Prognostic Significance In Hepatocellular Carcinoma Research Articles

Novel prognostic signature for hepatocellular carcinoma using a comprehensive machine learning framework to predict prognosis and guide treatment.

Hepatocellular carcinoma (HCC) is highly aggressive, with delayed diagnosis, poor prognosis, and a lack of comprehensive and accurate prognostic models to assist clinicians. This study aimed to construct an HCC prognosis-related gene signature (HPRGS) and explore its clinical application value. TCGA-LIHC cohort was used for training, and the LIRI-JP cohort and HCC cDNA microarray were used for validation. Machine learning algorithms constructed a prognostic gene label for HCC. Kaplan-Meier (K-M), ROC curve, multiple analyses, algorithms, and online databases were used to analyze differences between high- and low-risk populations. A nomogram was constructed to facilitate clinical application. We identified 119 differential genes based on transcriptome sequencing data from five independent HCC cohorts, and 53 of these genes were associated with overall survival (OS). Using 101 machine learning algorithms, the 10 most prognostic genes were selected. We constructed an HCC HPRGS with four genes (SOCS2, LCAT, ECT2, and TMEM106C). Good predictive performance of the HPRGS was confirmed by ROC, C-index, and K-M curves. Mutation analysis showed significant differences between the low- and high-risk patients. The low-risk group had a higher response to transcatheter arterial chemoembolization (TACE) and immunotherapy. Treatment response of high- and low-risk groups to small-molecule drugs was predicted. Linifanib was a potential drug for high-risk populations. Multivariate analysis confirmed that HPRGS were independent prognostic factors in TCGA-LIHC. A nomogram provided a clinical practice reference. We constructed an HPRGS for HCC, which can accurately predict OS and guide the treatment decisions for patients with HCC.

Proteogenomic Identification and Analysis of KIF5B as a Prognostic Signature for Hepatocellular Carcinoma.

Metabolic disorders are significant risk factors for liver cancer, particularly Hepatocellular Carcinoma (HCC). However, the molecular genetic basis of metabolic reprogramming in the liver remains largely uncertain. This study aimed to investigate some novel prognostic biomarkers in HCC by using proteogenomic and transcriptomic analysis and explore the potential role of specific prognostic genes in HCC. Here, we have presented a proteogenomic analysis of 10 pairs of HCC. Protein co-expression and pathway analysis were performed to investigate the biological characteristics of HCC. Protein and mRNA expression profiles of multi-cohorts were integrated to detect novel prognostic protein markers of HCC. The carcinogenic roles of candidate prognostic markers were further evaluated by MTS assay, colony formation, monolayer wound healing assay, and xenograft models. A total of 2086 proteins with significantly different expressions were detected in HCC. Pathways related to oncogenic signaling and insulin-related metabolism have been found to be dysregulated and differentially regulated in HCC. We have identified the novel prognostic biomarkers, KIF5B, involved in liver metabolic reprogramming. The biomarkers were identified using multivariable COX regression analysis from two independent proteomic datasets (Fudan Cohort and our recruited cohort) and the TCGA mRNA database. Both the protein and mRNA up-regulation of KIF5B have been found to be associated with a poor clinical outcome in HCC. Insulin activated the protein expression of KIF5B in HCC. Knocking out KIF5B expression by sgRNA decreased the protein expression of FASN and SCD1 and the intracellular triglyceride concentration. Silencing KIF5B suppressed HCC cell proliferation and colony formation in vitro, as well as HCC growth in xenograft models. Our findings have suggested KIF5B protein to function as a novel prognostic biomarker in HCC. KIF5B expression has been found to activate the AKT/mTOR pathway and reprogram triglyceride metabolism, leading to HCC development. Targeting KIF5B may be an effective strategy in the clinical treatment of HCC.

