Discovery of differentially expressed proteins for CAR-T therapy of ovarian cancers with a bioinformatics analysis

Results

Identification and functional examination of differentially expressed genes (DEGs)

The selected gene set enrichments (GSEs), namely GSE36668 [9], GSE27651 [10], GSE26712 [11, 12], and GSE14407 [13], were microarray datasets from OC patient samples, and a detailed profile of the datasets is provided in Table 1. Based on a log [fold change (FC)] cutoff of > 1.5 and a p-value of < 0.5, the gene distribution of the sample is clearly visible on the left and right of the volcano plot to ensure the significance of their expressions in the sample. Based on a Venn analysis, it was found that the numbers of unique genes from the GSEs were sequentially 1484, 586, 297, and 1374 genes, and 153 genes, which were DEGs, were expressed in all four GSEs (Figure 1A–1C). Furthermore, results of the gene ontology (GO) term and pathway analysis of DEGs showed a high confidence score (false discovery rate; FDR) for GO biological processes (BPs) and cellular components (CCs) and a low confidence score for GO molecular functions (MFs). GO terms that involved the most GSEs were GO:0005515-protein binding (125 genes: p < 0.001), GO:0050896-response to stimulus (102 genes: p < 0.0001), and GO:0005886-plasma membrane (60 genes: p < 0.001). Based on the results of this analysis, it can be seen that expressed proteins were dominated by those located in plasma membranes and played roles in responding to stimuli, including binding between proteins. Furthermore, the pathway analysis of DEGs showed a relatively low average confidence score (p-value). The induced pathways with the highest confidence scores (p < 0.0001) were hsa05200: Pathways in Cancer (KEGG: 16 genes) and R-HSA-162582: Signal Transduction (Reactome: 40 genes). Based on the top pathway that was induced in general, it is known that several DEGs act as oncogenes, which can be seen from the term-related pathways to cancer, especially in the Wnt and Janus kinase (JAK)-signal transduction and activator of transcription (STAT) signaling pathways (Figure 1D, 1E).

Table 1. Ovarian cancer patient gene expression omnibus (GEO) microarray profiles.

GEO profile	Type	Source	No. of cases	No. of controls	Platform	Annotation platform
GSE36668	mRNA	Serous ovarian carcinoma	8	4	GPL570	[HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array
GSE27651	mRNA	High-grade serous ovarian carcinomas	22	6	GPL570	[HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array
GSE26712	mRNA	Ovarian cancer	185	10	GPL96	[HG-U133A] Affymetrix Human Genome U133A Array
GSE14407	mRNA	Serous ovarian cancer epithelia	12	12	GPL570	[HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array

Figure 1. Determination of gene set enrichments (GSEs) and differentially expressed genes (DEGs). (A) Volcano plot of gene distributions in control and ovarian cancer samples. Gray dots represent genes that are not significantly expressed between normal and ovarian cancer cell samples. (B) Venn diagram of overlapping gene between four GSEs from which we obtained 153 DEGs. Also, the highest unique genes are GSE36668, GSE14407, and GSE27651. (C) List of DEGs. (D) Pathway terms related to DEGs, colored by –log10(p-values). (E) Gene ontology terms related to DEGs, colored by –log10 (p-values). (F) Plasma membrane-related genes from six GO terms (p > 0.05) with localization confidence scores. A higher score means a greater probability that the protein will be situated there.

