Construction of a novel predictive model with seven metabolism-related genes for hepatocellular carcinoma by machine learning.
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ABSTRACT:
SUBMITTER: Mou L
PROVIDER: S-EPMC10363621 | biostudies-literature | 2023 Sep
REPOSITORIES: biostudies-literature
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Construction of a novel predictive model with seven metabolism-related genes for hepatocellular carcinoma by machine learning.
Mou Lisha L Liu Lin L Qiu Yumiao Y Liang Yingying Y Pu Zuhui Z
Genes & diseases 20230123 5
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Project description:BackgroundTo exploit hepatocellular carcinoma (HCC) diagnostic substances, we identify potential predictive markers based on machine learning and to explore the significance of immune cell infiltration in this pathology.MethodThree HCC gene expression datasets were used for weighted gene co-expression network analysis (WGCNA) and differential expression analysis. Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest were applied to identify candidate biomarkers. The diagnostic value of HCC diagnostic gene biomarkers was further assessed by the area under the ROC curve observed in the validation dataset. CIBERSORT was used to analyze 22 immune cell fractions from HCC patients and to analyze their correlation with diagnostic markers. In addition, the prognostic value of the markers and the sensitivity of the drugs were analyzed.ResultWGCNA and differential expression analysis were used to screen 396 distinct gene signatures in HCC tissues. They were mostly engaged in cytoplasmic fusion and the cell division cycle, according to gene enrichment analyses. Five genes were shown to have a high diagnostic value for use as diagnostic biomarkers for HCC, including EFHD1 (AUC = 0.77), KIF4A (AUC = 0.97), UBE2C (AUC = 0.96), SMYD3 (AUC = 0.91), and MCM7 (AUC = 0.93). T cells, NK cells, macrophages, and dendritic cells were found to be related to diagnostic markers in HCC tissues by immune cell infiltration analysis, indicating that these cells are intimately linked to the onset and spread of HCC. Concurrently, these five genes and their constructed models have considerable prognostic value.ConclusionThese five genes (EFHD1, KIF4A, UBE2C, SMYD3, and MCM7) may serve as new candidate molecular markers for HCC, providing new insights for future diagnosis, prognosis, and molecular therapy of HCC.
| S-EPMC10835357 | biostudies-literature
Project description:One of the most common cancers is hepatocellular carcinoma (HCC). Numerous studies have shown the relationship between abnormal lipid metabolism-related genes (LMRGs) and malignancies. In most studies, the single LMRG was studied and has limited clinical application value. This study aims to develop a novel LMRG prognostic model for HCC patients and to study its utility for predictive, preventive, and personalized medicine. We used the single-cell RNA sequencing (scRNA-seq) dataset and TCGA dataset of HCC samples and discovered differentially expressed LMRGs between primary and metastatic HCC patients. By using the least absolute selection and shrinkage operator (LASSO) regression machine learning algorithm, we constructed a risk prognosis model with six LMRGs (AKR1C1, CYP27A1, CYP2C9, GLB1, HMGCS2, and PLPP1). The risk prognosis model was further validated in an external cohort of ICGC. We also constructed a nomogram that could accurately predict overall survival in HCC patients based on cancer status and LMRGs. Further investigation of the association between the LMRG model and somatic tumor mutational burden (TMB), tumor immune infiltration, and biological function was performed. We found that the most frequent somatic mutations in the LMRG high-risk group were CTNNB1, TTN, TP53, ALB, MUC16, and PCLO. Moreover, naïve CD8+ T cells, common myeloid progenitors, endothelial cells, granulocyte-monocyte progenitors, hematopoietic stem cells, M2 macrophages, and plasmacytoid dendritic cells were significantly correlated with the LMRG high-risk group. Finally, gene set enrichment analysis showed that RNA degradation, spliceosome, and lysosome pathways were associated with the LMRG high-risk group. For the first time, we used scRNA-seq and bulk RNA-seq to construct an LMRG-related risk score model, which may provide insights into more effective treatment strategies for predictive, preventive, and personalized medicine of HCC patients.
