DNA methylation-driven genes for constructing diagnostic, prognostic, and recurrence models for hepatocellular carcinoma

Apelin (APLN) is a biomarker contributing to the diagnosis and prognosis of hepatocellular carcinoma

Liver cancer, classified as a malignant hepatic tumor, can be divided into two categories: primary, originating within the liver, and secondary, resulting from metastasis to the liver from other organs. Hepatocellular carcinoma (HCC) is the main form of primary liver cancer and the third leading cause of cancer-related deaths. The diagnosis and prognosis of HCC using current methods still face numerous challenges. This study aims to develop novel diagnostic and prognostic models while identifying new biomarkers for improved HCC treatment. Diagnostic and prognostic models for HCC were constructed using traditional binary classification methods and machine learning algorithms based on the TCGA database (Downloaded in August 2023). The mechanisms by which APLN (Apelin) affects HCC were investigated using single-cell sequencing data sourced from the GEO database (GSE149614). The diagnostic models yielded by various algorithms could effectively distinguished HCC samples from normal ones. The prognostic model, composed of four genes, was constructed using LASSO and Cox regression algorithms, demonstrating good performance in predicting the three-year survival rate of HCC patients. The HCC biomarker Apelin (APLN) was identified in this study. APLN in liver cancer tissues mainly comes from endothelial cells and is associated with the carcinogenesis of these cells. APLN expression is significantly upregulated in liver cancer tissues, marking it as a viable indicator of endothelial cell malignancy in HCC. Furthermore, APLN expression was determined to be an independent predictor of tumor endothelial cell carcinogenesis, unaffected by its modifications such as single nucleotide variation, copy number variation, and methylation. Additionally, liver cancers characterized by high APLN expression are likely to progress rapidly after T2 stage. Our study presents diagnostic and prognostic models for HCC with appreciably improved accuracy and reliability compared to previous reports. APLN is a reliable HCC biomarker and contributes to the establishment of our models.

The resistance to anoikis, mediated by Spp1, and the evasion of immune surveillance facilitate the invasion and metastasis of hepatocellular carcinoma

Anoikis-Related Genes (ARGs) lead to the organism manifesting resistance to anoikis and are associated with unfavorable prognostic outcomes across various malignancies.Therefore, it is crucial to identify the pivotal target genes related to anoikis in HCC .We found that ARGs were significantly correlated with prognosis and immune responses in HCC. The core gene, SPP1, notably promoted anoikis resistance and metastasis in HCC through both in vivo and in vitro studies. The PI3K-Akt-mTOR pathway played a critical role in anoikis suppression within HCC contexts. Our research unveiled SPP1's role in enhancing PKCα phosphorylation, which in turn activated the PI3K-Akt-mTOR cascade. Additionally, SPP1 was identified as a key regulator of MDSCs and Tregs migration, directly affecting their immunosuppressive capabilities.These findings indicate that in HCC, SPP1 promoted anoikis resistance and facilitated immune evasion by modulating MDSCs and Tregs.

Identifying a Risk Signature of Methylation-Driven Genes as a Predictor of Survival Outcome for Colon Cancer Patients

Aberrant expression of gene is driven by its promoter methylation and is the key molecular basis of carcinogenic processes. This study aimed at identifying a risk signature of methylation-driven (MD) genes and evaluating its prognostic value for colon cancer (CC) patients. The expression profiles of methylation and mRNA in CC samples were obtained from the TCGA database, and the MethylMix algorithm was used to identify MD genes. The relationships between their expression levels and overall survival (OS) of CC patients were analyzed, and a prognostic signature of MD genes was established. The risk score of gene signature was calculated, and the median was used to divide all patients into high (H) and low (L) risk groups. The prognostic value of gene signature was tested by the TCGA cohort and an independent validation cohort (GSE17538 dataset). In total, 69 MD genes were identified, and 7 were associated with OS of CC patients. Ultimately, 4 (TWIST1, LDOC1, EPHX3, and STC2) were screened out to establish a risk signature. The H-risk patients (>0.923) had a worse OS than L-risk patients (≤0.923) in both the TCGA (5-year cumulative survival: 52.9% vs 72.0%, P=0.005) and GSE17538 cohort (49.4% vs 69.3%, P=0.004). The AUC values of MD genes signature for the prediction of 3- and 5-year OS were 0.648 and 0.643 in the TCGA dataset and 0.634 and 0.624 in the GSE17538 dataset, respectively. The risk signature of four MD genes was identified as an independent predictor of OS for CC patients (HR for TCGA dataset: 2.071, 95% CI=1.196-3.586, P=0.009; HR for GSE17538 dataset: 2.021, 95% CI=1.290-3.166, P=0.002). The risk signature of four MD genes might be a useful prognostic tool and help doctors improve the clinical management of CC patients.

Human pan-cancer analysis of the predictive biomarker for the CDKN3

BACKGROUND

Cell cycle protein-dependent kinase inhibitor protein 3 (CDKN3), as a member of the protein kinase family, has been demonstrated to exhibit oncogenic properties in several tumors. However, there are no pan-carcinogenic analyses for CDKN3.

METHODS

Using bioinformatics tools such as The Cancer Genome Atlas (TCGA) and the UCSC Xena database, a comprehensive pan-cancer analysis of CDKN3 was conducted. The inverstigation encompassed the examination of CDKN3 function actoss 33 different kinds of tumors, as well as the exploration of gene expressions, survival prognosis status, clinical significance, DNA methylation, immune infiltration, and associated signal pathways.

RESULTS

CDKN3 was significantly upregulated in most of tumors and correlated with overall survival (OS) of patients. Methylation levels of CDKN3 differed significantly between tumors and normal tissues. In addition, infiltration of CD4 + T cells, cancer-associated fibroblasts, macrophages, and endothelial cells were associated with CDKN3 expression in various tumors. Mechanistically, CDKN3 was associated with P53, PI3K-AKT, cell cycle checkpoints, mitotic spindle checkpoint, and chromosome maintenance.

CONCLUSION

Our pan-cancer analysis conducted in the study provides a comprehensive understanding of the involvement of CDKN3 gene in tumorigenesis. The findings suggest that targeting CDKN3 may potentially lead to novel therapeutic strategies for the treatment of tumors.

A Prognostic Methylation-Driven Two-Gene Signature in Medulloblastoma

Medulloblastoma (MB) is one of the most common pediatric brain tumors and it is estimated that one-third of patients will not achieve long-term survival. Conventional prognostic parameters have limited and unreliable correlations with MB outcome, presenting a major challenge for patients' clinical improvement. Acknowledging this issue, our aim was to build a gene signature and evaluate its potential as a new prognostic model for patients with the disease. In this study, we used six datasets totaling 1679 samples including RNA gene expression and DNA methylation data from primary MB as well as control samples from healthy cerebellum. We identified methylation-driven genes (MDGs) in MB, genes whose expression is correlated with their methylation. We employed LASSO regression, incorporating the MDGs as a parameter to develop the prognostic model. Through this approach, we derived a two-gene signature (GS-2) of candidate prognostic biomarkers for MB (CEMIP and NCBP3). Using a risk score model, we confirmed the GS-2 impact on overall survival (OS) with Kaplan-Meier analysis. We evaluated its robustness and accuracy with receiver operating characteristic curves predicting OS at 1, 3, and 5 years in multiple independent datasets. The GS-2 showed highly significant results as an independent prognostic biomarker compared to traditional MB markers. The methylation-regulated GS-2 risk score model can effectively classify patients with MB into high and low-risk, reinforcing the importance of this epigenetic modification in the disease. Such genes stand out as promising prognostic biomarkers with potential application for MB treatment.