Prediction of oncogenicity of plasma membrane-related genes as CAR antigens

In the analysis of target antigen CARs, in addition to significant oncogenicity, the protein must be located in the plasma membrane for CARs to interact and work effectively. A DEG localization analysis was performed to determine the protein position of each expressed gene. Results of the GO term analysis showed 60 selected plasma membrane-related genes (PMGs), and their confidence scores were analyzed using the GeneCard database. The result was an average confidence score of 4.6, with details of 56 genes at > 3 and 4 genes at 3. This means that PMGs are known to have a high probability of being found in plasma cell membranes (Figure 1F). The oncogenicity of PMGs was analyzed based on their expressions in OC and normal samples. As a result, it was found that 21 genes were highly expressed and 25 genes were poorly expressed in OC. In contrast, 14 expressed genes did not significantly differ in expression levels between OC and normal tissue in the database. (Supplementary Figure 1) To analyze further, 18 CAR clinical trial antigen genes were selected as control genes, which were examined for their associations with 19 highly expressed genes (CD24 and FOLR1, which had been used in clinical trials, so they were categorized as control genes) by gene ontology (GO) terms, pathways, expressions, and correlations. (Figure 2A, 2B). The control genes listed have been or are being developed as treatment targets using CAR T cells [14–24]. We obtained the predicted genes in this study and compared their potential bioactivity against control genes. (Table 2, Figure 2C).

Table 2. Clinical trial antigens for chimeric antigen receptors (CARs) and used as control genes.

Gene	NCT	Gene transfer	Reference
CEACAM5	01212887	RV	[14]
CXCR1	n.p	TF	[15, 16]
EGFR	01869166	LV	[14]
FSHR	n.p	RV	[16]
KLRK1	03018405	n.p.	[14]
PDL1	n.p	LI	[16, 17]
ANXA2	n.p	LV	[16, 18]
CD133	02541370	RV	[14]
CTAG1B	02366546	RV	[14]
ERBB2	01935843	LV	[29–31]
L1CAM	n.p	LV	[16, 20]
MAGEA4	02096614	RV	[14]
MSLN	02580747	RV, mRNA, LV	[14, 16, 21]
MUC16	02498912	RV, LI	[14, 16, 17]
TPBG	n.p	LV	[16, 22]
WT1	00562640	n.p.	[14]
CD24	n.p	LV	[14, 23]
FOLR1	00019136	RV, LV	[14, 16, 24]
Abbreviation: RV, retroviral vector; LV, lentiviral vector; GT, mRNA gene transduction; TF, mRNA transfection; LI, lentiviral infection; n.p., not provided.

Figure 2. Oncogenicity analysis. (A) Twenty-one genes were significantly expressed in ovarian cancer among plasma membrane-related genes. (B) Plasma membrane-related genes expressions in ovarian cancer. (C) Overlap between gene lists: high expression gene list (Predicted) and Control gene list, (D) including the shared term level, where blue curves connect genes that share an enriched ontology term. Gene lists are represented by the inner circle, with hits arranged along the arc. Genes that appear on multiple lists are depicted in dark orange, while genes that only appear on one list are displayed in light orange. (E) Heatmap of enriched terms across input gene lists, colored by p-values. (F) Heatmap of biological processes across input gene lists, colored by p-values. (G) Pattern genes related to predicted and control genes. (H) DisGeNET is a discovery platform containing one of the largest publicly available collections of genes and variants associated with human diseases. The heatmap is colored by p-values. Dark orange indicates a greater probability that bioactivity will occur.

Enrichment analysis of predicted and control genes

Bioactivity, diseases, and expression-specific analyses were carried out to analyze the potential of these 37 genes (highly expressed genes are now called predicted genes). Based on a bioactivity enrichment and pathway analysis, control genes were predicted to have GO enrichment, including T cell costimulation (GO: 0031295) and renal filtration cell differentiation (GO: 0061318); the predicted pathways were endocytosis (hsa04144), GPCR downstream signaling (R-HAS-388396), and extracellular vesicle-mediating signaling in recipient cells (WP2870). Meanwhile, predicted genes were predicted to have GO enrichment, including endocytosis (GO:0006897), inorganic anion transport (GO:0015698), myelination (GO:0042552), metal ion transport (GO:0030001), and mitotic cell cycle process (GO:1903047). Also, the predicted genes induced a pathway in the epithelial-to-mesenchymal transition in colorectal cancer (WP4239). Furthermore, based on the top evidence of GO BP, the predicted gene was predicted to induce fewer BPs than the control gene. According to these results, it could also be predicted that the BP data related to the predicted gene were still limited, so the chance of discovery was higher (Figure 2E, 2F).

