Evidence map›Paper›PMID 34975338›Full record

ArticleInternational journal of biological sciences2022

Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients.

Dongjie Chen, Jixing Liu, Longjun Zang, Tijun Xiao, Xianlin Zhang, Zheng Li, Hongwei Zhu, Wenzhe Gao, Xiao Yu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of biological sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
113citing papers in PubMed, 3 pooled it
–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

113 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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53 more citing papers are in PubMed but not listed here.

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

9 authors.

Dongjie ChenDepartment of Hepatopancreatobiliary Surgery, The Third Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China.
Jixing LiuDepartment of Hepatopancreatobiliary Surgery, The Third Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China.
Longjun ZangDepartment of Hepatopancreatobiliary Surgery, The Third Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China.
Tijun XiaoDepartment of General Surgery, Shaoyang University Affiliated Second Hospital, Shaoyang University, Shaoyang, Hunan, P.R. China.
Xianlin ZhangDepartment of General Surgery, Affiliated Renhe Hospital of China Three Gorges University, Yichang, Hubei, P.R. China.
Zheng LiDepartment of General Surgery, Affiliated Renhe Hospital of China Three Gorges University, Yichang, Hubei, P.R. China.
Hongwei ZhuDepartment of Hepatopancreatobiliary Surgery, The Third Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China.
Wenzhe GaoDepartment of Hepatopancreatobiliary Surgery, The Third Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China.
Xiao YuDepartment of Hepatopancreatobiliary Surgery, The Third Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immunotherapy has made great progress in hepatocellular carcinoma (HCC), yet there is still a lack of biomarkers for predicting response to it. Cancer stem cells (CSCs) are the primary cause of the tumorigenesis, metastasis, and multi-drug resistance of HCC. This study aimed to propose a novel CSCs-related cluster of HCC to predict patients' response to immunotherapy. Based on RNA-seq datasets from The Cancer Genome Atlas (TCGA) and Progenitor Cell Biology Consortium (PCBC), one-class logistic regression (OCLR) algorithm was applied to compute the stemness index (mRNAsi) of HCC patients. Unsupervised consensus clustering was performed to categorize HCC patients into two stemness subtypes which further proved to be a predictor of tumor immune microenvironment (TIME) status, immunogenomic expressions and sensitivity to neoadjuvant therapies. Finally, four machine learning algorithms (LASSO, RF, SVM-RFE and XGboost) were applied to distinguish different stemness subtypes. Thus, a five-hub-gene based classifier was constructed in TCGA and ICGC HCC datasets to predict patients' stemness subtype in a more convenient and applicable way, and this novel stemness-based classification system could facilitate the prognostic prediction and guide clinical strategies of immunotherapy and targeted therapy in HCC.

Indexed as

Machine LearningCarcinoma, HepatocellularComputational BiologyHumansImmunotherapyLiver NeoplasmsNeoplastic Stem CellsPrognosisCancer stem cellHepatocellular carcinomaImmunotherapyMachine learning

Identifiers

PMID34975338
PMCPMC8692161

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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.