Collagen type VII α1 chain: A promising prognostic and immune infiltration biomarker of pancreatic cancer
Affiliations: Department of Hepatobiliary Surgery, Beijing Chao‑Yang Hospital Affiliated to Capital Medical University, Beijing 100020, P.R. China, Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, P.R. China
- Published online on: January 5, 2023 https://doi.org/10.3892/ol.2023.13663
- Article Number: 77
Copyright: © Ding et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
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Pancreatic cancer (PC), which has a 5-year overall survival (OS) rate of only ~10% (1), remains one of the most malignant cancer types worldwide. PC is notorious for its high morbidity and mortality rates (2). It has been reported that close to 460,000 individuals were diagnosed with PC and that there were >430,000 PC-related deaths worldwide in 2018 (2). Unlike other malignancies, the incidence of PC has been slowly growing for decades, which brings a tremendous economic burden (3).
The extracellular matrix (ECM) is an important non-cellular component of the tumor microenvironment. The ECM not only provides a physical scaffold for cells but also a depot for cytokines that promote tumor development (4). The crosstalk between the ECM and tumor-infiltrating immune cells (TIICs) serves a crucial role in the progression of tumors (5). Through remodeling of the ECM, cancer-associated fibroblasts can facilitate the occurrence and development of cancer (6). Mast cells can serve as tumor contributors to simulate angiogenesis and degradation of the ECM (7,8). Furthermore, the ECM and immune-associated mechanisms are involved in the development and progression of PC (9,10). Due to the low radical resection rate, chemotherapy and immune-therapy resistance, little progress has been made in the management of PC during the past decades (11). Therefore, investigation of effective and reliable biomarkers for the diagnosis and treatment of PC is urgently required to improve survival rates.
Type VII collagen, which is encoded by collagen type VII α1 chain (COL7A1), is distributed to the basal area beneath the squamous epithelium (12,13). It is composed of three α collagen chains and acts as an anchoring fiber between the external epithelium and underlying substrate (12). Mutations of COL7A1 can result in recessive dystrophic epidermolysis bullosa (RDEB), which is an incurable autoimmune disease and associated with increased risk of skin carcinoma (13). In squamous cell carcinoma, the loss of type VII collagen can enhance tumor cell invasive behavior and promotes epithelial-mesenchymal transition (14). Aberrant COL7A1 expression has been reported in esophageal cancer and COL7A1 expression has been reported to be positively associated with depth of invasion and lymph node metastasis, and negatively associated with survival (15,16). In gastric carcinoma, COL7A1 expression has been reported to be upregulated in cancer tissues compared with normal tissues (17). High intracellular COL7A1 expression has been reported to indicate a poor 5-year OS and a high immunohistochemistry score is associated with distant metastasis (17). However, little is known regarding the prognostic role of COL7A1 and its association with TME in PC.
To better evaluate the value of COL7A1 in the assessment of the progression of PC, RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Genotype-Tissue Expression (GTEx) databases were downloaded. Cox and logistic regression analyses, Kaplan-Meier survival analysis and nomograms were used to evaluate the prognostic role of COL7A1. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Set Enrichment Analysis (GSEA) were used to evaluate the underlying mechanisms of COL7A1. Single-sample GSEA (ssGSEA) was used to assess the relationship between COL7A1 expression and TIICs. Furthermore, reverse transcription-quantitative PCR (RT-qPCR) was used to validate COL7A1 mRNA expression in PC cell lines.
Materials and methods
RNA-seq data acquisition and analysis
All transcriptome RNA-seq data with clinical information of patients with PC were accessed from TCGA [https://portal.gdc.cancer.gov; pancreatic adenocarcinoma (PAAD) dataset] and normal pancreas expression data were retrieved from the GTEx database (https://gtexportal.org), the aforementioned data was downloaded using the University of California, Santa Cruz Xena browser (https://xenabrowser.net/datapages/). RNA expression data were retrieved from GEO (https://www.ncbi.nlm.nih.gov/geo/) (GSE15471 and GSE101448 datasets).