Additionally, based on a disease-related gene analysis result, it was found that the control gene was related to the top 20 diseases by confidence scores, compared to the predicted gene, which was only predicted to be related to nine diseases. Based on the confidence scores, C1368683: epithelioma, C0025500: mesothelioma, and C0007138: carcinoma, was the disease with the highest confidence score, where genes in both groups were predicted to play roles in the disease. In addition, the control gene was only expressed (p < 0.05) in lung (PGB:00018). In contrast, the predicted gene was expressed (p < 0.05) in renal epithelial (PGB:00119), colon tissue (PGB:00007), and K562 cell line [25], which are lymphoblast cells isolated from the bone marrow of a 53-year-old chronic myelogenous leukemia patient (Figure 2G, 2H).

Protein-protein interaction (PPI) network analysis

To discover more pathways that the 37 genes may induce, a network was built by adding 100 neighboring genes as enriched genes. The PPI network analysis was used because it was proven to be a valuable tool in enhancing the efficacy of CAR T cell therapy by providing insights into critical protein interactions [26], optimizing T cell functions [27], and identifying potential therapeutic targets [28]. Based on the network formed, it was found that EGFR and ERBB2 were genes with the highest betweenness-centrality scores in the control group, while MUC1 and CXCR4 were genes with the highest betweenness-centrality scores among predicted genes. In addition to those two genes, EPCAM, RACGAP1, UBE2C, PRAME, SORT1, JUP, and CLDN3 sequentially had the top seven betweenness-centrality scores. Based on MCODE clustering, it was found that there were nine clusters in the main network. Some of the predicted genes in the cluster included JUP (cluster 2), CXCR4 (cluster 4), RSCGAP1 and UBE2C (cluster 5), MUC1 (cluster 6), CLDN3, and CLDN4 (cluster 8) (Figure 3A, 3B). Furthermore, GO and pathway analyses were also carried out to evaluate which bioactivities and pathways were associated with this network. Based on the GO analysis, the top two from each GO category are shown in Figure 3C. At the same time, MUC1, CXCR4, EPCAM, RACGAP1, UBE2C, PRAME, SORT1, JUP, and CLDN3 were labeled as the recommendation of tumor-associated antigens (TAAr) in a subsequent analysis.

Figure 3. Protein-protein interaction (PPI) network analysis. (A) PPI network of 37 enriched genes (20 control and 17 predicted genes) with 100 genes, which were analyzed with NetworkAnalyzer and CytoHubba for node and edge scoring. Nine genes with the highest betweenness-centrality scores were designated as TAAr. (B) Clustered network with the Molecular Complex Detection (MCODE) algorithm. (C) Gene ontology (GO) terms related to control and predicted genes. (C) GO term related to control and predicted genes. (D) Interaction network of the TAAr gene: yellow genes are TAAr, and blue genes are interactor proteins. (STRING database, high confidence: 0.7). (E) GeneMania network of TAAr. The nine biggest nodes with shading are TAAr genes. Edge width is labeled for confidence scores.

Furthermore, to further explore the TAAr genes, co-expression and correlation analyses were carried out on control genes; this analysis was carried out to see relationships between gene expressions that occurred. Based on a STRING co-expression analysis in Homo sapiens (cutoff, high confidence: 0.7), it was found that several TAAr genes were co-expressed with control genes. The top five co-expression scores were for RACGAP1-UBE2C, CLDN3-EPCAM, CD24-EPCAM, CTAG1B-MAGEA4, and MSLN-MUC1. Then, a correlation analysis was performed to explore correlations of TAAr with control genes specific to the OC sample. TAAr interactions with control genes with the highest positive correlation scores were JUP-ERBB2, MUC1-MUC16, MUC1-CEACAM5, CXCR4-WY1, and SORT1-MUC16. In contrast, interactions with the lowest negative correlation scores were UBE2C-MSLN, EPCAM-KLRK1, RACGAP1-MSLN, UBE2C-EGFR, and UBE2C-CXCR1 (Figure 4A, 4B).

