Evidence map›Paper›PMID 39633110›Full record

ArticleApoptosis : an international journal on programmed cell death2025

Integrated explainable machine learning and multi-omics analysis for survival prediction in cancer with immunotherapy response.

Alphonse Houssou Hounye, Li Xiong, Muzhou Hou

Abstract read
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In one paragraph

Article in Apoptosis : an international journal on programmed cell death, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
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

15 citing papers in PubMed.

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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

3 authors.

Alphonse Houssou HounyeGeneral surgery department of Second Xiangya Hospital, Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China. hounyeal@csu.edu.cn.
Li XiongGeneral surgery department of Second Xiangya Hospital, Central South University, 139 Renmin Road, Changsha, 410011, Hunan, China. lixionghn@163.com.
Muzhou HouSchool of Mathematics and Statistics, Central South University, Changsha, 410083, China.

Funding

Hunan Provincial Natural Science Foundation of China 2022JJ50229Natural Science Foundation of Hunan Province 2023JJ60506
6 · The paper itself

Abstract

To demonstrate the efficacy of machine learning models in predicting mortality in melanoma cancer, we developed an interpretability model for better understanding the survival prediction of cancer. To this end, the optimal features were identified, ten different machine learning models were utilized to predict mortality across various datasets. Then we have utilized the important features identified by those machines learning methods to construct a new model named NKECLR to forecast mortality of patient with cancer. To explicitly clarify the model's decision-making process and uncover novel findings, an interpretable technique incorporating machine learning and SHapley Additive exPlanations (SHAP), as well as LIME, has been employed, and four genes EPGN, PHF11, RBM34, and ZFP36 were identified from those machine learning(ML). The experimental analysis conducted on training and validation datasets demonstrated that the proposed model has a good performance com- pared to existing methods with AUC value 81.8%, and 79.3%, respectively. Moreover, when combined our NKECLR with PD-L1, PD-1, and CTLA-4 the AUC value was 83%0. Finally, these findings have been applied to comprehend the response of drugs and immunotherapy. Our research introduced an innovative predictive NKECLR model utilizing natural killer(NK) cell marker genes for cohorts with melanoma cancer. The NKECLR model can effectively predict the survival of melanoma cancer cohorts and treatment results, revealing distinct immune cell infiltration in the high-risk group.

Indexed as

ImmunotherapyMachine LearningMelanomaNeoplasmsBiomarkers, TumorHumansMultiomicsPrognosisBiomarkers, TumorBioinformaticCell–cell communicationImmunotherapyMachine leaningNK cell marker genesPan-cancerSingle-cell RNA-sequencing

Identifiers

PMID39633110

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.