Exploring the mechanism of LncRNA CASC15 affecting hepatocellular carcinoma through miRNA.

This study aimed to determine the potential mechanisms through which long noncoding (Lnc) RNA cancer susceptibility candidate 15 (CASC15) affects hepatocellular carcinoma (HCC). We retrieved HCC RNA-seq and clinical information from the UCSC Xena database. The differential expression (DE) of CASC15 was detected. Overall survival was analyzed using Kaplan-Meier (K-M) curves. Molecular function and signaling pathways affected by CASC15 were determined using Gene Set Enrichment Analysis. Associations between CASC15 and the HCC microenvironment were investigated using immuno-infiltration assays. A differential CASC15-miRNA-mRNA network and HCC-specific CASC15-miRNA-mRNA ceRNA network were constructed. The overexpression of CASC15 in HCC tissues was associated with histological grade, clinical stage, pathological T stage, poor survival, more complex immune cell components, and 12 immune checkpoints. We identified 27 DE miRNAs and 270 DE mRNAs in the differential CASC15-miRNA-mRNA network, and 10 key genes that were enriched in 12 cancer-related signaling pathways. Extraction of the HCC-specific CASC15-miRNA-mRNA network revealed that IGF1R, MET, and KRAS were associated with HCC progression and occurrence. Our bioinformatic findings confirmed that CASC15 is a promising prognostic biomarker for HCC, and elevated levels in HCC are associated with the tumor microenvironment. We also constructed a disease-specific CASC15-miRNA-mRNA regulatory ceRNA network that provides a new perspective for the precise indexing of patients with elevated levels of CASC15.

Prognostic significance of LRRC1 in hepatocellular carcinoma and construction of relevant prognostic model.

This study aimed to elucidate the prognostic value of the leucine rich repeat containing 1 (LRRC1) gene in hepatocellular carcinoma (HCC) and to determine the effects of high and low LRRC1 expression on mutation and immune cell infiltration. We downloaded HCC mRNA-seq expression and clinical data from University of California Santa Cruz Xena. The expression of LRRC1 was compared between HCC tumor and normal samples. Tumor samples were divided according to high and low LRRC1 expression. Differentially expressed genes between the 2 groups were identified, and function, mutation, and immune cell infiltration were analyzed. Genes associated with immune cells were identified using weighted gene co-expression network analysis, and transcription factors of these genes were predicted. Moreover, a prognostic model was developed and its performance was evaluated. The expression of LRRC1 was upregulated in HCC tissues, and this indicated a poor prognosis for patients with HCC. Differentially expressed genes between high and low LRRC1 expression were significantly enriched in pathways associated with cancer, amino acid metabolism, carbohydrate metabolism, and the immune system. We identified 15 differentially infiltrated immune cells between tumors with high and low LRRC1 expression and 14 of them correlated with LRRC1 gene expression. Weighted gene co-expression network analysis identified 83 immune cell-related genes, 27 of which had prognostic value. Cyclic AMP-response element binding protein regulated annexin A5, matrix metallopeptidase 9, and LRRC1 in the transcription factor regulatory network. Finally, a prognostic model composed of 7 genes were generated, which could accurately predict the prognosis of HCC patients. The LRRC1 gene might serve as a potential immune-associated prognostic biomarker for HCC.

Identification of molecular subtypes and prognostic signatures based on transient receptor potential channel-related genes to predict the prognostic risk of hepatocellular carcinoma: A review.

Abnormal transient receptor potential (TRP) channel function interferes with intracellular calcium-based signaling and causes malignant phenotypes. However, the effects of TRP channel-related genes on hepatocellular carcinoma (HCC) remain unclear. This study aimed to identify HCC molecular subtypes and prognostic signatures based on TRP channel-related genes to predict prognostic risks. Unsupervised hierarchical clustering was applied to identify HCC molecular subtypes using the expression data of TRP channel-related genes. This was followed by a comparison of the clinical and immune microenvironment characteristics between the resulting subtypes. After screening for differentially expressed genes among subtypes, prognostic signatures were identified to construct risk score-based prognostic and nomogram models and predict HCC survival. Finally, tumor drug sensitivities were predicted and compared between the risk groups. Sixteen TRP channel-related genes that were differentially expressed between HCC and non-tumorous tissues were used to identify 2 subtypes. Cluster 1 had higher TRP scores, better survival status, and lower levels of clinical malignancy. Immune-related analyses also revealed higher infiltration of M1 macrophages and higher immune and stromal scores in Cluster 1 than in Cluster 2. After screening differentially expressed genes between subtypes, 6 prognostic signatures were identified to construct prognostic and nomogram models. The potential of these models to assess the prognostic risk of HCC was further validated. Furthermore, Cluster 1 was more distributed in the low-risk group, with higher drug sensitivities. Two HCC subtypes were identified, of which Cluster 1 was associated with a favorable prognosis. Prognostic signatures related to TRP channel genes and molecular subtypes can be used to predict HCC risk.