| S-EPMC10014552 | biostudies-literature
Project description:BackgroundThe occult onset and rapid progression of hepatocellular carcinoma (HCC) lead to an unsatisfactory overall survival (OS) rate. Established prognostic predictive models based on tumor-node-metastasis staging and predictive factors do not report satisfactory predictive efficacy. Arachidonic acid plays pivotal roles in biological processes including inflammation, regeneration, immune modulation, and tumorigenesis. We, therefore, constructed a prognostic predictive model based on seven genes linked to arachidonic acid metabolism, using samples of HCC patients from databases to analyze the genomic profiles. We also assessed the predictive stability of the constructed model.MethodsSample data of 365 patients diagnosed with HCC were extracted from The Cancer Genome Atlas (TCGA, training set) and HCCDB18, GSE14520, and GSE76427 databases (validation sets). Patient samples were clustered using ConsensusClusterPlus analysis based on the expression levels of 12 genes involved in arachidonic acid metabolism that were significantly associated with HCC prognosis. Differentially expressed genes (DEGs) within different clusters were distinguished and compared using WebGestaltR. Immunohistochemistry (IHC) analysis was performed using a human HCC tissue microarray (TMA). Tumor immune microenvironment assessment was performed using ESTIMATE, ssGSEA, and TIDE.ResultsSamples of patients with HCC were classified into three clusters, with significant differences in OS. Cluster 2 showed the best prognosis, whereas cluster 1 presented the worst. The three clusters showed significant differences in immune infiltration. We then performed Cox and LASSO regression analyses, which revealed CYP2C9, G6PD, CDC20, SPP1, PON1, TRNP1, and ADH4 as prognosis-related hub genes, making it a simplified prognostic model. TMA analysis for the seven target genes showed similar results of regression analyses. The high-risk group showed a significantly worse prognosis and reduced immunotherapy efficacy. Our model showed stable prognostic predictive efficacy.ConclusionsThis seven-gene-based model showed stable outcomes in predicting HCC prognosis as well as responses to immunotherapy.
| S-EPMC11561968 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is one of the most general malignant tumors. Ferroptosis, a type of necrotic cell death that is oxidative and iron-dependent, has a strong correlation with the development of tumors and the progression of cancer. The present study was designed to identify potential diagnostic Ferroptosis-related genes (FRGs) using machine learning. From GEO datasets, two publicly available gene expression profiles (GSE65372 and GSE84402) from HCC and nontumor tissues were retrieved. The GSE65372 database was used to screen for FRGs with differential expression between HCC cases and nontumor specimens. Following this, a pathway enrichment analysis of FRGs was carried out. In order to locate potential biomarkers, an analysis using the support vector machine recursive feature elimination (SVM-RFE) model and the LASSO regression model were carried out. The levels of the novel biomarkers were validated further using data from the GSE84402 dataset and the TCGA datasets. In this study, 40 of 237 FRGs exhibited a dysregulated level between HCC specimens and nontumor specimens from GSE65372, including 27 increased and 13 decreased genes. The results of KEGG assays indicated that the 40 differential expressed FRGs were mainly enriched in the longevity regulating pathway, AMPK signaling pathway, the mTOR signaling pathway, and hepatocellular carcinoma. Subsequently, HSPB1, CDKN2A, LPIN1, MTDH, DCAF7, TRIM26, PIR, BCAT2, EZH2, and ADAMTS13 were identified as potential diagnostic biomarkers. ROC assays confirmed the diagnostic value of the new model. The expression of some FRGs among 11 FRGs was further confirmed by the GSE84402 dataset and TCGA datasets. Overall, our findings provided a novel diagnostic model using FRGs. Prior to its application in a clinical context, there is a need for additional research to evaluate the diagnostic value for HCC.
| S-EPMC9981290 | biostudies-literature
Project description:Predicting the prognosis of hepatocellular carcinoma (HCC) is a major medical challenge and of guiding significance for treatment. This study explored the actual relevance of RNA expression in predicting HCC prognosis. Cox's multiple regression was used to establish a risk score staging classification and to predict the HCC patients' prognosis on the basis of data in the Cancer Genome Atlas (TCGA). We screened seven gene biomarkers related to the prognosis of HCC from the perspective of oxidative stress, including Alpha-Enolase 1(ENO1), N-myc downstream-regulated gene 1 (NDRG1), nucleophosmin (NPM1), metallothionein-3, H2A histone family member X, Thioredoxin reductase 1 (TXNRD1) and interleukin 33 (IL-33). Among them we measured the expression of ENO1, NGDP1, NPM1, TXNRD1 and IL-33 to investigate the reliability of the multi-index prediction. The first four markers' expressions increased successively in the paracellular tissues, the hepatocellular carcinoma samples (from patients with better prognosis) and the hepatocellular carcinoma samples (from patients with poor prognosis), while IL-33 showed the opposite trend. The seven genes increased the sensitivity and specificity of the predictive model, resulting in a significant increase in overall confidence. Compared with the patients with higher-risk scores, the survival rates with lower-risk scores are significantly increased. Risk score is more accurate in predicting the prognosis HCC patients than other clinical factors. In conclusion, we use the Cox regression model to identify seven oxidative stress-related genes, investigate the reliability of the multi-index prediction, and develop a risk staging model for predicting the prognosis of HCC patients and guiding precise treatment strategy.
