Introduction

Worldwide, lung cancer cases and deaths are increasing. In 2018, GLOBOCAN [1] estimated that there were 2.09 million new cases (11.6% of the total number of cancer cases) and 1.76 million deaths (18.4% of the total number of cancer deaths), which is higher than the rate reported in 2012 (1.8 million new cases and 1.6 million deaths), making it the most common cause of cancer and cancer deaths in both men and women [2]. Lung cancer includes multiple subtypes, and the proportion of lung adenocarcinoma (LUAD) has increased in recent years. Despite significant advances in chemotherapy and molecular targeted therapy, the survival rate of LUAD remains unsatisfactory. Tumor recurrence and metastasis are major challenges in the clinical treatment of LUAD [3]. To improve the prognosis of patients with LUAD, more targeted molecules should be identified to diagnose, treat, and determine the prognosis of patients. We suggest that ARHGAP30 might have potential as a new targeting molecule.

The Rho protein family belongs to the small GTP-binding proteins of the Ras superfamily (including the Ras, Rho, Rab, Ran, and Rrf families), which have a molecular weight between 20 and 30 kDa and control numerous signal transduction pathways as molecular switches in eukaryotic cells [4]. Rho proteins act as signal converters in the signal transduction pathway of cells, acting on the cytoskeleton or target proteins, and produce a variety of biological effects [5]. Rho GTPase activating protein 30 (ARHGAP30), a Rho-specific Rho GAP, has been reported to enhance the intrinsic hydrolysis of GTP and might regulate Rho GTPase negatively [6].

Recent studies have demonstrated a close relationship between Rho-GTPases and the development and metastasis of various human tumors [7]. In some studies on the relationship between ARHGAP30 and cancer, upregulation of ARHGAP30 attenuated pancreatic cancer progression by inactivating the β-catenin pathway [8]. In addition, ARHGAP30 promotes p53 acetylation and function in colorectal cancer [9]. However, whether there is a difference in the expression of ARHGAP30 in LUAD, a relationship between the expression of ARHGAP30 in LUAD and DNA methylation, and whether these affect patient’s prognosis, survival, and tumor immune infiltration, are unclear and require further study.

This present study aimed to investigate the differential expression of ARHGAP30 between LUAD tissues and normal tissues and the relationship between ARHGAP30 expression and DNA methylation in LUAD. The role of ARHGAP30 expression in the prognosis and survival of patients with LUAD was studied. In addition, gene set enrichment analysis (GSEA) of ARHGAP30 was performed using various bioinformatic analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Panther pathways, Reactome pathways, Wikipathways, Gene ontology (GO; biological process, cellular component, and molecular function), Kinase Target Network, Transcription Factor Network, and a protein-protein interaction (PPI) network in the Biological General Repository for Interaction Datasets (BI-OGRID). The association of ARHGAP30 expression with tumor-infiltrating lymphocytes (TILs), immunostimulators, major histocompatibility complex (MHC) molecules, chemokines, and chemokine receptors in LUAD tissues were also analyzed. We believe that ARHGAP30 can be developed as a new biomarker for LUAD. The study of ARHGAP30-associated immune infiltration will provide a new direction for immunotherapy of lung adenocarcinoma.

Results

Differential expression of the ARHGAP30 mRNA and protein in LUAD tissues and normal tissues

Figure 1A shows a summary view of the different transcriptional levels of ARHGAP30 in various cancers in the Oncomine [10] database. The red line in the figure shows that the expression level of ARHGAP30 in lung cancer tissue was significantly lower than that in normal tissue. Figure 1B1–1B6 show that the mRNA expression levels of ARHGAP30 were considerably higher in LUAD than in normal tissue. Figure 1B1–1B3 show the fold change, associated p-values, and overexpression Gene Rank, based on Oncomine 4.5 analysis [10], including box plots of ARHGAP30 mRNA levels in the Hou Lung, Selamat Lung, and Okayama Lung datasets. Figure 1B4, 1B5 show the expression of ARHGAP30 in LUAD based on SurvExpress [11] analysis. Figure 1 (B6) shows the expression of ARHGAP30 in LUAD based on GEPIA [12]. P values as described in the figure are statistically significant. According to analysis at the Warner [13] database, the abundance of the different exons of the ARHGAP30 gene show an uneven balance between normal and tumor tissues in patients with LUAD (Figure 2A). Figure 2A1 shows the expression of ARHGAP30 in normal tissues (n = 58) and Figure 2A2 shows the expression of ARHGAP30 in tumor tissues (n = 488). The data shown in Figure 2A4, 2A5 indicates that ARHGAP30 expression correlated negatively with the level of DNA methylation.

Figure 1.
Comparison of mRNA and protein expression of ARHGAP30 in lung cancer tissues and normal tissues. (A) Summary view of ARHGAP30. The transcription level of ARHGAP30 in different types of cancer. P-value < 0.05, Note: The Z-score standardizes the color to describe the relative value in the row. Among them, red indicates overexpression or copy acquisition of genes in the analysis; blue indicates low expression or copy loss of genes in these analyses. Datasets comprised samples represented as microarray data measuring mRNA expression in primary tumors, cell lines, or xenografts. (B) Transcription of ARHGAP30 in lung adenocarcinoma (from Oncomine, SurvExpress, and GEPIA databases). mRNA expression levels of ARHGAP30 were significantly higher in lung adenocarcinoma than in normal tissue. (B1–B3) The fold change, associated p-values, and overexpression Gene Rank, based on Oncomine 4.5 analysis. Box plots show ARHGAP30 mRNA levels in the Hou Lung, Selamat Lung, and Okayama Lung datasets. (B4, B5) The expression of ARHGAP30 in LUAD based on SurvExpress analysis; (B6) The expression of ARHGAP30 in LUAD based on GEPIA analysis; P values as described in the figure are statistically significant. (C) ARHGAP30 transcription in subgroups of patients with lung adenocarcinoma, stratified based on sex, age, and other criteria (UALCAN). (C1) Sample types. (C2) Individual cancer stages. (C3) Ethnicity. (C4) Sex. (C5) Age. (C6) Smoking habits. (C7) Nodal metastasis status. (C8) TP53 mutation status. ☆, P < 0.05; ☆☆, P < 0.01; ☆☆☆, P < 0.001. (D) Differential abundance of the ARHGAP30 protein in patients with lung adenocarcinoma, stratified by sex, age, and other criteria. (D1) Sample types. (D2) Individual cancer stages. (D3) Ethnicity. (D4) Sex. (D5) Age. (D6) Weight. (D7) Tumor grade. (D8) Tumor histology. ☆, P < 0.05; ☆☆, P < 0.01; ☆☆☆, P < 0.001.
Figure 2.
DNA methylation and the differential expression of ARHGAP30 between lung adenocarcinoma and normal tissues. (A) The abundance of the different exons of the ARHGAP30 gene shows an uneven balance in normal and tumor tissues in patients with lung adenocarcinoma according to the Wanderer database. (A1) Expression of ARHGAP30 in normal tissues (n = 58); (A2) Expression of ARHGAP30 in tumor tissues (n = 488); (A3) Comparison of the mean expression of ARHGAP30 between normal tissue and lung adenocarcinoma tissue. (A4, A5) The expression of ARHGAP30 correlated negatively with the level of DNA methylation. (B) Highly mutated genes and the expression of ARHGAP30 in the TCGAportal database. The value adjacent to the highly mutated gene is the permutation test p-value of gene expression between the driver mutated (red) and not-mutated (gray) samples. (C1, C2) Box plots of the mRNA expression of ARHGAP30 in lung adenocarcinoma before and after mutation of highly mutated genes (KEAP1, STK11) in the Linkedomics database. (D) Heat map of ARHGAP30 methylation in lung adenocarcinoma. (E1, E2) Kaplan–Meier plots of the survival of patients with lung adenocarcinoma with different ARHGAP30 DNA methylation levels (Different methylation probes cg07837534 and cg00045607 in the MethSurv database). (F) Gene expression and methylation of ARHGAP30 in samples of primary tumors and solid tissues analyzed at the TCGAportal database. Spearman T: Spearman correlation between expression and methylation in primary tumor samples. Spearman N: Spearman correlation between expression and methylation in solid tissue standard samples. Mean T: Mean value of the methylation beta-value in primary tumor samples. Mean N: Mean value of methylation in normal solid tissue samples.

Differential expression of ARHGAP30 mRNA in LUAD tissues and normal tissues

Figure 1C shows mRNA expression levels of ARHGAP30 in subgroups of patients with LUAD, stratified based on sample type, individual cancer stage, ethnicity, sex, age, smoking habit, nodal metastasis status, and TP53 mutation status (UALCAN [14]). The P-value of the comparison between each is shown in Supplementary Table 1. Figure 1C1 shows a significant difference between normal tissue and lung adenocarcinoma tissue (P < 0.001). Figure 1C2–1C8 show that in addition to the differential expression between tumor tissues and normal tissues, there were statistically significant differences between Stage 1 and Stage 3, Stage 1 and Stage 4, Stage 2 and Stage 3, male and female, and N0 and N2.

