# The construction and evaluation of a prognostic risk score model for HCC based on MPT-related lncRNAs

## TL;DR

This study builds a risk model using specific long non-coding RNAs to predict outcomes in liver cancer patients and explores their role in immune response.

## Contribution

A novel prognostic model for HCC using MPT-related lncRNAs and its impact on immune microenvironment is developed and validated.

## Key findings

- A three-lncRNA risk model was constructed with good survival prediction (AUC = 0.725).
- Low-risk patients showed better response to immunotherapy based on immune cell infiltration and scores.
- Prognostic genes were validated using RT-qPCR on patient tissue samples.

## Abstract

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths in China. It has a high rate of postoperative recurrence and lacks prognostic markers. In this study, we first analyzed mitochondrial permeability transition (MPT) necrosis-associated long non-coding RNAs (lncRNAs), integrated multi-omics, and constructed a prognostic model. We also revealed the mechanism by which it regulates the immune microenvironment. This provides a new target for targeted therapy in HCC.

Screening and construction of a prognostic risk score model for MPT-driven necrosis-associated lncRNAs in HCC and exploration of their potential role in HCC.

Pearson’s correlation analysis, in conjunction with The Cancer Genome Atlas (TCGA) and gene set enrichment analysis (GSEA) databases, was utilized for the identification of lncRNAs associated with mitochondrial permeability transition-driven necrosis. The development of a risk prognostic score for mitochondrial permeability transition-driven necrosis-associated lncRNAs was accomplished through the implementation of one-way regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Bioinformatics analysis was performed to validate the prognostic ability and clinical application efficacy of the risk score model and prognostic genes and to explore their biological significance.

MPT-driven necrosis-related lncRNAs (MPTDNRlncRNAs) strongly correlated with HCC were obtained through Pearson’s correlation analysis. Additionally, MPT-driven necrosis-related prognostic lncRNAs were obtained through univariate Cox regression analysis. A new prognostic risk model consisting of three MPTDNRlncRNAs was constructed using LASSO-Cox regression. The model was tested using multiple bioinformatics methods, which suggested that it could significantly differentiate between high- and low-risk groups (p < 0.05) and demonstrated good survival prediction efficacy [area under the curve (AUC) = 0.725]. Differential genes in the high- and low-risk groups were enriched in pathways related to the cell cycle and cellular composition. Combined with immune cell infiltration and immune function scores, these results showed that the patients in the low-risk group had a more significant clinical response to immunotherapy (p < 0.05). Furthermore, the expression level of prognostic genes was verified using the RT-qPCR method on cancerous and paracancerous tissues from HCC patients who underwent HCC resection at our hospital.

The risk scoring model and prognostic genes in this study have been shown to possess satisfactory predictive values, which may prove beneficial for the assessment of risk and the selection of individualized chemotherapy regimens for patients with HCC. A preliminary discussion is presented on the potential biological significance of risk scores in HCC.