Identification and Verification of a Glycolysis-Related lncRNA Prognostic Signature for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a primary liver cancer with a high mortality rate. The search for a new biomarker could help the prognosis of HCC patients. We identified the glycolytic gene set associated with HCC and the glycolytic lncRNA based on TCGA and MsigDB databases. According to these lncRNAs, K-means clustering, and regression analysis were performed on the patients. Two groups of HCC patients with different lncRNA expression levels were obtained based on K-means clustering results. The results of difference analysis and enrichment analysis showed that DEmRNA in the two HCC populations with significant survival differences was mainly enriched in transmembrane transporter complex, RNA polymerase II specificity, cAMP signaling pathway, and calcium signaling pathway. In addition, a prognostic model of HCC with 4 DElncRNAs was constructed based on regression analysis. ROC curve analysis showed that the model had good predictive performance. Drug predictionresults showed that the efficacy of JQ1, niraparib, and teniposide was higher in the low-risk group than in the high-risk group. In conclusion, this study preliminarily identified glycolytic-related prognostic features of lncRNAs in HCC and constructed a risk assessment model. The results of this study are expected to guide the prognosis assessment of clinical HCC patients.

The m6A methyltransferase METTL5 promotes neutrophil extracellular trap network release to regulate hepatocellular carcinoma progression.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide, it has a poor prognosis due to its highly invasive and metastatic nature. Consequently, identifying effective prognostic markers and potential therapeutic targets has been extensively investigated. METTL5, an 18S rRNA methyltransferase, is abnormally high in HCC. But its biological function and prognostic significance in HCC remain largely unelucidated. This study aimed to investigate the role of METTL5 in HCC progression, and elucidate its possible molecular mechanisms in HCC via transcriptome sequencing, providing new insights for identifying new HCC prognostic markers and therapeutic targets. The METTL5 expression in HCC and paracancerous tissues was analyzed using HCC immunohistochemical microarrays and bioinformatic retrieval methods to correlate METTL5 with clinicopathological features and survival prognosis. We constructed a METTL5 knockdown hepatocellular carcinoma cell line model and an animal model to determine the effect of METTL5 on hepatocellular carcinoma progression. Subsequently, RNA sequencing was performed to analyze the molecular mechanism of METTL5 in HCC based on the sequencing results, and relevant experiments were performed to verify it. We found that METTL5 expression was elevated in hepatocellular carcinoma tissues and correlated with poor patient prognosis, and in the analysis of clinicopathological features showed a correlation with TNM staging. In hepatocellular carcinoma cell lines with knockdown of METTL5, the malignant biological behavior was significantly reduced both invitro and invivo. Based on the sequencing results as well as the results of GO functional enrichment analysis and KEGG pathway enrichment analysis, we found that METTL5 could promote the generation and release of neutrophil extracellular capture network (NETs) and might further accelerate the progression of HCC. The m6A methyltransferase METTL5 is overexpressed in hepatocellular carcinoma (HCC) and correlates with poor prognosis. METTL5 accelerates malignant progression of HCC by promoting generation and release of the neutrophil extracellular traps (NETs) network, providing new insights for clinical biomarkers and immunotherapeutic targets in HCC prognosis.

Combined OLA1 and CLEC3B Gene Is a Prognostic Signature for Hepatocellular Carcinoma and Impact Tumor Progression.

Hepatocellular carcinoma (HCC), partly because of its complexity and high heterogeneity, has a poor prognosis and an extremely high mortality rate. In this study, mRNA sequencing expression profiles and relevant clinical data of HCC patients were gathered from different public databases. Kaplan-Meier survival curves as well as ROC curves validated that OLA1|CLEC3B was an independent predictor with better predictive capability of HCC prognosis compared to OLA1 and CLEC3B separately. Further, the cell transfection experiment verified that knockdown of OLA1 inhibited cell proliferation, facilitated apoptosis, and improved sensitivity of HCC cells to gemcitabine. In this study, the prognostic model of HCC composed of OLA1/CLEC3B genes was constructed and verified, and the prediction ability was favorable. A higher level of OLA1 along with a lower level of CEC3B is a sign of poor prognosis in HCC. We revealed a novel gene pair OLA1|CLEC3B overexpressed in HCC patients, which may serve as a promising independent predictor of HCC survival and an approach for innovative diagnostic and therapeutic strategies.

Comprehensive pan-cancer investigation of carnosine dipeptidase 1 and its prospective prognostic significance in hepatocellular carcinoma.