Differential abundance of the ARHGAP30 protein in LUAD tissues and normal tissues

Figure 1D shows the protein levels of ARHGAP30 in subgroups of patients with LUAD, stratified based on sample type, individual cancer stage, ethnicity, sex, age, weight, tumor grade, and tumor histology (assessed using UALCAN [14] and CPTAC [15]). The P-value of the comparison between each is shown in Supplementary Table 2. Figure 1D1 shows a significant difference between normal tissue and LUAD tissue (P < 0.001). Figure 1D1–1D8 show that in addition to the differential abundance between tumor tissues and normal tissues, there were statistically significant differences between age 41–60 years and 61–80 years; and Grade 2 and Grade 3.

Effect of mutations in common hypermutated genes and DNA methylation of ARHGAP30 on the expression of ARHGAP30 in lung adenocarcinoma versus normal tissues

The location of ARHGAP30 methylation in the lung adenocarcinoma cases was on chromosome 1, 161015000 to 161,069905. Figure 2B shows that ARHGAP30 expression was affected by some highly mutated genes in the analysis using the TCGAportal [16] database. Among them, KRAS (encoding KRAS proto-oncogene, GTPase), KEAP1 (encoding kelch like ECH associated protein 1), STK11 (encoding serine/threonine kinase 11), and NF1 (encoding neurofibromin 1) genes had statistically significant P values. Figure 2C1, 2C2 show that ARHGAP30 mRNA expression in LUAD was significantly lower than that in normal tissues after mutation of highly mutated genes (KEAP1 and STK11) in the Linkedomics [17] database. These results indicate that mutations in KEAP1 and STK11 significantly reduce ARHGAP30 gene expression and affect LUAD development.

Figure 2D shows a heatmap of ARHGAP30 DNA methylation (using four probes: cg07837534, cg12081303, cg00045607, cg03089651) in LUAD based on analysis at the Methsurv [18] database, which showed that ARHGAP30 DNA methylation levels were markedly increased in LUAD. A Kaplan–Meier map for patients with LUAD with different levels of ARHGAP30 DNA methylation showed that patients with hypomethylation had a statistically significant better survival prognosis (Figure 2E1, 2E2) [18]. The Spearman correlation between expression and methylation in primary tumor samples was significantly higher than the Spearman correlation between expression and methylation in normal samples of solid tissues (Figure 2F) [16].

Prediction of the prognosis of patients with LUAD according to ARHGAP30 mRNA levels

We found that the prognosis of patients with LUAD with high ARHGAP30 mRNA expression levels was significantly better than that of patients with low ARHGAP30 mRNA expression levels, as demonstrated by the 12 overall survival curves shown in Figure 3 (all P < 0.01). Figure 3A1, 3A2 represent the two overall survival curves from the GEPIA [12] database; Figure 3C–3J represent the eight overall survival curves from the Oncolnc [19], Ualcan [14], UCSC [20], TCGA portal [16], TISIDB [21], KMplot [22], TIMER [23], and Linkedomics [17] databases. The two survival curves in Figure 3K1, 3K2 represent the overall survival curves from the PrognoScan [24] database. Figure 3B1, 3B2 show two disease-free survival curves from the GEPIA database, which indicate that the prognosis of patients with LUAD with high expression of ARHGAP30 mRNA was significantly higher than that of patients with low expression of ARHGAP30 mRNA (P < 0.01). The two survival curves in Figure 3L1, 3L2 represent recurrence-free survival curves from the PrognoScan [24] database), which show that the prognosis of patients with LUAD with high expression of ARHGAP30 mRNA were significantly higher than that of patients with low expression of ARHGAP30 mRNA (P < 0.05).

Figure 3.
Overall survival curves, recurrence-free survival curves, and disease-free survival curves of ARHGAP30 in lung adenocarcinoma. The blue curves represent patients with lung adenocarcinoma with low ARHGAP30 expression, and the red curves represent patients with lung adenocarcinoma with high ARHGAP30 expression. (A1, A2) Two overall survival curves (in months and days, respectively) from the GEPIA database; (B1, B2) Two disease-free survival (DFS) curves for ARHGAP30 in the GEPIA database (in months and days, respectively). (C–J) Eight overall survival curves from the databases of Oncolnc, Ualcan, UCSC, TCGAportal, TISIDB, KMplot, TIMER, and Linkedomics, respectively. (K1, K2) Two survival curves representing the overall survival curves from the PrognoScan database. (L1, L2) Two survival curves representing recurrence-free survival curves from the PrognoScan database.

Genes, miRNAs, and lncRNAs correlated highly with ARHGAP30 in lung adenocarcinoma

We analyzed the genes and microRNAs (miRNAs) that correlated with ARHGAP30 based on the Linkedomics [17] database. Figure 4A shows a volcano plot of genes that correlated highly with ARHGAP30 in LUAD. Figure 4B shows a heatmap of genes that correlated highly and positively with ARHGAP30 in LUAD. Figure 4C shows a heatmap of genes that correlated highly and negatively with ARHGAP30 in LUAD. Figure 4D1–4D18 show scatter plots of the top 18 genes that correlated positively with ARHGAP30 in LUAD: ITGAL, DOCK2, MYO1F, SNX20, IL10RA, SASH3, IKZF1, NCKAP1L, SPN, CSF2RB, FAM78A, WAS, ARHGAP25, PIK3R5, CD37, FGD2, PTPRC, and CYTH4. Figure 4E1–4E18 show scatter plots of the top 18 genes that correlated negatively with ARHGAP30 in LUAD: SNRPE, HSPE1, DPY30, PSMB5, TMEM223, MRPS18A, PFDN6, C15orf63, YWHAE, APOA1BP, ACP1, TMEM9, TMEM183A, ILF2, SRP9, FBXO22OS, SF3B14, and CCT3.

Figure 4.
Genes that correlated highly with ARHGAP30 in lung adenocarcinoma (LUAD). (A) Volcano map of ARHGAP30-correlated genes in LUAD, the red dots on the right represent the positively related genes, and the green dots on the left represent the negatively related genes. (B, C) Heat maps showing the genes that correlated positively and negatively with ARHGAP30 in LUAD (top 50). Red indicates positively correlated genes; green indicates negatively correlated genes. (D1–D18) Scatter plots of the first 18 genes that correlated positively with ARHGAP30 in LUAD. (E1–E18) Scatter plots of the first 18 genes that correlated negatively with ARHGAP30 in LUAD.

Figure 5A shows a volcano plot of miRNAs that correlated highly with ARHGAP30 in LUAD. Figure 5B shows a heatmap of miRNAs that correlated highly and positively with ARHGAP30 in LUAD. Figure 5C shows a heatmap of miRNAs that correlated highly and negatively with ARHGAP30 in LUAD. Figure 5D1–5D18 show scatter plots of the top 18 miRNAs that correlated positively with ARHGAP30 in LUAD: hsa-mir-150, hsa-mir-155, hsa-mir-146a, hsa-mir-511-1, hsa-mir-140, hsa-mir-142, hsa-mir-342, hsa-mir-511-2, hsa-mir-146b, hsa-mir-598, hsa-mir-378, hsa-mir-101-2, hsa-mir-133a-1, hsa-mir-1976, hsa-mir-218-2, hsa-mir-29c, hsa-mir-139, and hsa-mir-223. Figure 5E1–5E18 show scatter plots of the top 18 mRNAs that corelated negatively with ARHGAP30 in LUAD: hsa-mir-183, hsa-mir-182, hsa-mir-877, hsa-mir-1276, hsa-mir-3691, hsa-mir-151, hsa-mir-96, hsa-mir-760, hsa-mir-18b, hsa-mir-130b, hsa-mir-1254, hsa-mir-556, hsa-mir-200c, hsa-mir-421, hsa-mir-301b, hsa-mir-106b, hsa-mir-1266 and hsa-mir-561.

Figure 5.
MiRNAs correlated highly with ARHGAP30 in lung adenocarcinoma (LUAD). (A) Volcano map of ARHGAP30-correlated miRNAs in LUAD, the red dots on the right represent the positively associated miRNAs, and the green dots on the left represent the negatively associated miRNAs. (B, C) Heat maps showing the miRNAs that correlated positively and negatively with ARHGAP30 in LUAD (top 50). Red indicates positively correlated miRNAs; green indicates negatively correlated miRNAs. (D1–D18) Scatter plots of the first 18 miRNAs that correlated positively with ARHGAP30 in LUAD. (E1–E18) Scatter plots of the first 18 miRNAs that correlated negatively with ARHGAP30 in LUAD.

