Coexpression Module Construction by Weighted Gene Coexpression Network Analysis and Identify Potential Prognostic Markers of Breast Cancer

Role of vascular endothelial growth factor D in lung adenocarcinoma immunotherapy response

OBJECTIVE

To identify key genes associated with tumor-associated macrophages (TAMs), tumor immunotherapy, in the prognosis of lung adenocarcinoma (LUAD).

METHODS

The mRNA expression profiles of LUAD samples were obtained from The Cancer Genome Atlas (TCGA) database. The "CIBERSORT" R package was employed to calculate the proportion of innate immune cell infiltration in both tumor and adjacent normal tissues. TAM-associated genes in LUAD were identified to construct a prognostic risk model using weighted gene correlation network analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression analyses (COX). The IMvigor210 cohort was utilized to validate the roles of these genes as predictors of immunotherapy response. Tissue microarrays, immunofluorescence staining, and mRNA level detection methods were used to determine the correlation of risk factors in LUAD tissues.

RESULTS

CIBERSORT analysis revealed significant differences in innate immune cells between tumor and adjacent tissues. Seventy-four differential genes linked to these cells were identified from WGCNA. Four hub genes (endothelin receptor type B, vascular endothelial growth factor D (VEGFD), latent transforming growth factor beta binding protein 4 (LTBP4), and fibroblast growth factor receptor 4 (FGFR4)) in the TAM prognostic model were identified as independent prognostic risk factors (P < 0.05). VEGFD expression was identified as a low-risk factor for LUAD prognosis prediction (P < 0.05). Moreover, low-risk patients exhibited higher sensitivity to anti-PD-L1 therapy compared to high-risk patients (P < 0.05). VEGFD levels were negatively correlated with programmed cell death 1 (PD-1) levels (r = -0.363; P < 0.05), suggesting that VEGFD may serve as a predictor for anti-PD-1 treatment.

CONCLUSIONS

VEGFD is associated with innate immunity in LUAD, it can predict LUAD prognosis, and therefor may be a potential predictor for anti-PD-1 treatment in patients with LUAD.

Identification of Hub Genes and Construction of a Transcriptional Regulatory Network Associated With Tumor Recurrence in Colorectal Cancer by Weighted Gene Co-expression Network Analysis

Tumor recurrence is one of the most important risk factors that can negatively affect the survival rate of colorectal cancer (CRC) patients. However, the key regulators dictating this process and their exact mechanisms are understudied. This study aimed to construct a gene co-expression network to predict the hub genes affecting CRC recurrence and to inspect the regulatory network of hub genes and transcription factors (TFs). A total of 177 cases from the GSE17536 dataset were analyzed via weighted gene co-expression network analysis to explore the modules related to CRC recurrence. Functional annotation of the key module genes was assessed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. The protein and protein interaction network was then built to screen hub genes. Samples from the Cancer Genome Atlas (TCGA) were further used to validate the hub genes. Construction of a TFs-miRNAs–hub genes network was also conducted using StarBase and Cytoscape approaches. After identification and validation, a total of five genes (TIMP1, SPARCL1, MYL9, TPM2, and CNN1) were selected as hub genes. A regulatory network of TFs-miRNAs-targets with 29 TFs, 58 miRNAs, and five hub genes was instituted, including model GATA6-MIR106A-CNN1, SP4-MIR424-TPM2, SP4-MIR326-MYL9, ETS1-MIR22-TIMP1, and ETS1-MIR22-SPARCL1. In conclusion, the identification of these hub genes and the prediction of the Regulatory relationship of TFs-miRNAs-hub genes may provide a novel insight for understanding the underlying mechanism for CRC recurrence.