Identification and functional analysis of lactic acid metabolism-related differentially expressed genes in hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a malignant tumor with high morbidity and mortality rate that seriously threatens human health. We aimed to investigate the expression, prognostic value, and immune cell infiltration of lactic acid metabolism-related genes (LAMRGs) in HCC using bioinformatics. Methods: The HCC database (The Cancer Genome Atlas-Liver Hepatocellular Carcinoma) was downloaded from the Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) between normal and tumor groups were identified. The LAMRGs were obtained from literature and GeneCards and MSigDB databases. Lactic acid metabolism-related differentially expressed genes (LAMRDEGs) in HCC were screened from the DEGs and LAMRGs. Functional enrichment analyses of the screened LAMRDEGs were further conducted using Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Set Enrichment Analysis (GSEA). The genes were used in multivariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses to construct a prognostic model. Then, a protein-protein interaction network was constructed using STRING and CTD databases. Furthermore, the CIBERSORTx online database was used to assess the relationship between immune cell infiltration and hub genes. Results: Twenty-eight lactic acid metabolism-related differentially expressed genes (LAMRDEGs) were identified. The GO and KEGG analyses showed that the LAMRDEGs were related to the prognosis of HCC. The GSEA indicated that the LAMRDEGs were significantly enriched in tumor related pathways. In the multivariate Cox regression analysis, 14 key genes (E2F1, SERPINE1, GYS2, SPP1, PCK1, CCNB1, CYP2C9, IGFBP3, KDM8, RCAN1, ALPL, FBP1, NQO1, and LCAT) were found to be independent prognostic factors of HCC. Finally, the LASSO and Cox regression analyses showed that six key genes (SERPINE1, SPP1, CCNB1, CYP2C9, NQO1, and LCAT) were associated with HCC prognosis. Moreover, the correlation analyses revealed that the expression of the six key genes were associated with immune infiltrates of HCC. Conclusion: The LAMRDEGs can predict the prognosis and may be associated with immune cells infiltration in patients with HCC. These genes might be the promising biomarkers for the prognosis and treatment of HCC.

Extracellular cancer‑associated fibroblasts: A novel subgroup in the cervical cancer microenvironment that exhibits tumor‑promoting roles and prognosis biomarker functions

Tumor invasion and metastasis are the processes that primarily cause adverse outcomes in patients with cervical cancer. Cancer-associated fibroblasts (CAFs), which participate in cancer progression and metastasis, are novel targets for the treatment of tumors. The present study aimed to assess the heterogeneity of CAFs in the cervical cancer microenvironment through single-cell RNA sequencing. After collecting five cervical cancer samples and obtaining the CAF-associated gene sets, the CAFs in the cervical cancer microenvironment were divided into myofibroblastic CAFs and extracellular (ec)CAFs. The ecCAFs appeared with more robust pro-tumorigenic effects than myCAFs according to enrichment analysis. Subsequently, through combining the ecCAF hub genes and bulk gene expression data for cervical cancer obtained from The Cancer Genome Atlas and Gene Ontology databases, univariate Cox regression and least absolute shrinkage and selection operator analyses were performed to establish a CAF-associated risk signature for patients with cancer. The established risk signature demonstrated a stable and strong prognostic capability in both the training and validation cohorts. Subsequently, the association between the risk signature and clinical data was evaluated, and a nomogram to facilitate clinical application was established. The risk score was demonstrated to be associated with both the tumor immune microenvironment and the therapeutic responses. Moreover, the signature also has predictive value for the prognosis of head and neck squamous cell carcinoma, and bladder urothelial carcinoma, which were also associated with human papillomavirus infection. In conclusion, the present study assessed the heterogeneity of CAFs in the cervical cancer microenvironment, and a subgroup of CAFs that may be closely associated with tumor progression was defined. Moreover, a signature based on the hub genes of ecCAFs was shown to have biomarker functionality in terms of predicting survival rates, and therefore this CAF subgroup may become a therapeutic target for cervical cancer in the future.

Identification of ASF1A and HJURP by global H3-H4 histone chaperone analysis as a prognostic two-gene model in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a malignancy with poor prognosis. Abnormal expression of H3-H4 histone chaperones has been identified in many cancers and holds promise as a biomarker for diagnosis and prognosis. However, systemic analysis of H3-H4 histone chaperones in HCC is still lacking. Here, we investigated the expression of 19 known H3-H4 histone chaperones in HCC. Integrated analysis of multiple public databases indicated that these chaperones are highly expressed in HCC tumor tissues, which was further verified by immunohistochemistry (IHC) staining in offline samples. Additionally, survival analysis suggested that HCC patients with upregulated H3-H4 histone chaperones have poor prognosis. Using LASSO and Cox regression, we constructed a two-gene model (ASF1A, HJURP) that accurately predicts prognosis in ICGC-LIRI and GEO HCC data, which was further validated in HCC tissue microarrays with follow-up information. GSEA revealed that HCCs in the high-risk group were associated with enhanced cell cycle progression and DNA replication. Intriguingly, HCCs in the high-risk group exhibited increased immune infiltration and sensitivity to immune checkpoint therapy (ICT). In summary, H3-H4 histone chaperones play a critical role in HCC progression, and the two-gene (ASF1A, HJURP) risk model is effective for predicting survival outcomes and sensitivity to immunotherapy for HCC patients.

Developing risk models and subtypes of autophagy-associated LncRNAs for enhanced prognostic prediction and precision in therapeutic approaches for liver cancer patients

Background

Limited research has been conducted on the influence of autophagy-associated long non-coding RNAs (ARLncRNAs) on the prognosis of hepatocellular carcinoma (HCC).

Methods

We analyzed 371 HCC samples from TCGA, identifying expression networks of ARLncRNAs using autophagy-related genes. Screening for prognostically relevant ARLncRNAs involved univariate Cox regression, Lasso regression, and multivariate Cox regression. A Nomogram was further employed to assess the reliability of Riskscore, calculated from the signatures of screened ARLncRNAs, in predicting outcomes. Additionally, we compared drug sensitivities in patient groups with differing risk levels and investigated potential biological pathways through enrichment analysis, using consensus clustering to identify subgroups related to ARLncRNAs.

Results

The screening process identified 27 ARLncRNAs, with 13 being associated with HCC prognosis. Consequently, a set of signatures comprising 8 ARLncRNAs was successfully constructed as independent prognostic factors for HCC. Patients in the high-risk group showed very poor prognoses in most clinical categories. The Riskscore was closely related to immune cell scores, such as macrophages, and the DEGs between different groups were implicated in metabolism, cell cycle, and mitotic processes. Notably, high-risk group patients demonstrated a significantly lower IC50 for Paclitaxel, suggesting that Paclitaxel could be an ideal treatment for those at elevated risk for HCC. We further identified C2 as the Paclitaxel subtype, where patients exhibited higher Riskscores, reduced survival rates, and more severe clinical progression.

Conclusion

The 8 signatures based on ARLncRNAs present novel targets for prognostic prediction in HCC. The drug candidate Paclitaxel may effectively treat HCC by impacting ARLncRNAs expression. With the identification of ARLncRNAs-related isoforms, these results provide valuable insights for clinical exploration of autophagy mechanisms in HCC pathogenesis and offer potential avenues for precision medicine.