We analyzed the long noncoding RNAs (lncRNAs) that correlated with ARHGAP30 based on the TANRIC [25] database. Figure 6A1–6A20 show scatter plots of lncRNAs that are highly and positively correlated with ARHGAP30 in LUAD: ENSG00000257824.1, ENSG00000268802.1, ENSG00000261644.1, ENSG00000255197.1, ENSG00000267074.1, ENSG00000233038.1, ENSG00000245164.2, ENSG00000229645.4, ENSG00000272908.1, ENSG00000265148.1, ENSG00000247774.2, ENSG00000238121.1, ENSG00000270107.1, ENSG00000242258.1, ENSG00000237484.5, ENSG00000239636.1, ENSG00000225331.1, ENSG00000228427.1, ENSG00000258810.1, ENSG00000224875.2. Figure 6B1–6B10 show survival curves with a better prognosis for those lncRNAs with low expression associated with ARHGAP30: ENSG00000182057.4, ENSG00000235570.1, ENSG00000250838.1, ENSG00000251059.1, ENSG00000229656.2, ENSG00000232527.3, ENSG00000261521.1, ENSG00000233903.2, ENSG00000186615.6, and ENSG00000215394.4 (all P < 0.05). Figure 6C1–6C10 show survival curves with a better prognosis for highly expressed lncRNAs associated with ARHGAP30: ENSG00000256691.1, ENSG00000266312.1, ENSG00000270182.1, ENSG00000231335.1, ENSG00000249717.1, ENSG00000267259.1, ENSG00000256984.1, ENSG00000178977.3, ENSG00000264469.1, and ENSG00000258670.1 (all P < 0.05).

Figure 6.
LncRNAs correlated highly with ARHGAP30 in lung adenocarcinoma (LUAD). (A1–A20) Scatter plots of lncRNAs that are positively associated with ARHGAP30 in LUAD. (B1–B10) ARHGAP30 correlated lncRNAs, in which low expression has a better prognosis according to the survival curve of the lncRNAs. (C1–C10) ARHGAP30 correlated lncRNAs, in which high expression has a better prognosis according to the survival curve of lncRNAs.

Gene set enrichment analysis of ARHGAP30 in lung adenocarcinoma

We performed gene set enrichment analysis (GSEA) [26] of ARHGAP30 using the Linkedomics [17] database for KEGG Pathway [27], Panther Pathway [28], Reactome Pathway [29], Wikipathway [30], Gene ontology Biological Process [31, 32], Gene ontology Cellular Component [31, 32], Gene ontology Molecular Function [31, 32], Kinase Target Network, Transcription Factor Network, and PPI BIOGRID Network [33]. We identified many genes related to tumor immunity in the enrichment results.

The results of KEGG pathway enrichment analysis are shown in Figure 7A. Significantly enriched pathways were identified using false discovery rate (FDR) less than 0.05 and the absolute value of the normalized enrichment score greater than 1. Figure 7B1, 7B2 show the enrichment profiles of some statistically significant gene sets in the KEGG analysis. Supplementary Figures 1–9 show the bar charts and enrichment profiles for ARHGAP30 GSEA of the Panther Pathway, Reactome Pathway, Wikipathway, Gene ontology Biological Process, Gene ontology Cellular Component, Gene ontology Molecular Function, Kinase Target Network, Transcription Factor Network, and PPI BIOGRID Network. Tables 1–10 detail the results of ARHGAP30 GSEA for the Panther Pathway, Reactome Pathway, Wikipathway, Gene ontology Biological Process, Gene ontology Cellular Component, Gene ontology Molecular Function, Kinase Target Network, Transcription Factor Network, and PPI BIOGRID Network, respectively, which were statistically significant (absolute normalized enrichment score (NES values greater than 1, FDR and P values less than 0.05).

Figure 7.
KEGG pathway-based GSEA of ARHGAP30 in lung adenocarcinoma (LUAD). (A) Bar chart of KEGG pathway-based GSEA of ARHGAP30 in LUAD (FDR < 0.05). (B1–B16) GSEA enrichment analysis Plots of 16 tumor immune-related KEGG gene sets (FDR < 0.05).

Table 1. KEGG pathway based gene set enrichment analysis of ARHGAP30 in lung adenocarcinoma.

Gene setDescriptionSizeLeading edge numberESNESP ValueFDR
hsa05310Asthma28180.924551.693900
hsa05340Primary immunodeficiency36240.895981.682200
hsa05320Autoimmune thyroid disease48270.883341.679900
hsa05140Leishmaniasis71410.882061.679400
hsa04672Intestinal immune network for IgA production45340.889491.67600
hsa05330Allograft rejection35310.892871.664300
hsa04640Hematopoietic cell lineage93580.862191.662800
hsa05150Staphylococcus aureus infection52360.873921.657600
hsa05321Inflammatory bowel disease (IBD)63410.857681.641700
hsa04658Th1 and Th2 cell differentiation90460.852021.639100
hsa05416Viral myocarditis56320.86281.636800
hsa05332Graft-versus-host disease37270.867831.632700
hsa04940Type I diabetes mellitus41300.862961.631300
hsa04514Cell adhesion molecules (CAMs)137540.84321.629500
hsa05012Parkinson disease11566-0.59262-2.231900
hsa03020RNA polymerase3121-0.74745-2.23700
hsa00970Aminoacyl-tRNA biosynthesis4330-0.67799-2.241300
hsa03430Mismatch repair2311-0.80357-2.249500
hsa00020Citrate cycle (TCA cycle)3019-0.7776-2.353400
hsa03060Protein export2217-0.79558-2.353900
hsa03030DNA replication3619-0.76463-2.415200
hsa03010Ribosome131100-0.83153-3.496100
hsa04062Chemokine signaling pathway185730.829041.603909.82E-05
hsa05323Rheumatoid arthritis85420.831171.608400.000104
hsa05144Malaria46270.841721.610400.000111
hsa04660T cell receptor signaling pathway98350.836621.600200.000176
hsa04659Th17 cell differentiation105540.832771.601900.000185
hsa04380Osteoclast differentiation126570.835911.595700.00025
hsa04064NF-kappa B signaling pathway90420.814361.575700.00091
hsa04666Fc gamma R-mediated phagocytosis86170.818811.572400.001016
hsa03008Ribosome biogenesis in eukaryotes7037-0.65008-2.176300.001061
hsa05152Tuberculosis174650.801991.557500.001535
hsa00900Terpenoid backbone biosynthesis2217-0.74335-1.942800.002387
hsa03420Nucleotide excision repair4516-0.59231-1.980600.002387
hsa00563Glycosylphosphatidylinositol (GPI)-anchor biosynthesis2510-0.71989-1.959700.002546
hsa01230Biosynthesis of amino acids6927-0.59257-2.005200.002604
hsa05010Alzheimer disease15267-0.49713-1.971700.002728
hsa01200Carbon metabolism11038-0.47465-1.911800.002808
hsa03050Proteasome4434-0.60203-2.010800.002865
hsa03022Basal transcription factors4418-0.63104-1.978100.002938
hsa03018RNA degradation7327-0.50119-1.904100.003183
hsa03410Base excision repair3313-0.68657-1.868200.004523
hsa04932Non-alcoholic fatty liver disease (NAFLD)14355-0.46729-1.844600.005729
hsa00240Pyrimidine metabolism9642-0.4934-1.83600.005911
hsa00130Ubiquinone and other terpenoid-quinone biosynthesis115-0.76111-1.656700.027882

Table 2. Panther pathway gene set enrichment analysis of ARHGAP30 in lung adenocarcinoma.

Gene setDescriptionSizeLeading edge numberESNESP ValueFDR
P00053T cell activation75300.875721.675400
P02738De novo purine biosynthesis2616-0.79062-2.241200
P00017DNA replication1910-0.79041-2.262500
P00023General transcription regulation2814-0.72986-2.10100.001287
P00010B cell activation58190.842161.581900.004295
P00055Transcription regulation by bZIP transcription factor4514-0.58101-1.896100.005792
P00038JAK/STAT signaling pathway1590.90351.55430.0023810.006872
P02746Heme biosynthesis126-0.73501-1.73370.0113640.013998
P02740De novo pyrimidine ribonucleotides biosynthesis107-0.79533-1.75490.0099010.014894
P00031Inflammation mediated by chemokine and cytokine signaling pathway196720.783111.52400.015463
P00051TCA cycle105-0.83656-1.758800.017377
P02739De novo pyrimidine deoxyribonucleotide biosynthesis138-0.74772-1.777200.019307
P00009Axon guidance mediated by netrin30120.814391.49410.0085110.035736
P00014Cholesterol biosynthesis128-0.76183-1.64430.0101010.039902

Table 3. Wikipathway gene set enrichment analysis of ARHGAP30 in lung adenocarcinoma.

