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Fatty Acid Metabolism-Related lncRNAs Are Potential Biomarkers for Predicting the Overall Survival of Patients With Colorectal Cancer.

ABSTRACT: Abnormal metabolism, including abnormal fatty acid metabolism, is an emerging hallmark of cancer. The current study sought to investigate the potential prognostic value of fatty acid metabolism-related long noncoding RNAs (lncRNAs) in colorectal cancer (CRC). To this end, we obtained the gene expression data and clinical data of patients with CRC from The Cancer Genome Atlas (TCGA) database. Through gene set variation analysis (GSVA), we found that the fatty acid metabolism pathway was related to the clinical stage and prognosis of patients with CRC. After screening differentially expressed RNAs, we constructed a fatty acid metabolism-related competing endogenous RNA (ceRNA) network based on the miRTarBase, miRDB, TargetScan, and StarBase databases. Next, eight fatty acid metabolism-related lncRNAs included in the ceRNA network were identified to build a prognostic signature with Cox and least absolute shrinkage and selection operator (LASSO) regression analyses, and a nomogram was established based on the lncRNA signature and clinical variables. The signature and nomogram were further validated by Kaplan-Meier survival analysis, Cox regression analysis, calibration plots, receiver operating characteristic (ROC) curves, decision curve analysis (DCA). Besides, the TCGA internal and the quantitative real-time polymerase chain reaction (qRT-PCR) external cohorts were applied to successfully validate the robustness of the signature and nomogram. Finally, in vitro assays showed that knockdown of prognostic lncRNA TSPEAR-AS2 decreased the triglyceride (TG) content and the expressions of fatty acid synthase (FASN) and acetyl-CoA carboxylase 1 (ACC1) in CRC cells, which indicated the important role of lncRNA TSPEAR-AS2 in modulating fatty acid metabolism of CRC. The result of Oil Red O staining showed that the lipid content in lncRNA TSPEAR-AS2 high expression group was higher than that in lncRNA TSPEAR-AS2 low expression group. Our study may provide helpful information for fatty acid metabolism targeting therapies in CRC.

SUBMITTER: Peng Y

PROVIDER: S-EPMC8386021 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Metabolic reprogramming of energy is a newly recognized characteristic of cancer. In our current investigation, we examined the possible predictive importance of long noncoding RNAs (lncRNAs) associated to fatty acid metabolism in clear cell renal cell carcinoma (ccRCC). We conducted an analysis of the gene expression data obtained from patients diagnosed with ccRCC using the Cancer Genome Atlas (TCGA) database and the ArrayExpress database. We performed a screening to identify lncRNAs that are differentially expressed in fatty acid metabolism. Based on these findings, we developed a prognostic risk score model using these fatty acid metabolism-related lncRNAs. We then validated this model using Cox regression analysis, Kaplan-Meier survival analysis, and principal-component analysis (PCA). Furthermore, the prognostic risk score model was successfully validated using both the TCGA cohort and the E-MTAB-1980 cohort. We utilized gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) to determine the correlation between fatty acid metabolism and the PPAR signaling pathway in patients with ccRCC at various clinical stages and prognoses. We have discovered compelling evidence of the interaction between immune cells in the tumor microenvironment and tumor cells, which leads to immune evasion and resistance to drugs. This was achieved by the utilization of advanced techniques such as the CIBERSORT method, ESTIMATE R package, ssGSEA algorithm, and TIMER database exploration. Ultimately, we have established a network of competing endogenous RNA (ceRNA) that is related to fatty acid metabolism. The findings of our study suggest that medicines focused on fatty acid metabolism could be clinically significant for individuals with ccRCC. The utilization of this risk model, which is centered around the lncRNAs associated with fatty acid metabolism, could potentially provide valuable prognostic information and hold immunotherapeutic implications for patients with ccRCC.

Project description:ObjectiveGastric cancer (GC) is one of the most prevalent malignant tumors in Asian countries. Studies have proposed that lncRNAs can be used as diagnostic and prognostic indicators of GC due to the high specificity of lncRNAs expression involvement in GC. Recently, N6-methyladenosine (m6A) has also emerged as an important modulator of the expression of lncRNAs in GC. This study aimed at establishing a novel m6A-related lncRNAs prognostic signature that can be used to construct accurate models for predicting the prognosis of GC in the Asian population.MethodsFirst, the levels of m6A modification and m6A methyltransferases expression in GC samples were determined using dot blot and western blot analyses. Next, we evaluated the lncRNAs expression profiles and the corresponding clinical data of 88 Asian GC patients retrieved from The Cancer Genome Atlas (TCGA) database. Differential expression of m6A-related lncRNAs between GC and normal tissues was investigated. The relationship between these target lncRNAs and potential immunotherapeutic signatures was also analyzed. Gene set enrichment analysis (GSEA) was performed to identify the malignancy-associated pathways. Univariate Cox regression, LASSO regression, and multivariate Cox regression analyses were performed to establish a novel prognostic m6A-related lncRNAs prognostic signature. Moreover, we constructed a predictive nomogram and determined the expression levels of nine m6A-related lncRNAs in 12 pairs of clinical samples.ResultsWe found that m6A methylation levels were significantly increased in GC tumor samples compared to adjacent normal tissues, and the increase was positively correlated with tumor stage. Patients were then divided into two clusters (cluster 1 and cluster 2) based on the differential expression of the m6A-related lncRNAs. Results showed that there was a significant difference in survival probability between the two clusters (p = 0.018). Notably, the low survival rate in cluster 2 may be associated with high expression of immune cells (resting memory CD4+ T cells, p = 0.027; regulatory T cells, p = 0.0018; monocytes, p = 0.00095; and resting dendritic cells, p = 0.015), and low expression of immune cells (resting NK cells, p = 0.033; and macrophages M1, p = 0.045). Enrichment analysis indicated that malignancy-associated biological processes were more common in the cluster 2 subgroup. Finally, the risk model comprising of six m6A-related lncRNAs was identified as an independent predictor of prognoses, which could divide patients into high- or low-risk groups. Time-dependent ROC analysis suggested that the risk score could accurately predict the prognosis of GC patients. Patients in the high-risk group had worse outcomes compared to patients in the low-risk group, and the risk score showed a positive correlation with immune cells (resting memory CD4+ T cells, R = 0.31, P = 0.038; regulatory T cells, R = 0.42, P = 0.0042; monocytes, R = 0.42, P = 0.0043). However, M1 macrophages (R = -0.37, P = 0.012) and resting NK cells (R = -0.31, P = 0.043) had a negative correlation with risk scores. Furthermore, analysis of clinical samples validated the weak positive correlation between the risk score and tumor stage.ConclusionsThe risk model described here, based on the six m6A-related lncRNAs signature, and may predict the clinical prognoses and immunotherapeutic response in Asian GC patients.

