ArticleFrontiers in immunology2026
Immune - cell death index in hepatocellular carcinoma: a multi-omics and machine learning study for prognosis and immunotherapy prediction.
Article in Frontiers in immunology, 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
The heterogeneity of hepatocellular carcinoma (HCC) and individual disparities in immunotherapy response necessitate the urgent development of accurate evaluation tools. Programmed cell death (PCD) is implicated in the occurrence and development of HCC. Moreover, immune-related genes have a crucial role in cancer progression and patient prognosis. This study employed 10 clustering algorithms to conduct high-resolution molecular subtyping based on PCD-related genes, immune-related genes, microRNA, long non-coding RNA, and methylation data. Subsequently, we developed hepatocellular carcinoma consensus immune-cell death index (HICDI) by employing subtype-specific genes and merging 10 commonly used machine learning algorithms into 101 unique combination frameworks. Our HICDI score exhibited enhanced predictive ability compared to previously published HCC biomarkers. Patients with a low HICDI score exhibited higher overall survival and improved responses to immunotherapy. The high HICDI group exhibited a propensity for "cold" tumors marked by immune suppression and exclusion; however, drugs such as paclitaxel may present viable therapeutic options for these patients. We verified the model gene kinesin family member 2C through
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.