Gene setDescriptionSizeLeading edge numberESNESP ValueFDR
R-HSA-110373Resolution of AP sites via the multiple-nucleotide patch replacement pathway2615-0.80592-2.164300
R-HSA-114604GPVI-mediated activation cascade34140.868461.61300.003124
R-HSA-1268020Mitochondrial protein import5235-0.82458-2.78400
R-HSA-1461973Defensins2150.928431.713500
R-HSA-162599Late Phase of HIV Life Cycle12159-0.61857-2.439500
R-HSA-191859snRNP Assembly4919-0.78096-2.518600
R-HSA-194441Metabolism of non-coding RNA4919-0.78096-2.518600
R-HSA-198933Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell122790.864271.665400.000368
R-HSA-202427Phosphorylation of CD3 and TCR zeta chains20200.933561.66730.0023530.00042
R-HSA-202430Translocation of ZAP-70 to Immunological synapse17160.942741.684400
R-HSA-202433Generation of second messenger molecules30220.941771.741100
R-HSA-2029482Regulation of actin dynamics for phagocytic cup formation60140.833481.595400.005648
R-HSA-2172127DAP12 interactions38210.875911.658200.000327
R-HSA-2299718Condensation of Prophase Chromosomes6947-0.66539-2.189500
R-HSA-2424491DAP12 signaling29150.887441.633200.00084
R-HSA-379724tRNA Aminoacylation4232-0.71306-2.369400
R-HSA-380108Chemokine receptors bind chemokines45260.848551.599100.004982
R-HSA-388841Costimulation by the CD28 family67340.884591.706400
R-HSA-389948PD-1 signaling21200.938321.704900
R-HSA-451927Interleukin-2 family signaling44280.892011.692400
R-HSA-512988Interleukin-3, Interleukin-5 and GM-CSF signaling47240.869931.651200.000294
R-HSA-5621480Dectin-2 family24100.901221.650300.000245
R-HSA-5668599RHO GTPases Activate NADPH Oxidases1350.949771.607500.003718
R-HSA-5696399Global Genome Nucleotide Excision Repair (GG-NER)8431-0.63145-2.207300
R-HSA-606279Deposition of new CENPA-containing nucleosomes at the centromere6336-0.70907-2.550700
R-HSA-6781827Transcription-Coupled Nucleotide Excision Repair (TC-NER)7734-0.6929-2.39900
R-HSA-6782135Dual incision in TC-NER6427-0.72714-2.313600
R-HSA-6783783Interleukin-10 signaling45280.869741.650300.000267
R-HSA-6790901rRNA modification in the nucleus and cytosol5235-0.80009-2.639200
R-HSA-69202Cyclin E associated events during G1/S transition8250-0.61312-2.250800
R-HSA-69206G1/S Transition12475-0.64433-2.482100
R-HSA-69618Mitotic Spindle Checkpoint10156-0.67804-2.339700
R-HSA-69656Cyclin A:Cdk2-associated events at S phase entry8450-0.60739-2.450100
R-HSA-72165mRNA Splicing - Minor Pathway4620-0.74059-2.325200
R-HSA-73863RNA Polymerase I Transcription Termination3012-0.81293-2.519600
R-HSA-73864RNA Polymerase I Transcription10643-0.61126-2.321100
R-HSA-73884Base Excision Repair3917-0.77946-2.317700
R-HSA-73933Resolution of Abasic Sites (AP sites)3917-0.77946-2.317700
R-HSA-774815Nucleosome assembly6336-0.70907-2.550700
R-HSA-877300Interferon gamma signaling90530.824091.593300.00553
R-HSA-912526Interleukin receptor SHC signaling27150.884411.634500.000905
R-HSA-983695Antigen activates B Cell Receptor (BCR) leading to generation of second messengers32180.861.617900.002744

Table 4. Reactome pathway gene set enrichment analysis of ARHGAP30 in lung adenocarcinoma.

Gene setDescriptionSizeLeading edge numberESNESP ValueFDR
WP3937Microglia Pathogen Phagocytosis Pathway40250.932211.752300
WP69T-Cell antigen Receptor (TCR) Signaling Pathway89390.865661.682500
WP3863T-Cell antigen Receptor (TCR) pathway during Staphylococcus aureus infection61260.866621.661500
WP3945TYROBP Causal Network59400.881461.659300
WP2328Allograft Rejection87550.861191.649900
WP286IL-3 Signaling Pathway48220.873431.633400
WP78TCA Cycle (aka Krebs or citric acid cycle)1813-0.79775-2.105300
WP4752Base Excision Repair3113-0.76263-2.22400
WP4521Glycosylation and related congenital defects2513-0.78449-2.226100
WP466DNA Replication3619-0.75101-2.366500
WP623Oxidative phosphorylation3727-0.81707-2.390400
WP405Eukaryotic Transcription Initiation4224-0.77435-2.467600
WP477Cytoplasmic Ribosomal Proteins8872-0.77946-2.470700
WP107Translation Factors5028-0.76662-2.488400
WP4324Mitochondrial complex I assembly model OXPHOS system4439-0.84395-2.671100
WP111Electron Transport Chain (OXPHOS system in mitochondria)7361-0.83256-2.945600
WP4595Urea cycle and associated pathways219-0.73691-2.079500.000281
WP531DNA Mismatch Repair2210-0.77183-2.048400.000515
WP619Type II interferon signaling (IFNG)37220.876091.62500.000533
WP4753Nucleotide Excision Repair4416-0.59965-2.037300.000713
WP2446Retinoblastoma Gene in Cancer8645-0.55877-1.970700.001443
WP4022Pyrimidine metabolism8339-0.49658-1.971800.001546
WP4559Interactions between immune cells and microRNAs in tumor microenvironment28200.864241.601300.001864
WP4585Cancer immunotherapy by PD-1 blockade23150.887151.601600.00205
WP49IL-2 Signaling Pathway42170.844451.603600.002278
WP22IL-9 Signaling Pathway1790.922711.604200.00233
WP205IL-7 Signaling Pathway25120.899981.592800.003417
WP4146Macrophage markers980.974731.586300.003594
WP3929Chemokine signaling pathway163620.825241.587600.003728
WP4494Selective expression of chemokine receptors during T-cell polarization29200.869871.575200.003837
WP581EPO Receptor Signaling2680.871231.576800.003844
WP2849Hematopoietic Stem Cell Differentiation55180.840731.580700.003852
WP4582Cancer immunotherapy by CTLA4 blockade1470.916431.572500.004038
WP2583T-Cell Receptor and Co-stimulatory Signaling29130.861681.567900.004807
WP23B Cell Receptor Signaling Pathway96390.810891.563600.005498
WP453Inflammatory Response Pathway30150.843111.559500.005676
WP24Peptide GPCRs73190.817151.560400.005858
WP2453TCA Cycle and Deficiency of Pyruvate Dehydrogenase complex1611-0.77333-1.901800.006183
WP127IL-5 Signaling Pathway40130.829341.556500.006321
WP4553FBXL10 enhancement of MAP/ERK signaling in diffuse large B-cell lymphoma3210-0.59305-1.836800.011093
WP1946Cori Cycle178-0.72333-1.821400.012022
WP4629Computational Model of Aerobic Glycolysis117-0.77655-1.812400.013017
WP197Cholesterol Biosynthesis Pathway139-0.76865-1.77150.0099010.019786
WP4240Regulation of sister chromatid separation at the metaphase-anaphase transition159-0.68148-1.714900.035479
WP438Non-homologous end joining102-0.78427-1.68350.0241940.040727
WP4320The effect of progerin on the involved genes in Hutchinson-Gilford Progeria Syndrome3614-0.57494-1.683600.042578

Table 5. Gene ontology biological process based gene set enrichment analysis of ARHGAP30 in lung adenocarcinoma.