Project description:Background: N6-methyladenosine (m6A) is the most common form of mRNA- and long noncoding RNA (lncRNA)-specific internal modification encountered in eukaryotes, with important effects on mRNA stability, translation, and splicing. The role of m6A-modified lncRNAs (m6A-lncRNAs) in bladder cancer (BLCA) is rarely reported. This study aimed to evaluate an efficient prognostic model of BLCA in patients, based on m6A-lncRNAs, and to discover potential biological targets. Methods: Differentially expressed lncRNAs were investigated in 433 BLCA samples derived from The Cancer Genome Atlas (TCGA) database. Kaplan–Meier and univariate Cox regression analyses were performed to screen for m6A-lncRNAs with prognostic roles in BLCA. We implemented Pearson correlation analysis to analyze 18 potentially prognostic lncRNAs and 20 known m6A-associated genes. Next, the data were imputed using least absolute shrinkage and selection operator (LASSO) Cox regression to establish an m6A-lncRNA prognostic signature. Results: We established an integrated risk score (RS) containing five m6A-lncRNAs and constructed a nomogram that had the ability to forecast the overall survival (OS) of patients with BLCA. We showed that the predictive accuracy of the RS for BLCA prognosis was high, which was confirmed by the area under the receiver operating characteristic (ROC) curve. We analyzed the correlation between tumor immune infiltrating cells and RS in high- and low-risk patients with BLCA and used tumor immune dysfunction and exclusion to predict the effect of immunotherapy. We screened out the most relevant modules of RS through the weighted gene co-expression network analysis network and explored their potential biological functions using GO and KEGG analyses. Conclusion: Our findings demonstrate that, compared with nomograms constructed using a single prognostic factor, the integrated RS represents a superior model for predicting survival in patients with BLCA, which may improve the clinical management of BLCA.

Project description:BackgroundStomach adenocarcinoma (STAD), caused by mutations in stomach cells, is characterized by poor overall survival. Chemotherapy is commonly administered for stomach cancer patients following surgical resection. An imbalance in tumor metabolic pathways is connected to tumor genesis and growth. It has been discovered that glutamine (Gln) metabolism plays a crucial role in cancer. Metabolic reprogramming is associated with clinical prognosis in various cancers. However, the role of glutamine metabolism genes (GlnMgs) in the fight against STAD remains poorly understood.MethodsGlnMgs were determined in STAD samples from the TCGA and GEO datasets. The TCGA and GEO databases provide information on stemness indices (mRNAsi), gene mutations, copy number variations (CNV), tumor mutation burden (TMB), and clinical characteristics. Lasso regression was performed to build the prediction model. The relationship between gene expression and Gln metabolism was investigated using co-expression analysis.ResultsGlnMgs, found to be overexpressed in the high-risk group even in the absence of any symptomatology, demonstrated strong predictive potential for STAD outcomes. GSEA highlighted immunological and tumor-related pathways in the high-risk group. Immune function and m6a gene expression differed significantly between the low- and high-risk groups. AFP, CST6, CGB5, and ELANE may be linked to the oncology process in STAD patients. The prognostic model, CNVs, single nucleotide polymorphism (SNP), and medication sensitivity all revealed a strong link to the gene.ConclusionGlnMgs are connected to the genesis and development of STAD. These corresponding prognostic models aid in predicting the prognosis of STAD GlnMgs and immune cell infiltration in the tumor microenvironment (TME) may be possible therapeutic targets in STAD. Furthermore, the glutamine metabolism gene signature presents a credible alternative for predicting STAD outcomes, suggesting that these GlnMgs could open a new field of study for STAD-focused therapy Additional trials are needed to validate the results of the current study.

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Fatty Acid Metabolism-Related lncRNAs Are Potential Biomarkers for Predicting the Overall Survival of Patients With Colorectal Cancer.

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