Figure 4. Correlation, co-expression, and pathway analysis of TAAr. (A) Co-expressed genes are colored by a dot; a darker dot means a higher co-expression score. Co-expression score: the higher the score, the higher the probability that co-expression will occur. (B) Spearman’s correlation scores for gene expressions in ovarian cancer (OC), which are comprised of 27 proteins consisting of nine TAAr and 18 control genes. (C) The complete pathway of the Reactome database related to TAAr. This pathway was constructed with the Voronoi tessellation method termed ReacFoam, which provides user-friendly access and visualization. (D) Pathway related to reproduction where the specification of primordial germ cells pathway is located. (E) Rho GTPase-related and neighbor pathways. (F) The expression data of TAAr in brain, retina, and liver tissue, provided by GeneCard, were used to analyze the possible toxic effects of targeting TAAr.

A further co-expression analysis was conducted using GeneMania to investigate other genes correlated with TAAr expressions. We obtained other proteins, CLDN1, CLDN4, CLDN7, cytochrome P450 family 27 subfamily B member 1 (CYP27B1), polypeptide N-acetylgalactosaminyltransferase 12 (GALNT12), Homeobox A4 (HOXA4), nerve growth factor (NGF), Rho family GTPase 2 (RND2), SSH3 and multiple ankyrin repeat domain 2 (HANK2), solute carrier family 26 member 8 (SLC26A8), sorting nexin 7 (SNX7), serine/threonine-protein kinase 19 (STK19), and ubiquitin protein ligase E3D (UBE3D), which developed interactions with and were co-expressed with TAAr. Several other proteins appeared in the pathway we highlighted for each TAAr, but others did not, so we will not examine them further (Figure 3D, 3E and Table 3).

Table 3. Networks according to a GeneMania analysis.

Network	Interaction from	Coverage	Proteins
Co-expression	Burington-Shaughxnessy-2008	25.46%	CLDN3, CYP27B1, EPCAM, HOXA4, JUP, MUC1, PRAME, SHANK2, SNX7, SORT1, UBE2C
Co-expression	Dobbin-Giordano-2005	14.07%	CLDN4, CLDN7, EPCAM, JUP, MUC1, SNX7, SORT1
Co-expression	Bild-Nevins-2006 B	11.68%	CLDN3, CLDN4, JUP, MUC1, STK19
Co-expression	Ramaswamy-Golub-2001	10.98%	CLDN3, CLDN4, EPCAM
Co-expression	Perou-Botstein-2000	8.30%	CLDN1, CLDN3, CLDN4, CLDN7, EPCAM, MUC1
Co-expression	Wu-Garvey-2007	8.16%	CLDN3, CLDN4, CLDN7, CYP27B1, MUC1, NGF, STK19, UBE2C
Co-expression	Roth-Zlotnik-2006	7.68%	CLDN3, CLDN4, CLDN7, EPCAM, GALNT12, PRAME, SLC26A8
Co-expression	Wang-Cheung-2015	2.33%	CLDN4, CLDN7, JUP, NGF, RACGAP1, SHANK2, UBE2C
Co-expression	Arijs-Rutgeers-2009	0.49%	CLDN1, CLDN3, CLDN4, GALNT12, MUC1, PRAME, RACGAP1, RND2, UBE2C, UBE3D
Physical Interaction	Biogrid-small-scale-studies	4.13%	ANAPC11, BDNF, CLDN1, CLDN3, CLDN5, GALNT4, GALNT15, JUP, MUC1, NGF, PRAME, RACGAP1, RND2, SLC26A8, STK19, SORCS1, SORT1, UBE2C, UBE3D
Physical Interaction	IREF-hprd	2.14%	ANAPC11, BDNF, CDLN1, CLDN3, CLDN5, CLDN7, EPCAM, GALNT12, GALNT15, JUP, NGF, PRAME, RACGAP1, RND2, SLC16A10, SLC26A8, SORT1, STK19, UBE2C
Physical Interaction	IREF-biogrid	1.79%	ANAPC11, CLDN1, CLDN3, CLDN5, GALNT4, GALNT12, MUC1, JUP, RACGAP1, RND2, UBE2C
Genetic Interaction	Lin-Smith-2010	2.80%	CLDN1, CXCR4, EPCAM, GALNT13, GALNT15, HOXA4, JUP, MUC1, NGF, PRAME, RACGAP1, RND2, SHANK2, SLC16A10, SLC26A8, SNX7, SORCS1, UBE3D
*) Underlined proteins are the recommendation of tumor-associated antigens (TAAr).