Carnosine dipeptidase 1 (CNDP1), an enzyme integral to the hydrolysis of dipeptides containing histidine, plays an indispensable role in myriad physiological processes, including hydrolysis of proteins, maturation of specific biochemical functionalities within proteins, tissue regeneration, and regulation of cell cycle. However, the implications of CNDP1 in oncogenesis and its prognostic value are not yet fully elucidated. Initially, we procured the GSE40367 dataset from the Gene Expression Omnibus and established a protein-protein interaction network. Thereafter, we conducted functional and pathway enrichment analyses utilizing GO, KEGG, and GSEA. Moreover, we undertook an association analysis concerning the expression of CNDP1 with immune infiltration, along with survival analysis across various cancers and specifically in hepatocellular carcinoma (HCC). Our study uncovered a total of 2,248 differentially expressed genes, with a down-regulation of CNDP1 in HCC and other cancers. Our explorations into the relationship between CNDP1 and immune infiltration disclosed a negative correlation between CNDP1 expression and the presence of immune cells in HCC. Survival analyses revealed that diminished expression of CNDP1 correlates with an adverse prognosis in HCC and several other types of cancer. These observations intimate that CNDP1 holds promise as a novel prognostic biomarker for both pan-cancer and HCC.

Prognosis Prediction of CRAFITY Score in HCC Undergoing TACE Combined with PD-(L)1 Inhibitors and Molecular Targeted Therapy.

The CRAFITY (C-reactive protein and alpha-fetoprotein in immunotherapy) score has demonstrated prognostic significance in hepatocellular carcinoma (HCC) patients undergoing immunotherapy. The study aimed to validate accuracy of CRAFITY score on predicting prognosis for patients with HCC treated with transarterial chemoembolization (TACE) combined with PD-(L)1 inhibitors and molecular targeted therapy. Eighty-five HCC patients who underwent TACE in combination with molecular targeted therapy (MTT) and PD-(L)1 Inhibitors were consecutively enrolled from November 2019 to November 2022. Patients were divided into CRAFITY 0 score (n=32), CRAFITY 1 score (n=31), and CRAFITY 2 score (n=22), respectively. The primary outcomes were overall survival (OS) and progression-free survival (PFS), and the secondary outcomes included tumor response rate and treatment-related adverse events (TRAEs). Factors affecting survival were identified via Cox regression analysis. The median overall survival (OS) for HCC patients with CRAFITY scores of 0, 1, and 2 was 33.4 months (95% confidence interval [CI]: 27.1-39.7), 34.5 months (95% CI: 23.1-45.9), and 24.2 months (95% CI: 13.9-39.3), respectively, there were statistical differences among the three groups (p<0.05). The progression-free survival (PFS) was 14.1 months (95% CI: 10.0-18.2), 14.1 months (95% CI: 9.0-19.2), and 9.3 months (95% CI: 7.2-11.4) for patients with CRAFITY scores of 1, 2, and 3, respectively, with a significant difference between the three groups (p<0.05). In patients with CRAFITY scores of 1, 2, and 3, the disease control rates (DCR) were 94%, 84%, and 73%, respectively (p < 0.05), while the overall response rates (ORR) were 78.1%, 67.7%, and 59.1%, respectively (p = 0.318). A higher CRAFITY score showed a correlation with an increased frequency of fatigue and grade 3 fever (p<0.05). Moreover, CRAFITY 2 score was an independent risk factor for both OS (HR = 2.610(1.281-4.564), p = 0.014) and PFS (HR = 2.419(1.281-4.564), p = 0.006). The CRAFITY score may provide an efficient predictive capacity for prognosis in HCC patients undergoing TACE combined with PD-(L)1 inhibitors and molecular targeted therapy.

PPIH gene regulation system and its prognostic significance in hepatocellular carcinoma: a comprehensive analysis.

