Dataset Information

System analysis based on the lysosome-related genes identifies HPS4 as a novel therapy target for liver hepatocellular carcinoma.

ABSTRACT:

Background

Liver cancer is a leading cause of cancer-related deaths worldwide. Lysosomal dysfunction is implicated in cancer progression; however, prognostic prediction models based on lysosome-related genes (LRGs) are lacking in liver cancer. This study aimed to establish an LRG-based model to improve prognosis prediction and explore potential therapeutic targets in liver cancer.

Methods

Expression profiles of 61 LRGs were analyzed in The Cancer Genome Atlas liver cancer cohorts. There were 14 LRGs identified, and their association with clinical outcomes was evaluated. Unsupervised clustering, Cox regression, and functional assays were performed.

Results

Patients were classified into high-risk and low-risk subgroups based on the 14 LRGs. The high-risk group had significantly worse overall survival. Aberrant immune infiltration and checkpoint expression were observed in the high-risk group. Furthermore, HPS4 was identified as an independent prognostic indicator. Knockdown of HPS4 suppressed liver cancer cell proliferation and induced apoptosis.

Conclusion

This study developed an LRG-based prognostic model to improve risk stratification in liver cancer. The potential value of HPS4 as a therapeutic target and biomarker was demonstrated. Regulation of HPS4 may offer novel strategies for precision treatment in liver cancer patients.

SUBMITTER: He KJ

PROVIDER: S-EPMC10535104 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

System analysis based on the lysosome-related genes identifies HPS4 as a novel therapy target for liver hepatocellular carcinoma.

He Ke-Jie KJ Nie Zhiqiang Z

Frontiers in oncology 20230913

<h4>Background</h4>Liver cancer is a leading cause of cancer-related deaths worldwide. Lysosomal dysfunction is implicated in cancer progression; however, prognostic prediction models based on lysosome-related genes (LRGs) are lacking in liver cancer. This study aimed to establish an LRG-based model to improve prognosis prediction and explore potential therapeutic targets in liver cancer.<h4>Methods</h4>Expression profiles of 61 LRGs were analyzed in The Cancer Genome Atlas liver cancer cohorts. ...[more]

PMID: 37781184

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Project description:BackgroundLiver hepatocellular carcinoma (LIHC) ranks sixth among the most common types of cancer with a high mortality rate. Cuproptosis is a newly discovered type of cell death in tumor, which is characterized by accumulation of intracellular copper leading to the aggregation of mitochondrial lipoproteins and destabilization of proteins. Thus, understanding the exact effects of cuproptosis-related genes in LIHC and determining their prognosticvalue is critical. However, the prognostic model of LIHC based on cuproptosis-related genes has not been reported.MethodsFirstly, we downloaded transcriptome data and clinical information of LIHC patients from TCGA and GEO (GSE76427), respectively. We then extracted the expression of cuproptosis-related genes and established a prognostic model by lasso cox regression analysis. Afterwards, the prediction performance of the model was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic curve (ROC). Then, the prognostic model and the expression levels of the three genes were validated using the dataset from GEO. Subsequently, we divided LIHC patients into two subtypes by non-negative matrix factorization (NMF) classification and performed survival analysis. We constructed a Sankey plot linking different subtypes and prognostic models. Next, we calculate the drug sensitivity of each sample from patients in the high-risk group and low-risk group by the R package pRRophetic. Finally, we verified the function of LIPT1 in LIHC.ResultsUsing lasso cox regression analysis, we developed a prognostic risk model based on three cuproptosis-related genes (GCSH, LIPT1 and CDKN2A). Both in the training and in the test sets, the overall survival (OS) of LIHC patients in the low-risk group was significantly longer than that in the high-risk group. By performing NMF cluster, we identified two molecular subtypes of LIHC (C1 and C2), with C1 subtype having significantly longer OS and PFS than C2 subtype. The ROC analysis indicated that our model had a precisely predictive capacity for patients with LIHC. The multivariate Cox regression analysis indicated that the risk score is an independent predictor. Subsequently, we identified 71 compounds with IC50 values that differed between the high-risk and low-risk groups. Finally, we determined that knockdown of LIPT1 gene expression inhibited proliferation and invasion of hepatoma cells.ConclusionIn this study, we developed a novel prognostic model for hepatocellular carcinoma based on cuproptosis-related genes that can effectively predict the prognosis of LIHC patients. The model may be helpful for clinicians to make clinical decisions for patients with LIHC and provide valuable insights for individualized treatment. Two distinct subtypes of LIHC were identified based on cuproptosis-related genes, with different prognosis and immune characteristics. In addition, we verified that LIPT1 may promote proliferation, invasion and migration of LIHC cells. LIPT1 might be a new potential target for therapy of LIHC.

