A prognostic index based on a fourteen long non-coding RNA signature to predict the recurrence-free survival for muscle-invasive bladder cancer patients

The Roles of miRNAs in Predicting Bladder Cancer Recurrence and Resistance to Treatment

Bladder cancer (BCa) is associated with significant morbidity, with development linked to environmental, lifestyle, and genetic causes. Recurrence presents a significant issue and is managed in the clinical setting with intravesical chemotherapy or immunotherapy. In order to address challenges such as a limited supply of BCG and identifying cases likely to recur, it would be advantageous to use molecular biomarkers to determine likelihood of recurrence and treatment response. Here, we review microRNAs (miRNAs) that have shown promise as predictors of BCa recurrence. MiRNAs are also discussed in the context of predicting resistance or susceptibility to BCa treatment.

Construction of an Immune-Related Six-lncRNA Signature to Predict the Outcomes, Immune Cell Infiltration, and Immunotherapy Response in Patients With Hepatocellular Carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the world's most lethal malignant tumors with a poor prognosis. Growing evidence has been demonstrating that immune-related long non-coding RNAs (lncRNAs) are relevant to the tumor microenvironment (TME) and can help assess the effects of immunotherapy and evaluate one's prognosis. This study aims to identify an immune-related lncRNA signature for the prospective assessment of the immunotherapy and prognosis in HCC.

METHOD

We downloaded HCC RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) project database. We first used ESTIMATE to evaluate the TME. Then, we conducted a cox regression analysis to construct a prognostic signature and the riskScore. We then applied the univariate Cox regression, multivariate Cox regression, principal components analysis (PCA), receiver operating characteristic (ROC) curve, and stratification analyses to confirm our previous assessments. Afterward, we employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways. Besides, we used CIBERSORT to estimate the abundance of tumor-infiltrating immune cells (TIICs). Furthermore, we investigated the relationship between the immune-related lncRNA signature and immune checkpoint genes. Finally, we used the quantitative real-time polymerase chain reaction (qRT-PCR) assays to demonstrate the expression of the six lncRNAs.

RESULTS

We identified six immune-related lncRNAs - MSC-AS1, AC145207.5, SNHG3, AL365203.2, AL031985.3, NRAV - which show the ability to stratify patients into high-risk and low-risk groups with significantly different survival rates. The univariate Cox regression, multivariate Cox regression, ROC, and stratification analyses confirmed that the immune-related six-lncRNA signature was a novel independent prognostic factor in HCC patients. The high-risk group and low-risk group illustrated contrasting distributions in PCA. The GSEA suggested that the immune-related six-lncRNA signature was involved in the immune-related biological processes and pathways. Besides, the immune-related six-lncRNA signature was associated with the infiltration of immune cells. Furthermore, it was linked with the expression of critical immune genes and could predict immunotherapy's clinical response. Finally, the qRT-PCR demonstrated that the six lncRNAs were significantly differentially expressed in HCC cell lines and normal hepatic cell lines.

CONCLUSION

In summary, we identified an immune-related six-lncRNA signature that can predict the outcomes, immune cell infiltration, and immunotherapy response in patients with hepatocellular carcinoma.