This research demonstrated that the mRNA and protein levels of SAMM50 in HCC tissues were elevated compared to those in normal liver and adjacent tissues, suggesting its potential as a diagnostic marker for HCC, though further validation in independent cohorts is needed.
Abstract
Identifying diagnostic and prognostic biomarkers and therapeutic targets for hepatocellular carcinoma (HCC) is essential to improve risk stratification, guide individualized treatment, and enhance therapeutic efficacy.The expression of SAMM50 (Sorting and Assembly Machinery Component 50) was initially analyzed in publicly accessible curated genomic and proteomic databases, such as the Cancer Cell Line Encyclopedia, the Human Protein Atlas, and other HCC-specific repositories. This analysis revealed differential expression patterns between HCC and non-neoplastic liver tissue. Subsequently, clinicopathological data and tissue specimens were collected from 200 HCC patients who underwent treatment at our institution. The protein and transcript levels of SAMM50 were experimentally measured in paired HCC and adjacent non-tumorous tissues using immunohistochemistry (IHC) and quantitative reverse transcription polymerase chain reaction (qRT-PCR). The association between SAMM50 expression and key clinicopathological features was further evaluated. Univariate and multivariate Cox proportional hazards analyses were performed to determine the independent prognostic value of SAMM50 expression in HCC. Based on these results, a reproducible and clinically applicable nomogram, supported by a forest plot, was constructed to facilitate prognostic prediction and support individualized therapeutic decision-making. Finally, in vitro and in vivo experiments were conducted to characterize the phenotypic alterations in HCC cells after SAMM50 knockdown, thereby confirming its involvement in critical oncogenic behaviors.This research demonstrated that the mRNA and protein levels of SAMM50 in HCC tissues were elevated compared to those in normal liver and adjacent tissues. Immunohistochemistry findings confirmed that SAMM50 protein levels were persistently higher in HCC tissues than in paired adjacent tissues. High expression of SAMM50 was correlated with unfavorable clinicopathological factors, encompassing pretreatment alpha-fetoprotein (AFP) levels, tumor size, T stage, American Joint Committee on Cancer (AJCC) stage, histological grade, and worse overall survival.Specifically, high expression of SAMM50 was linked to shorter overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS). Moreover, univariate and multivariate Cox analyses were conducted to investigate the association between SAMM50 expression and clinicopathological features in HCC patients and to identify independent prognostic factors. The area under the receiver operating characteristic (ROC) curve (AUC) for SAMM50 was 0.863, suggesting its potential as a diagnostic marker for HCC, though further validation in independent cohorts is needed. Silencing of SAMM50 inhibited HCC cell proliferation, migration, and invasion, promoted apoptosis in vitro, and suppressed HCC growth in vivo.This research demonstrates that SAMM50 shows potential diagnostic value for HCC, though this observation requires further validation in larger, independent, and prospective cohorts. The results of this study not only contribute to the evaluation of baseline data and risk stratification in HCC but also offer novel approaches for the development of precise treatment strategies and targeted therapies.
Hepatocellular carcinoma (HCC) is one of the malignant tumors with high incidence and mortality rates worldwide. Given the poor prognosis of patients with HCC, it is crucial to explore the molecular mechanisms underlying HCC development and to evaluate prognostic markers. Differential expression analysis followed by univariate Cox, LASSO, and multivariate Cox regression identified four genes (EPO, SOCS2, IL18RAP, and KPNA2), and a Cox-based risk score was evaluated in the TCGA-LIHC cohort and externally in GSE14520 using Kaplan–Meier and time-dependent ROC analyses. Bulk, single-cell, and protein resources provided convergent expression context. Survival machine-learning analysis using observed overall-survival time and censoring status identified Cox–Ridge as the best-performing model in TCGA-LIHC, with more modest performance in GSE14520, and immune profiling revealed risk-group-associated differences in estimated immune and stromal components, immune-cell composition, and immune-checkpoint expression. The oncoPredict/GDSC2 screen highlighted five potential drug candidates for experimental prioritization. Because the drug screen is based on computationally predicted sensitivities, these findings should be regarded as hypothesis-generating and require validation in prospective cohorts and experimental systems before clinical translation.
Yu-Xian Liu, Xing-Jie Chen, Junyuan Zhang et al.· International Journal of Mol...· 0 citations
OBJECTIVE
To analyze SNRPD1 expression in hepatocellular carcinoma (HCC) and investigate its impact on post-liver transplantation recurrence and prognosis.
