Clinical Correlation of Wnt2 and COL8A1 With Colon Adenocarcinoma Prognosis

Zhang, Lihua; Jiang, Xin; Li, Yan; Fan, Qianqian; Li, Hongjuan; Jin, Lei; Li, Liqi; Jin, Yinzhe; Zhang, Ting; Mao, Yong; Dong, Hua

doi:10.3389/fonc.2020.01504

Cited by 12 publications

(10 citation statements)

References 25 publications

(20 reference statements)

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“…Recent research has shown that COL8A1 is dysregulated in a variety of cancers. COL8A1 may affect the progression of colorectal cancer and the prognosis of patients by regulating focal adhesion-related pathways, and the expression of COL8A1 in colorectal cancer is related to the expression of Wnt2 and is linked to the poor survival of patients [ 28 , 29 ]. COL8A1 may promote breast cancer migration by affecting ECM receptor interactions and cooperation with other genes [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

COL8A1 Predicts the Clinical Prognosis of Gastric Cancer and Is Related to Epithelial-Mesenchymal Transition

She

Zhao

et al. 2022

BioMed Research International

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Background. Gastric cancer (GC) is the fifth most common malignant tumor and the third leading cause of cancer-related deaths. Because GC has the characteristics of high heterogeneity, unclear mechanism, limited treatment methods, and low five-year survival rate, it is necessary to find the prognostic biomarkers of GC and explore the mechanism of GC. Methods. We first identified differentially expressed genes (DEGs) between gastric cancer and normal gastric cells through expression analysis. A protein-protein interaction (PPI) network was constructed to find tightly connected modules. We performed survival analysis on the DEGs in the modules to identify genes with prognostic significance. Gene set enrichment analysis (GSEA) was used to identify gene enrichment pathways. Finally, we used our own collected clinical samples of 119 gastric adenocarcinoma (STAD) tissues and 40 normal gastric tissues to perform immunohistochemical (IHC) staining to verify the differential expression of COL8A1 in STAD tissues and normal gastric tissues and its correlation with epithelial-mesenchymal transition- (EMT-) related factors. Results. We identified 356 DEGs through differential expression analysis. Through PPI analysis and survival analysis, we determined that the collagen type VII alpha-1 chain (COL8A1) gene has prognostic significance. GSEA analysis showed that COL8A1 was significantly enriched in the EMT. IHC results showed that COL8A1 was upregulated in STAD tissues and could be used as an independent prognostic factor and was related to EMT. Conclusion. This study shows that COL8A1 is related to the prognosis of GC patients and might affect the progress of GC through the EMT pathway. Therefore, COL8A1 may be a biomarker for predicting the prognosis of GC.

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Section: Discussionmentioning

confidence: 99%

COL8A1 Predicts the Clinical Prognosis of Gastric Cancer and Is Related to Epithelial-Mesenchymal Transition

She

Zhao

et al. 2022

BioMed Research International

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“…COAD remains one of the most life-threatening malignancies in the world due to the complex molecular mechanisms. With the development of gene sequencing technology, some potential gene markers, including INHBA ( 24 ), COL8A1 ( 25 ), and GABRD ( 26 ), correlated with poor prognosis for COAD patients have been identified. Nevertheless, the number of such biomarkers in COAD tumorigenesis is still limited.…”

Section: Discussionmentioning

confidence: 99%

Integrative Proteo-Genomic Analysis for Recurrent Survival Prognosis in Colon Adenocarcinoma

Wang

Liu

et al. 2022

Front. Oncol.

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BackgroundThe survival prognosis is the hallmark of cancer progression. Here, we aimed to develop a recurrence-related gene signature to predict the prognosis of colon adenocarcinoma (COAD).MethodsThe proteomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and genomic data from the cancer genomic maps [The Cancer Genome Atlas (TCGA)] dataset were analyzed to identify co-differentially expressed genes (cDEGs) between recurrence samples and non-recurrence samples in COAD using limma package. Functional enrichment analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was conducted. Univariate and multivariate Cox regressions were applied to identify the independent prognostic feature cDEGs and establish the signature whose performance was evaluated by Kaplan–Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index (C-index), and calibration curve. The area under the receiver operating characteristic (ROC) curve (AUROC) and a nomogram were calculated to assess the predictive accuracy. GSE17538 and GSE39582 were used for external validation. Quantitative real-time PCR and Western blot analysis were carried out to validate our findings.ResultsWe identified 86 cDEGs in recurrence samples compared with non-recurrence samples. These genes were primarily enriched in the regulation of carbon metabolic process, fructose and mannose metabolism, and extracellular exosome. Then, an eight-gene-based signature (CA12, HBB, NCF1, KBTBD11, MMAA, DMBT1, AHNAK2, and FBLN2) was developed to separate patients into high- and low-risk groups. Patients in the low-risk group had significantly better prognosis than those in the high-risk group. Four prognostic clinical features, including pathological M, N, T, and RS model status, were screened for building the nomogram survival model. The PCR and Western blot analysis results suggested that CA12 and AHNAK2 were significantly upregulated, while MMAA and DMBT1 were downregulated in the tumor sample compared with adjacent tissues, and in non-recurrent samples compared with non-recurrent samples in COAD.ConclusionThese identified recurrence-related gene signatures might provide an effective prognostic predictor and promising therapeutic targets for COAD patients.