Elucidating hepatocellular carcinoma progression: a novel prognostic miRNA-mRNA network and signature analysis

There is increasing evidence that miRNAs play an important role in the prognosis of HCC. There is currently a lack of acknowledged models that accurately predict patient prognosis. The aim of this study is to create a miRNA-based model to precisely forecast a patient's prognosis and a miRNA-mRNA network to investigate the function of a targeted mRNA. TCGA miRNA dataset and survival data of HCC patients were downloaded for differential analysis. The outcomes of variance analysis were subjected to univariate and multivariate Cox regression analyses and LASSO analysis. We constructed and visualized prognosis-related models and subsequently used violin plots to probe the function of miRNAs in tumor cells. We predicted the target mRNAs added those to the String database, built PPI protein interaction networks, and screened those mRNA using Cytoscape. The hub mRNA was subjected to GO and KEGG analysis to determine its biological role. Six of them were associated with prognosis: hsa-miR-139-3p, hsa-miR-139-5p, hsa-miR-101-3p, hsa-miR-30d-5p, hsa-miR-5003-3p, and hsa-miR-6844. The prognostic model was highly predictive and consistently performs, with the C index exceeding 0.7 after 1, 3, and 5 years. The model estimated significant differences in the Kaplan-Meier plotter and the model could predict patient prognosis independently of clinical indicators. A relatively stable miRNA prognostic model for HCC patients was constructed, and the model was highly accurate in predicting patients with good stability over 5 years. The miRNA-mRNA network was constructed to explore the function of mRNA.

Targeting host-specific metabolic pathways-opportunities and challenges for anti-infective therapy

Microorganisms can takeover critical metabolic pathways in host cells to fuel their replication. This interaction provides an opportunity to target host metabolic pathways, in addition to the pathogen-specific ones, in the development of antimicrobials. Host-directed therapy (HDT) is an emerging strategy of anti-infective therapy, which targets host cell metabolism utilized by facultative and obligate intracellular pathogens for entry, replication, egress or persistence of infected host cells. This review provides an overview of the host lipid metabolism and links it to the challenges in the development of HDTs for viral and bacterial infections, where pathogens are using important for the host lipid enzymes, or producing their own analogous of lecithin-cholesterol acyltransferase (LCAT) and lipoprotein lipase (LPL) thus interfering with the human host's lipid metabolism.

Construction of a prognostic prediction model in liver cancer based on genes involved in integrin cell surface interactions pathway by multi-omics screening

Background: Liver cancer is a common malignant tumor with an increasing incidence in recent years. We aimed to develop a model by integrating clinical information and multi-omics profiles of genes to predict survival of patients with liver cancer. Methods: The multi-omics data were integrated to identify liver cancer survival-associated signal pathways. Then, a prognostic risk score model was established based on key genes in a specific pathway, followed by the analysis of the relationship between the risk score and clinical features as well as molecular and immunologic characterization of the key genes included in the prediction model. The function experiments were performed to further elucidate the undergoing molecular mechanism. Results: Totally, 4 pathways associated with liver cancer patients' survival were identified. In the pathway of integrin cell surface interactions, low expression of COMP and SPP1, and low CNVs level of COL4A2 and ITGAV were significantly related to prognosis. Based on above 4 genes, the risk score model for prognosis was established. Risk score, ITGAV and SPP1 were the most significantly positively related to activated dendritic cell. COL4A2 and COMP were the most significantly positively associated with Type 1 T helper cell and regulatory T cell, respectively. The nomogram (involved T stage and risk score) may better predict short-term survival. The cell assay showed that overexpression of ITGAV promoted tumorigenesis. Conclusion: The risk score model constructed with four genes (COMP, SPP1, COL4A2, and ITGAV) may be used to predict survival in liver cancer patients.

Disulfidptosis-related signature predicts prognosis and characterizes the immune microenvironment in hepatocellular carcinoma

BACKGROUND

Disulfidptosis is a type of programmed cell death caused by excessive cysteine-induced disulfide bond denaturation leading to actin collapse. Liver cancer has a poor prognosis and requires more effective intervention strategies. Currently, the prognostic and therapeutic value of disulfidptosis in liver cancer is not clear.

METHODS

We investigated the features of 16 disulfidptosis-related genes (DRGs) of HCC patients in the TCGA and classified the patients into two disulfidptosis pattern clusters by consensus clustering analysis. Then, we constructed a prognostic model using LASSO Cox regression. Next, the microenvironment and drug sensitivity were evaluated. Finally, we used qPCR and functional analysis to verify the reliability of hub DRGs.

RESULTS

Most of the DRGs showed significantly higher expression in cancer tissues than in adjacent tissues. Our prognostic model, the DRG score, can well predict the survival of HCC patients. There were significant differences in survival, features of the microenvironment, effects of immunotherapy, and drug sensitivity between the high- and low-DRG score groups. Ultimately, we demonstrated that a few hub DRGs have differential mRNA expression between liver cancer cells and normal cells and that the protective gene LCAT can inhibit liver cancer metastasis in vitro.

CONCLUSION

We established a novel risk model based on DRG scores to predict HCC patient prognosis, drug sensitivity and immunotherapy efficacy, which provides new insight into the relationship between disulfidptosis and HCC and provides valuable assistance for the personalized treatment of HCC.

Analyzing aberrant DNA methylation in colorectal cancer uncovered intangible heterogeneity of gene effects in the survival time of patients

Colorectal cancer (CRC) involves epigenetic alterations. Irregular gene-methylation alteration causes and advances CRC tumor growth. Detecting differentially methylated genes (DMGs) in CRC and patient survival time paves the way to early cancer detection and prognosis. However, CRC data including survival times are heterogeneous. Almost all studies tend to ignore the heterogeneity of DMG effects on survival. To this end, we utilized a sparse estimation method in the finite mixture of accelerated failure time (AFT) regression models to capture such heterogeneity. We analyzed a dataset of CRC and normal colon tissues and identified 3406 DMGs. Analysis of overlapped DMGs with several Gene Expression Omnibus datasets led to 917 hypo- and 654 hyper-methylated DMGs. CRC pathways were revealed via gene ontology enrichment. Hub genes were selected based on Protein-Protein-Interaction network including SEMA7A, GATA4, LHX2, SOST, and CTLA4, regulating the Wnt signaling pathway. The relationship between identified DMGs/hub genes and patient survival time uncovered a two-component mixture of AFT regression model. The genes NMNAT2, ZFP42, NPAS2, MYLK3, NUDT13, KIRREL3, and FKBP6 and hub genes SOST, NFATC1, and TLE4 were associated with survival time in the most aggressive form of the disease that can serve as potential diagnostic targets for early CRC detection.

miR-4270 suppresses hepatocellular carcinoma progression by inhibiting DNMT3A-mediated methylation of HGFAC promoter

Background

miR-4270 is a regulatory factor has been linked with the progression of various cancers, such as nasopharyngeal carcinoma, hepatocellular carcinoma (HCC), and gastric cancer. However, the underlying mechanisms through which miR-4270 modulates HCC development are not fully understood.

Methods

miR-4270 expression levels were analyzed in various HCC cell lines and tissue samples. An online bioinformatics tool was then utilized to predict the miR-4270 target gene. The binding relationship between miR-4270 and its target gene DNMT3A was verified using dual-luciferase reporter and Ago2-RIP assays. Then, co-immunoprecipitation (Co-IP) and chromatin immunoprecipitation (ChIP) assays were conducted to investigate the association between DNMT3A and the hepatocyte growth factor activator (HGFAC) promoter region. To assess the methylation level of the HGFAC promoter, methylation-specific PCR (MSP) was employed. Furthermore, rescue analyses were carried out to evaluate the functional relevance of miR-4270 and HGFAC in the modulation of the malignant properties of HCC cells. Finally, HepG2 cells overexpressing miR-4270 were subcutaneously injected into nude mice to estimate the impact of miR-4270 on the xenograft tumor growth of HCC.