COL7A1 mRNA expression in PC samples and normal tissues
Boxplots and scatter plots were used to compare the mRNA expression levels of COL7A1 in tumor and normal samples, the ggplot2 package (v3.3.3) (18) was used to generate the visualization. The expression levels of COL7A1 in TCGA and GTEx databases were compared using a Wilcoxon rank sum test, COL7A1 expression was compared between normal and pancreatic cancer tissues in GSE15471 using a Wilcoxon signed rank test, and COL7A1 expression was compared between pancreatic cancer and normal samples in GSE101448 using an unpaired t-test. In the present study, the samples in GSE15471 dataset were paired tissues, while in other instances, the normal tissues referred to unpaired tissues. The diagnostic value of COL7A1 in patients with PC was estimated using receiver operating characteristic (ROC) curves, the pROC (v22.214.171.124) (19) and ggplot2 (v3.3.3) (18) packages were used to analyze and visualize these data. The patients were divided into two groups, COL7A1-high and COL7A1-low, based on the median expression level.
Identification of differentially expressed genes (DEGs)
The DESeq2 package (v1.26.0) (20) was used to identify DEGs between COL7A1-high and COL7A1-low groups from TCGA. The criteria used were |log (fold change)|>1 and adjusted P<0.05. The results were presented as volcano plots and heat maps using the ggplot2 package (v3.3.3) (18).
Enrichment analysis and immune cell infiltration
To evaluate the biological effects of DEGs, GO (geneontology.org) and KEGG (www.kegg.jp) enrichment analyses were performed. The criteria for both GO and KEGG analysis were as follows: A minimum count of 5, a maximum count of 5,000, P<0.05 and false discovery rate (FDR)<0.25 were considered statistically significant. Furthermore, GSEA was performed to assess potential biological functions and pathways in the COL7A1-high and COL7A1-low groups. Gene sets with absolute normalized enrichment score >1, nominal P<0.05 and FDR<0.25 were considered to be statistically significant. The c2.cp.kegg.v7.4.symbols.gmt and h.all.v7.4.symbols.gmt were downloaded from the Molecular Signature Database (MSigDB) (www.gsea-msigdb.org). All of the enrichment analyses were performed using the clusterProfiler package (v3.14.3) (21). Furthermore, ssGSEA was used to perform immune infiltration analysis using the GSVA package (v1.34.0) (22) and based on gene expression profiles, the infiltration levels of 24 immune cell types (23) were quantified. To further evaluate the association between COL7A1 mRNA expression and immune cell infiltration levels, the data were assessed using Spearman correlation and Wilcoxon rank sum tests.
Association of COL7A1 with prognosis and model construction
OS, disease-specific survival (DSS) and progression-free interval (PFI) were used to evaluate the relationship between COL7A1 expression and prognosis in PC. The Kaplan-Meier Plotter (kmplot.com) was used to evaluate survival according to mRNA expression levels using the log-rank test and the parameters selected were as follows: mRNA(RNA-seq) for pan-cancer, split patient by median (24). To analyze the prognostic value of COL7A1 mRNA expression in PC, univariate and multivariate Cox analysis of TCGA-PAAD dataset was performed. The median COL7A1 mRNA expression level was defined as the cut-off value. Multivariate Cox analysis was then applied to validate the independent prognostic value of COL7A1 mRNA expression levels. Nomograms were constructed to evaluate the prognosis for 1-, 2- and 3-year OS for patients with PC a using the rms (v6.2-0) (https://cran.r-project.org/web/packages/rms) and survival (v3.2-10) (https://cran.r-project.org/web/packages/survival) packages for analysis and visualization respectively.
