Evidence map›Paper›PMID 37275878›Full record

ArticleFrontiers in immunology2023

Machine learning-based prognostic modeling of lysosome-related genes for predicting prognosis and immune status of patients with hepatocellular carcinoma.

Wenhua Li, Qianwen Wang, Junxia Lu, Bin Zhao, Yuqing Geng, Xiangwei Wu, Xueling Chen

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
3.4field-weighted citation impact, top 7% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 13 citations in OpenAlex.

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  5. CSPG4 overexpression implicates higher risks of recurrence and tumorigenesis after surgical intervention of vocal fold Leukoplakia.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Wenhua LiKey Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi, China.
Qianwen WangKey Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi, China.
Junxia LuKey Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi, China.
Bin ZhaoKey Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi, China.
Yuqing GengKey Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi, China.
Xiangwei WuKey Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi, China.
Xueling ChenKey Laboratory for Prevention and Treatment of High Morbidity in Central Asia, National Health and Health Commission, Shihezi, China.
National Health and Family Planning Commission · CNShihezi University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide. Lysosomes are organelles that play an important role in cancer progression by breaking down biomolecules. However, the molecular mechanisms of lysosome-related genes in HCC are not fully understood. Methods: We downloaded HCC datasets from TCGA and GEO as well as lysosome-related gene sets from AIMGO. After univariate Cox screening of the set of lysosome-associated genes differentially expressed in HCC and normal tissues, risk models were built by machine learning. Model effects were assessed using the concordance index (C-index), Kaplan-Meier (K-M) and receiver operating characteristic curves (ROC). Additionally, we explored the biological function and immune microenvironment between the high- and low-risk groups, and analyzed the response of the high- and low-risk groups to immunotherapy responsiveness and chemotherapeutic agents. Finally, we explored the function of a key gene (RAMP3) at the cellular level. Results: Univariate Cox yielded 46 differentially and prognostically significant lysosome-related genes, and risk models were constructed using eight genes (RAMP3, GPLD1, FABP5, CD68, CSPG4, SORT1, CSPG5, CSF3R) derived from machine learning. The risk model was a better predictor of clinical outcomes, with the higher risk group having worse clinical outcomes. There were significant differences in biological function, immune microenvironment, and responsiveness to immunotherapy and drug sensitivity between the high and low-risk groups. Finally, we found that RAMP3 inhibited the proliferation, migration, and invasion of HCC cells and correlated with the sensitivity of HCC cells to Idarubicin. Conclusion: Lysosome-associated gene risk models built by machine learning can effectively predict patient prognosis and offer new prospects for chemotherapy and immunotherapy in HCC. In addition, cellular-level experiments suggest that RAMP3 may be a new target for the treatment of HCC.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsFatty Acid-Binding ProteinsHumansImmunotherapyLysosomesPrognosisTumor MicroenvironmentFABP5 protein, humanFatty Acid-Binding Proteinsdrug sensitivityhepatocellular carcinomaimmune infiltrationlysosomemachine learningprognostic modelRAMP3

Identifiers

PMID37275878
PMCPMC10237352
OpenAlexW4377138739

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LicenceCC BY
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Registered trials

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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.