| S-EPMC10781471 | biostudies-literature
Project description:Background: Hepatocellular carcinoma (HCC) is one of the most malignant tumors with a poor prognosis. There is still a lack of effective biomarkers to predict its prognosis. Exosomes participate in intercellular communication and play an important role in the development and progression of cancers. Methods: In this study, two machine learning methods (univariate feature selection and random forest (RF) algorithm) were used to select 13 exosome-related genes (ERGs) and construct an ERG signature. Based on the ERG signature score and ERG signature-related pathway score, a novel RF signature was generated. The expression of BSG and SFN, members of 13 ERGs, was examined using real-time quantitative polymerase chain reaction and immunohistochemistry. Finally, the effects of the inhibition of BSG and SFN on cell proliferation were examined using the cell counting kit-8 (CCK-8) assays. Results: The ERG signature had a good predictive performance, and the ERG score was determined as an independent predictor of HCC overall survival. Our RF signature showed an excellent prognostic ability with the area under the curve (AUC) of 0.845 at 1 year, 0.811 at 2 years, and 0.801 at 3 years in TCGA, which was better than the ERG signature. Notably, the RF signature had a good performance in the prediction of HCC prognosis in patients with the high exosome score and high NK score. Enhanced BSG and SFN levels were found in HCC tissues compared with adjacent normal tissues. The inhibition of BSG and SFN suppressed cell proliferation in Huh7 cells. Conclusion: The RF signature can accurately predict prognosis of HCC patients and has potential clinical value.
| S-EPMC9527306 | biostudies-literature
Project description:Liver cancer, a global menace, ranked as the sixth most prevalent and third deadliest cancer in 2020. The challenge of early diagnosis and treatment, especially for hepatocellular carcinoma (HCC), persists due to late-stage detections. Understanding HCC's complex pathogenesis is vital for advancing diagnostics and therapies. This study combines bioinformatics and machine learning, examining HCC comprehensively. Three datasets underwent meticulous scrutiny, employing various analytical tools such as Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein interaction assessment, and survival analysis. These rigorous investigations uncovered twelve pivotal genes intricately linked with HCC's pathophysiological intricacies. Among them, CYP2C8, CYP2C9, EPHX2, and ESR1 were significantly positively correlated with overall patient survival, while AKR1B10 and NQO1 displayed a negative correlation. Moreover, the Adaboost prediction model yielded an 86.8 % accuracy, showcasing machine learning's potential in deciphering complex dataset patterns for clinically relevant predictions. These findings promise to contribute valuable insights into the elusive mechanisms driving liver cancer (HCC). They hold the potential to guide the development of more precise diagnostic methods and treatment strategies in the future. In the fight against this global health challenge, unraveling HCC's intricacies is of paramount importance.
| S-EPMC10724547 | biostudies-literature
Project description:Background and aimsAs a result of increasing numbers of studies most recently, mitophagy plays a vital function in the genesis of cancer. However, research on the predictive potential and clinical importance of mitophagy-related genes (MRGs) in hepatocellular carcinoma (HCC) is currently lacking. This study aimed to uncover and analyze the mitophagy-related diagnostic biomarkers in HCC using machine learning (ML), as well as to investigate its biological role, immune infiltration, and clinical significance.MethodsIn our research, by using Least absolute shrinkage and selection operator (LASSO) regression and support vector machine- (SVM-) recursive feature elimination (RFE) algorithm, six mitophagy genes (ATG12, CSNK2B, MTERF3, TOMM20, TOMM22, and TOMM40) were identified from twenty-nine mitophagy genes, next, the algorithm of non-negative matrix factorization (NMF) was used to separate the HCC patients into cluster A and B based on the six mitophagy genes. And there was evidence from multi-analysis that cluster A and B were associated with tumor immune microenvironment (TIME), clinicopathological features, and prognosis. After then, based on the DEGs (differentially expressed genes) between cluster A and cluster B, the prognostic model (riskScore) of mitophagy was constructed, including ten mitophagy-related genes (G6PD, KIF20A, SLC1A5, TPX2, ANXA10, TRNP1, ADH4, CYP2C9, CFHR3, and SPP1).ResultsThis study uncovered and analyzed the mitophagy-related diagnostic biomarkers in HCC using machine learning (ML), as well as to investigate its biological role, immune infiltration, and clinical significance. Based on the mitophagy-related diagnostic biomarkers, we constructed a prognostic model(riskScore). Furthermore, we discovered that the riskScore was associated with somatic mutation, TIME, chemotherapy efficacy, TACE and immunotherapy effectiveness in HCC patients.ConclusionMitophagy may play an important role in the development of HCC, and further research on this issue is necessary. Furthermore, the riskScore performed well as a standalone prognostic marker in terms of accuracy and stability. It can provide some guidance for the diagnosis and treatment of HCC patients.