The MIA PaCa-2 (CRM-CRL-1420), BxPC-3 (CRL-1687), Capan-1 (HTB-79) and PATU-8988 (ACC 162) PC cell lines, and the hTERT-HPNE (CRL-4023) human normal pancreatic duct cell line were purchased from American Type Culture Collection. PANC-1, MIA PaCa-2, PATU-8988 and HPNE cells were cultured in Dulbecco's modified Eagle's medium (HyClone; Cytiva) containing 10% FBS (HyClone; Cytiva), BxPC-3 cells were cultured in RPMI-1640 medium (HyClone; Cytiva) containing 10% FBS (HyClone; Cytiva) and Capan-1 cells were cultured in Iscove's Modified Dulbecco's Medium (HyClone; Cytiva) containing 20% FBS (HyClone; Cytiva). All cell lines were cultured at 37°C in a 5% carbon dioxide cell culture incubator.
Total RNA was extracted from cells using TRIzol® (Invitrogen; Thermo Fisher Scientific, Inc.). GoScript™ Reverse Transcription Mix (cat. no. A25742; Promega Corporation) and PowerUP™ SYBR™ Green Master Mix (Thermo Fisher Scientific, Inc.) were used for RT-qPCR assays according to the manufacturers' protocols. The thermocycling conditions for qPCR were as follows: Initial denaturation and polymerase activation at 95°C for 2 min, followed by 40 cycles of 95°C for 15 sec and 60°C for 1 min. The 2−∆∆Cq (25) method was used to calculate relative mRNA expression levels and GAPDH was used as the reference. The primer sequences used in the present study were as follows: COL7A1 forward, 5′-GTTGGAGAGAAAGGTGACGAGG-3′ and reverse, 3′-TGGTCTCCCTTTTCACCCACAG-5′; and GAPDH forward, 5′-GTCTCCTCTGACTTCAACAGCG-3′ and reverse, 3′-ACCACCCTGTTGCTGTAGCCAA-5′.
RStudio software(version 1.4.171; http://www.rstudio.com) and R software (version 3.6.3; http://www.r-project.org)were used to perform statistical analyses. Two-tailed, unpaired Student's t-test was used to compare two groups. The RT-qPCR data were analyzed using one-way ANOVA and Dunnett's post hoc test. P<0.05 was considered to indicate a statistically significant difference.
COL7A1 mRNA expression in pan-cancerous and PC samples
Using data from TCGA and GTEx databases the mRNA expression levels of COL7A1 in human cancer types and normal tissues were assessed. Compared with those in normal samples, COL7A1 mRNA expression levels were significantly higher in bladder urothelial carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, cholangiocarcinoma, colon adenocarcinoma, lymphoid neoplasm diffuse large B-cell lymphoma, esophageal carcinoma, head and neck squamous cell carcinoma, liver hepatocellular carcinoma, PAAD, pheochromocytoma and paraganglioma, stomach adenocarcinoma and thymoma; however, COL7A1 was significantly downregulated in adrenocortical carcinoma, breast invasive carcinoma, glioblastoma multiforme, kidney chromophobe, kidney renal clear cell carcinoma and kidney renal papillary cell carcinoma (P<0.05; Fig. 1A). Additionally, in PC, COL7A1 mRNA expression levels were significantly higher compared with those in normal pancreas tissues (Fig. 1B). Furthermore, the mRNA expression levels of COL7A1 in PC and normal tissues were assessed in the GSE15471 and GSE101448 datasets. In the GSE15471 dataset, COL7A1 mRNA expression was significantly higher (P<0.001) in PC tissues (6.84±0.71) than in normal tissues (6.20±0.24). Similarly, in the GSE101448 dataset, COL7A1 mRNA expression was significantly higher (P<0.001) in PC tissues (10.08±1.29) than in normal tissues (8.32±0.93) (Fig. 1C and D). Furthermore, a ROC curve was used to evaluate the diagnostic value of COL7A1 in PC. The area under the curve of COL7A1 was 0.963 (95% CI, 0.942-0.983; Fig. 1E), which suggested that COL7A1 could serve as an effective marker for the diagnosis of PC. To identify DEGs in PC, 89 COL7A1-high samples were compared with 89 COL7A1-low PC samples. A total of 1,288 DEGs were identified, which included 1,007 downregulated genes and 281 upregulated genes (Fig. 1F).