Gene setDescriptionSizeLeading edge numberESNESP ValueFDR
GO:0006968cellular defense response53260.856071.666700
GO:0000959mitochondrial RNA metabolic process3322-0.67592-2.053800
GO:0002181cytoplasmic translation8450-0.58607-2.059700
GO:0098781ncRNA transcription9346-0.54515-2.064100
GO:0071806protein transmembrane transport5927-0.70316-2.103100
GO:0034502protein localization to chromosome6839-0.61386-2.125700
GO:0042769DNA damage response, detection of DNA damage3815-0.70411-2.142800
GO:0006490oligosaccharide-lipid intermediate biosynthetic process209-0.8074-2.167800
GO:0006354DNA-templated transcription, elongation8427-0.54275-2.189800
GO:0045454cell redox homeostasis5924-0.65482-2.191500
GO:0061641CENP-A containing chromatin organization2416-0.77476-2.231200
GO:0036260RNA capping3013-0.79033-2.313500
GO:0006353DNA-templated transcription, termination6926-0.69744-2.351100
GO:0072350tricarboxylic acid metabolic process3821-0.73574-2.427600
GO:0033108mitochondrial respiratory chain complex assembly6853-0.82238-2.448900
GO:0010257NADH dehydrogenase complex assembly4941-0.83836-2.480700
GO:0006289nucleotide-excision repair10639-0.64825-2.499600
GO:0006414translational elongation12382-0.83503-3.215500
GO:0032623interleukin-2 production63310.835781.610500.000291
GO:0032609interferon-gamma production102560.842411.610700.000317
GO:0070661leukocyte proliferation2721220.841381.634900.000349
GO:0002285lymphocyte activation involved in immune response165680.835271.613700.000349
GO:0007159leukocyte cell-cell adhesion3101350.830541.614200.000388
GO:0001773myeloid dendritic cell activation27150.865611.609500.000403
GO:0050690regulation of defense response to virus by virus29120.859411.63900.000437
GO:0002250adaptive immune response3661750.8351.617700.000437
GO:0042110T cell activation4371840.835991.625500.000499
GO:0050867positive regulation of cell activation2981260.826591.60800.000499
GO:0032633interleukin-4 production34210.885571.650800.000582
GO:0045730respiratory burst27100.905361.625600.000582
GO:0031123RNA 3'-end processing11148-0.62236-1.983700.000584
GO:0016073snRNA metabolic process8242-0.56867-1.986500.000611
GO:0051131chaperone-mediated protein complex assembly196-0.74976-2.002100.00064
GO:0042107cytokine metabolic process106430.830011.602400.000698
GO:0071706tumor necrosis factor superfamily cytokine production133540.821671.601300.000764
GO:1990868response to chemokine86440.848521.652400.000873
GO:0030101natural killer cell activation79300.833761.596700.000873
GO:0002694regulation of leukocyte activation4611990.821491.598700.000924
GO:0042113B cell activation221860.822211.588700.000998
GO:0050866negative regulation of cell activation172780.826991.591400.001011
GO:0002764immune response-regulating signaling pathway4521590.808131.581800.001215
GO:0032613interleukin-10 production46240.833411.573400.001293

Table 6. Gene ontology cellular component based gene set enrichment analysis of ARHGAP30 in lung adenocarcinoma.

Gene setDescriptionSizeLeading edge numberESNESP ValueFDR
GO:0042611MHC protein complex19160.912351.639700
GO:0036452ESCRT complex2312-0.7271-1.981400
GO:0101031chaperone complex2113-0.7488-2.08900
GO:0005732small nucleolar ribonucleoprotein complex2014-0.84007-2.235700
GO:0005844polysome7044-0.64071-2.284300
GO:0009295nucleoid3627-0.76327-2.321100
GO:1905368peptidase complex8554-0.68339-2.479300
GO:0005681spliceosomal complex15564-0.60446-2.567600
GO:0030964NADH dehydrogenase complex4339-0.82377-2.622100
GO:0070069cytochrome complex2922-0.87423-2.675600
GO:0070469respiratory chain8462-0.82349-2.685800
GO:0120114Sm-like protein family complex6928-0.78085-2.732600
GO:0030684preribosome6639-0.73361-2.735500
GO:0001772immunological synapse32170.857131.592800.000759
GO:1905348endonuclease complex2310-0.7109-1.895400.003019
GO:0098552side of membrane4591710.804841.573400.00354
GO:0098636protein complex involved in cell adhesion35140.833271.550900.00531
GO:0042629mast cell granule2190.853421.541700.006069
GO:0001891phagocytic cup21120.853941.53600.006575
GO:0042581specific granule152440.776621.508300.010431
GO:0070820tertiary granule155430.779581.513600.010837
GO:0005657replication fork6221-0.52303-1.767400.012616
GO:1990204oxidoreductase complex9561-0.47317-1.732700.017008
GO:0031970organelle envelope lumen7328-0.44485-1.719600.017172
GO:0030667secretory granule membrane279760.751061.474400.023264
GO:0005697telomerase holoenzyme complex2010-0.62191-1.67130.0172410.032323
GO:0043235receptor complex3911430.737261.43700.047337
GO:0036019endolysosome1990.821881.43170.0045870.047999

Table 7. Gene ontology molecular function-based gene set enrichment analysis of ARHGAP30 in lung adenocarcinoma.

Gene setSizeLeading edge numberESNESP ValueFDRDescription
GO:0042287MHC protein binding24160.907831.645100
GO:0008135translation factor activity, RNA binding8134-0.59488-2.106700
GO:0043021ribonucleoprotein complex binding11744-0.55984-2.120500
GO:0000049tRNA binding5032-0.61345-2.133200
GO:0015002heme-copper terminal oxidase activity2416-0.84644-2.300200
GO:0030515snoRNA binding2819-0.80939-2.305300
GO:0016675oxidoreductase activity, acting on a heme group of donors2516-0.84613-2.375700
GO:0019843rRNA binding6042-0.74059-2.408100
GO:0051082unfolded protein binding10852-0.69233-2.649900
GO:0003735structural constituent of ribosome154119-0.83969-3.28900
GO:0016502nucleotide receptor activity22140.878111.611500.00054724
GO:0035586purinergic receptor activity26160.868251.612600.00082086
GO:0004896cytokine receptor activity88490.846391.608700.0016417
GO:0017069snRNA binding3410-0.67977-1.937500.0022837
GO:0003684damaged DNA binding6726-0.49758-1.923900.0028547
GO:0016779nucleotidyltransferase activity11444-0.47695-1.924300.0031142
GO:0035004phosphatidylinositol 3-kinase activity81250.820411.590500.0032834
GO:0019865immunoglobulin binding22120.863621.58310.00222720.003518
GO:0038187pattern recognition receptor activity20110.879261.583300.0041043
GO:0052813phosphatidylinositol bisphosphate kinase activity73240.813061.574300.0045147
GO:0043548phosphatidylinositol 3-kinase binding30110.841911.54600.0073877
GO:0003823antigen binding52250.833571.54820.00203670.0080262
GO:0019239deaminase activity2790.844491.536800.010149
GO:0042169SH2 domain binding3390.835811.528900.010229
GO:0015026coreceptor activity39200.831081.532400.010261
GO:0019955cytokine binding119530.79231.518300.012547
GO:1990782protein tyrosine kinase binding76180.795681.515800.012587
GO:0031491nucleosome binding6620-0.49926-1.789100.016689
GO:0017056structural constituent of nuclear pore223-0.61094-1.75800.023653
GO:0016790thiolester hydrolase activity3113-0.5909-1.729200.028166
GO:0038024cargo receptor activity77260.767161.469400.03776
GO:0104005hijacked molecular function70140.775661.464600.039884
GO:0004713protein tyrosine kinase activity174560.750631.458800.042685
GO:0003697single-stranded DNA binding9341-0.46853-1.655100.044247
GO:0051087chaperone binding9627-0.46803-1.635700.045003
GO:0030506ankyrin binding2020.815151.44980.00907030.04856
GO:0051540metal cluster binding5926-0.53488-1.619600.048846

Table 8. Kinase target network gene set enrichment analysis of ARHGAP30 in lung adenocarcinoma.

Gene setDescriptionSizeLeading edge numberESNESP ValueFDR
Kinase_LYNLYN proto-oncogene, Src family tyrosine kinase50230.881631.6900
Kinase_SYKspleen associated tyrosine kinase35200.888071.663800
Kinase_LCKLCK proto-oncogene, Src family tyrosine kinase43250.877541.640900
Kinase_HCKHCK proto-oncogene, Src family tyrosine kinase23140.905681.623600.000453
Kinase_BTKBruton tyrosine kinase940.962451.556900.014843
Kinase_FGRFGR proto-oncogene, Src family tyrosine kinase1270.902911.53540.0048190.023015
Kinase_FYNFYN proto-oncogene, Src family tyrosine kinase66210.796741.530900.023306
Kinase_PRKCQprotein kinase C theta28100.833131.53860.0021790.023834
Kinase_ITKIL2 inducible T-cell kinase860.958051.516300.030592
Kinase_JAK3Janus kinase 31280.89141.51640.0050510.033991

Table 9. Transcription factor network gene set enrichment analysis of ARHGAP30 in lung adenocarcinoma.

