Identification of long non-coding RNA signatures for squamous cell carcinomas and adenocarcinomas

Results

Discriminative lncRNA signatures

Squamous cell carcinomas

Separate logistic elastic-net regression models were fit for the LUSC and HNSC studies (the CESC study were excluded due to the non-availability of normal tissues). A 173-lncRNA discriminative signature for HNSC and a 277-lncRNA signature for the LUSC study were identified. The gene lists for these two studies resulted in 35 overlapped lncRNAs (Table 1). Of those, 10 genes have been reported in the literature to correlate to one specific cancer type or more using real experiments (rather than being predicted using a computational method) according to the lncRNADisease 2.0 database.

Table 1. Pan discriminative lncRNAs for the squamous cell carcinoma type.

Symbols	Target mRNA	Cancer types (experimentally validated)	Recent publications recording associated cancer types
WDFY3-AS2	WDFY3		Ovarian [23]; ESCC [24]; LUAD [25]; breast [26, 27]; HCC [28]
CFAP99
DUXAP8		Stomach	HCC [29–31]; colon [32, 33]; bladder [34, 35]; pancreatic [36]; ESCC [37]; esophageal [38]; renal cell carcinoma [39, 40]; NSCLC [41]
FIRRE			Diffuse large B-cell lymphoma [42]; colon [43]
HAGLR	EVX2, HOXD13, HOXD12, HOXD11, HOXD10, HOXD9, HOXD8, HOXD3, AC009336.2, HOXD4, HOXD1, MTX2	Stomach, cervical, NSCLC, neuroblastoma, glioma, ovarian, urinary bladder, HCC, prostate, thyroid	Esophageal [44]; LUAD [45]; colon [46]
LOC101929331
DUXAP10		Ovarian, colorectal	Myeloid leukemia [47]; ESCC [48]; bladder [49]
ABHD11-AS1	VPS37D, DNAJC30, BUD23, STX1A, ABHD11, CLDN3, CLDN4, METTL27	Urinary bladder, stomach	Thyroid [50, 51]; pancreatic [52, 53]; endometrial carcinoma [54]; ovarian [55]; colon [56, 57]
LOC101928118
SLC16A1-AS1	SLC16A1, LRIG2	Cervical, lung, astrocytoma	NSCLC [58]; OSCC [59]; HCC [60]
MFI2-AS1	MFI2		Colon [61]; glioma [62]; HCC [63]
LINC00443	ARGLU1		Renal cell carcinoma [64]
DLEU2	SPRYD7, TRIM13, KCNRG	Lymphoma, laryngeal, leukemia, pancreatic, astrocytoma	Gastric [65]; NSCLC [66, 67]; pancreatic [68]; HCC [69]; esophageal [70]
LOC101927596
LOC105375401
LOC100128164
ADAMTS9-AS1	ADAMTS9	Ovarian epithelial cancer, malignant glioma	Prostate [71]; COAD [72]
ADAMTS9-AS2	ADAMTS9	Malignant glioma, renal, NSCLC	Ovarian [73]; gastric [74–76]; glioblastoma [77]; breast [26, 78]; TSCC [79]; esophageal [80]
LINC00996	LRRC61, RARRES2, REPIN1, AC073111.3, ZNF775, AC073111.5, GIMAP8, GIMAP7
LINC01296		Prostate, stomach, urinary bladder, colorectal	Osteosarcoma [81]; neuroblastoma [82]; ESCC [83, 84]; NSCLC [85]; HCC [86]; breast [87]; ovarian ([88])
LOC101929066
LOC101929595
LOC440028
LOC101929340
LINC00958			Cervical [89, 90]; OSCC [91, 92]; NSCLC [93]; pancreatic [94]; nasopharyngeal [95]; HNSCC [96]; HCC [97]; gastric [98]
LOC101927392
RBPMS-AS1	RBPMS
BBOX1-AS1	FIBIN, BBOX1		Colon [99]; cervical [100]
TMPO-AS1	TMPO, SLC25A3, IKBIP	Astrocytoma
GS1-120K12.4
TTC39A-AS1	RNF11, TTC39A, EPS15
LOC400568
TMEM220-AS1	MYH3, SCO1, ADPRM, TMEM220, TMEM238L, PIRT
LDLRAD4-AS1
LINC00551	EFNB2, ARGLU1
* Cancer types (with experimental validations) to be associated with that specific lncRNA according to the lncRNAdisease2.0 database.
** Only a few examples are given since the number of relevant publications is large.
COAD: colon adenocarcinoma; ESCC: esophageal squamous cell carcinoma; HCC: Hepatocellular carcinoma; HNSCC: head and neck squamous cell carcinoma; LUAD: lung adenocarcinoma; NSCLC: non-small cell lung cancer; OSCC: oral squamous cell carcinoma; TSCC: tongue squamous cell carcinoma.