mRNA expression levels of COL7A1 in pan-cancerous and PC samples. (A) mRNA expression levels of COL7A1 in tumor and normal (unpaired) samples based on TCGA and GTEx datasets. (B) mRNA expression levels of COL7A1 in PC and normal (unpaired) pancreas tissues based on TCGA and GTEx datasets. (C) COL7A1 mRNA expression between tumor and normal (paired) samples in the GSE15471 dataset. (D) COL7A1 mRNA expression between tumor and normal (unpaired) samples in the GSE101448 dataset. (E) Receiver operating characteristic curve analysis was used to evaluate the diagnostic value of COL7A1 mRNA expression in PC. (F) Volcano plot of the differentially expressed genes. *P<0.05, **P<0.01 and ***P<0.001. AUC, area under the curve; COL7A1, collagen type VII α1 chain; GTEx, Genotype-Tissue Expression; ns, not significant; P.adj, adjusted P-value; PC, pancreatic cancer; TCGA, The Cancer Genome Atlas; TPM, transcripts per million; TPR, true positive rate; FPR, false positive rate.
GO and KEGG enrichment analyses and GSEA
To evaluate the relative biofunctions and pathways associated with COL7A1 in PC, the clusterProfiler package was used to perform GO and KEGG analysis. The results demonstrated that COL7A1-associated genes participated in multiple biological processes, cellular components and molecular functions, including ‘Regulation of biological quality’, ‘Cell-cell signaling’, ‘Plasma membrane part’, ‘Neuron part’, ‘Transporter activity’ and ‘Transmembrane transporter activity’ (Fig. 2A-C). Furthermore, KEGG enrichment analysis demonstrated that COL7A1-associated DEGs were involved in ‘Neuroactive ligand-receptor interaction’, ‘cAMP signaling pathway’, ‘cGMP-PKG signaling pathway’, ‘ECM-receptor interaction’ and numerous other signaling pathways (Fig. 2D). To better evaluate COL7A1-related signaling pathways, GSEA was performed. The GSEA results showed that COL7A1-associated genes were mainly involved in ‘ECM-receptor interaction’, ‘cell cycle’, ‘G2M checkpoint’ and ‘epithelial-mesenchymal-transition (EMT)’ pathways (Fig. 3A and B).
GO and KEGG enrichment analysis of DEGs. GO enrichment analysis of DEGs for (A) biological processes, (B) cellular components and (C) molecular functions. (D) KEGG pathway enrichment analysis of DEGs. DEGs, differentially expressed genes; ECM, extracellular matrix; GABA, γ-aminobutyric acid; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PKG, protein kinase G.
Enrichment plots from Gene Set Enrichment Analysis. (A) COL7A1-related signaling pathways in the c2.cp.kegg.v7.4.symbols.gmt dataset. (B) COL7A1-related signaling pathways in h.all.v7.4.symbols.gmt. COL7A1, collagen type VII α1 chain; E2F, E2 factor; ECM, extracellular matrix; ES, enrichment score; FDR, false discovery rate.
Correlation between COL7A1 expression and immune cell infiltration
ssGSEA and spearman correlation analysis were used to evaluate the correlation between COL7A1 mRNA expression levels and immune cell infiltration levels. As shown in Fig. 4, high COL7A1 mRNA expression in PC was significantly positively associated with the abundance of T helper (Th)2 cells (R=0.32; P<0.001) (Fig. 4A, B and F), natural killer (NK) CD56bright cells (R=0.25; P<0.001) (Fig. 4A, C and G), NK cells (R=0.19; P<0.05), Th1 cells (R=0.18; P<0.05) and regulatory T cells (R=0.15; P<0.05), and negatively related to infiltration levels of Th17 cells (R=−0.24; P<0.01)(Fig. 4A, E and I), eosinophils (R=−0.22; P<0.01) (Fig. 4A, D and H), T follicular helper cells (R=−0.18; P<0.05) and plasmacytoid dendritic cells (R=−0.15; P<0.05).