Enrichment analysis of a novel tumor-associated antigen candidate for ovarian cancer

Three pathways with the highest confidence scores based on Reactome were primordial germ cell specification, RhoB GTPase cycle, and RhoC GTPase cycle, with TAAr genes involved in these pathways being CXCR4, JUP, and RACGAP1. Complete information is given in Table 4. Primordial germ cell specifications are developmental processes of primordial germ cells (PGCs) that occur around the third to fourth week of pregnancy in humans to become gametes. In the process of becoming gametes, PGCs undergo various metabolic cycles in which the expression or suppression of each gene has an important role, and disruption of this critical role can cause developmental disorders, from disabilities to an inability to fertilize and germ cell-specific tumors [33, 34]. Furthermore, the Rho GTPase cycle plays a role in various cellular responses, including regulating cell migration. This cycle is controlled by Rho family GTPases, members of the Ras superfamily, which can be grouped into two typical and atypical subdivisions. Rho family members in the typical subdivision are RhoA, Cdc42, and Rac1, with a cycle related to GTP-bound activation and GDP-bound inactivation conformation. Next, Rho family members in the atypical subdivision are those proteins that possess amino acid substitutions that affect their ability to interact with GTP or GDP and are regulated by certain mechanisms [35]. Also, the Rho GTPase cycle is known to play an essential role in regulating the actin cytoskeleton, where the actin cytoskeleton plays a role in cellular processes such as cell division, migration, chemotaxis, and endocytosis [36, 37]. Finally, the link of this pathway to OC is through the process of tumor cell growth, where tumor germ cells can end up as ovarian germ cell tumors due to unregulated metabolic processes, and Rho GTPases can increase cellular activity related to cell division, proliferation, and migration [37, 38].

Table 4. The most significant pathways of the Reactome.

Pathway name	Entities founda)	p-value	FDR
Specification of primordial germ cells	2 / 64	< 0.001	0.175
RhoB GTPase cycle [32]	2 / 75	0.001	0.175
RhoC GTPase cycle [32]	2 / 85	0.002	0.175
Formation of definitive endoderm	3 / 85	0.002	0.175
RhoA GTPase cycle [32]	2 / 154	0.005	0.193
a)Entities found are a count of entities input with pathway-related entities in Reactome. FDR, false discovery rate.

