Identification of Novel Prognostic Signatures for Clear Cell Renal Cell Carcinoma Based on ceRNA Network Construction and Immune Infiltration Analysis

Abstract

Objective. Clear cell renal cell carcinoma (ccRCC) carries significant morbidity and mortality globally and is often resistant to conventional radiotherapy and chemotherapy. Immune checkpoint blockade (ICB) has received attention in ccRCC patients as a promising anticancer treatment. Furthermore, competitive endogenous RNA (ceRNA) networks are crucial for the occurrence and progression of various tumors. This study was aimed at identifying reliable prognostic signatures and exploring potential mechanisms between ceRNA regulation and immune cell infiltration in ccRCC patients. Methods and Results. Gene expression profiling and clinical information of ccRCC samples were obtained from The Cancer Genome Atlas (TCGA) database. Through comprehensive bioinformatic analyses, differentially expressed mRNAs (DEmRNAs; n = 131 ), lncRNAs (DElncRNAs; n = 12 ), and miRNAs (DEmiRNAs; n = 25 ) were identified to establish ceRNA networks. The CIBERSORT algorithm was applied to calculate the proportion of 22 types of tumor-infiltrating immune cells (TIICs) in ccRCC tissues. Subsequently, univariate Cox, Lasso, and multivariate Cox regression analyses were employed to construct ceRNA-related and TIIC-related prognostic signatures. In addition, we explored the relationship between the crucial genes and TIICs via coexpression analysis, which revealed that the interactions between MALAT1, miR-1271-5p, KIAA1324, and follicular helper T cells might be closely correlated with the progression of ccRCC. Ultimately, we preliminarily validated that the potential MALAT1/miR-1271-5p/KIAA1324 axis was consistent with the ceRNA theory by qRT-PCR in the ccRCC cell lines. Conclusion. On the basis of the ceRNA networks and TIICs, we constructed two prognostic signatures with excellent predictive value and explored possible molecular regulatory mechanisms, which might contribute to the improvement of prognosis and individualized treatment for ccRCC patients.

Mapping the tumor microenvironment in clear cell renal carcinoma by single-cell transcriptome analysis

“…In ccRCC, MALAT1 has been implicated in various metabolic processes. It has been shown to interact with SCD, an enzyme involved in fatty acid biosynthesis, to participate in the regulation of lipid metabolism ( Zhou et al, 2022 ) and lipid uptake and insulin resistance through multiple pathways ( Yan et al, 2016 ; Zhao et al, 2021 ). In the TCGA-KIRC cohort, compared to normal tissues, tumor tissues exhibit higher levels of MALAT1 ( Supplementary Figure S2B ), and elevated MALAT1 expression was significantly positively associated with poor prognosis ( Figure 2I ).…”

Identification of a Novel Tumor Inflammation Signature for Risk Stratification, Prognosis Prediction, and Immune Status in Colorectal Cancer

“…[8][9][10][11], have been demonstrated with prognostic value in multiple cancers and have good predictive value in the prognostic evaluation of CRC [12,13]. With the development of sequencing technology and bioinformatic approaches, TIS has been proved to predict the prognosis of renal carcinoma BioMed Research International [14], pancreatic ductal adenocarcinoma [15], and lung cancer [16]. But the research on the correlation between TIS and the prognosis of patients with CRC is still limited.…”

Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network

“…Recently, studies investigated the ceRNA network and gene signature association in ccRCC. Most of them focus on the relationship between ceRNA, immune response regulation, and prognosis [27][28][29][30][31][32][33][34][35][36], and there is a lack of information about gene signatures involving the ceRNA network and metastasis in ccRCC.…”