Association between immune cell infiltration and COL7A1 expression in the tumor microenvironment. (A) Correlation between COL7A1 mRNA expression and immune cell levels. (B-E) Comparison of the abundances of (B) Th2 cells, (C) NK CD56bright cells, (D) eosinophils and (E) Th17 cells between the COL7A1-low and COL7A1-high groups. (F-I) Scatter plots demonstrated the correlation between the abundances of (F) Th2 cells, (G) NK CD56 bright cells, (H) eosinophils and (I) Th17 cells and COL7A1 mRNA expression in pancreatic cancer. aDC, activated dendritic cells. COL7A1, collagen type VII α1 chain; DC, dendritic cells; iDC, immature dendritic cells; NK, natural killer; pDC, plasmacytoid dendritic cells; Tcm, central memory T cells; TFH, T follicular helper cells; TGD, γδ T cells; Th, T helper; TPM, transcripts per million; TReg, regulatory T cells.
Association of COL7A1 mRNA expression with clinicopathological variables in PC
Based on the median mRNA expression levels of COL7A1, the patients were divided into COL7A1-high and COL7A1-low groups, and the clinical features and the expression levels of COL7A1 were presented in Table I. Logistic regression analysis was used to evaluate the correlation between COL7A1 expression and clinical features, the results indicated that COL7A1 mRNA expression levels were not significantly associated with any of the 10 clinical characteristics (gender, age, T stage, N stage, M stage, pathologic stage, histologic stage, history of diabetes, history of chronic pancreatitis and primary therapy outcome) analyzed (P>0.05; Table II).
Association between clinicopathological variables and COL7A1 expression in patients with pancreatic cancer.
Association between type VII collagen α1 chain expression and clinicopathological characteristics (logistic regression).
High COL7A1 expression is associated with poor survival in PC
The Kaplan-Meier survival analysis demonstrated that high mRNA expression levels of COL7A1 were significantly associated with poor OS, DSS and PFI (Fig. 5A-C), in line with the results of the Kaplan-Meier Plotter analysis (Fig. 5D). Univariate analyses demonstrated that COL7A1 mRNA expression was a prognostic factor for OS, DSS and PFI. Furthermore, multivariate analysis demonstrated that COL7A1 mRNA expression was an independent prognostic indicator for PC (Tables III, SI and SII). Furthermore, the nomogram utilized T stage, N stage, age, histologic grade and COL7A1 mRNA expression to predict the 1-, 2- and 3-year OS, DSS and PFI in PC (Figs. 6A, S1A and S2A). The calibration curve was constructed to evaluate the efficiency of the nomogram (Fig. 6B, S1B and S2B). The 1, 2 and 3-year OS, DSS and PFI lines were close to ideal line, which indicated that this nomogram model demonstrated a high level of accuracy. Furthermore, ROC analysis was performed to predict 1, 2 and 3-year OS, DSS and PFI (Figs. 6C, S1C and S2C).
Kaplan-Meier survival plots comparing the COL7A1-high and COL7A1-low groups in pancreatic cancer. Analysis of (A) overall survival, (B) disease-specific survival and (C) progression-free interval using The Cancer Genome Atlas-pancreatic adenocarcinoma dataset. (D) Relapse-free survival analysis using Kaplan-Meier plotter. COL7A1, collagen type VII α1 chain; HR, hazard ratio.
