A Novel Risk-Score Model With Eight MiRNA Signatures for Overall Survival of Patients With Lung Adenocarcinoma

Screening of miRNAs as prognostic biomarkers and their associated hub targets across Hepatocellular carcinoma using survival-based bioinformatics approach

BACKGROUND

The hepatocellular carcinoma (HCC) incident rate is gradually increasing yearly despite all the research and efforts taken by scientific communities and governing bodies. Approximately 90% of all liver cancer cases belong to HCC. Usually, HCC patients approach the treatment in the late stages of this malignancy which becomes the primary cause of high mortality rate. The knowledge about molecular pathogenesis of HCC is limited and needs more attention from researchers to identify the driver genes and miRNAs, which causes to translate this information into clinical practice. Therefore, the key regulators identification of miRNA-mRNA regulatory network is essential to identify HCC-associated genes.

METHODOLOGY

We extracted microRNA (miRNA) and messenger RNA (mRNA) expression datasets of normal and tumor HCC patient samples from UCSC Xena followed by identifying differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs). Univariate and multivariate cox-proportional hazard models were utilized to identify DEMs having significant association with overall survival (OS). Kaplan-Meier (KM) plotter was used to validate the presence of prognostic DEMs. A risk-score model was used to evaluate the effectiveness of KM-plotter validated DEMs combination on risk of samples. Target DEGs of prognostic miRNAs were identified via sources such as miRTargetLink and miRWalk followed by their validation in an external microarray cohort and enrichment analysis.

RESULTS

562 DEGs and 388 DEMs were identified followed by seven prognostic miRNAs (i.e., miR-19a, miR-19b, miR-30d-5p, miR-424-5p, miR-3677-5p, miR-3913-5p, miR-7705) post univariate, multivariate, risk-score model evaluation and KM-plotter analyses. ANLN, MRO, CPEB3 were their targets and were also validated in GSE84005 dataset.

CONCLUSIONS

The findings of this study decipher that most significant miRNAs and their identified target genes have association with apoptosis, inflammation, cell cycle regulation and cancer-related pathways, which appear to contribute to HCC pathogenesis and therefore, the discovery of new targets.

Bioinformatics algorithm for lung adenocarcinoma based on macropinocytosis-related long noncoding RNAs as a reliable indicator for predicting survival outcomes and selecting suitable anti-tumor drugs

As a highly conserved endocytic mechanism during evolution, macropinocytosis is enhanced in several malignant tumors, which promotes tumor growth by ingesting extracellular nutrients. Recent research has emphasized the crucial role of macropinocytosis in tumor immunity. In the present study, we established a new macropinocytosis-related algorithm comprising molecular subtypes and a prognostic signature, in which patients with lung adenocarcinoma (LUAD) were classified into different clusters and risk groups based on the expression of 16 macropinocytosis-related long noncoding RNAs. According to the molecular subtypes, we discovered that patients with LUAD in cluster1 had a higher content of stromal cells and immune cells, stronger intensity of immune activities, higher expression of PD1, PDL1, and HAVCR2, and a higher tumor mutational burden, while patients in cluster2 exhibited better survival advantages. Furthermore, the constructed prognostic signature revealed that low-risk patients showed better survival outcomes, earlier tumor stage, higher abundance of stromal cells and immune cells, higher immune activities, higher expression of PD1, PDL1, CTLA4, and HAVCR2, and more sensitivity to Paclitaxel and Erlotinib. By contrast, patients with high scores were more suitable for Gefitinib treatment. In conclusion, the novel algorithm that divided patients with LUAD into different groups according to their clusters and risk groups, which could provide theoretical support for predicting their survival outcomes and selecting drugs for chemotherapy, targeted therapy, and immunotherapy.