Peptidyl-prolyl isomerase H (PPIH) is a member of the cyclophilin protein family, which functions as a molecular chaperone and is involved in the splicing of pre-mRNA. According to reports, the malignant progression of HCC related to hepatitis B virus (HBV) is tightly associated with RNA-binding proteins. Nevertheless, there is no research on PPIH expression or its function in the occurrence and progression of HCC. We are the first to reveal that the mRNA and protein levels of Ppih are substantially overexpressed in HCC, as the outcomes show. A significant correlation existed between enriched expression of Ppih within HCC and more advanced, poorly differentiated, and TP53-mutated tumors. These findings, which suggest that Ppih may serve as a predictive biomarker for people with HCC, serve as a starting point for further investigation into the function of Ppih in the progression of carcinogenesis. Accordingly, we utilized clinical samples and bioinformatics analysis to assess Ppih's mRNA, protein expression, and gene regulatory system in HCC. Additionally, Wilcoxon signed-rank testing and logistic regression were utilized to inspect the association between clinicopathological factors and Ppih. Clinical pathological traits linked to overall survival (OS) among HCC patients were examined via TCGA data via Cox regression and the Kaplan-Meier approach. Additionally, via TCGA data collection, gene set enrichment assessment was also conducted.

Retracted: Development and Validation of a Novel Mitophagy-Related Gene Prognostic Signature for Hepatocellular Carcinoma Based on Immunoscore Classification of Tumor.

[This retracts the article DOI: 10.1155/2021/5070099.].

Combining bulk and single-cell RNA-sequencing data to develop an NK cell-related prognostic signature for hepatocellular carcinoma based on an integrated machine learning framework

BackgroundThe application of molecular targeting therapy and immunotherapy has notably prolonged the survival of patients with hepatocellular carcinoma (HCC). However, multidrug resistance and high molecular heterogeneity of HCC still prevent the further improvement of clinical benefits. Dysfunction of tumor-infiltrating natural killer (NK) cells was strongly related to HCC progression and survival benefits of HCC patients. Hence, an NK cell-related prognostic signature was built up to predict HCC patients’ prognosis and immunotherapeutic response.MethodsNK cell markers were selected from scRNA-Seq data obtained from GSE162616 data set. A consensus machine learning framework including a total of 77 algorithms was developed to establish the gene signature in TCGA–LIHC data set, GSE14520 data set, GSE76427 data set and ICGC–LIRI–JP data set. Moreover, the predictive efficacy on ICI response was externally validated by GSE91061 data set and PRJEB23709 data set.ResultsWith the highest C-index among 77 algorithms, a 11-gene signature was established by the combination of LASSO and CoxBoost algorithm, which classified patients into high- and low-risk group. The prognostic signature displayed a good predictive performance for overall survival rate, moderate to high predictive accuracy and was an independent risk factor for HCC patients’ prognosis in TCGA, GEO and ICGC cohorts. Compared with high-risk group, low-risk patients showed higher IPS–PD1 blocker, IPS–CTLA4 blocker, common immune checkpoints expression but lower TIDE score, which indicated low-risk patients might be prone to benefiting from ICI treatment. Moreover, a real-world cohort, PRJEB23709, also revealed better immunotherapeutic response in low-risk group.ConclusionsOverall, the present study developed a gene signature based on NK cell-related genes, which offered a novel platform for prognosis and immunotherapeutic response evaluation of HCC patients.

A novel prognostic signature for hepatocellular carcinoma based on SUMOylation-related genes

SUMOylation (SUMO modification) has been confirmed to play an essential role in the progression of various malignancies. As the value of SUMOylation-related genes (SRGs) in prognosis prediction of hepatocellular carcinoma (HCC) has not been explored, we aim to construct an HCC SRGs signature. RNA sequencing was utilized to identify differentially expressed SRGs. The 87 identified genes were used in Univariate Cox regression analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) analysis to build a signature. The accuracy of the model was validated by the ICGC and GEO datasets. The GSEA revealed that the risk score was associated with common cancer-related pathways. The ssGSEA showed that NK cells in the high-risk group were significantly reduced. The sensitivities of anti-cancer drugs confirmed the sensitivity of the high-risk group to sorafenib was lower. Further, our cohort showed that risk scores were correlated with advanced grade and vascular invasion (VI). Finally, the results of H&E staining and immunohistochemistry of Ki67 showed that higher-risk patients are more malignant.

Integrative analysis of lactylation-related genes and establishment of a novel prognostic signature for hepatocellular carcinoma.