| S-EPMC10014545 | biostudies-literature
Project description:BackgroundChemokines not only regulate immune cells but also play significant roles in development and treatment of tumors and patient prognoses. However, these effects have not been fully explained in hepatocellular carcinoma (HCC).Materials and methodsWe conducted a clustering analysis of chemokine-related genes. We then examined the differences in survival rates and analyzed immune levels using single-sample Gene Set Enrichment Analysis (ssGSEA) for each subtype. Based on chemokine-related genes of different subtypes, we built a prognostic model in The Cancer Genome Atlas (TCGA) dataset using the survival package and glmnet package and validated it in the Gene Expression Omnibus (GEO) dataset. We used univariate and multivariate regression analyses to select independent prognostic factors and used R package rms to draw a nomogram reflecting patient survival rates at 1, 3, and 5 years.ResultsWe identified two chemokine subtypes and, after screening, found that Cluster1 had higher survival rates than Cluster2. In addition, in terms of immune evaluation, stromal evaluation, ESTIMATE evaluation, immune abundance, immune function, and expressions of various immune checkpoints, immune levels of Cluster1 were significantly better than those of Cluster2. The immunophenoscore (IPS) of HCC patients in Cluster1 was significantly higher than that in Cluster2. Furthermore, we established a prognostic model consisting of 9 genes, which correlated with chemokines. Through testing, Riskscore was revealed as an independent prognostic factor, and the model could effectively predict HCC patients' prognoses in both TCGA and GEO datasets.ConclusionThis study resulted in the development of a novel prognostic model related to chemokine genes, providing new targets and theoretical support for HCC patients.
| S-EPMC10727522 | biostudies-literature
Project description:Background/aimsHepatocellular carcinoma (HCC) represents a primary liver malignancy with a multifaceted molecular landscape. The interplay between liquid-liquid phase separation (LLPS) and ferroptosis-a regulated form of cell death-has garnered interest in tumorigenesis. However, the precise role of LLPS and ferroptosis-related genes in HCC progression and prognosis remains obscure. Unraveling this connection could pave the way for innovative diagnosis and therapeutic strategies.Materials and methodsThe differentially expressed genes (DEGs) were identified based on 3 GEO datasets, followed by overlapping with LLPS-related and ferroptosis-related genes. Based on central hub genes, a diagnostic model was developed through LASSO regression and validated using KM survival analysis and real-time quantitative polymerase chain reaction (RT-qPCR). Then the effects of NRAS on the development of HCC and ferroptosis were also detected.ResultsWe identified 24 DEGs overlapping among HCC-specific, LLPS, and ferroptosis-related genes. A diagnostic model, centered on 5 hub genes, was developed and validated. Lower expression of these genes corresponded with enhanced patient survival rates, and they were distinctly overexpressed in HCC cells. NRAS downregulation significantly inhibited HepG2 cell proliferation and migration (P < .01). Fe2+ content and ROS levels were both significantly increased in the si-NRAS group when compared to those in the si-NC group (P < .01), while opposite results were observed for the protein level of GPX4 and GSH content.ConclusionThe diagnostic model with 5 hub genes (EZH2, HSPB1, NRAS, RPL8, and SUV39H1) emerges as a potential innovative tool for the diagnosis of HCC. NRAS promotes the carcinogenesis of HCC cells and inhibits ferroptosis.
| S-EPMC11843271 | biostudies-literature