Gene setSizeLeading edge numberESNESP ValueFDR
V$PU1_Q6211480.74561.453900.027156
V$PEA3_Q6242730.748371.448300.027497
RACCACAR_V$AML_Q6241660.740251.443400.028904
RGAGGAARY_V$PU1_Q64601070.74621.455300.030226
STTTCRNTTT_V$IRF_Q6175680.755241.461400.030856
V$PAX5_021570.856891.46650.0093460.035138
V$ISRE_01234770.755081.472200.038255
V$IRF_Q6229780.764361.48200.039042
V$ELF1_Q6220690.771761.513800.039672
V$ETS_Q4238550.726161.410800.043159
TGTYNNNNNRGCARM_UNKNOWN81260.733661.411300.046284
V$ICSBP_Q6230750.712521.388500.047526
V$ETS1_B237760.713141.391400.047543
V$STAT6_02241600.712561.385200.0477
V$AML_Q6239750.727961.413500.047915
GGGNNTTTCC_V$NFKB_Q6_01130510.761831.487900.048173
YNTTTNNNANGCARM_UNKNOWN66160.732941.39270.002020.048562

Table 10. PPI BIOGRID network gene set enrichment analysis of ARHGAP30 in lung adenocarcinoma.

Gene setSizeLeading edge numberESNESP ValueFDR
PPI_BIOGRID_M8562720-0.80351-2.238500
PPI_BIOGRID_M2994323-0.77865-2.332300
PPI_BIOGRID_M4224125-0.78055-2.3800
PPI_BIOGRID_M2985037-0.8034-2.622500
PPI_BIOGRID_M3004942-0.88664-3.080100
PPI_BIOGRID_M2728544-0.53652-2.110300.000404
PPI_BIOGRID_M4284323-0.62913-2.114800.000471
PPI_BIOGRID_M4413615-0.63714-2.077200.000706
PPI_BIOGRID_M7343011-0.69304-2.025800.00113
PPI_BIOGRID_M8482211-0.67958-1.992400.001177
PPI_BIOGRID_M8571413-0.83146-2.037100.001256
PPI_BIOGRID_M5815623-0.63221-2.006200.001284
PPI_BIOGRID_M1723114-0.63806-1.948800.001507
PPI_BIOGRID_M5442012-0.7468-1.964600.001521
PPI_BIOGRID_M438166-0.74768-1.945900.001589
PPI_BIOGRID_M597136-0.85103-1.951100.001614
PPI_BIOGRID_M309238890.838851.628600.003523
PPI_BIOGRID_M1853221-0.63805-1.899100.003822
PPI_BIOGRID_M702158-0.76267-1.867200.006592
PPI_BIOGRID_M7224624-0.58535-1.857500.007286
PPI_BIOGRID_M189117-0.86049-1.847500.008616
PPI_BIOGRID_M7172312-0.67161-1.839800.008732
PPI_BIOGRID_M5836927-0.54002-1.841200.008744
PPI_BIOGRID_M951105-0.81538-1.82930.0089290.010809
PPI_BIOGRID_M190117-0.79575-1.81760.0169490.012359
PPI_BIOGRID_M819108-0.80619-1.811700.012656

From the results of KEGG pathway GSEA (Table 1), Primary immunodeficiency, Th1 and Th2 cell differentiation, Chemokine signaling pathway, T cell receptor signaling pathway, Th17 cell differentiation, and Fc gamma R-mediated phagocytosis were associated with immunity. From the results of Panther Pathway GSEA (Table 2), T cell activation, B cell activation, Inflammation mediated by chemokine and cytokine signaling pathway, Interleukin signaling pathway, and Toll receptor signaling pathway were associated with immunity. From the results of Reactome Pathway GSEA (Table 3), Defensins, Translocation of ZAP-70 to Immunological synapse, Generation of second messenger molecules, Costimulation by the CD28 family, PD-1 signaling, Interleukin-2 family signaling, Interleukin-10 signaling, Interleukin-3, Interleukin-5 and GM-CSF signaling, DAP12 interactions, Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell, Phosphorylation of CD3 and TCR zeta chains, DAP12 signaling, Interleukin receptor SHC signaling, Antigen activates B Cell Receptor (BCR) leading to generation of second messengers, RHO GTPases Activate NADPH Oxidases, Chemokine receptors bind chemokines, Interferon-gamma signaling, and Regulation of actin dynamics for phagocytic cup formation were associated with immunity. From the results of Wikipathway GSEA analysis (Table 4), T-Cell antigen Receptor (TCR) Signaling Pathway, T-Cell antigen Receptor (TCR) pathway during Staphylococcus aureus infection, Allograft Rejection, IL-3 Signaling Pathway, Type II interferon signaling (IFNG), Interactions between immune cells and microRNAs in the tumor microenvironment, Cancer immunotherapy by PD-1 blockade, IL-2 Signaling Pathway, IL-9 Signaling Pathway, IL-7 Signaling Pathway, Macrophage markers, Chemokine signaling pathway, Selective expression of chemokine receptors during T-cell polarization, Cancer immunotherapy by CTLA4 blockade, T-Cell Receptor and Co-stimulatory Signaling, B Cell Receptor Signaling Pathway, Inflammatory Response Pathway, and IL-5 Signaling Pathway were associated with immunity. From the results of Gene ontology Biological Process GSEA (Table 5), the GO terms cellular defense response, interleukin-2 production, interferon-gamma production, leukocyte proliferation, lymphocyte activation involved in immune response, leukocyte cell-cell adhesion, myeloid dendritic cell activation, adaptive immune response, T cell activation, interleukin-4 production, cytokine metabolic process, tumor necrosis factor superfamily cytokine production, response to chemokine, natural killer cell activation, regulation of leukocyte activation, B cell activation, immune response-regulating signaling pathway, and interleukin-10 production were associated with immunity. From the results of the Gene ontology Cellular Component GSEA (Table 6), the GO terms MHC protein complex, immunological synapse, and mast cell granule were associated with immunity. From the results of Gene ontology Molecular Function GSEA (Table 7–10) the GO terms MHC protein binding, cytokine receptor activity, immunoglobulin binding, antigen binding, and cytokine binding were associated with immunity.

The relationship between TILs, immunostimulators, MHC molecules, chemokines, and chemokine receptors and the expression and DNA methylation of ARHGAP30 in lung adenocarcinoma

The relationship between ARHGAP30 expression and TILs, immunostimulators, MHC molecules, chemokines, and chemokine receptors in LUAD

Figures 8A, 9A, 10A, 11A, 12A, respectively, show heat maps of the relationship between the abundance of TILs, immunostimulators, MHC molecules, chemokines, and chemokine receptors and the expression of ARHGAP30. These heatmaps were mostly red, indicating that most of the TILs, immunostimulators, MHC molecules, chemokines, and chemokine receptors correlated positively with the expression of ARHGAP30. Also, dark red areas indicated that some of them had a strong positive correlation with the expression of ARHGAP30.

Figure 8.
The correlation between the abundance of tumor-infiltrating lymphocytes (TILs) and expression of ARHGAP30. (A) Heat map of the relationship between the abundance of TILs and ARHGAP30 expression. (B1–B28) Scatter plots showing the positive correlation between ARHGAP30 expression and TILs in the treatment of lung adenocarcinoma. Act_CD8, Activated CD8 T cell; Tcm_CD8, Central memory CD8 T cell; Tem_CD8, Effector memory CD8 T cell; Act_CD4, Activated CD4 T cell; Tcm_CD4, Central memory CD4 T cell; Tem_CD4, Effector memory CD4 T cell; Tgd, Gamma delta T cell; Tfh, T follicular helper cell; Th1, Type 1 T helper cell; Th17, Type 17 T helper cell; Th2, Type 2 T helper cell; Treg, Regulatory T cell; MDSC, Myeloid derived suppressor cell; Act_B, Activated B cell; Imm_B, Immature B cell; Mem_B, Memory B cell; NK, Natural killer cell; CD56brigh, CD56bright natural killer cell; CD56dim, CD56dim natural killer cell; NKT, Natural killer T cell; Act_DC, Activated dendritic cell; iDC, Immature dendritic cell; pDC, Plasmacytoid dendritic cell; Mast, Mast cell.
Figure 9.
The correlation between the abundance of tumor-infiltrating lymphocytes (TILs) and the methylation of ARHGAP30. (A) Heat map of the relationship between the abundance of TILs abundance and ARHGAP30 DNA methylation. (B1–B39) Scatter plots showing the negative correlation between ARHGAP30 DNA methylation and TILs in the treatment of lung adenocarcinoma. Act_CD8, Activated CD8 T cell; Tcm_CD8, Central memory CD8 T cell; Tem_CD8, Effector memory CD8 T cell; Act_CD4, Activated CD4 T cell; Tcm_CD4, Central memory CD4 T cell; Tem_CD4, Effector memory CD4 T cell; Tgd, Gamma delta T cell; Tfh, T follicular helper cell; Th1, Type 1 T helper cell; Th17, Type 17 T helper cell; Th2, Type 2 T helper cell; Treg, Regulatory T cell; MDSC, Myeloid derived suppressor cell; Act_B, Activated B cell; Imm_B, Immature B cell; Mem_B, Memory B cell; NK, Natural killer cell; CD56brigh, CD56bright natural killer cell; CD56dim, CD56dim natural killer cell; NKT, Natural killer T cell; Act_DC, Activated dendritic cell; iDC, Immature dendritic cell; pDC, Plasmacytoid dendritic cell; Mast, Mast cell.
Figure 10.
The correlation between the expression of ARHGAP30 and immune inhibitors. (A) Heat map of Spearman correlations between ARHGAP30 expression and immune inhibitors across human cancers. (B1–B21) Scatter plots showing the positive correlation between ARHGAP30 expression and immune inhibitors in the treatment of lung adenocarcinoma.
Figure 11.
The correlation between the DNA methylation of ARHGAP30 and immune inhibitors. (A) Heat map of Spearman correlations between DNA methylation of ARHGAP30 and immune inhibitors across human cancers. (B1–B30) Scatter plots showing the negative correlation between DNA methylation of ARHGAP30 and immune inhibitors in the treatment of lung adenocarcinoma.
Figure 12.
The correlation between the expression of ARHGAP30 and immunostimulators. (A) Heat map of Spearman correlations between ARHGAP30 expression and immunostimulators across human cancers. (B1–B15) Scatter plots showing the positive correlation between ARHGAP30 expression and immunostimulators in the treatment of lung adenocarcinoma.

