Identification of multi-omics biomarkers and construction of the novel prognostic model for hepatocellular carcinoma

Abstract

Genome changes play a crucial role in carcinogenesis, and many biomarkers can be used as effective prognostic indicators in various tumors. Although previous studies have constructed many predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance is unsatisfactory. Because multi-omics data can more comprehensively reflect the biological phenomenon of disease, we hope to build a more accurate predictive model by multi-omics analysis. We use the TCGA to identify crucial biomarkers and construct prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. The performances of predictive models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Multiple mRNAs, lncRNAs, miRNAs, CNV genes, and SNPs were significantly associated with the prognosis of HCC. We constructed five single-omic models, and the mRNA and lncRNA models showed good performance with c-indexes over 0.70. The multi-omics model presented a robust predictive ability with a c-index over 0.77. This study identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC. In addition, we constructed multiple single-omic models and an integrated multi-omics model that may provide practical and reliable guides for prognosis assessment.

Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers

“…Integrating multiple omics data, including genomics, transcriptomics, proteomics, and metabolomics, can provide a more comprehensive understanding of cancer biology and help identify novel biomarkers. Analyzing multiple layers of molecular information can enhance diagnostic accuracy and predictive capabilities. − …”

“…Liquid biopsies offer a non-invasive and real-time approach for detecting cancer-specific alterations, monitoring treatment response, and detecting minimal residual disease or cancer recurrence. , Integrating multiple omics data, including genomics, transcriptomics, proteomics, and metabolomics, can provide a more comprehensive understanding of cancer biology and help identify novel biomarkers. Analyzing multiple layers of molecular information can enhance diagnostic accuracy and predictive capabilities. − The application of artificial intelligence (AI) and machine learning (ML) algorithms can aid in the analysis and interpretation of complex biomarker data. AI can assist in identifying patterns, predicting disease outcomes, and improving the accuracy and efficiency of cancer diagnosis. − MicroRNAs (miRNAs) and other non-coding RNAs (ncRNAs) have shown promise as potential biomarkers due to their involvement in gene regulation and their dysregulation in various cancers.…”

Biomarkers for early cancer detection: A landscape view of recent advancements, spotlighting pancreatic and liver cancers

“…Analyzing multiple layers of molecular information can enhance diagnostic accuracy and predictive capabilities. [220][221][222][223][224] − The application of artificial intelligence (AI) and machine learning algorithms can aid in the analysis and interpretation of complex biomarker data. AI can assist in identifying patterns, predicting disease outcomes, and improving the accuracy and efficiency of cancer diagnosis.…”

Artificial Intelligence, Machine Learning, and Deep Learning in the Diagnosis and Management of Hepatocellular Carcinoma

“…Recent multi-omic investigations have advanced our understanding of the carcinogenic mechanisms responsible for HCC, revealing many potential biomarkers [26]. Evaluating these large data sets with AI methods may improve current prognostic ability by identifying more aggressive subtypes and patients at high risk of recurrence (Table 2).…”