Also, based on an ingenuity pathway analysis (IPA), we obtained 15 pathways with the highest confidence scores out of three annotations (Table 5). IPA results showed predicted pathways related to the nine recommended antigens, especially toxicity pathways that may occur with or be associated with these proteins. Based on toxicity results, it was observed that the liver and kidneys may be organs that need to be examined for adverse effects that may occur. To verify expressions of TAAr in brain, retina, and liver tissues, we obtained data from GeneCard (Figure 4F). These data showed that the TAAr had the lowest RNAseq counts in the retina, while the brain and liver had varied RNAseq counts (100× FPKB). We highlight three proteins, SORT1, JUP, and CLDN3, with upper-middle RNAseq expressions in these two organs. CAR-T-cell therapy may have adverse effects on these three organs. Neurotoxicity is a frequent complication after CAR-T-cell therapy, and the exact cause of which is still under research and debate. Apart from that, the neurotoxicity of CAR-T-cell therapy is probably due to high cytokine levels in the brain and cerebrospinal fluid associated with the blood-brain barrier [39]. Also, CAR-T-cell therapy commonly induces neurological complications termed immune effector cell-associated neurotoxicity syndrome (ICANS). The ICANS is a significant concern in the context of chimeric antigen receptor (CAR) T cell therapy [40]. Liver toxicity was also reported in an earlier CAR-T-cell therapy clinical trial. Three metastatic renal carcinoma patients who received autologous T cells transduced with CAR-targeting carboxyanhydrase-IX (CAIX) experienced cholangitis due to T-cell infiltration around the bile ducts, because bile duct epithelial cells unusually express CAIX [41, 42]. Furthermore, CAR-T-cell therapy was also reported to affect the patient, even though it does not seem to be severe. Treatment results in 1421 cases showed that 28 of them had eye disorders, with abnormalities in the form of vision impairment or changes, impaired pupil responses, papilledema, mydriasis, photophobia, and visual tracking test abnormalities [43, 44]. Then, the final enrichment results, namely single-cell RNA sequencing (scRNA-seq) analysis of the tumor microenvironment (TME). Single-cell RNA analysis provides numerous advantages over traditional bulk RNA sequencing methods. This technology has been pivotal in detecting novel cell types with distinct expression signatures and understanding the stochasticity of gene expression within a cell population. Additionally, single-cell RNA sequencing allows for differential expression analysis, clustering, cell type annotation, and pseudotime analysis at a single-cell level, leading to significant progress in the field of transcriptomics [45, 46]. According to this analysis, the result indicated that EPCAM, MUC1, UBE2C, and CLDN3 were highly expressed in malignant samples in two scRNA-seq databases, thereby increasing the potential of TAAr (Figure 5).

Table 5. The five canonical, disease and function, and toxicity pathways based on an ingenuity pathway analysis result of nine genes.

Annotation	Name	p-value
Canonical pathway	Granulocyte adhesion and diapedesis	2.34E-03
Canonical pathway	Leukocyte extravasation signaling	2.75E-03
Canonical pathway	Agranulocyte adhesion and diapedesis	2.87E-03
Canonical pathway	Sertoli cell-Sertoli cell junction signaling	3.14E-03
Canonical pathway	FAT10 cancer signaling pathway	2.11E-02
Disease and function	Primary ovarian cancer	1.17E-10
Disease and function	Serous ovarian adenocarcinoma	1.77E-09
Disease and function	Primary clear cell ovarian carcinoma	3E-09
Disease and function	Primary serous ovarian carcinoma	3.63E-08
Disease and function	Clear-cell ovarian carcinoma	2.92E-07
Toxicity	Hepatic fibrosis	8.64E-03
Toxicity	Hypoxia-inducible factor signaling	2.90E-02
Toxicity	Increases liver damage	4.18E-02
Toxicity	Increases renal damage	4.63E-02
Toxicity	Decreases transmembrane potential of mitochondria and the mitochondrial membrane potential	6.10E-02

