Progressive and Prognostic Performance of an Extracellular Matrix-Receptor Interaction Signature in Gastric Cancer

Yang, Xiangchou; Chen, Liping; Mao, Yuting; Hu, Zijing; He, Min

doi:10.1155/2020/8816070

Cited by 29 publications

(23 citation statements)

References 60 publications

(55 reference statements)

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“…Several works describe biomarkers and signatures for assessment of colorectal cancer prognosis based on the expression of particular genes involved in ECM–receptor interaction, including genes encoding integrins ( Boudjadi et al, 2013 ; Gong et al, 2019 ), E- and P-cadherin ( Sun et al, 2011 ; Christou et al, 2017 ), and different laminins ( Galatenko et al, 2018 ). ECM-based prognostic gene signatures were constructed for gastric ( Yang et al, 2020 ), breast ( Bergamaschi et al, 2008 ), prostate ( Pang et al, 2019 ), and bladder ( Qing et al, 2020 ) cancers. However, to the best of our knowledge, no comprehensive prognostic analysis of ECM–receptor interaction–based colorectal cancer gene signatures has been done so far.…”

Section: Introductionmentioning

confidence: 99%

ECM–Receptor Regulatory Network and Its Prognostic Role in Colorectal Cancer

et al. 2021

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Interactions of the extracellular matrix (ECM) and cellular receptors constitute one of the crucial pathways involved in colorectal cancer progression and metastasis. With the use of bioinformatics analysis, we comprehensively evaluated the prognostic information concentrated in the genes from this pathway. First, we constructed a ECM–receptor regulatory network by integrating the transcription factor (TF) and 5’-isomiR interaction databases with mRNA/miRNA-seq data from The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD). Notably, one-third of interactions mediated by 5’-isomiRs was represented by noncanonical isomiRs (isomiRs, whose 5’-end sequence did not match with the canonical miRBase version). Then, exhaustive search-based feature selection was used to fit prognostic signatures composed of nodes from the network for overall survival prediction. Two reliable prognostic signatures were identified and validated on the independent The Cancer Genome Atlas Rectum Adenocarcinoma (TCGA-READ) cohort. The first signature was made up by six genes, directly involved in ECM–receptor interaction: AGRN, DAG1, FN1, ITGA5, THBS3, and TNC (concordance index 0.61, logrank test p = 0.0164, 3-years ROC AUC = 0.68). The second hybrid signature was composed of three regulators: hsa-miR-32-5p, NR1H2, and SNAI1 (concordance index 0.64, logrank test p = 0.0229, 3-years ROC AUC = 0.71). While hsa-miR-32-5p exclusively regulated ECM-related genes (COL1A2 and ITGA5), NR1H2 and SNAI1 also targeted other pathways (adhesion, cell cycle, and cell division). Concordant distributions of the respective risk scores across four stages of colorectal cancer and adjacent normal mucosa additionally confirmed reliability of the models.

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Section: Introductionmentioning

confidence: 99%

ECM–Receptor Regulatory Network and Its Prognostic Role in Colorectal Cancer

et al. 2021

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“…For example, ECM may provide support as well as maintain normal epithelial architecture [29]. Yang et al found that ECM receptor interaction signatures such as CD36, COL5A2, and ITGB5 displayed distinct correlations to clinical outcomes of gastric cancer subjects [30]. Our data demonstrated that stage and the risk score were independent risk factors for gastric cancer, which were incorporated into the nomogram.…”

Section: Discussionmentioning

confidence: 56%

Development and Verification of an Immune-Based Gene Signature for Risk Stratification and Immunotherapeutic Efficacy Assessment in Gastric Cancer