Results

A substantial miR-4270 downregulation was revealed in HCC patient samples and cell lines. miR-4270 upregulation suppressed both cell proliferation and invasion while promoting apoptosis. At the molecular level, miR-4270 was found to bind to the 3'untranslated region (3'UTR) of DNMT3A, thereby inhibiting DNMT3A-mediated methylation of the HGFAC promoter. Functional assays indicated that inhibition of miR-4270 stimulated HCC cell growth, an effect counteracted by overexpression of HGFAC. In vivo assays further verified that miR-4270 effectively suppressed the progression of HCC xenograft tumors.

Conclusions

miR-4270 was found to mitigate the malignant characteristics of HCC by inhibiting DNMT3A-mediated methylation of the HGFAC promoter, suggesting a potential therapeutic avenue for the management of HCC.

Development of a prognostic model for anoikis and identifies hub genes in hepatocellular carcinoma

Considering the high fatality of hepatocellular carcinoma (HCC), current prognostic systems are insufficient to accurately forecast HCC patients' outcomes. In our study, nine anoikis‑related genes (PTRH2, ITGAV, ANXA5, BIRC5, BDNF, BSG, DAP3, SKP2, and EGF) were determined to establish a risk scoring model using LASSO regression, which could be validated in ICGC dataset. Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curve analysis confirmed the risk score possessed an accurate predictive value for the prognosis of HCC patients. The high-risk group showed a higher infiltration of aDCs, macrophages, T-follicular helper cells, and Th2 cells. Besides, PD-L1 was significantly higher in the high-risk group compared to the low-risk group. Several anoikis‑related genes, such as ANX5, ITGAV, BDNF and SKP2, were associated with drug sensitivity in HCC. Finally, we identified BIRC5 and SKP2 as hub genes among the nine model genes using WGCNA analysis. BIRC5 and SKP2 were over-expressed in HCC tissues, and their over-expression was associated with poor prognosis, no matter in our cohort by immunohistochemical staining or in the TCGA cohort by mRNA-Seq. In our cohort, BIRC5 expression was highly associated with the T stage, pathologic stage, histologic grade and AFP of HCC patients. In general, our anoikis-related risk model can enhance the ability to predict the survival outcomes of HCC patients and provide a feasible therapeutic strategy for immunotherapy and drug resistance in HCC. BIRC5 and SKP2 are hub genes of anoikis‑related genes in HCC.

A robust panel based on genomic methylation sites for recurrence-free survival in early hepatocellular carcinoma

Purpose

Altered gene methylation precedes altered gene expression and the onset of disease. This study aimed to develop a potential model for predicting recurrence of early to mid-stage hepatocellular carcinoma (HCC) using methylation loci.

Methods

We used data from early to mid-stage HCC patients (TNM I-II) in the TCGA-LIHC dataset and lasso-cox regression model to identify an 18-DNA methylation site panel from which to calculate the riskScore of patients. The correlation of high/low riskScore with recurrence-free survival (RFS) and immune microenvironment in HCC patients was analyzed by bioinformatics. It was also validated in the GSE56588 dataset and the final dynamic nomogram was constructed.

Results

The results showed that riskScore was significantly correlated with RFS in HCC patients. The differential mutated genes between the two groups of HCC patients with high/low riskScore were mainly enriched in the TP53 signaling pathway. The immune microenvironment was better in HCC patients in the low-riskScore group compared to the high-riskScore group. This was validated in the GSE56588 dataset. Based on the subgroup stratification analysis of the relationship between high/low riskScore and RFS, as well as univariate and multivariate cox analyses, the riskScore was found to be independent of clinical indicators. We found that riskScore, vascular invasion and cirrhosis status could effectively differentiate RFS in HCC patients, and we also constructed prediction model based on these three factors. The model we constructed were validated in the TCGA-LIHC database and a web calculator was built for clinical use.

Conclusion

The methylation riskScore is a predictor of RFS independent of clinical factors and can be used as a marker to predict recurrence in HCC patients.

Association analysis of hepatocellular carcinoma-related hub proteins and hub genes

PURPOSE

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. The occurrence and development of HCC are closely related to epigenetic modifications. Epigenetic modifications can regulate gene expression and related functions through DNA methylation. This paper presents an association analysis method of HCC-related hub proteins and hub genes.

EXPERIMENTAL DESIGN

Bioinformatics analysis of HCC-related DNA methylation data is carried out to clarify the molecular mechanism of HCC-related genes and to find hub genes (genes with more connections in the network) by constructing in the gene interaction network. This paper proposes an accurate prediction method of protein-protein interaction (PPI) based on deep learning model DeepSG2PPI. The trained DeepSG2PPI model predicts the interaction relationship between the synthetic proteins regulated by HCC-related genes.

RESULTS

This paper finds that four genes are the intersection of hub genes and hub proteins. The four genes are: FBL, CCNB2, ALDH18A1, and RPLP0. The association of RPLP0 gene with HCC is a new finding of this study. RPLP0 is expected to become a new biomarker for the treatment, diagnosis, and prognosis of HCC. The four proteins corresponding to the four genes are: ENSP00000221801, ENSP00000288207, ENSP00000360268, and ENSP00000449328.

CONCLUSIONS AND CLINICAL RELEVANCE

The association between the hub genes with the hub proteins is analyzed. The mutual verification of the hub genes and the hub proteins can obtain more credible HCC-related genes and proteins, which is helpful for the diagnosis, treatment, and drug development of HCC.

Hypoxia-based classification and prognostic signature for clinical management of hepatocellular carcinoma

OBJECTIVE

Intratumoral hypoxia is an essential feature of hepatocellular carcinoma (HCC). Herein, we investigated the hypoxia-based heterogeneity and relevant clinical implication in HCC.

METHODS

Three HCC cohorts: TCGA-LIHC, LICA-FR, and LIRI-JP were retrospectively gathered. Consensus clustering analysis was utilized for hypoxia-based classification based upon transcriptome of hypoxia genes. Through LASSO algorithm, a hypoxia-relevant prognostic signature was built. Immunotherapeutic response was inferred through analyzing immune checkpoints, T cell inflamed score, TIDE score, and TMB score. RNF145 expression was measured in normoxic or hypoxic HCC cells. In RNF145-knockout cells, CCK-8, TUNEL, and scratch tests were implemented.

RESULTS

HCC patients were classified into two hypoxia subtypes, with more advanced stages and poorer prognosis in cluster2 than cluster1. The heterogeneity in tumor infiltrating immune cells and genetic mutation was found between subtypes. The hypoxia-relevant prognostic model was proposed, composed of ANLN, CBX2, DLGAP5, FBLN2, FTCD, HMOX1, IGLV1-44, IL33, LCAT, LPCAT1, MKI67, PFN2, RNF145, S100A9, and SPP1). It was predicted that high-risk patients presented worse prognosis with an independent and reliable manner. Based upon high expression of immune checkpoints (CD209, CTLA4, HAVCR2, SIRPA, TNFRSF18, TNFRSF4, and TNFRSF9), high T cell inflamed score, low TIDE score and high TMB score, high-risk patients might respond to immunotherapy. Experimental validation showed that RNF145 was upregulated in hypoxic HCC cells, RNF145 knockdown attenuated proliferation and migration, but aggravated apoptosis in HCC cells.

CONCLUSION

Altogether, the hypoxia-based classification and prognostic signature might be useful for prognostication and guiding treatment of HCC.

