ArticlePloS one2026
Prognostic stratification in hepatocellular carcinoma using a telomerase-related lncRNA signature derived from TCGA database.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCharacterized 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.
methodsFor 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.
resultsBy 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.
conclusionThe 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.