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“…With respect to the four identified markers in the model, elevated expression of COL8A1 has been found in CAFs and was significantly associated with a high risk of death in head and neck squamous cell carcinoma (Lai et al, 2019). Zhang et al identified COL8A1 as the prognostic hub gene highly correlated with Wnt2, which is elevated selectively in CAFs, and high coexpression of COL8A1 and Wnt2 was an independent adverse prognostic factor for colon cancer patients (Katoh, 2001;Zhang et al, 2020) proliferation and promoted the apoptosis of GC cells (Zhou et al, 2020). As for SPOCK1, studies have proved SPOCK1 as an EMT-related marker that was closely correlated with tumorigenesis and invasiveness in gastric cancer (Yan et al, 2017;Chen et al, 2018), prostate cancer (Wang et al, 2016), pancreatic cancer (Li et al, 2020), gallbladder cancer (Shu et al, 2015), and lung cancer (Miao et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Weighted Gene Co-expression Network Analysis Identifies a Cancer-Associated Fibroblast Signature for Predicting Prognosis and Therapeutic Responses in Gastric Cancer

Zheng

Liu

et al. 2021

Front. Mol. Biosci.

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Background: Cancer-associated fibroblasts (CAFs) are the most prominent cellular components in gastric cancer (GC) stroma that contribute to GC progression, treatment resistance, and immunosuppression. This study aimed at exploring stromal CAF-related factors and developing a CAF-related classifier for predicting prognosis and therapeutic effects in GC.Methods: We downloaded mRNA expression and clinical information of 431 GC samples from Gene Expression Omnibus (GEO) and 330 GC samples from The Cancer Genome Atlas (TCGA) databases. CAF infiltrations were quantified by the estimate the proportion of immune and cancer cells (EPIC) method, and stromal scores were calculated via the Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm. Stromal CAF-related genes were identified by weighted gene co-expression network analysis (WGCNA). A CAF risk signature was then developed using the univariate and least absolute shrinkage and selection operator method (LASSO) Cox regression model. We applied the Spearman test to determine the correlation among CAF risk score, CAF markers, and CAF infiltrations (estimated via EPIC, xCell, microenvironment cell populations-counter (MCP-counter), and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms). The TIDE algorithm was further used to assess immunotherapy response. Gene set enrichment analysis (GSEA) was applied to clarify the molecular mechanisms.Results: The 4-gene (COL8A1, SPOCK1, AEBP1, and TIMP2) prognostic CAF model was constructed. GC patients were classified into high– and low–CAF-risk groups in accordance with their median CAF risk score, and patients in the high–CAF-risk group had significant worse prognosis. Spearman correlation analyses revealed the CAF risk score was strongly and positively correlated with stromal and CAF infiltrations, and the four model genes also exhibited positive correlations with CAF markers. Furthermore, TIDE analysis revealed high–CAF-risk patients were less likely to respond to immunotherapy. GSEA revealed that epithelial–mesenchymal transition (EMT), TGF-β signaling, hypoxia, and angiogenesis gene sets were significantly enriched in high–CAF-risk group patients.Conclusion: The present four-gene prognostic CAF signature was not only reliable for predicting prognosis but also competent to estimate clinical immunotherapy response for GC patients, which might provide significant clinical implications for guiding tailored anti-CAF therapy in combination with immunotherapy for GC patients.

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Clinical Correlation of Wnt2 and COL8A1 With Colon Adenocarcinoma Prognosis

Cited by 12 publications

References 25 publications

COL8A1 Predicts the Clinical Prognosis of Gastric Cancer and Is Related to Epithelial-Mesenchymal Transition

COL8A1 Predicts the Clinical Prognosis of Gastric Cancer and Is Related to Epithelial-Mesenchymal Transition

Integrative Proteo-Genomic Analysis for Recurrent Survival Prognosis in Colon Adenocarcinoma

Weighted Gene Co-expression Network Analysis Identifies a Cancer-Associated Fibroblast Signature for Predicting Prognosis and Therapeutic Responses in Gastric Cancer

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