Comprehensive molecular classification predicted microenvironment profiles and therapy response for hepatocellular carcinoma
Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc..
BACKGROUND AND AIMS: Tumor microenvironment (TME) heterogeneity leads to a discrepancy in survival prognosis and clinical treatment response for hepatocellular carcinoma (HCC) patients. The clinical applications of documented molecular subtypes are constrained by several issues.
APPROACH AND RESULTS: We integrated three single-cell datasets to describe the TME landscape and identified six prognosis-related cell subclusters. Unsupervised clustering of subcluster-specific markers was performed to generate transcriptomic subtypes. The predictive value of these molecular subtypes for prognosis and treatment response was explored in multiple external HCC cohorts and the Xiangya HCC cohort. TME features were estimated using single-cell immune repertoire sequencing, mass cytometry and multiplex immunofluorescence. The prognosis-related score (PRS) was constructed based on machine learning algorithm. Comprehensive single-cell analysis described TME heterogeneity in HCC. The five transcriptomic subtypes possessed different clinical prognoses, stemness characteristics, immune landscapes and therapeutic responses. Class 1 exhibited an inflamed phenotype with better clinical outcomes, while Classes 2 and 4 were characterized by a lack of T cell infiltration. Classes 5 and 3 indicated an inhibitory tumor immune microenvironment. Analysis of multiple therapeutic cohorts suggested that Classes 5 and 3 were sensitive to ICB and targeted therapy, whereas Classes 1 and 2 were more responsive to transcatheter arterial chemoembolization treatment. Class 4 displayed resistant to all conventional HCC therapies. Three potential therapeutic agents and four targets were further identified for high-PRS HCC patients.
CONCLUSION: Our study generated a clinically valid molecular classification to guide precision medicine in patients with HCC.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
|---|---|
| Enthalten in: |
Hepatology (Baltimore, Md.) - (2024) vom: 27. März |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Chen, Yihong [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Revised 27.03.2024 published: Print-Electronic Citation Status Publisher |
|---|
| doi: |
10.1097/HEP.0000000000000869 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM370266714 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM370266714 | ||
| 003 | DE-627 | ||
| 005 | 20240329001607.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240329s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1097/HEP.0000000000000869 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1353.xml |
| 035 | |a (DE-627)NLM370266714 | ||
| 035 | |a (NLM)38537130 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Chen, Yihong |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Comprehensive molecular classification predicted microenvironment profiles and therapy response for hepatocellular carcinoma |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 27.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. | ||
| 520 | |a BACKGROUND AND AIMS: Tumor microenvironment (TME) heterogeneity leads to a discrepancy in survival prognosis and clinical treatment response for hepatocellular carcinoma (HCC) patients. The clinical applications of documented molecular subtypes are constrained by several issues | ||
| 520 | |a APPROACH AND RESULTS: We integrated three single-cell datasets to describe the TME landscape and identified six prognosis-related cell subclusters. Unsupervised clustering of subcluster-specific markers was performed to generate transcriptomic subtypes. The predictive value of these molecular subtypes for prognosis and treatment response was explored in multiple external HCC cohorts and the Xiangya HCC cohort. TME features were estimated using single-cell immune repertoire sequencing, mass cytometry and multiplex immunofluorescence. The prognosis-related score (PRS) was constructed based on machine learning algorithm. Comprehensive single-cell analysis described TME heterogeneity in HCC. The five transcriptomic subtypes possessed different clinical prognoses, stemness characteristics, immune landscapes and therapeutic responses. Class 1 exhibited an inflamed phenotype with better clinical outcomes, while Classes 2 and 4 were characterized by a lack of T cell infiltration. Classes 5 and 3 indicated an inhibitory tumor immune microenvironment. Analysis of multiple therapeutic cohorts suggested that Classes 5 and 3 were sensitive to ICB and targeted therapy, whereas Classes 1 and 2 were more responsive to transcatheter arterial chemoembolization treatment. Class 4 displayed resistant to all conventional HCC therapies. Three potential therapeutic agents and four targets were further identified for high-PRS HCC patients | ||
| 520 | |a CONCLUSION: Our study generated a clinically valid molecular classification to guide precision medicine in patients with HCC | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Deng, Xiangying |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yin |e verfasserin |4 aut | |
| 700 | 1 | |a Han, Ying |e verfasserin |4 aut | |
| 700 | 1 | |a Peng, Yinghui |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Wantao |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xinwen |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Jiayao |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Erya |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Xin |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Edward |e verfasserin |4 aut | |
| 700 | 1 | |a Zeng, Shan |e verfasserin |4 aut | |
| 700 | 1 | |a Cai, Changjing |e verfasserin |4 aut | |
| 700 | 1 | |a Qin, Yiming |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Hong |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Hepatology (Baltimore, Md.) |d 1983 |g (2024) vom: 27. März |w (DE-627)NLM012603902 |x 1527-3350 |7 nnns |
| 773 | 1 | 8 | |g year:2024 |g day:27 |g month:03 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1097/HEP.0000000000000869 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |j 2024 |b 27 |c 03 | ||