Adenocarcinomas

Among the AC cohort, separate logistic elastic-net regression models identified a 185-lncRNA signature for the LUAD study, a 173-lncRNA signature for the STAD study and a 114-lncRNA signature for the PRAD study. The intersection of these three gene lists has 6 lncRNAs: UBXN10-AS1, SNHG20, ADAMTS9-AS1, ADAMTS9-AS2, PVT1 and VPS9D1-AS1 (Table 2). Of note, both ADAMTS9-AS1 and ADAMTS9-AS2 also belong to the 35-overlapped lncRNA list for SCC discriminative analysis.

Table 2. Pan discriminative lncRNAs for the adenocarcinoma type.

Symbols	Target mRNAs	Cancer types (experimentally validated)*	Recent publications recording associated cancer types
UBXN10-AS1	PLA2G5, PLA2G2D, PLA2G2F, PLA2G2C, UBXN10
SNHG20	SEC14L1	HCC, ovarian, colorectal, NSLCL, stomach	Breast [101]; cervical [102]; bladder [103]; prostate [104]; ESCC [105]; OSCC [106, 107]; nasopharyngeal [108]
ADAMTS9-AS1	ADAMTS9	Ovarian epithelial cancer, glioma	Prostate [32]; COAD [72]
ADAMTS9-AS2	ADAMTS9	Glioma, renal, NSCLC	Ovarian [73]; gastric [74–76]; clear cell renal cell carcinoma [109]; glioblastoma [77]; breast [26, 78]; TSCC [79]; esophageal [80]; NSCLC [110]
PVT1	MYC	Colorectal, HCC, prostate, cervical, stomach, lung, esophageal and others	EAC [111] **
VPS9D1-AS1	DPEP1, CHMP1A, SPATA33, CDK10, SPATA2L, VPS9D1, ZNF276, FANCA	Stomach	NSCLC [112, 113]; prostate [114, 115]
*Cancer types (with experimental validations) to be associated with that specific lncRNA according to the lncRNAdisease2.0 database.
**Only one example is given since the number of relevant publications is large.
COAD: colon adenocarcinoma; EAC: esophageal adenocarcinoma; ESCC: esophageal squamous cell carcinoma; HCC: Hepatocellular carcinoma; NSCLC: non-small cell lung cancer; OSCC: oral squamous cell carcinoma; TSCC: tongue squamous cell carcinoma.

Furthermore, the number of overlaps between LUAD and STAD is 34; 18 between LUAD and PRAD and 11 between STAD and PRAD. All these overlaps took substantial proportions of the identified lncRNA signatures.

Prognostic lncRNA signatures

Squamous cell carcinoma

Cox elastic-net regression models selected a 462-lncRNA list for the LUSC study, a 597-lncRNA list for the HNSC study and a 263-lncRNA list for the CESC study, respectively. Among the 3 lists were 11 overlaps (Table 3), and 5 of them (CFLAR-AS1, SLC16A1-AS1, SIRPG-AS1, LOC389641 and LINC00593) were experimentally validated as cancer related genes according to the lncRNADisease 2.0 database. In Table 3, the target mRNAs of these 11 lncRNAs are given.

Table 3. Pan prognostic lncRNAs for the squamous cell carcinoma type.