METHODS
Differentially expressed genes were identified from the TCGA-LIHC cohort using R-based bioinformatics tools. Kaplan-Meier survival analysis, ROC curves, nomograms, and Cox regression models assessed the diagnostic and prognostic value of SNRPD1 in HCC. Immunohistochemistry was performed to detect SNRPD1 protein expression in 102 paired HCC and adjacent non-tumor tissues, with correlation analysis of clinicopathological parameters and follow-up data.
RESULTS
SNRPD1 expression was significantly higher in HCC than in adjacent tissues (89.2% vs. 15.7%, P < 0.001) and positively correlated with microvascular invasion (MVI) and microvascular density (MVD) (P < 0.05; r = 0.26). Multivariate Cox regression confirmed high SNRPD1 expression as an independent risk factor for poor overall survival (hazard ratio (HR) = 0.679, P < 0.001) and progression-free survival (HR = 2.08, P = 0.025) in transplant recipients. The diagnostic AUC was 0.86. High SNRPD1 expression was associated with significantly shorter median overall survival (OS, 45 vs. 88 months) and progression-free survival (PFS, 46 vs. 75 months) (both P < 0.01).
CONCLUSION
SNRPD1 is highly expressed in HCC and correlates with MVI and MVD. High SNRPD1 expression independently predicts poorer post-transplant OS and PFS, suggesting it as a promising prognostic biomarker and therapeutic target.
Zhihong Wei, Xuan-Hua Lin, Jian-Wei Chen et al.· American journal of translat...· 0 citations
Background Hepatocellular carcinoma (HCC) demonstrates significant prognostic variability that is not entirely accounted for by traditional staging systems. Necrosis by sodium overload (NECSO) is an emerging programmed cell death pathway, but its clinical relevance in HCC remains undefined. Therefore, this study aimed to identify TRPM4-associated core genes, develop and validate a prognostic signature, and investigate its relationship with the tumor immune microenvironment, tumor mutational burden, and single-cell expression patterns in HCC. Methods We integrated transcriptomic, clinical, and mutational datasets from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) (n=421) and Gene Expression Omnibus (GEO) cohorts (n=115) to identify genes co-expressed with TRPM4—a key NECSO mediator—and those differentially expressed in HCC. A prognostic signature was developed using least absolute shrinkage and selection operator (LASSO)-Cox regression and validated through survival analysis, time-dependent receiver operating characteristic (ROC) curves, and multivariate Cox regression analysis. The immune landscape was characterized using CIBERSORT, somatic mutation data were used to calculate tumor mutational burden (TMB) and assess its correlation with the risk score, and single-cell RNA sequencing (scRNA-seq) resolved cell-type-specific expression patterns. Results From 294 TRPM4-associated core genes, we identified an 11-gene signature (BRSK1, MMP1, GRIN2D, GP6, MYOM2, N4BP3, CCDC112, TSEN54, MAP3K9, SPP1, B3GNT4) that independently predicted overall survival (OS) (hazard ratio =5.419, P<0.001) with areas under the curve (AUCs) of 0.779, 0.693, and 0.701 at 1, 3, and 5 years. These values were superior or comparable to conventional clinicopathologic variables after direct comparison. High-risk patients exhibited an immunosuppressive microenvironment, characterized by enrichment of M0 macrophage, a higher M2/M1 ratio (P<0.001) and distinct immune checkpoint profiles. When integrated with TMB, the prognostic stratification was further refined: high-TMB/high-risk patients had poorest outcomes (median OS, 15.3 months), while low-TMB/low-risk patients had the most favorable survival (median OS, 68.7 months). Single-cell analysis revealed that MMP1 was induced in cancer-associated fibroblasts (CAFs) and SPP1 was downregulated in macrophages, single-cell risk scores confirmed TAFs and macrophages as the main contributors to the prognostic model. Conclusions The TRPM4-centered 11-gene signature provides robust and independent prognostic stratification in HCC by integrating immune, mutational, and single-cell features. This signature serves as a potential tool for prognostic evaluation and may help inform immunotherapeutic strategies for HCC.
Jun-Ze Chen, Jia-Mei Li, Zhi-Yong Lin et al.· Journal of Gastrointestinal...· 0 citations
Functional enrichment analysis revealed that IFFO2 is significantly associated with immune regulation, cell cycle, and complement activation pathways, and its strong association with adverse prognosis and its potential to modulate the immune microenvironment underscore its dual potential as a diagnostic biomarker and a promising therapeutic target.
Qi Li, Sheng-Ke Wen, Hong Chen et al.· Current Cancer Therapy Revie...· 0 citations
A four-gene hypoxia-related prognostic signature was established and successfully stratified patients into high- and low-risk groups and was associated with distinct metabolic and immune characteristics in HCC.
Chengting Wu, Yuanqin Du, Juhong Jia et al.· Discover Oncology· 0 citations
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