Association of Plasma Vitamins and Carotenoids, DNA Methylation of LCAT, and Risk of Age-Related Macular Degeneration

Dysregulation of lipid metabolism has been implicated in age-related macular degeneration (AMD), the leading cause of blindness among the elderly. Lecithin cholesterol acyltransferase (LCAT) is an important enzyme responsible for lipid metabolism, which could be regulated by DNA methylation during the development of various age-related diseases. This study aimed to assess the association between LCAT DNA methylation and the risk of AMD, and to examine whether plasma vitamin and carotenoid concentrations modified this association. A total of 126 cases of AMD and 174 controls were included in the present analysis. LCAT DNA methylation was detected by quantitative real-time methylation-1specific PCR (qMSP). Circulating vitamins and carotenoids were measured using reversed-phase high-performance liquid chromatography (RP-HPLC). DNA methylation of LCAT was significantly higher in patients with AMD than those in the control subjects. After multivariable adjustment, participants in the highest tertile of LCAT DNA methylation had a 5.37-fold higher risk (95% CI: 2.56, 11.28) of AMD compared with those in the lowest tertile. Each standard deviation (SD) increment of LCAT DNA methylation was associated with a 2.23-fold (95% CI: 1.58, 3.13) increased risk of AMD. There was a J-shaped association between LCAT DNA methylation and AMD risk (P_{non-linearity} = 0.03). Higher concentrations of plasma retinol and β-cryptoxanthin were significantly associated with decreased levels of LCAT DNA methylation, with the multivariate-adjusted β coefficient being -0.05 (95% CI: -0.08, -0.01) and -0.25 (95% CI: -0.42, -0.08), respectively. In joint analyses of LCAT DNA methylation and plasma vitamin and carotenoid concentrations, the inverse association between increased LCAT DNA methylation and AMD risk was more pronounced among participants who had a lower concentration of plasma retinol and β-cryptoxanthin. These findings highlight the importance of comprehensively assessing LCAT DNA methylation and increasing vitamin and carotenoid status for the prevention of AMD.

A transcriptomic intratumour heterogeneity-free signature overcomes sampling bias in prognostic risk classification for hepatocellular carcinoma

Background & Aims

Intratumour heterogeneity (ITH) fosters the vulnerability of RNA expression-based biomarkers derived from a single biopsy to tumour sampling bias, and is regarded as an unaddressed confounding factor for patient precision stratification using molecular biomarkers. This study aimed to identify an ITH-free predictive biomarker in hepatocellular carcinoma (HCC).

Methods

We interrogated the confounding effect of ITH on performance of molecular biomarkers and quantified transcriptomic heterogeneity utilising three multiregional HCC transcriptome datasets involving 142 tumoural regions from 30 patients. A de novo strategy based on the heterogeneity metrics was devised to develop a surveillant biomarker (a utility gadget using RNA; AUGUR) using three datasets involving 715 liver samples from 509 patients with HCC. The performance of AUGUR was assessed in seven cross-platform HCC cohorts that encompassed 1,206 patients.

Results

An average discordance rate of 39.9% at the level of individual patients was observed applying 13 published prognostic signatures to classify tumour regions. We partitioned genes into four heterogeneity quadrants, from which we developed and validated a reproducible robust ITH-free expression signature AUGUR that showed significant positive associations with adverse features of HCC. High AUGUR risk increased the risk of disease progression and mortality independent of established clinicopathological indices, which maintained concordance across seven cohorts. Moreover, AUGUR compared favourably to the discriminative ability, prognostic accuracy, and patient risk concordant rates of 13 published signatures. Finally, a well-calibrated predictive nomogram integrating AUGUR and tumour-node-metastasis (TNM) stage was established, which generated a numerical probability of mortality.

Conclusions

We constructed and validated an ITH-free AUGUR and nomogram that overcame sampling bias and provided reliable prognostic information for patients with HCC.

Impact and Implications

Intratumour heterogeneity (ITH) is prevalent in hepatocellular carcinoma (HCC), and is regarded as an unaddressed confounding factor for biomarker design and application. We examined the confounding effect of transcriptomic ITH in patient risk classification, and found existing molecular biomarkers of HCC were vulnerable to tumour sampling bias. We then developed an ITH-free expression biomarker (a utility gadget using RNA; AUGUR) that overcame clinical sampling bias and maintained prognostic reproducibility and generalisability across multiple HCC patient cohorts from different commercial platforms. Furthermore, we established and validated a well-calibrated nomogram based on AUGUR and tumour-node-metastasis (TNM) stage that provided an individualised prognostic information for patients with HCC.

The combination therapy of isomucronulatol 7-O-beta-glucoside (IMG) and CEP-9722 targeting ferroptosis-related biomarkers in non-small cell lung cancer (NSCLC)

BACKGROUND

NSCLC is a malignant tumor with a high incidence. Ferroptosis presents an essential function in regulating carcinogenesis and tumor progression. However, the ferroptosis-associated prognostic model based on single-cell sequencing of NSCLC remains unexplored. Our study aims to establish a potential predictive model for NSCLC patients and provide available targeted drugs for clinical treatment.

METHODS

The data on NSCLC patients were collected from TCGA and GEO databases to analyze their gene expression profiles. ConsensusCluster was adopted to divide the patients into different groups based on ferroptosis-related genes. Then, the univariable Cox and LASSO analyses were applied to data analysis and model establishment. Single-cell analysis was used to explore the risk score genes in different cell populations and states. The protein levels of these genes were also investigated through the HPA database. Drug sensitivity was evaluated in CellMiner database. CCK8 and colony formation assays were performed to validate potential drugs' effects on lung cancer cell lines.

RESULTS

A ferroptosis-related prognostic model involving 14 genes in NSCLC patients was established. The risk score model was developed in training set GSE31210 and validated in the test set TCGA. The low-risk score group showed a better prognosis than the high-risk score group. The single-cell analysis revealed that the risk score genes were mainly derived from lung tumor cells. Most risk score genes were more highly expressed in tumor tissue than in normal tissue, according to the HPA database. Besides, these genes were associated with 106 drugs in CellMiner database. Finally, the drug effects on NSCLC cell growth were evaluated by cck8 and colony formation.

CONCLUSIONS

We identified an effective ferroptosis-related prognostic model based on single-cell sequencing. The potential prediction model is devoted to exploring clinical therapeutic targets for NSCLC.

Transcriptional and Epigenetic Alterations in the Progression of Non-Alcoholic Fatty Liver Disease and Biomarkers Helping to Diagnose Non-Alcoholic Steatohepatitis

Non-alcoholic fatty liver disease (NAFLD) encompasses a broad spectrum of conditions from simple steatosis (non-alcoholic fatty liver (NAFL)) to non-alcoholic steatohepatitis (NASH), and its global prevalence continues to rise. NASH, the progressive form of NAFLD, has higher risks of liver and non-liver related adverse outcomes compared with those patients with NAFL alone. Therefore, the present study aimed to explore the mechanisms in the progression of NAFLD and to develop a model to diagnose NASH based on the transcriptome and epigenome. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) among the three groups (normal, NAFL, and NASH) were identified, and the functional analysis revealed that the development of NAFLD was primarily related to the oxidoreductase-related activity, PPAR signaling pathway, tight junction, and pathogenic Escherichia coli infection. The logistic regression (LR) model, consisting of ApoF, THOP1, and BICC1, outperformed the other five models. With the highest AUC (0.8819, 95%CI: 0.8128-0.9511) and a sensitivity of 97.87%, as well as a specificity of 64.71%, the LR model was determined as the diagnostic model, which can differentiate NASH from NAFL. In conclusion, several potential mechanisms were screened out based on the transcriptome and epigenome, and a diagnostic model was built to help patient stratification for NAFLD populations.