Prognostic model for the prediction of 1-, 2- and 3-year OS in PC. (A) A nomogram for the prediction of the probability of 1-, 2- and 3-year OS for patients with PC. (B) Calibration plots of the nomogram for the estimation of the probability of OS at 1, 2 and 3 years. (C) Receiver operating characteristic curve of COL7A1 mRNA expression for the prediction of 1-, 2- and 3-year OS for patients with PC. AUC, area under the curve; COL7A1, collagen type VII α1 chain; OS, overall survival; PC, pancreatic cancer; TPR, true positive rate; FPR, false positive rate.
Univariate and multivariate analysis (overall survival) for prognostic factors in pancreatic cancer.
Upregulation of COL7A1 expression in PC cell lines assessed using RT-qPCR
To evaluate the accuracy of the bioinformatics analysis results, RT-qPCR was performed in PC cell lines (MIA PaCa-2, BxPC-3, Capan-1 and PATU-8988) and hTERT-HPNE cells. The results demonstrated that the mRNA expression levels of COL7A1 in the BxPC-3, Capan-1 and PATU-8988 cell lines were significantly higher than that in the hTERT-HPNE cell line. However, there was no significant difference in the COL7A1 mRNA expression levels between the MIA PaCa-2 and hTERT-HPNE cell lines. The RT-qPCR results indicated that COL7A1 was commonly overexpressed in PC cell lines, which was in accordance with the results bioinformatics analysis of public datasets (Fig. 7).
Reverse transcription-quantitative PCR assessment of COL7A1 mRNA expression in pancreatic cancer cell lines and a normal pancreatic duct cell line. *P<0.05 and ****P<0.0001. COL7A1, collagen type VII α1 chain; ns, not significant.
According to a previous study, the ECM serves crucial roles in the progression of cancer (26) and the basement membrane is one type of ECM structure. Type VII collagen, a type of basement membrane collagen, is an important component for basement membrane functions as it can form anchoring fibrils which mediate dermal-epidermal adhesion (27). Therefore, loss of function of type VII collagen can lead to a RDEB and increases the risk of skin cancer (7). Martins et al (28) reported that type VII collagen suppressed TGF-β signaling and angiogenesis in cutaneous SCC. However, certain studies have reported that high COL7A1 expression was associated with poor prognosis in gastric cancer and esophageal squamous cell carcinoma (15,17). However, to the best of our knowledge, the mechanism is still unclear and it may serve different roles in different cancer types.
Despite vigorous research focusing on PC, the improvement in its prognosis remains poor due to its aggressive nature and insensitivity to treatment (11). Therefore, it is imperative to identify efficient and compelling diagnostic and prognostic biomarkers for patients with PC. As such, the present study evaluated COL7A1 expression and the prognostic value of COL7A1 mRNA expression in PC using RT-qPCR and public databases. The results demonstrated that COL7A1 mRNA expression was significantly higher in PC samples and cell lines compared with in normal pancreas tissues. The results of the present study demonstrated that COL7A1 was expressed differentially in human cancer types. For example, it was expressed at significantly higher levels in digestive carcinomas (esophageal cancer, gastric cancer, hepatocellular carcinoma, colorectal cancer and PC) (Fig. 1A) compared with normal tissues, which indicated that COL7A1 might serve as an oncogene in digestive system cancers. Furthermore, the diagnostic value of COL7A1 in PC was evaluated and the ROC curve indicated that the mRNA expression levels of COL7A1 could be an effective biomarker for PC diagnosis.