Lactylation has been found to involve in regulating many types of biological process in cancers. However, research on lactylation-related genes in predicting the prognosis of hepatocellular carcinoma (HCC) remains limited. The differential expression of lactylation-related genes (EP300 and HDAC1-3) in pan-cancer were examined in public databases. HCC patient tissues were obtained for mRNA expression and lactylation level detection by RT-qPCR and western blotting. Transwell migration assay, CCK-8 assay, EDU staining assay and RNA-seq were performed to verify the potential function and mechanisms in HCC cell lines after lactylation inhibitor apicidin treatment. lmmuCellAI, quantiSeq, xCell, TIMER and CIBERSOR were used to analyze the correlation between transcription levels of lactylation-related genes and immune cell infiltration in HCC. Risk model of lactylation-related genes was constructed by LASSO regression analysis, and prediction effect of the model was evaluated. The mRNA levels of lactylation-related genes and lactylation levels were higher in HCC tissues than normal samples. The lactylation levels, cell migration, and proliferation ability of HCC cell lines were suppressed after apicidin treatment. The dysregulation of EP300 and HDAC1-3 was associated with proportion of immune cell infiltration, especially B cell. Upregulation of HDAC1 and HDAC2 was closely associated with poorer prognosis. Finally, a novel risk model, based on HDAC1 and HDAC2, was developed for prognosis prediction in HCC. HDAC1 and HDAC2 are expected to become new biomarkers for HCC. Risk scoring model based on HDAC1 and HDAC2 can be used to predict the prognosis of HCC patients.

Development and validation of a selenium metabolism regulators associated prognostic model for hepatocellular carcinoma

BackgroundSelenium metabolism has been implicated in human health. This study aimed to identify a selenium metabolism regulator-based prognostic signature for hepatocellular carcinoma (HCC) and validate the role of INMT in HCC.MethodsTranscriptome sequencing data and clinical information related to selenium metabolism regulators in TCGA liver cancer dataset were analysed. Next, a selenium metabolism model was constructed by multiple machine learning algorithms, including univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses. Then, the potential of this model for predicting the immune landscape of different risk groups was evaluated. Finally, INMT expression was examined in different datasets. After knockdown of INMT, cell proliferation and colony formation assays were conducted.ResultsA selenium metabolism model containing INMT and SEPSECS was established and shown to be an independent predictor of prognosis. The survival time of low-risk patients was significantly longer than that of high-risk patients. These two groups had different immune environments. In different datasets, including TCGA, GEO, and our PUMCH dataset, INMT was significantly downregulated in HCC tissues. Moreover, knockdown of INMT significantly promoted HCC cell proliferation.ConclusionsThe current study established a risk signature of selenium metabolism regulators for predicting the prognosis of HCC patients. INMT was identified as a biomarker for poor prognosis of HCC.

Establishment and validation of a ferroptosis-related prognostic signature for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer with high heterogeneity. The prognosis of HCC is quite poor and the prognostic prediction also has challenges. Ferroptosis is recently recognized as a kind of iron-dependent cell death, which is involved in tumor progression. However, further study is needed to validate the influence of drivers of ferroptosis (DOFs) on the prognosis of HCC. The FerrDb database and the Cancer Genome Atlas (TCGA) database were applied to retrieve DOFs and information of HCC patients respectively. HCC patients were randomly divided into training and testing cohorts with a 7:3 ratio. Univariate Cox regression, LASSO and multivariate Cox regression analyses were carried out to identify the optimal prognosis model and calculate the risk score. Then, univariate and multivariate Cox regression analyses were performed to assess the independence of the signature. At last, gene functional, tumor mutation and immune-related analyses were conducted to explore the underlying mechanism. Internal and external databases were used to confirm the results. Finally, the tumor tissue and normal tissue from HCC patients were applied to validate the gene expression in the model. Five genes were identified to develop as a prognostic signature in the training cohort relying on the comprehensive analysis. Univariate and multivariate Cox regression analyses confirmed that the risk score was able to be an independent factor for the prognosis of HCC patients. Low-risk patients showed better overall survival than high-risk patients. Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Furthermore, internal and external cohorts were consistent with our results. There was a higher proportion of nTreg cell, Th1 cell, macrophage, exhausted cell and CD8+T cell in the high-risk group. The Tumor Immune Dysfunction and Exclusion (TIDE) score suggested that high-risk patients could respond better to immunotherapy. Besides, the experimental results showed that some genes were differentially expressed between tumor and normal tissues. In summary, the five ferroptosis gene signature showed potential in prognosis of patients with HCC and could also be regarded as a value biomarker for immunotherapy response in these patients.