Symbols	Target mRNAs	Cancer types (experimentally validated)*	Recent publications recording associated cancer types with experimental validations
SLC16A1-AS1	SLC16A1, LRIG2	Cervical, lung, astrocytoma	NSCLC [58]; OSCC [59]; HCC [60]
CFLAR-AS1	FAM126B, NDUFB3 CFLAR, CASP10, CASP8	ESCC, astrocytoma
SPATA13-AS1	AL359736.1, SPATA13, C1QTNF9
LINC00311	GSE1		Thyroid [117]
LINC01305	OLA1, SP9, CIR1, SCRN3		Cervical [118]; NSCLC [119]
LINC01399	ISX, HMGXB4, TOM1
FGF14-AS1	FGF14
SIRPG-AS1	SIRPD, AL049634.2, SIRPB1, SIRPG	Astrocytoma
LOC389641	TNFRSF10D, TNFRSF10A, CHMP7, R3HCC1, LOXL2	PADA	PDAC [120]
LINC00593		Astrocytoma
MKNK1-AS1	DMBX1, AL136373.1, KNCN, MKNK1, MOB3C, ATPAF1, TEX38
*Cancer types (with experimental validations) to be associated with that specific lncRNA according to the lncRNAdisease2.0 database.
ESCC: esophageal squamous cell carcinoma; HCC: Hepatocellular carcinoma; NSCLC: non-small cell lung cancer; OSCC: oral squamous cell carcinoma; PDAC: pancreatic ductal adenocarcinoma.

Adenocarcinoma

Cox elastic-net regression models identified a 53-lncRNA set for the STAD study and a 95-lncRNA list for the LUAD study. The PRAD study was excluded from the prognosis analysis since the number of events (deaths) was too small to guarantee a valid analysis. The intersection set of the two gene lists includes 5 lncRNAs (EIF1AX-AS1, LINC00115, LINC01237, MALAT1 and LINC00528), among which only LINC00115 and MALAT1 have been experimentally validated to correlate with cancers according to the lncRNADisease2.0 database. The associated cancer types of and the target mRNAs by these five lncRNAs given by the lncRNADisease 2.0 database are listed in Table 4.

Table 4. Pan prognostic lncRNAs for the adenocarcinoma type.

Symbols	Target mRNAs	Cancer types (experimentally validated)*	Recent publications recording associated cancer types with experimental validations
EIF1AX-AS1	MAP7D2, EIF1AX, RPS6KA3
LINC00115	SAMD11	Astrocytoma, lung adenocarcinoma	Glioma [126]; breast [127]
LINC01237	GAL3ST2, NEU4, PDCD1, RTP5, AC131097.2
LINC00528	BCL2L13, BID, MICAL3		LSCC [128]
MALAT1	FRMD8, SCYL1, LTBP3, SSSCA1, FAM89B, EHBP1L1, KCNK7, MAP3K11	Pancreatic, prostate, breast, gallbladder, OSCC, stomach, NSCLC, ESCC, and others	Breast [129]; ovarian [130]; glioblastoma [131] **
*Cancer types (with experimental validations) to be associated with that specific lncRNA according to the lncRNAdisease2.0 database.
**Only a few examples are given since the number of relevant publications is large.
ESCC: esophageal squamous cell carcinoma; LSCC: laryngeal squamous cell carcinoma; LUAD: lung adenocarcinoma; OSCC: oral squamous cell carcinoma.

Performance evaluation

Using ROC curves (Figure 1), the discriminative ability of identified pan-SCC and pan-AC lncRNA signatures was evaluated. The AUCs of these two signatures are 0.951 and 0.920 for the SCC type and the AC type, respectively. Overall, these two signatures perform well. According to the log-rank tests (Figure 2), the identified pan-SCC and pan-AC prognostic lncRNA signatures have good prognostic values as well.

Figure 1. ROC curves showing the performance of identified discriminative lncRNA lists. AC: adenocarcinoma; AUC: area under the ROC curve; ROC: receiver characteristic operator; SCC: squamous cell carcinoma.

Figure 2. Kaplan-Meier curves showing if identified prognostic lncRNA lists are associated with survival rates for adenocarcinomas and squamous cell carcinomas. AC: adenocarcinoma; SCC: squamous cell carcinoma. p: the corresponding p-values of log-rank tests to test if the survival curves of high-risk group and low-risk group are same. Here, the ridge Cox regression models were used to estimate the coefficients of lncRNAs and then risk scores were calculated. Then the median value of risk scores was used as the cutoff to divide the AC/SCC patients into the high-risk group and the low-risk group.

Enriched pathways

Using String software, pathway enrichment analysis was conducted. The results showed that no pathway or GO biological process term was enriched by the AC prognostic signature. Enriched pathways/GO biological process terms for the SCC and AC discriminative signatures and the SCC prognostic signatures are given in Figure 3. As far as the enriched KEGG pathways are considered, there is one overlap – necroptosis between the AC discriminative category and the SCC prognostic category. Necroptosis is a programmed caspase-independent cell death. Studies have shown that tumor undertakes necroptosis in vivo and the process has pro- or anti- tumor effects in cancer development and progression [116]. At the level of pathways, both SCCs and ACs may share some common features.

