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MicroRNAs play an indispensable role in numerous biological processes rangingfrom organismic development to tumor progression.In oncology,these microRNAsconstitute a fundamental regulation role in the pathology of cancer thatprovides the basis for probing into the influences on clinical features throughtranscriptome data. Previous work focused on machine learning (ML) forsearching biomarkers in different cancer databases, but the functions of thesebiomarkers are fully not clear. Taking lung cancer as a prototype case ofstudy. Through integrating clinical information into the transcripts expressiondata, we systematically analyzed the effect of microRNA on diagnostic andprognostic factors at deteriorative lung adenocarcinoma (LUAD). After dimensionreduction, unsupervised hierarchical clustering was used to find the diagnosticfactors which represent the unique expression patterns of microRNA at variouspatient's stages. In addition, we developed a classification framework, LightGradient Boosting Machine (LightGBM) and SHAPley Additive explanation (SHAP)algorithm, to screen out the prognostic factors. Enrichment analyses show thatthe diagnostic and prognostic factors are not only enriched in cancer-relatedathways, but also involved in many vital cellular signaling transduction andimmune responses. These key microRNAs also impact the survival risk of LUADpatients at all (or a specific) stage(s) and some of them target some importantTranscription Factors (TF).The key finding is that five microRNAs(hsa-mir-196b, hsa-mir-31, hsa-mir-891a, hsa-mir-34c, and hsa-mir-653) can thenserve as not only potential diagnostic factors but also prognostic tools in themonitoring of lung cancer.

Systematic analysis reveals key microRNAs as diagnostic and prognostic factors in progressive stages of lung cancer