Identification of an immune-related 6-lncRNA panel with a good performance for prognostic prediction in hepatocellular carcinoma by integrated bioinformatics analysis
Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc..
Hepatocellular carcinoma (HCC) is one of the most malignant tumors with a poor prognosis. The long non-coding RNA (lncRNA) has been found to have great potential as a prognostic biomarker or therapeutic target for cancer patients. However, the prognostic value and tumor immune infiltration of lncRNAs in HCC has yet to be fully elucidated. To identify prognostic biomarkers of lncRNA in HCC by integrated bioinformatics analysis and explore their functions and relationship with tumor immune infiltration. The prognostic risk assessment model for HCC was constructed by comprehensively using univariate/multivariate Cox regression analysis, Kaplan-Meier survival analysis, and the least absolute shrinkage and selection operator regression analysis. Subsequently, the accuracy, independence, and sensitivity of our model were evaluated, and a nomogram for individual prediction in the clinic was constructed. Tumor immune microenvironment (TIME), immune checkpoints, and human leukocyte antigen alleles were compared in high- and low-risk patients. Finally, the functions of our lncRNA signature were examined using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and gene set enrichment analysis. A 6-lncRNA panel of HCC consisting of RHPN1-AS1, LINC01224, CTD-2510F5.4, RP1-228H13.5, LINC01011, and RP11-324I22.4 was eventually identified, and show good performance in predicting the survivals of patients with HCC and distinguishing the immunomodulation of TIME of high- and low-risk patients. Functional analysis also suggested that this 6-lncRNA panel may play an essential role in promoting tumor progression and immune regulation of TIME. In this study, 6 potential lncRNAs were identified as the prognostic biomarkers in HCC, and the regulatory mechanisms involved in HCC were initially explored.
Medienart: |
E-Artikel |
---|
Erscheinungsjahr: |
2023 |
---|---|
Erschienen: |
2023 |
Enthalten in: |
Zur Gesamtaufnahme - volume:102 |
---|---|
Enthalten in: |
Medicine - 102(2023), 29 vom: 21. Juli, Seite e33990 |
Sprache: |
Englisch |
---|
Beteiligte Personen: |
Lu, Shan [VerfasserIn] |
---|
Links: |
---|
Themen: |
Biomarkers |
---|
Anmerkungen: |
Date Completed 24.07.2023 Date Revised 23.11.2023 published: Print Citation Status MEDLINE |
---|
doi: |
10.1097/MD.0000000000033990 |
---|
funding: |
|
---|---|
Förderinstitution / Projekttitel: |
|
PPN (Katalog-ID): |
NLM359785905 |
---|
LEADER | 01000naa a22002652 4500 | ||
---|---|---|---|
001 | NLM359785905 | ||
003 | DE-627 | ||
005 | 20231226081623.0 | ||
007 | cr uuu---uuuuu | ||
008 | 231226s2023 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1097/MD.0000000000033990 |2 doi | |
028 | 5 | 2 | |a pubmed24n1199.xml |
035 | |a (DE-627)NLM359785905 | ||
035 | |a (NLM)37478241 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 1 | |a Lu, Shan |e verfasserin |4 aut | |
245 | 1 | 0 | |a Identification of an immune-related 6-lncRNA panel with a good performance for prognostic prediction in hepatocellular carcinoma by integrated bioinformatics analysis |
264 | 1 | |c 2023 | |
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 Completed 24.07.2023 | ||
500 | |a Date Revised 23.11.2023 | ||
500 | |a published: Print | ||
500 | |a Citation Status MEDLINE | ||
520 | |a Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. | ||
520 | |a Hepatocellular carcinoma (HCC) is one of the most malignant tumors with a poor prognosis. The long non-coding RNA (lncRNA) has been found to have great potential as a prognostic biomarker or therapeutic target for cancer patients. However, the prognostic value and tumor immune infiltration of lncRNAs in HCC has yet to be fully elucidated. To identify prognostic biomarkers of lncRNA in HCC by integrated bioinformatics analysis and explore their functions and relationship with tumor immune infiltration. The prognostic risk assessment model for HCC was constructed by comprehensively using univariate/multivariate Cox regression analysis, Kaplan-Meier survival analysis, and the least absolute shrinkage and selection operator regression analysis. Subsequently, the accuracy, independence, and sensitivity of our model were evaluated, and a nomogram for individual prediction in the clinic was constructed. Tumor immune microenvironment (TIME), immune checkpoints, and human leukocyte antigen alleles were compared in high- and low-risk patients. Finally, the functions of our lncRNA signature were examined using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and gene set enrichment analysis. A 6-lncRNA panel of HCC consisting of RHPN1-AS1, LINC01224, CTD-2510F5.4, RP1-228H13.5, LINC01011, and RP11-324I22.4 was eventually identified, and show good performance in predicting the survivals of patients with HCC and distinguishing the immunomodulation of TIME of high- and low-risk patients. Functional analysis also suggested that this 6-lncRNA panel may play an essential role in promoting tumor progression and immune regulation of TIME. In this study, 6 potential lncRNAs were identified as the prognostic biomarkers in HCC, and the regulatory mechanisms involved in HCC were initially explored | ||
650 | 4 | |a Journal Article | |
650 | 7 | |a RNA, Long Noncoding |2 NLM | |
650 | 7 | |a Biomarkers |2 NLM | |
650 | 7 | |a Biomarkers, Tumor |2 NLM | |
700 | 1 | |a Liu, Xinkui |e verfasserin |4 aut | |
700 | 1 | |a Wu, Chao |e verfasserin |4 aut | |
700 | 1 | |a Zhang, Jingyuan |e verfasserin |4 aut | |
700 | 1 | |a Stalin, Antony |e verfasserin |4 aut | |
700 | 1 | |a Huang, Zhihong |e verfasserin |4 aut | |
700 | 1 | |a Tan, Yingying |e verfasserin |4 aut | |
700 | 1 | |a Wu, Zhishan |e verfasserin |4 aut | |
700 | 1 | |a You, Leiming |e verfasserin |4 aut | |
700 | 1 | |a Ye, Peizhi |e verfasserin |4 aut | |
700 | 1 | |a Fu, Changgeng |e verfasserin |4 aut | |
700 | 1 | |a Zhang, Xiaomeng |e verfasserin |4 aut | |
700 | 1 | |a Wu, Jiarui |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Medicine |d 1945 |g 102(2023), 29 vom: 21. Juli, Seite e33990 |w (DE-627)NLM000020737 |x 1536-5964 |7 nnns |
773 | 1 | 8 | |g volume:102 |g year:2023 |g number:29 |g day:21 |g month:07 |g pages:e33990 |
856 | 4 | 0 | |u http://dx.doi.org/10.1097/MD.0000000000033990 |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a GBV_NLM | ||
951 | |a AR | ||
952 | |d 102 |j 2023 |e 29 |b 21 |c 07 |h e33990 |