Prognostic 7-SLC-Gene Signature Identified via Weighted Gene Co-Expression Network Analysis for Patients with Hepatocellular Carcinoma.

Solute carrier (SLC) proteins play an important role in tumor metabolism. But SLC-associated genes' prognostic significance in hepatocellular carcinoma (HCC) remained elusive. We identified SLC-related factors and developed an SLC-related classifier to predict and improve HCC prognosis and treatment. From the TCGA database, corresponding clinical data and mRNA expression profiles of 371 HCC patients were acquired, and those of 231 tumor samples were derived from the ICGC database. Genes associated with clinical features were filtered using weighted gene correlation network analysis (WGCNA). Next, univariate LASSO Cox regression studies developed SLC risk profiles, with the ICGC cohort data being used in validation. Univariate Cox regression analysis revealed that 31 SLC genes (P < 0.05) were related to HCC prognosis. 7 (SLC22A25, SLC2A2, SLC41A3, SLC44A1, SLC48A1, SLC4A2, and SLC9A3R1) of these genes were applied in developing a SLC gene prognosis model. Samples were classified into the low-andhigh-risk groups by the prognostic signature, with those in the high-risk group showing a significantly worse prognosis (P < 0.001 in the TCGA cohort and P=0.0068 in the ICGC cohort). ROC analysis validated the signature's prediction power. In addition, functional analyses showed enrichment of immune-related pathways and different immune status between the two risk groups. The 7-SLC-gene prognostic signature established in this study helped predict the prognosis, and was also correlated with the tumor immune status and infiltration of different immune cells in the tumor microenvironment. The current findings may provide important clinical indications for proposing a novel combination therapy consists of targeted anti-SLC therapy and immunotherapy for HCC patients.

Potential Common Molecular Mechanisms Between Periodontitis and Hepatocellular Carcinoma: A Bioinformatic Analysis and Validation.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and has a poor prognosis. Periodontitis, or tooth loss, is considered to be related to hepatocarcinogenesis and its poor prognosis. This study aimed to explore potential associations and cross-talk mechanisms between periodontitis and HCC. Periodontitis and HCC microarray datasets were acquired from the Gene Expression Omnibus (GEO) database and were analyzed to obtain differentially expressed (DE) lncRNAs, miRNAs and mRNAs. Functional enrichment analysis was used to detect the functions of these mRNAs. Then, a ceRNA network of periodontitis-related HCC was constructed. Least absolute shrinkage and selection operator (LASSO) regression, random forest algorithm, and support vector machine-recursive feature elimination (SVM-RFE) were performed to explore the diagnostic significance of mRNAs in periodontitis-related HCC. Cox regression analyses were conducted to screen mRNAs with prognostic significance in HCC. Quantitative real-time PCR (qRT-PCR) and immunohistochemistry (IHC) were conducted to validate the expression of these mRNAs in HCC tissues. A ceRNA network was constructed. Functional enrichment analysis indicated that the network is associated with immune and inflammatory responses, the cell cycle and liver metabolic function. LASSO, random forest algorithm and SVM-RFE showed the diagnostic significance of DE mRNAs in HCC. Cox regression analyses revealed that MSH2, GRAMD1C and CTHRC1 have prognostic significance for HCC, and qRT-PCR and IHC validated this finding. Periodontitis may affect the occurrence of HCC by changing the immune and inflammatory response, the cell cycle and liver metabolic function. MSH2, GRAMD1C and CTHRC1 are potential prognostic biomarkers for HCC.

PP202 Stem cell-like subtype from developmental hierarchies of fetal liver shows clinical relevance with prognostic significance in hepatocellular carcinoma

PP202 Stem cell-like subtype from developmental hierarchies of fetal liver shows clinical relevance with prognostic significance in hepatocellular carcinoma

Constructing and validating of m7G-related genes prognostic signature for hepatocellular carcinoma and immune infiltration: potential biomarkers for predicting the overall survival