Figure 5. Gene expression levels are based on single-cell RNA sequencing based on a human sample based on the TISCH2 database. (A) Sample from GSE154600 based on 5 non-treatment patients, 42583 cells, analyzed with the 10x genomics platform. (B) UMAP plot of nine TAAr on GSE154600, where EPCAM, MUC1, RACGAP1, CLDN3, and JUP are highly expressed in malignant cell samples. (C) Sample from GSE118828 based on 9 non-treatment patients, 1909 cells, analyzed with the Smart-Seq2 platform. (D) UMAP plot of nine TAAr on GSE118828, where CXCR4, MUC1, EPCAM, UBE2C, CLDN3, JUP, PRAME, and SORT1 are highly expressed in malignant cell samples. (E) Violin diagram of TAAr expression in each sample cell in GSE118828. (F, G) Diversification and comparison of the number of samples analyzed in GSE118828. (H) Violin diagram of TAAr expression in each sample cell on GSE154600. (I, J) Diversification and comparison of the number of samples analyzed on GSE154600. (K) Transcription factor induced by TAAr in GSE118828: the higher the induction, the redder it is. (L) Number of cell-cell interactions in sample GSE118828. (M) Transcription factor induced by TAAr in GSE154600: the higher the induction, the redder it is. (N) Number of cell-cell interactions in the GSE154600 sample. (O, P) The genetical hallmarks that are up-downregulated based on the TAAr gene in GSE118828. (Q, R) The genetical hallmarks that are up-downregulated based on the TAAr gene in GSE154600.

Materials and Methods

Data sources

The study was conducted following the general steps detailed in Figure 6. There was a determination of DEGs, followed by examination of protein localization and oncogenicity, and network and enrichment analyses of the recommendation of target genes. The microarray dataset was obtained from the NCBI-GEO database, which is a public gene profile database (https://www.ncbi.nlm.nih.gov/gds/). The four datasets relevant to OC, namely GSE36668, [9] GSE276512, [10] GSE26712, [11, 12] and GSE14407, [13] are datasets related to OC that use three levels on the GPL570 platform: [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array, and GSE26712 using the GPL96 platform: [HG-U133A] Affymetrix Human Genome U133A Array. The dataset above is used because each dataset is submitted no later than the last 20 years, can be analyzed with the GEO2R tool, and has samples of ovarian cancer tissue and normal cell tissue in one dataset. GSE36668 had 12 samples (eight OC and four normal samples). GSE27651 had 49 samples (43 OC and six normal samples). GSE26712 had 195 samples (185 OC and 10 normal samples). GSE14407 had 24 samples (12 OC and 12 normal samples). In this study, we used a balanced count of data samples between normal and OC samples from each GSE.

Figure 6. Research stages and workflow. The four research red lines are differential expression analysis, differentially expressed genes (DEGs), protein localization analysis, oncogenicity analysis, and pathway and gene ontology analyses. Therefore, in this study, we carried out multilevel screening to reduce the potential for errors or discrepancies later. What we mean by multilevel screening is first looking at the significance of the expression of a gene in normal and cancer samples from the dataset, then checking the significance of the significant gene again in a different database to ensure that the gene is significantly expressed only in cancer cells.

DEG identification

Identification of DEGs was carried out using the GEO2R interactive tool (http://www.ncbi.nlm.nih.gov/geo/geo2r) provided by NCBI to compare a number of sample datasets from the microarray dataset series. The threshold used was logFC > 1.5 and a p-value of < 0.05, which were considered to be of statistical significance. Next, GEO2R results were analyzed using Microsoft Excel format to separate genes with the same ID, and online Venn software [82] was used to determine gene intersections of the four datasets, which are DEGs.

Functional examination of DEGs

Functional annotation analysis of gene ontology (GO) and pathway of DEGs was performed with the Database for Annotation, Visualization, and Integrated Discovery (DAVID; https://david.ncifcrf.gov/). Then, genes associated with the seven GO terms with the highest confidence score (-log10 (FDR)) for GO biological processes (BPs), cellular components (CCs), and molecular functions (MFs) were visualized, and plasma membrane-related genes (PMGs) were selected for analysis. Advanced confidence scores were from the GeneCard database (https://www.genecards.org/). The analysis plays a role in ensuring that the expressed protein is located in the plasma membrane so that it can be targeted as a CAR antigen.