Figure 3. Dot plots showing the enriched gene ontology biological process terms and KEGG pathways. (A) Enriched GO biological process terms by SCC discriminative lncRNA signature. (B) Enriched GO biological process terms by AC discriminative lncRNA signature. (C) Enriched GO biological process terms by SCC prognostic lncRNA signature. (D) Enriched KEGG pathways by AC discriminative lncRNA signature. (E) Enriched KEGG pathways by SCC prognostic lncRNA signature. AC: adenocarcinoma; FDR: false discovery rate; GO: gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; FDR: false discovery rate; SCC: squamous cell carcinoma.

The overlapped proportions/ratios of these prognostic lncRNAs are substantially less than those taken by the discriminative lncRNAs, which may be explained by two reasons. First, many studies in the literature have pointed out that prognosis is more difficult than discrimination/diagnosis. For instance, for the LUAD patients at the same stage, distinct molecular subtypes with different prognoses exist. Correspondingly, prognostic gene signatures are anticipated to be more type-specific, while the sizes of these signatures are to be much larger. Indeed, several studies suggested that possibly dozens of genes can make a perfect segmentation of tumors and controls or of different subtypes, but this is not so for the segmentation of patients with good prognosis versus poor prognosis. Second, the overall survival time may not be a good surrogate for prognosis. Also, these AC and SCC cohorts may not be followed up for a period long enough to develop adequate events/deaths for an accurate survival analysis. With these two disadvantages, many false positives may be produced and included in the resulting gene lists.

Materials and Methods

Experimental data

The Atlas of ncRNA in Cancer (Tanric) database [134] included lncRNA expression profiles (RNA-Seq data) for 20 cancer types in the TCGA project. We identified 8 cancers that are either the SCC type or AC type with 100 % confidence: cervical squamous cell carcinoma (CESC), head and neck squamous cell carcinoma (HNSC) and lung squamous cell carcinoma (LUSC), lung adenocarcinoma (LUAD), prostate adenocarcinoma (PRAD), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and rectum adenocarcinoma (READ). For the colon and rectum cohorts, no normal tissues were provided and the number of deaths was also very small, rendering both the discrimination and prognosis analyses less trustworthy or even impossible. Consequently, these two cohorts were excluded, and the final SCC study included cervical, head and neck, and lung SCC cohorts. The AC cohorts included lung, prostate and stomach.

For the 6 cohorts, expression profiles were downloaded fromtheTanricwebpage(https://ibl.mdanderson.org/tanric/_design/basic/download.html). Corresponding clinical information such as overall survival time, American Joint Committee on Cancer staging status and age were downloaded from TCGA’s Genomic Data Commons (http://www.cbioportal.org/). Tanric [134] includes expression profiles of 12,727 lncRNAs quantified as the RPKM (fragments per kilo-bases per million) counts. Table 5 summarizes clinical characteristics of these six studies.

Table 5. Clinical characteristics of squamous cell carcinoma studies and adenocarcinoma studies.

	# of patients(stages I-IV)	# of deaths	Missing of survival time/Missing of stage information/ No. of all zero expression	# of normal tissues
Squamous Cell Carcinoma
Head and Neck	426 (24/63/73/200)	187	1/66/43	42
Lung	220 (116/55/43/4)	105	4/2/17	17
Cervical1	196 (--/--/--/--)	42	0/--/220	3
Adenocarcinoma
Lung	488 (262/112/81/23)	174	9/10/76	58
Stomach	285 (39/99/103/23)	94	3/21/10	33
Prostate2	374 (--/--/--/--)	6	0/--/53	52
--: not available.
1Since no stage information is available and there are only 3 normal tissues, this cohort was excluded from and discriminative analysis.
2The prostate cohort was not included in the cancer-type specific analysis for survival analysis since the number of events/deaths is small. In summary, 2 studies were used for SCC discriminative analysis and 3 for survival analysis, while for AC 3 studies were used for discriminative analysis and 2 for survival analysis.

Next, ensemble IDs were mapped into gene symbols and lncRNAs without a valid gene symbol were deleted, leaving 3,173 lncRNAs retained for further analysis. Lastly, the RPKM were logarithm transformed (base 2) after being added 1s (in order to avoid having log transformation on zeros), to make the distribution of resulting lncRNA expression values approximate to a normal one.