et al. 2021

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Objective. Due to the molecular heterogeneity of gastric cancer, only minor patients respond to immunotherapeutic schemes. This study is aimed at developing an immune-based gene signature for risk stratification and immunotherapeutic efficacy assessment in gastric cancer. Methods. An immune-based gene signature was developed in gastric cancer by LASSO method in the training set. The predictive performance was validated in the external datasets. KEGG pathways related to risk scores were assessed by GSEA. Based on multivariate Cox regression analysis, a nomogram was established. Sensitivity to chemotherapy drugs was evaluated between high- and low-risk samples. The relationships of risk scores with infiltration levels of immune cells, stromal scores, immune scores, immune cell subgroups, and overall response to anti-PD-L1 therapy were determined. Results. Our results showed that high risk scores were indicative of undesirable survival outcomes both in the training set ( p < 0.0001 ) and the validation set ( p = 0.002 ). Moreover, this signature could independently predict patients’ survival (HR: 2.656 (1.919-3.676) and p < 0.001 ). Subgroup analysis confirmed the sensitivity of this signature in predicting prognosis (all p < 0.05 ). Cancer-related pathways were primarily enriched in high-risk samples, such as MAPK and TGF-β pathways ( p < 0.05 ). By incorporating stage and the risk score, we established a nomogram for predicting one-, three-, and five-year survival probability. Patients with high-risk scores were more sensitive to chemotherapy drugs ( p < 0.05 ). There was heterogeneity in immune cells between high- and low-risk samples ( p < 0.05 ). Samples with progressive disease exhibited the highest risk score, and those with complete response had the lowest risk score ( p < 0.05 ). Conclusion. This immune-based gene signature might be representative of a promising prognostic classifier for predicting risk stratification and immunotherapeutic efficacy in gastric cancer, assisting personalized therapy and follow-up plan.

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“…Our study evaluated that the biological function of the signature components was related to the ECM. The current study showed that ECM and ECM-related components play an essential role in the occurrence and development of GC [30]. We found that the 24 target genes were mainly composed of ECM-related genes, such as SPARC, BGN, FBN1, SPP1, and FN1.…”

Section: Discussionmentioning

confidence: 65%

Development and Validation of a Six-lncRNA Prognostic Signature in Gastric Cancer

Zeng

Nai

et al. 2021

Preprint

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Background: Gastric cancer (GC) has been a leading cause of cancer-related mortality for many years. It is thought that long noncoding RNAs (lncRNAs) can play a significant role in GC. This study aimed to construct a powerful six-lncRNA signature as a prognostic biomarker for GC patients.Methods: Based on The Cancer Genome Atlas (TCGA), the expression profiles of lncRNAs and the corresponding clinical data of GC patients were obtained. Cox regression and the least absolute shrinkage and selection operator (LASSO) regression model were used to identify the prognostic lncRNA signature. A total of 337 patients were included in the combined dataset (N = 337), which was divided into a training dataset (N= 169) and a test dataset (N = 168). The reliability of the lncRNA prognostic signature was validated in three datasets.Results: A six-lncRNA prognostic signature was constructed to predict the overall survival (OS) of GC patients. The signature had better discriminability than clinical characteristics. The prognostic risk score was as follows: (expression level of RP11-284F21.7×-0.243981) + (expression level of RP11-432J22.2×-0.502378) + (expression level of RP4-584D14.5×-0.447878) + (expression level of AC093850.2×0.261822) + (expression level of AP000695.6 ×0.654318) + (expression level of AC098973.2× 0.406603). In addition, the signature was confirmed to be a significant predictor for predicting the OS. The nomogram model precisely predicted the OS of GC. Enrichment analysis indicated that the signature was mainly enriched for extracellular matrix-related functions and tumor signaling pathways. The target genes IGFBP7, VCAN, and COL1A1 had prognostic value in GC. AC098973.2 and RP11-284F21.7 was verified for the first time in GC tissues and cell lines.Conclusions: The six-lncRNA prognostic signature could predict the OS and has high clinical application value in GC.

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Progressive and Prognostic Performance of an Extracellular Matrix-Receptor Interaction Signature in Gastric Cancer

Cited by 29 publications

References 60 publications

ECM–Receptor Regulatory Network and Its Prognostic Role in Colorectal Cancer

ECM–Receptor Regulatory Network and Its Prognostic Role in Colorectal Cancer

Development and Verification of an Immune-Based Gene Signature for Risk Stratification and Immunotherapeutic Efficacy Assessment in Gastric Cancer

Development and Validation of a Six-lncRNA Prognostic Signature in Gastric Cancer

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