To further understand the biological functions and mechanism of COL7A1 in PC, GO and KEGG analyses and GSEA were performed. The results of GO and KEGG enrichment analyses indicated that the DEGs were involved in ‘Ion transport’, ‘Ion channel activity’, ‘cAMP signaling pathway’, ‘Calcium signaling pathway’ and ‘ECM-receptor interaction’. Recently, numerous studies have reported that ion transport and calcium channels serve an important role in tumorigenesis and progression (29–31). These findings supported further investigation of the function of COL7A1 in PC. GSEA results suggested that COL7A1 was associated with ‘ECM receptor interaction’, ‘cell cycle’, ‘DNA replication’, ‘G2M checkpoint’, ‘epithelial mesenchymal transition’, ‘E2F targets’ and ‘mitotic spindle’. These pathways have all been reported to participate in the proliferation, invasion and metastasis of PC (32–34).
TIICs exhibit a dual role in the development of PC, as it can inhibit tumor progression as well as promote cancer cells' escape of immune surveillance (35–37). ssGSEA was used to evaluate the association between COL7A1 mRNA expression and TIICs in PC. The results demonstrated that COL7A1 mRNA expression was significantly correlated with Th2 and NK cell levels. Yang et al (38) reported that an elevated Th2/Th1 ratio could result in an immune-suppressive microenvironment and promote tumor growth. Marcon et al (39) reported that, in PC, the cytotoxicity of NK cells was inhibited and favored tumor escape of immune surveillance. These results partially indicated why COL7A1 mRNA expression exhibited a negative association with the survival of patients with PC. However, it must be acknowledged that the association between TIIC and COL7A1 mRNA expression was only based on the results of TCGA database analysis and the correlation coefficient of TIICs was not high. The complex interactions between TIICs and the PC tumor immune microenvironment still require further study.
The association between COL7A1 mRNA expression and the prognosis of patients with PC was also evaluated. In the present study, Kaplan-Meier survival analysis indicated that low COL7A1 mRNA expression was significantly associated with longer OS, DSS and PFI times. Univariate and multivariate Cox regression analysis demonstrated that COL7A1 mRNA expression was a powerful and independent prognostic biomarker for patients with PC. Furthermore, a prognostic nomogram involving clinicopathological factors and COL7A1 mRNA expression was constructed to predict the survival (OS, DSS and PFI) of patients with PC. This predictive model provides a novel option for assessing the prognosis of patients with PC.
Although a comprehensive analysis of the association between COL7A1 mRNA expression and PC was performed, the present study still included certain limitations. Firstly, the association between COL7A1 mRNA expression and the prognosis of patients with PC should be validated using more clinical samples. Secondly, the raw data, excluding the PCR data, were acquired from public databases and bias due to confounding factors may be unavoidable. Furthermore, the functional and molecular mechanisms associated with COL7A1 need to be further elucidated. Additionally, the clinical role of COL7A1 in PC requires further assessment in the near future.
To the best our knowledge, the present study was the first to evaluate the prognostic and immunologic values of COL7A1 in PC. However, it is limited by the aforementioned limitations and a deeper mechanistic investigation of COL7A1 is required.
Overall, the present study demonstrated that COL7A1 expression was upregulated in PC tissues and high mRNA expression levels of COL7A1 were an independent risk factor in patients with PC. Furthermore, its expression may be associated with tumor immune infiltration cells. Based on these findings, it was proposed that COL7A1 could be a novel biomarker for the diagnosis and prognosis of PC.
The costs of the present study were supported by Cheng Ding.
Availability of data and materials
The data used to support the bioinformatic results are available at the TCGA [pancreatic adenocarcinoma (PAAD) dataset; https://portal.gdc.cancer.gov], GTEx (https://xenabrowser.net/datapages/; TOIL RSEM tpm dataset) and GEO databases (accession no. GSE15471 and GSE101448; http://www.ncbi.nlm.nih.gov/geo/). The other datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
MD and QH designed the study. CD and ZY collected the data, performed the analysis and wrote the manuscript. ZY performed the RT-qPCR experiments. JZ and XL revised the statistical findings and edited and revised the manuscript. MD and QH confirm the authenticity of the raw data. All authors have read and approved the final manuscript.
Ethics approval and consent to participate
Patient consent for publication
All authors declare that they have no competing interests.
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