Statistical methods

Elastic-net regularized regression

A logistic elastic-net regularized regression model and a Cox elastic-net regularized regression model were fit for each cohort to identify the respective discriminative lncRNA signature and prognostic lncRNA signature for the specific cancer type. Briefly, elastic-net penalty is a linear combination of L1 (LASSO) and L2 (ridge) penalties. It is well known that the L1 penalty introduces sparseness into the model by offsetting the coefficients of insignificant genes to zeros. In contrast, the L2 penalty introduces a grouping effect by smoothing the coefficients of correlated genes to a common value. In this sense, it can also penalize large β coefficients. Therefore, the elastic-net penalty incorporates the advantages of both L1 and L2 penalties, i.e., being capable of feature selection and giving the grouping structure some consideration. The mathematical notation is,

$\lambda (\alpha |\beta {|}_1+(1-a)\left|\right|\beta |{|}_2)$

Here, the parameter controls the ratio of L1 penalty to L2 penalty. The tuning parameter λ determines the amount of regularization used, with a large value corresponding to a heavy penalization on β coefficients and a small value to a light one. Its optimal values were determined using 10-fold cross-validations.

Depending on the type of outcome, it may be combined with the corresponding objective function to frame into a variety of regularized regression models such as a logistic elastic-net regression for a binary outcome and a Cox elastic-net regression for survival time.

For individual cohorts, elastic-net regression models were fit. The respective intersections of identified lncRNA lists by the models were obtained and deemed as pan-AC discriminative lncRNAs, pan-SCC discriminative lncRNAs, pan-AC prognostic lncRNAs and pan-SCC prognostic lncRNAs. Their performance and biological relevance were investigated subsequently. Figure 4 shows how these four signatures were constructed.

Figure 4. Flowchart showing the procedure for identifying SCC- and AC-discriminative, and SCC- and AC-prognostic lncRNAs.

Pathway enrichment analysis

Connectivity (gene-to-gene interaction) information was retrieved for the target mRNAs by identified lncRNAs from the lncRNADisease 2.0 database [21], and upon those target mRNAs Kyoto Encyclopedia of Genes and Genomes (KEGG) [135] pathway/gene ontology (GO) [136] enrichment analysis was conducted using the String software [137]. A false discovery rate (FDR) of <0.05 was deemed to be statistically significant.

Performance evaluation

For discriminative values of identified lncRNAs, ridge logistic regression models were fit to estimate the coefficients of identified genes and then probabilities of having tumors were calculated. The receiver characteristic operator (ROC) curves were made, and the area under the ROC curve (AUC) statistics were calculated to evaluate the performance of these signatures.

Multivariate Cox regression models with ridge penalty terms were fit to estimate the coefficients of identified prognostic lncRNAs and calculate the risk scores of death for all patients. The median expression value was used as a cutoff to divide the patients into high-risk group and the low-risk groups. Lastly, log-rank tests were carried out to test if the survival curves of the two groups were the same.

Statistical language and packages

All statistical analysis was carried out in R version 3.5 (https://www.r-project.org/). Specifically, the Bioconductor org.Hs.eg.db package was used to map ENSEMBL IDs to gene symbols. The glmnet package [138] was used to fit the elastic net and the ridge regression models. The survival and survminer packages were used for making Kaplan-Meier curves and carrying out log-rank tests. ROCR and ggplot2 were used for making ROC curves and calculating AUC statistics.

Availability of data and materials

LncRNA expression profiles (RNA-Seq data) and the corresponding clinical information were downloaded from the Tanric (The Atlas of ncRNA in Cancer) webpage (https://ibl.mdanderson.org/tanric/_design/basic/download.html) and the Genomic Data Commons (http://www.cbioportal.org/) of TCGA (The Cancer Genome Atlas) database.

Author Contributions

Conceived and designed the study: ST CW WL. Analyzed the data: ST MT JL. Interpreted data analysis and results: ST WL MT JL CW. Wrote the paper: ST CW JL MT. All authors reviewed and approved the final manuscript.

Acknowledgments

We thank Donna Gilbreath in the Markey Cancer Center’s Research Communications Office for English editing.

Conflicts of Interest

No conflicts of interests have been declared.

Funding

This study was supported by the Education Department of Jilin Province (grant No. JJKH20190032KJ) and Jilin Province Department of Finance (grant No. 2018SCZWSZX-018).

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