Project description:BackgroundCancer cachexia can impact prognosis, cause resistance to anticancer treatments and affect the tolerability of treatments. This study aims to identify hepatocellular carcinoma (HCC) with cachexia by characterizing longitudinal body composition (BC) trajectories.MethodsThis longitudinal, multicentre cohort study included unresectable HCC patients treated with first-line programmed death-(ligand)1 inhibitors plus anti-vascular endothelial growth factor antibody/tyrosine kinase inhibitors between 01/2018-12/2022. BC measurements including skeletal muscle mass (SMM) and total adipose tissue area (TATA) were evaluated by computed tomography at the third lumbar vertebra at baseline and follow-up imaging. Unsupervised latent class growth mixed models were applied to distinguish potential longitudinal SMM and TATA trajectories for identifying cachexia. The primary study endpoint was overall survival (OS), with secondary endpoints including progression-free survival (PFS), objective response rate (ORR) and safety. Multiple Cox proportional hazards models were used to calculate adjusted hazard ratios (HRs) for survival.ResultsA total of 411 patients with 2138 time-point measurements were included. The median age was 56 years, and 50 (12.2%) patients were female. Two distinct trajectories were identified for SMM and TATA: sharp-falling and stable. SMM sharply declined in 58 patients (14.1%) and TATA in 71 of 406 patients (17.5%) with significant worse OS (for SMM, 17.0 vs. 24.9 months; p < 0.001; HR = 0.59; for TATA, 15.3 vs. 25.1 months; p < 0.001; HR = 0.44). Patients were categorized into three phases based on trajectories: pre-cachexia (SMM and TATA stable, n = 299, 73.6%), cachexia (SMM or TATA sharp-falling, n = 86, 21.2%) and refractory cachexia (SMM and TATA sharp-falling, n = 21, 5.2%). Patients with refractory cachexia exhibited the worst OS, PFS and ORR, followed by those with cachexia. The median OS was 11.5 months for refractory cachexia, 17.7 for cachexia and 26.0 for pre-cachexia; median PFS was 6.0, 7.9 and 10.9 months, respectively, with ORR of 4.8%, 39.5% and 54.2%, respectively (all ps < 0.001). Multivariable Cox analysis identified refractory cachexia as an independent risk factor for both OS (HR = 3.31; p < 0.001) and PFS (HR = 2.94; p < 0.001), with cachexia also showing significant impacts. Grade 3-4 adverse events were higher in patients with refractory cachexia (23.8%) and cachexia (8.1%) compared with pre-cachexia (6.0%; p = 0.010).ConclusionsHCC patients with cachexia and refractory cachexia were identified by longitudinal BC trajectories. Falling trajectories of BC identified refractory cachexia patients with worst response, survival and poor tolerability from systemic therapy combinations.Trial registrationClinicalTrials.gov identifier: NCT05278195.

Dataset Information

System analysis based on the lysosome-related genes identifies HPS4 as a novel therapy target for liver hepatocellular carcinoma.

Background

Methods

Results

Conclusion

Publications

System analysis based on the lysosome-related genes identifies HPS4 as a novel therapy target for liver hepatocellular carcinoma.

Similar Datasets

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