Evidence map›Paper›PMID 38091986›Full record

ArticleCell reports. Medicine2023

Integrated omics landscape of hepatocellular carcinoma suggests proteomic subtypes for precision therapy.

Xiaohua Xing, En Hu, Jiahe Ouyang, Xianyu Zhong, Fei Wang, Kaixin Liu, Linsheng Cai, Yang Zhou, Yingchao Wang, Geng Chen and 3 more

Abstract read
In one paragraph

Article in Cell reports. Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

31 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

13 authors.

Xiaohua XingThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China.
En HuThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China.
Jiahe OuyangThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China.
Xianyu ZhongThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China.
Fei WangThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China.
Kaixin LiuThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China.
Linsheng CaiThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China.
Yang ZhouThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China.
Yingchao WangThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China.
Geng ChenThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China.
Zhenli LiThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China.
Liming WuDepartment of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China. Electronic address: wlm@zju.edu.cn.
Xiaolong LiuThe United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China. Electronic address: xiaoloong.liu@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patients with hepatocellular carcinoma (HCC) at the same clinical stage can have extremely different prognoses, and molecular subtyping provides an opportunity for individualized precision treatment. In this study, genomic, transcriptomic, proteomic, and phosphoproteomic profiling of primary tumor tissues and paired para-tumor tissues from HCC patients (N = 160) are integrated. Proteomic profiling identifies three HCC subtypes with different clinical prognosis, which are validated in three publicly available external validation sets. A simplified panel of nine proteins associated with metabolic reprogramming is further identified as a potential subtype-specific biomarker for clinical application. Multi-omics analysis further reveals that three proteomic subtypes have significant differences in genetic alterations, microenvironment dysregulation, kinase-substrate regulatory networks, and therapeutic responses. Patient-derived cell-based drug tests (N = 26) show personalized responses for sorafenib in three proteomic subtypes, which can be predicted by a machine-learning response prediction model. Overall, this study provides a valuable resource for better understanding of HCC subtypes for precision clinical therapy.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsBiomarkers, TumorHumansMultiomicsProteomicsTumor MicroenvironmentBiomarkers, Tumorhepatocellular carcinomamachine learningmulti-omicsprecision therapyproteomic subtypesresponse prediction modelSorafenib

Identifiers

PMID38091986
PMCPMC10783603

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.