To investigate the prognostic significance of N7-methylguanosine (m7G) regulators and immune infiltration in liver hepatocellular carcinoma (LIHC). The research measured predictive m7G genes in LIHC samples from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. Data on the stemness index based on mRNA expression (mRNAsi), gene mutations, and corresponding clinical characteristics were obtained from TCGA and ICGC. Lasso regression was used to construct the prediction model to assess the m7G prognostic signals in LIHC. Based on these genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to identify key biological functions and pathways. The correlation between m7G RNA methylation regulators and the prognosis and immune infiltration of LIHC was evaluated. There were 21 m7G-related differentially expressed genes (DEGs) in LIHC and healthy tissues, and LIHC patients could be divided into two categories by consensus clustering of these DEGs. A five-gene predictive approach was employed using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Patients in the low-risk group showed a significantly higher survival rate compared with those in the high-risk group (P=0.001). Validations using the ICGC database. Also, univariate and multivariate Cox regression analyses suggested that the risk score produced by the predictive model is an independent predictor for LIHC [hazard ratio (HR): 1.848, 95% confidence interval (CI): 1.286-2.656; HR: 2.597, 95% CI: 1.358-4.965]. The ROC curves of the ICGC cohort revealed that the five-gene prediction model performed well [area under the curve (AUC) =0.642 at 1 year, AUC =0.686 at 2 years, and AUC =0.667 at 3 years]. Immuno-oncology scoring revealed that in the high-risk group, among 16 immune cells, the expressions of neutrophils and natural killer (NK) cells were low and that of regulatory T-cells (Tregs) was high. LIHC occurrence and progression are linked to m7G-related genes. Corresponding prognostic models help forecast the prognosis of LIHC patients. m7G-related genes and associated immune cell infiltration in the TME may serve as potential therapeutic targets in LIHC, which requires further trials. In addition, the m7G-related gene signature offers a viable alternative to predict LIHC, and these m7G-related genes show a prospective research area for LIHC targeted treatment in the future.

Identification and analysis of C17orf53 as a prognostic signature for hepatocellular carcinoma

C17orf53 is a novel gene for DNA synthesis and homologous recombination. However, the exact role of C17orf53 in hepatocellular carcinoma (HCC) remains unclear. In this study, we analyzed it using a set of public datasets. UALCAN, Human Protein Atlas (HPA), Kaplan‒Meier Plotter, Tumor Immune Estimation Resource (TIMER), cBioPortal, GEPIA, GeneMANIA, and LinkedOmics were used. Functional analysis was conducted in SK-Hep-1 cells by using small interfering RNA (siRNA). C17orf53 was highly expressed and predicted unfavorable survival in HCC patients. Moreover, it showed positive correlations with the abundance of B cells, macrophages and dendritic cells. In addition, we identified 126 genes that were positively correlated with C17orf53 and its coeffector minichromosome maintenance 8 (MCM8). These genes were mainly enriched in the cell cycle, DNA replication and Fanconi anemia pathways. Knockdown of C17orf53 significantly inhibited the proliferation of SK-Hep-1 cells and decreased the expression of MCM8, cyclin D1 and proliferating cell nuclear antigen (PCNA). Overall, C17orf53 is a novel prognostic signature for HCC.

Machine learning-based transcriptome analysis of lipid metabolism biomarkers for the survival prediction in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver with a very high fatality rate. Our goal in this study is to find a reliable lipid metabolism-related signature associated with prognostic significance for HCC. In this study, HCC lipid metabolism-related molecular subtype analysis was conducted based on the 243 lipid metabolism genes collected from the Molecular Signatures Database. Several significant disparities in prognosis, clinicopathological characteristics, and immune and ferroptosis-related status were found across the three subtypes, especially between C1 and C3 subgroups. Differential expression analysis yielded 57 differentially expressed genes (DEGs) between C1 and C3 subtypes. GO and KEGG analysis was employed for functional annotation. Three of 21 prognostic DEGs (CXCL8, SLC10A1, and ADH4) were finally selected through machine-learning-based discovery and validation strategy. The risk score = (0.103) × expression value of CXCL8 + (−0.0333) × expression value of SLC10A1 + (−0.0812) × expression value of ADH4. We used these three to construct a HCC prognostic risk model, which stratified the patients of the validation cohort into two risk subtypes with significantly different overall survival. Our work provides possible significance of the lipid metabolism-associated model in stratifying patient prognosis and its feasibility to guide therapeutic selection.