Multi-region sampling with paired sample sequencing analyses reveals sub-groups of patients with novel patient-specific dysregulation in Hepatocellular Carcinoma

BACKGROUND

Conventional differential expression (DE) testing compares the grouped mean value of tumour samples to the grouped mean value of the normal samples, and may miss out dysregulated genes in small subgroup of patients. This is especially so for highly heterogeneous cancer like Hepatocellular Carcinoma (HCC).

METHODS

Using multi-region sampled RNA-seq data of 90 patients, we performed patient-specific differential expression testing, together with the patients' matched adjacent normal samples.

RESULTS

Comparing the results from conventional DE analysis and patient-specific DE analyses, we show that the conventional DE analysis omits some genes due to high inter-individual variability present in both tumour and normal tissues. Dysregulated genes shared in small subgroup of patients were useful in stratifying patients, and presented differential prognosis. We also showed that the target genes of some of the current targeted agents used in HCC exhibited highly individualistic dysregulation pattern, which may explain the poor response rate.

DISCUSSION/CONCLUSION

Our results highlight the importance of identifying patient-specific DE genes, with its potential to provide clinically valuable insights into patient subgroups for applications in precision medicine.

Development and validation of a genomic instability-related lncRNA prognostic model for hepatocellular carcinoma

Genomic instability is a characteristic of tumors, and recent studies have shown that it is related to a poor prognosis of multiple cancers. Long non-coding RNAs (lncRNAs) have become a research hotspot in recent years, and many unknown biological functions are being explored. For example, some lncRNAs play a critical role in the initiation and progression of multiple cancer types by modulating genomic instability. However, the role of genomic instability-related lncRNAs in liver cancer remains unclear. Therefore, we screened genomic instability-related lncRNAs by combining somatic mutation data and RNA-Seq data in The Cancer Genome Atlas (TCGA) database. We established a genomic instability-related lncRNA model (GLncM) involving ZFPM2-AS1 and MIR210HG to predict the hepatocellular carcinoma (HCC) prognosis and further explore the clinical significance of these lncRNAs, and the robustness of the model was validated in the verification set. Thereafter, we calculated the immune score for each patient and explored the relationship between genome instability and the immune microenvironment. The analysis indicated that this model was better than the immune microenvironment in predicting the prognosis of HCC patients, suggesting that the GLncM may be an effective indicator of HCC prognosis and providing a new direction and strategy for estimating the prognosis of HCC patients.

A novel prognostic model based on six methylation-driven genes predicts overall survival for patients with clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a lethal urological malignancy. DNA methylation is involved in the regulation of ccRCC occurrence and progression. This study aimed to establish a prognostic model based on DNA methylation to predict the overall survival (OS) of patients with ccRCC. To create this model, we used the transcriptome and DNA methylation data of patients with ccRCC from The Cancer Genome Atlas (TCGA) database. We then used the MethylMix R package to identify methylation-driven genes, and LASSO regression and multivariate Cox regression analyses established the prognostic risk model, from which we derived risk scores. We incorporated these risk scores and clinical parameters to develop a prognostic nomogram to predict 3-, 5-, and 7-year overall survival, and its predictive power was validated using the ArrayExpress cohort. These analyses identified six methylation-driven genes (SAA1, FUT6, SPATA18, SHROOM3, AJAP1, and NPEPL1) that produced risk scores, which were sorted into high- and low-risk patient groups. These two groups differed in nomogram-predicted prognosis, the extent of immune cell infiltration, tumor mutational burden, and expected response to additional therapies. In conclusion, we established a nomogram based on six DNA methylation-driven genes with excellent accuracy for prognostic prediction in ccRCC patients. This nomogram model might provide novel insights into the epigenetic mechanism and individualized treatment of ccRCC.

Promotor methylation status of MAPK4 is a novel epigenetic biomarker for prognosis of recurrence in patients with thymic epithelial tumors

BACKGROUND

The prognosis of thymic epithelial tumors (TETs) currently relies on the commonly adopted WHO classification and Masaoka staging system, which cannot reflect the undefined biological behaviors limiting them as prognostic factors.

METHODS

In this study, we first identified 40 genes and 179 genes, respectively that were epigenetically upregulated and silenced, corresponding to a total of 509 functionally methylated CpG sites between thymomas and thymic carcinomas by using the TCGA dataset.

RESULTS

The methylation β-values of cg20068620 in MAPK4 and cg18770944 in USP51 were significantly associated with recurrence-free survival (RFS). In the independent validation cohort, only WHO classification and methylation β-values of cg20068620 in MAPK4 were independent prognostic factors for RFS in Chinese patients with TETs. A linear weighted model including these two factors was used to calculate the recurrence risk score (RRS). Time-dependent ROC curve analysis revealed that RRS was overwhelmingly superior to WHO classification for predicting 3-, 5-, and 10-year RFS and Masaoka stage for 3- and 5-year RFS.

CONCLUSIONS

These results suggested that the methylation site cg20068620 in MAPK4 can improve the accuracy of the WHO classification alone regarding the prognostic value of TETs recurrence.

Tumor-Promoting Actions of HNRNP A1 in HCC Are Associated with Cell Cycle, Mitochondrial Dynamics, and Necroptosis

Hepatocellular carcinoma (HCC) is one of the most frequent malignancies in the world. Although increasing evidence supports the role of heterogeneous ribonucleoprotein particle A1 (HNRNP A1) in tumor progression, the function of HNRNP A1 in HCC remains unclear. Here, we focused on the role of HNRNP A1 in the development of HCC. In this study, we found HNRNP A1 participates in many aspects of HCC, such as progression and prognosis. Our results showed that HNRNP A1 is upregulated in human HCC tissues and cell lines. High expression of HNRNP A1 can promote the proliferation, migration, and invasion in HCC cells and accelerate tumor progression in mice. Moreover, we found that HNRNP A1 prevents the senescence process of HCC cells. Knocking down of HNRNP A1 promotes the expression of P16^INK4, which arrests the cell cycle and then induces the senescence phenotype in HCC cells. Furthermore, we found that HNRNP A1 regulated necroptosis and mitochondrial dynamics. In summary, our study indicates that HNRNP A1 promotes the development of HCC, which suggests a potential therapeutic target for HCC.

DYSF promotes monocyte activation in atherosclerotic cardiovascular disease as a DNA methylation-driven gene

Dysferlin (DYSF) has drawn much attention due to its involvement in dysferlinopathy and was reported to affect monocyte functions in recent studies. However, the role of DYSF in the pathogenesis of atherosclerotic cardiovascular diseases (ASCVD) and the regulation mechanism of DYSF expression have not been fully studied. In this study, Gene Expression Omnibus (GEO) database and epigenome-wide association study (EWAS) literatures were searched to find the DNA methylation-driven genes (including DYSF) of ASCVD. The hub genes related to DYSF were also identified through weighted correlation network analysis (WGCNA). Regulation of DYSF expression through its promoter methylation status was verified using peripheral blood leucocytes (PBLs) from ASCVD patients and normal controls, and experiments on THP1 cells and Apoe^-/- mice. Similarly, the expressions of DYSF related hub genes, mainly contained SELL, STAT3 and TMX1, were also validated. DYSF functions were then evaluated by phagocytosis, transwell and adhesion assays in DYSF knock-down and overexpressed THP1 cells. The results showed that DYSF promoter hypermethylation up-regulated its expression in clinical samples, THP1 cells and Apoe^-/- mice, confirming DYSF as a DNA methylation-driven gene. The combination of DYSF expression and methylation status in PBLs had a considerable prediction value for ASCVD. Besides, DYSF could enhance the phagocytosis, migration and adhesion ability of THP1 cells. Among DYSF related hub genes, SELL was proven to be the downstream target of DYSF by wet experiments. In conclusion, DYSF promoter hypermethylation upregulated its expression and promoted monocytes activation, which further participated in the pathogenesis of ASCVD.

