Prognostic stratification in hepatocellular carcinoma using a telomerase-related lncRNA signature derived from TCGA database

TL;DR

This study identifies a 4-lncRNA signature linked to telomerase activity that predicts survival and immune profiles in hepatocellular carcinoma patients.

Contribution

A novel telomerase-related lncRNA signature is developed for prognostic stratification and treatment guidance in HCC.

Findings

The 4-TRLs signature accurately separates HCC patients into low- and high-risk groups with distinct survival outcomes.

High-risk patients show increased immune cell infiltration and checkpoint expression, suggesting poor immunotherapy response.

RT-qPCR validation confirms dysregulated TRLs in HCC tissues, supporting bioinformatic findings.

Abstract

Characterized by high recurrence rates and limited therapeutic options, hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide. Notwithstanding the fact that telomerase-related long non-coding RNAs (TRLs) have been implicated in tumorigenesis, it remains poorly understood about their prognostic and immunological roles in HCC.

For the purpose of identifying telomerase-related genes (TRGs) and TRLs, we used transcriptomic data from The Cancer Genome Atlas (TCGA). We built a prognostic signature using LASSO-Cox regression. Then, we validated it with time-dependent ROC curves. We assessed the model's clinical utility with nomogram calibration and DCA. We also evaluated immune profiling, tumor mutation burden, drug sensitivity and TIDE scores to characterize the tumor microenvironment. Using a pilot cohort of clinical samples, initial experimental validation was completed with RT-qPCR.

By using a 4-TRLs signature, HCC patients can be divided into Low-risk (L-R) and High-risk (H-R) groups. The signature acted as an independent prognostic factor. It provided a highly accurate prediction of patient survival at 1, 3, and 5 years (AUC: 0.744–0.770). H-R patients had more immune cells in their tumors. They also showed higher levels of checkpoint expression. Besides, their TIDE (tumor immune dysfunction and exclusion) scores were also higher. All these things mean they might not respond well to immunotherapy. Subtype-specific therapeutic vulnerabilities can be read from drug sensitivity analysis. By carrying out reverse transcription quantitative polymerase chain reaction (RT-qPCR), consistent dysregulation patterns of TRLs can be observed in HCC tissues. This providing basis supports for our bioinformatic findings. Mechanistically, lncRNA AC026356.1 linked to a telomerase-related ceRNA network. This network includes miR-126-5p and its downstream targets.

The 4-TRLs signature is a tool that can be applied in HCC clinical practice. It enables prognostic stratification and helps guide treatment. These lncRNAs are linked to both immune activity and drug response. This dual role shows they affect tumor progression and the microenvironment. This finding provides new insights for precision oncology in HCC.

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