Oncogenicity prediction analysis

Oncogenicity predictions were carried out on PMGs using Gene Expression Profiling Interactive Analysis (GEPIA2; http://gepia2.cancer-pku.cn/#index). Gene expressions were compared in OC and normal samples. Parameters used were a Log2FC cutoff of 2, a p-value cutoff of 0.01, and matching of The Cancer Genome Atlas (TCGA) normal and GTEx data. GEPIA is a web-based tool that provides interactive features such as profile plotting, patient survival analysis, differential expression analysis, similar gene detection, and dimensionality reduction analysis [83]. A highly expressed gene in OC was designated a predicted gene, and predicted genes were used in the next analysis.

Protein-protein interaction (PPI) network analysis

A PPI network analysis was accomplished using the Search Tool for the Retrieval of Interacting Genes and Proteins (STRING; https://string-db.org/) and Cytoscape 3.10. A network of highly expressed genes in OC was constructed in STRING with an interaction confidence score of > 0.7 [84] and an additional 100 enriched genes. Network PPIs were analyzed with NetworkAnalyzer [85] and CytoHubba [86, 87] to determine the betweenness score of each node. In addition, an MCODE clustering analysis, GO, and pathways were also carried out to more deeply investigate results of enrichment. MCDE clustering was carried out using CluserViz with a degree threshold of 2, a node score threshold of 0.2, a K-core threshold of 2, and a max depth of 100, resulting in nine clusters [88, 89].

Enrichment analysis

Basic GO and pathway analyses were carried out using the g: Profiler webserver [90]. GO terms and pathways with the best confidence score (FDR) were visualized. A TAAr enrichment analysis was also carried out using Metascape to obtain bioactivity comparisons between control and predicted genes [91, 92], TIMER to acquire co-expression scores of predicted genes to control genes [93], Reactome webserver (https://reactome.org/) and an Ingenuity pathway analysis (IPA) (Qiagen, USA) for analyzing pathway and bioactivity of predicted gene, and an scRNA-seq analysis by Tumor Immune Single-cell Hub 2 (TISCH2) database to provide detailed cell-type annotation at the single-cell level, enabling the exploration of TME across different cancer types focusing on tumor microenvironment (TME) [94], and the SRPlot online tool (https://www.bioinformatics.com.cn/en) to construct and analyze data graphics [95].

Data availability statement

The data analyzed in this study are freely available and can be accessed in the NCBI database (https://www.ncbi.nlm.nih.gov/). Another set of data presented in this paper is available from the corresponding author upon reasonable request.

Author Contributions

DA: Conceptualizing, writing original draft preparation, review and editing (lead), data curation. Y-HC: Supervision (equal), resources. Y-CH: Supervision (supporting), resources. JTQ: Writing review and editing (supporting), data curation, resources, supervision (equal), funding acquisition.

Acknowledgments

We would like to extend my heartfelt thanks to Huy Hoang Dang (IPCTRM, Taipei Medical University, Taiwan), Gangga Anuraga (Taipei Medical University, Taiwan), Prof. Jeanne Adiwinata Pawitan (University of Indonesia), Prof. Dr. Carina Citra Dewi Joe (University of Oxford), Prof. Dr.rer.nat. Arli Aditya Parikesit (Department of Bioinformatics, Indonesia International Institute for Life Sciences), and Maulida Mazaya, Ph.D. (National Research and Innovation Agency of the Republic of Indonesia, BRIN), for their meaningful insights and the time they generously devoted to engaging in discussions, all of which have contributed immensely to the successful completion of this manuscript. Thanks also go to Ahmad Hafidul Ahkam (Padjadjaran University) as technical support for figures.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Funding

This work is supported by TMU Research Center of Cancer Translational Medicine from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) Project number: DP2-TMU-112-101; DP2-TMU-113-C-06. TMU109-AE1-B11 from Taipei Medical University.

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