Evaluation of aliphatic acid metabolism in bladder cancer with the goal of guiding therapeutic treatment

Urothelial bladder cancer (BLCA) is a common internal malignancy with a poor prognosis. The re-programming of lipid metabolism is necessary for cancer cell growth, proliferation, angiogenesis and invasion. However, the role of aliphatic acid metabolism genes in bladder cancer patients has not been explored. The samples’ gene expression and clinicopathological data were obtained from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Univariate, multivariate, and LASSO Cox regression were used to develop a BLCA prognostic model. GSVA was used to assess function, whereas pRRophetic was used to assess chemotherapeutic drug sensitivity. The twelve-gene signature may define the tumor immune milieu, according to the risk score model. We compared the expression of aliphatic acid metabolism genes in malignant and non-cancerous tissues and chose 90 with a false discovery rate of 0.05 for The Cancer Genome Atlas cohort. The prognostic risk score model can effectively predict BLCA OS. A nomogram including age, clinical T stage, gender, grade, pathological stage, and clinical M stage was developed as an independent BLCA prognostic predictor. The halfmaximal inhibitory concentration (IC50) was used to assess chemotherapeutic medication response. Sorafenib and Pyrimethamine were used to treat patients with low risk scores more sensitively than patients with high risk scores. Immunotherapy candidates with CMS1 exhibited higher risk ratings. The aliphatic acid prognostic risk score model can assess metabolic trends. Clinical stage and molecular subtype may be used to categorize individuals using the risk score.With this new paradigm, future cancer treatment and immunotherapy may be tailored to the patient’s exact requirements.

High cathepsin A protein expression predicts poor prognosis and tumor recurrence of hepatocellular carcinoma patients after curative hepatectomy

Cathepsin A (CTSA) is overexpressed in various types of cancer and is linked to poor clinical outcomes. However, the clinical application of CTSA in HCC has not been explored. In this study, we examined the protein level of CTSA in the archived HCC samples from 161 patients by Immunohistochemistry (IHC). The high protein level of CTSA was significantly correlated to the poor clinicopathological parameters, such as TNM stage, serum AFP level, tumor differentiation, liver cirrhosis, Child-Pugh class, vascular invasion, tumor encapsulation, tumor recurrence, and patient death. In addition, multivariate Cox regression analysis indicated that high CTSA expression was an independent prognostic factor of OS and RFS. We also analyzed the area under the curve (AUC) of the time-dependent receiver operating characteristic (ROC) of CTSA expression for 1-, 3-, and 5-year OS and RFS prediction. Furthermore, we constructed a nomogram that exhibited excellent prediction performance, which was validated by the calibration curve and decision curve analysis. Together, our study demonstrated that CTSA protein level is strongly associated with poor clinical outcome of HCC patients and may be used as a potential diagnostic and prognostic biomarker in HCC.

Polymeric immunoglobulin receptor suppresses colorectal cancer through the AKT-FOXO3/4 axis by downregulating LAMB3 expression

Colorectal cancer (CRC) remains one of the most common malignancies worldwide and its mechanism is unclear. Polymeric immunoglobulin receptor (PIGR) which plays an important role in mucosal immunity is widely expressed in the mucosal epithelium and is dysregulated in different tumors. However, the role and underlying mechanisms of PIGR in CRC remain unclear. Here, we demonstrated that PIGR was hypermethylated and downregulated in our cohort (N = 272), and these features were associated with reduced overall survival in patients (HRmethylation 1.61, 95% CI [1.11-2.33]). These findings were validated by external TCGA and GEO data. Moreover, PIGR overexpression inhibits CRC cell malignant phenotypes in vitro and impedes CRC cells growth in male BALB/c nude mice. Mechanistically, PIGR physically associates with RE1 silencing transcription factor (REST) and blocks the transcription of laminin subunit beta 3 (LAMB3). Subsequently, the AKT-FOXO3/4 axis was suppressed by downregulated LAMB3. In the drug sensitive assay, PIGR-overexpressing cells were more sensitive to cisplatin and gemcitabine. Together, PIGR may serve as a powerful prognostic biomarker and putative tumor suppressor by suppressing the AKT-FOXO3/4 axis by downregulating LAMB3 in CRC. Our study may offer a novel therapeutic strategy for treating CRC patients who highly express PIGR with cisplatin and gemcitabine.

Integrated multi-omics analysis identifies ENY2 as a predictor of recurrence and a regulator of telomere maintenance in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and has a high recurrence rate. Accurate prediction of recurrence risk is urgently required for tailoring personalized treatment programs for individual HCC patients in advance. In this study, we analyzed a gene expression dataset from an HCC cohort with 247 samples and identified five genes including ENY2, GPAA1, NDUFA4L2, NEDD9, and NRP1 as the variables for the prediction of HCC recurrence, especially the early recurrence. The Cox model and risks score were validated in two public HCC cohorts (GSE76427 and The Cancer Genome Atlas (TCGA)) and one cohort from Huashan Hospital, which included a total of 641 samples. Moreover, the multivariate Cox regression analysis revealed that the risk score could serve as an independent prognostic factor in the prediction of HCC recurrence. In addition, we found that ENY2, GPAA1, and NDUFA4L2 were significantly upregulated in HCC of the two validation cohorts, and ENY2 had significantly higher expression levels than another four genes in malignant cells, suggesting that ENY2 might play key roles in malignant cells. The cell line analysis revealed that ENY2 could promote cell cycle progression, cell proliferation, migration, and invasion. The functional analysis of the genes correlated with ENY2 revealed that ENY2 might be involved in telomere maintenance, one of the fundamental hallmarks of cancer. In conclusion, our data indicate that ENY2 may regulate the malignant phenotypes of HCC via activating telomere maintenance.

Identification of chromosomal instability-associated genes as hepatocellular carcinoma progression-related biomarkers to guide clinical diagnosis, prognosis and therapy

Hepatocellular carcinoma (HCC) is a type of cancer characterized by high heterogeneity and a complex multistep progression process. Significantly-altered biomarkers for HCC need to be identified. Differentially expressed genes and weighted gene co-expression network analyses were used to identify progression-related biomarkers. LASSO-Cox regression and random forest algorithms were used to construct the progression-related prognosis (PRP) score. Three chromosomal instability-associated genes (KIF20A, TOP2A, and TTK) have been identified as progression-related biomarkers. The robustness of the PRP scores were validated using four independent cohorts. Immune status was observed using the single-sample gene set enrichment analysis (ssGSEA). Comprehensive analysis showed that the patients with high PRP score had wider genomic alterations, more malignant phenotypes, and were in a state of immunosuppression. The diagnostic models constructed via logistic regression based on the three genes showed satisfactory performances in distinguishing HCC from cirrhotic tissues or dysplastic nodules. The nomogram combining PRP scores with clinical factors had a better performance in predicting prognosis than the tumor node metastasis classification (TNM) system. We further confirmed that KIF20A, TOP2A, and TTK were highly expressed in HCC tissues than in cirrhotic tissues. Downregulation of all three genes aggravated chromosomal instabilities in HCC and suppressed HCC cells viability both in vitro and in vivo. Overall, our study highlights the important roles of chromosomal instability-associated genes during the progression of HCC and their potential clinical diagnosis and prognostic value and provides promising new ideas for developing therapeutic strategies to improve the outcomes of HCC patients.