Figure 8B1–8B28 show scatter plots of the relations the abundance of TILs and ARHGAP30 expression. The results showed that effector memory CD8 T cells, T follicular helper cells, type 1 T helper cells, regulatory T cells, myeloid derived suppressor cells, activated B cells, immature B cells, natural killer cells, natural killer T cells, macrophages, eosinophils, and mast cells showed a strong positive correlation with the expression of ARHGAP30 in LUAD (Spearman correlation coefficient, r > 0.6; p value < 0.01). Figure 9B1–9B39 shows scatter plots of the relationship between the abundance of immunostimulators and ARHGAP30 expression. The results showed that C10orf54, CD28, CD40LG, CD48, CD80, CD86, ICOS, KLRK1, LTA, and TNFRSF8 showed a strong positive correlation with the expression of ARHGAP30 in LUAD (Spearman correlation coefficient, r > 0.6; p value < 0.01). Figure 10B1–10B21 show scatter plots of the relationship between the abundance of MHC molecules and ARHGAP30 expression. The results showed that HLA-DMB, HLA-DOA, HLA-DPA1, HLA-DPB1, HLA-DQA1, and HLA-DRA showed a strong positive correlation with the expression of ARHGAP30 in LUAD (Spearman correlation coefficient, r > 0.6; p value < 0.01). Figure 11B1–11B30 show scatter plots of the relationship between the abundance of chemokines and ARHGAP30 expression. The results showed that CCL19 showed a strong positive correlation with the expression of ARHGAP30 in LUAD (Spearman correlation coefficient, r > 0.6; p value < 0.01). Figure 12B1–12B15 show scatter plots of the relationship between the abundance of chemokine receptors and ARHGAP30 expression. The results showed that CCR1, CCR2, CCR4, CCR5, CCR6, CCR7, CCR8, CXCR3, CXCR5, and CXCR6 showed a strong positive correlation with the expression of ARHGAP30 in LUAD (Spearman correlation coefficient, r > 0.6; p value < 0.01).

The relationship between DNA methylation of ARHGAP30 and TILs, immunostimulators, MHC molecules, chemokines, and chemokine receptors in LUAD

Figures 13A and Supplementary Figures 10A, 11A, 12A, 13A, respectively, show heat maps of the relationship between TILs, immunostimulators, MHC molecules, chemokines, and chemokine receptors and DNA methylation of ARHGAP30. The results showed that in LUAD, most of them were blue, indicating that most of the TILs, immunostimulators, MHC molecules, chemokines, and chemokine receptors correlated negatively with DNA methylation of ARHGAP30. Also, some of them were very dark blue, indicating that they had a strong negative correlation with DNA methylation of ARHGAP30.

Figure 13.
The correlation between the DNA methylation of ARHGAP30 and Immunostimulators. (A) Heat map of Spearman correlations between DNA methylation of ARHGAP30 and immunostimulators across human cancers. (B1–B28) Scatter plots showing the negative correlation between DNA methylation of ARHGAP30 and immunostimulators in the treatment of lung adenocarcinoma.

Figure 13B1–13B28 show scatter plots of the relationship between the abundance of TILs and DNA methylation of ARHGAP30. The results showed that activated B cell, immature B cell, myeloid derived suppressor cell, natural killer T cell, effector memory CD8 T cell, type 1 T helper cell, and regulatory T cell had a strong negative correlation with the DNA methylation of ARHGAP30 in LUAD (Spearman correlation coefficient, r < - 0.6; p value < 0.01). Supplementary Figure 10B1–10B39 show scatter plots of the relationship between the abundance of immunostimulators and DNA methylation of ARHGAP30. The results showed that CD28, CD48, LTA, and TNFRSF8 had a strong negative correlation with the DNA methylation of AGHGAP30 in LUAD (Spearman correlation coefficient, r < - 0.6; p value < 0.01). Supplementary Figure 11B1–11B21 show scatter plots of the relationship between the abundance of MHC molecules and DNA methylation of ARHGAP30. Supplementary Figure 12B1–12B30 show scatter plots of the relationship between the abundance of chemokines and DNA methylation of ARHGAP30. Supplementary Figure 13B1–13B15 show scatter plots of the relationship between the abundance of chemokine receptors and DNA methylation of ARHGAP30. The results showed that CCR5 and CCR6 had a strong negative correlation with the DNA methylation of ARHGAP30 in LUAD (Spearman correlation coefficient, r < - 0.6; p value < 0.01).

Discussion and conclusions

In this study, we showed that the expression of ARHGAP30 in LUAD tissues was significantly lower than that in normal tissues. There were differences in ARHGAP30 mRNA expression levels in patients with LUAD with different sexes, cancer stages, and nodal metastatic status (Figure 1). The expression of ARHGAP30 in LUAD tissues was significantly lower in the presence of KEAP1 and STK11 mutations. The correlation between DNA methylation of ARHGAP30 and its mRNA expression levels was considerably higher in LUAD tissues than in normal tissues (Figure 2). There are some studies on the differential expression of ARHGAP30 in cancer [8, 34, 35]. The high DNA methylation level of ARHGAP30 might also be one of the reasons for the decreased ARHGAP30 expression in LUAD tissues. Genetic mutations in KEAP1 and STK11 might also be another reason for decreased expression of ARHGAP30 in LUAD tissues. These were not reported in previous studies.

Patients with LUAD with low ARHGAP30 expression had a significantly better prognosis than those with high ARHGAP30 expression (Figure 3). A study by Mao and Tong [35] also supports this point. Although some prognostic molecular markers have been found in patients with LUAD [36–43], ARHGAP30 might be developed as a molecular marker to evaluate the prognosis of patients with LUAD after surgery or in patients with advanced disease. We identified genes, miRNAs, and lncRNAs that were highly associated with ARHGAP30 in LUAD (Figures 4–6), which could provide new ideas and targets for epigenetic studies of ARHGAP30 in LUAD.

We identified many pathways related to tumor immunity from the enrichment results of KEGG Pathway, Panther Pathway, Reactome Pathway, and Wikipathway (Figures 7, 14 and Supplementary Figures 1–3). Recent studies have demonstrated a close relationship between Rho GTPases and the development and metastasis of a variety of human tumors [7]. KEGG pathways included Primary immunodeficiency, Th1 and Th2 cell differentiation, Chemokine signaling pathway, T cell receptor signaling pathway, Th17 cell differentiation, and Fc gamma R-mediated phagocytosis. Panther pathways included T cell activation, B cell activation, Inflammation mediated by chemokine and cytokine signaling pathway, Interleukin signaling pathway and Toll receptor signaling pathway. Reactome Pathways Defensins, Translocation of ZAP-70 to Immunological synapse, Generation of second messenger molecules, Costimulation by the CD28 family, PD-1 signaling, Interleukin-2 family signaling, Interleukin-10 signaling, Interleukin-3, Interleukin-5 and GM-CSF signaling, DAP12 inter-actions, Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell, Phosphorylation of CD3 and TCR zeta chains, DAP12 signaling, Interleukin receptor SHC signaling, Antigen activates B Cell Receptor (BCR) leading to generation of second messengers, RHO GTPases Activate NADPH Oxidases, Chemokine receptors bind chemokines, Interferon gamma signaling and Regulation of actin dynamics for phagocytic cup formation. Wikipathways included T-Cell antigen Receptor (TCR) Signaling Pathway, T-Cell antigen Receptor (TCR) pathway during Staphylococcus aureus infection, Allograft Rejection, IL-3 Signaling Pathway, Type II interferon signaling (IFNG), Interactions between immune cells and microRNAs in tumor microenvironment, Cancer immunotherapy by PD-1 blockade, IL-2 Signaling Pathway, IL-9 Signaling Pathway, IL-7 Signaling Pathway, Macrophage markers, Chemokine signaling pathway, Selective expression of chemokine receptors during T-cell polarization, Cancer immunotherapy by CTLA4 blockade, T-Cell Receptor and Co-stimulatory Signaling, B Cell Receptor Signaling Pathway, Inflammatory Response Pathway, and IL-5 Signaling Pathway.

Figure 14.
Immune-related statistically significant KEGG pathway annotations. (A) Chemokine signaling pathway (hsa04062). (B) Th1 and Th2 cell differentiation (hsa04658). (C) Th17 cell differentiation (hsa04659). (D) T cell receptor signaling pathway (hsa04660). (E) Fc gamma R-mediated phagocytosis (hsa04666). (F) Primary immunodeficiency (hsa05340). Red denotes leading-edge genes; green denotes the remaining genes.

We further observed that the levels of TILs, immunostimulators, MHC molecules, chemokines, chemokine receptors and ARHGAP30 expression correlated positively in LUAD (Figures 8–13); however, these factors correlated negatively with the DNA methylation level of ARHGAP30 (Supplementary Figures 10–13). Anti-tumor immunotherapy is promising treatment modality in the fight against tumors; however, previous application found that its efficacy was not as good as expected. Through in-depth studies, it has been found that immune tolerance in the tumor microenvironment might be the most important reason leading to the unsatisfactory effects of immunotherapy [44, 45]. Defects in the development or function of CD8+ cytotoxic T lymphocytes (CTLs), CD4+ Th1 helper T cells, or natural killer (NK) cells all lead to more frequent tumorigenesis and/or more rapid growth [46]. Immunostimulators could accumulate in tumors and significantly inhibit tumor growth [47]. A tumor can escape T cell reactions by losing major histocompatibility complex (MHC) molecules [48]. Chemokines and chemokine receptors mediate the host response to cancer by directing leukocytes into the tumor microenvironment [49, 50]. Our results supported the above points. ARHGAP30 expression correlated positively with TILs, immunostimulators, MHC molecules, chemokines, and chemokine receptors in LUAD (Figures 8–12), which might be related to the significantly reduced ARHGAP30 expression in LUAD. Levels of TILs, immunostimulators, MHC molecules, chemokines, and chemokine receptors were decreased in LUAD. Reduced or functional defects in tumor immune function result in more frequent occurrence and more rapid proliferation and growth of LUAD.

Therefore, we proposed that DNA methylation of ARHGAP30 and mutations in KEAP1 and STK11 genes inhibit ARHGAP30 expression in LUAD. Decreased ARHGAP30 expression might inhibit TILs, immunostimulators, MHC molecules, chemokines, and chemokine receptors in lung adenocarcinoma through pathways identified in the enrichment analysis, which in turn inhibits tumor immunity and ultimately promotes the formation and growth of LUAD.

Our study is the first to perform prognostic analysis and evaluation of ARHGAP30 in patients with LUAD, to carry out GSEA of ARHGAP30, and to investigate the relationship between ARHGAP30 and TILs, immunostimulators, MHC molecules, chemokines, and chemokine receptors in LUAD. These findings have important implications for the diagnosis, prognostic evaluation, and cancer immunotherapy of patients with LUAD Our study was limited by a lack of further experimental validation. We could also assess the relationship of ARHGAP30 with other types of lung cancer to determine the specific role of ARHGAP30 expression in the diagnosis and treatment of different types of lung cancer.

Overall, our results suggest that DNA methylation of ARHGAP30, as well as mutations in KEAP1 and STK11, inhibit ARHGAP30 expression in LUAD, which in turn promotes LUAD formation and growth through multiple pathways that suppress tumor infiltrating immunity, thus contributing to poor prognosis of patients with LUAD.

Materials and Methods

We used the Oncomine 4.5 [10] database to analyze the differential expression of ARHGAP30 in various cancers and in the Hou lung, Selamat lung, and Okayama lung adenocarcinoma datasets. We used the SurvExpress [11] database to analyze the differential expression of ARHGAP30 in two lung adenocarcinoma datasets. We used the GEPIA [12] database to analyze the differential expression of ARHGAP30 in lung adenocarcinoma. We used the Warner [13] database to explore the abundance of different exons of the ARHGAP30 gene in normal and tumor tissues of patients with LUAD. We used the Ualcan [14] database to analyze the differences of ARHGAP30 mRNA expression in subgroups of patients with lung adenocarcinoma patients according to sample type, individual cancer stage, ethnicity, sex, age, smoking habit, nodal metastasis status, and TP53 mutation status. We used the Ualcan [14] and CPTAC [15] databases to analyze the differential expression of ARHGAP30 protein in patients with LUAD stratified by sample type, individual cancer stage, ethnicity, sex, age, weight, tumor grade, and tumor histology.

We used the TCGA portal [16] database to analyze the differential expression of ARHGAP30 after highly mutated gene mutation. We also used the TCGA portal database to analyze the correlation between ARHGAP30 gene expression and DNA methylation in primary tumors and normal tissue samples. We analyzed the mRNA expression of ARHGAP30 in LUAD before and after mutation of highly mutated genes (KEAP1, STK11) using the Linkedomics [17] database. We analyzed the heatmap of ARHGAP30 methylation in lung adenocarcinoma using the MethSurv [18] database. The Kaplan–Meier plots of patients with LUAD assessed using different ARHGAP30 methylation probes (cg07837534 and cg00045607) were analyzed.

We used GEPIA [12], Oncolnc [19], Ualcan [14], UCSC [20], TCGAportal [16], TISIDB [21], KMplot [22], TIMER [23], Linkedomics [17], and PrognoScan [24] databases to analyze the overall survival (OS) curves for patients with LUAD. We used the GEPIA [12] database to analyze the disease-free survival (DFS) curves for patients with LUAD (in months and days, respectively). We used the PrognoScan database to analyze the recurrence-free survival (RFS) curves in patients with LUAD.

We analyzed the genes and mRNAs that were highly associated with ARHGAP30 in LUAD using the Linkedomics [17] database and obtained the corresponding volcano plots, heat plots, and scatter plots. We analyzed the lncRNAs that were highly associated with ARHGAP30 in LUAD using the TANRIC [25] database and obtained the corresponding scatter plots and survival curves.

We used the TISIDB [21] database to analyze the relationship between TILs, immunostimulators, MHC molecules, chemokines, chemokine receptors and the expression and DNA methylation of ARHGAP30 in LUAD.

Statistical methods

We used a t-test to analyze the differential expression levels of ARHGAP30 in normal and tumor samples. We analyzed the DNA methylation expression levels of ARHGAP30 in normal and tumor samples using the Wilcoxon rank sum test. We used Pearson correlation [51–54] to analyze ARHGAP30-associated genes, miRNAs, and lncRNAs. We performed survival analysis and plotted Kaplan–Meier curves for ARHGAP30. We performed gene set enrichment analysis (GSEA) [26] of ARHGAP30 for KEGG Pathway [27], Panther Pathway [28], Reactome Pathway [29], Wikipathway [30], Gene ontology Biological Process [31, 32], Gene ontology Cellular Component [31, 32], Gene ontology Molecular Function [31, 32], Kinase Target Network, Transcription Factor Network, and PPI BIOGRID Network [33].

Ethics approval and declaration

This study was approved by the academic ethics review board of the Second Affiliated Hospital of Nanchang University. Human participants and research animals were not involved in this study. All software applications are freely and publicly available without custom code. All data in this article were obtained from publicly available databases, and all the data and pictures in this article are authorized.

Supplementary Materials

Supplementary Figures
Supplementary Tables

Author Contributions

Conceptualization, S.H. and YP.W.; methodology, S.H., JY. Y, WB. Z, WX. Z, Y.Z, DY. Z, JJ.X, DL.Y, YP. W, J.P; software, S.H.; validation, X.X., Y.Y. and Z.Z.; formal analysis, S.H.; investigation, S.H.; resources, S.H.; data curation, S.H.; writing—original draft preparation, S.H., JY. Y, WB. Z, WX. Z, Y.Z, DY. Z, JJ.X, DL.Y, YP. W, J.P; writing—review and editing, S.H., YP. W., JH. P; visualization, S.H.; supervision, S.H., YP. W., JH. P.; project administration, S.H., YP. W., JH. P.; funding acquisition, YP.W.

Acknowledgments

We are grateful to the staff from the Department of Thoracic Surgery of the Second Affiliated Hospital of Nanchang University, China for their support during the preparation of this manuscript.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Funding

This work was supported by grants from the National Natural Science Foundation of China [grant number 81860379], the Preeminence Youth Fund of Jiangxi Province [grant number 20162BCB23058], and the Natural Science Foundation of Jiangxi Province, China [grant number 20171BAB 205075].

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