Identification of multi-omics biomarkers and construction of the novel prognostic model for hepatocellular carcinoma

Genome changes play a crucial role in carcinogenesis, and many biomarkers can be used as effective prognostic indicators in various tumors. Although previous studies have constructed many predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance is unsatisfactory. Because multi-omics data can more comprehensively reflect the biological phenomenon of disease, we hope to build a more accurate predictive model by multi-omics analysis. We use the TCGA to identify crucial biomarkers and construct prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. The performances of predictive models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Multiple mRNAs, lncRNAs, miRNAs, CNV genes, and SNPs were significantly associated with the prognosis of HCC. We constructed five single-omic models, and the mRNA and lncRNA models showed good performance with c-indexes over 0.70. The multi-omics model presented a robust predictive ability with a c-index over 0.77. This study identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC. In addition, we constructed multiple single-omic models and an integrated multi-omics model that may provide practical and reliable guides for prognosis assessment.

Prognostic and therapeutic prediction by screening signature combinations from transcriptome-methylome interactions in oral squamous cell carcinoma

DNA methylation pattern in oral squamous cell carcinoma (OSCC) remains poorly described. This study aimed to perform a genome-wide integrated analysis of the transcriptome and methylome and assess the efficacy of their prognostic signature model in patients with OSCC. We analyzed transcriptome and methylome data from 391 OSCC samples and 41 adjacent normal samples. A total of 8074 differentially expressed genes (DEGs) and 10,084 differentially expressed CpGs (DMCpGs) were identified. Then 241 DEGs with DMCpGs were identified. According to the prognostic analysis, the prognostic signature of methylation-related differentially expressed genes (mrDEGPS) was established. mrDEGPS consisted of seven prognostic methylation-related genes, including ESRRG, CCNA1, SLC20A1, COL6A6, FCGBP, CDKN2A, and ZNF43. mrDEGPS was a significant stratification factor of survival (P < 0.00001) irrespective of the clinical stage. The immune effector components, including B cells, CD4⁺ T cells, and CD8⁺ T cells, were decreased in the tumor environment of patients with high mrDEGPS. Immune checkpoint expressions, including CTLA-4, PD-1, LAG3, LGALS9, HAVCR2, and TIGHT, were comprehensively elevated (P < 0.001). The estimated half-maximal inhibitory concentration difference between low- and high-risk patients was inconsistent among chemotherapeutic drugs. In conclusion, the transcriptome–methylome interaction pattern in OSCC is complex. mrDEGPS can predict patient survival and responses to immunotherapy and chemotherapy and facilitate clinical decision-making in patients with OSCC.

Epigenetic Alterations of DNA Methylation and miRNA Contribution to Lung Adenocarcinoma

This study focused on the epigenetic alterations of DNA methylation and miRNAs for lung adenocarcinoma (LUAD) diagnosis and treatment using bioinformatics analyses. DNA methylation data and mRNA and miRNA expression microarray data were obtained from The Cancer Genome Atlas (TCGA) database. The differentially methylated genes (DMGs), differentially expressed genes (DEGs), and differentially expressed miRNAs were analyzed by using the limma package. The DAVID database performed GO and KEGG pathway enrichment analyses. Using STRING and Cytoscape, we constructed the protein-protein interaction (PPI) network and achieved visualization. The online analysis tool CMap was used to identify potential small-molecule drugs for LUAD. In LUAD, 607 high miRNA-targeting downregulated genes and 925 low miRNA-targeting upregulated genes, as well as 284 hypermethylated low-expression genes and 315 hypomethylated high-expression genes, were obtained. They were mainly enriched in terms of pathways in cancer, neuroactive ligand-receptor interaction, cAMP signaling pathway, and cytosolic DNA-sensing pathway. In addition, 40 upregulated and 84 downregulated genes were regulated by both aberrant alternations of DNA methylation and miRNAs. Five small-molecule drugs were identified as a potential treatment for LUAD, and five hub genes (SLC2A1, PAX6, LEP, KLF4, and FGF10) were found in PPI, and two of them (SLC2A1 and KLF4) may be related to the prognosis of LUAD. In summary, our study identified a series of differentially expressed genes associated with epigenetic alterations of DNA methylation and miRNA in LUAD. Five small-molecule drugs and five hub genes may be promising drugs and targets for LUAD treatment.

Integrative Analysis Identifies Cell-Type-Specific Genes Within Tumor Microenvironment as Prognostic Indicators in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, but effective early detection and prognostication methods are lacking.

Methods

The Cox regression model was built to stratify the HCC patients. The single-cell RNA sequencing data analysis and gene set enrichment analysis were employed to investigate the biological function of identified markers. PLCB1 gain- or loss-of-function experiments were performed, and obtained HCC samples were analyzed using quantitative real-time PCR and immunohistochemistry assay to validate the biological function of identified markers.

Results

In this study, we developed a model using optimized markers for HCC recurrence prediction. Specifically, we screened out 8 genes through a series of data analyses, and built a multivariable Cox model based on their expression. The risk stratifications using the Eight-Gene Cox (EGC) model were closely associated with the recurrence-free survivals (RFS) in both training and three validation cohorts. We further demonstrated that this risk stratification could serve as an independent predictor in predicting HCC recurrence, and that the EGC model could outperform other models. Moreover, we also investigated the cell-type-specific expression patterns of the eight recurrence-related genes in tumor microenvironment using single-cell RNA sequencing data, and interpreted their functional roles from correlation and gene set enrichment analyses, in vitro and in vivo experiments. Particularly, PLCB1 and SLC22A7 were predominantly expressed in malignant cells, and they were predicted to promote angiogenesis and to help maintain normal metabolism in liver, respectively. In contrast, both FASLG and IL2RB were specifically expressed in T cells, and were highly correlated with T cell marker genes, suggesting that these two genes might assist in maintaining normal function of T cell-mediated immune response in tumor tissues.

Conclusion

In conclusion, the EGC model and eight identified marker genes could not only facilitate the accurate prediction of HCC recurrence, but also improve our understanding of the mechanisms behind HCC recurrence.

Identification of the Immune Subtype of Hepatocellular Carcinoma for the Prediction of Disease-Free Survival Time and Prevention of Recurrence by Integrated Analysis of Bulk- and Single-Cell RNA Sequencing Data

Background

The main factors affecting the long-term prognosis of hepatocellular carcinoma (HCC) patients undergoing radical surgery are recurrence and metastasis. However, the methods for predicting disease-free survival (DFS) time and preventing postoperative recurrence of HCC are still very limited.

Methods

In this study, immune cell abundances in HCC samples were analyzed by single-sample gene set enrichment analysis (ssGSEA), while the prognostic values of immune cells for DFS time prediction were evaluated by the least absolute shrinkage and selection operator (LASSO) and subsequent univariate and multivariate Cox analyses. Next, a risk score was constructed based on the most prognostic immune cells and their corresponding coefficients. Interactions among prognostic immune cells and the specific targets for the prevention of recurrence were further identified by single-cell RNA (scRNA) sequencing data and CellMiner.

Results

A novel efficient T cell risk score (TCRS) was constructed based on data from the three most prognostic immune cell types (effector memory CD8 T cells, regulatory T cells and follicular helper T cells) for identifying an immune subtype of HCC patients with longer DFS times and inflammatory immune characteristics. Functional differences between the high- and low-score groups separated by TCRS were clarified, and the cell-cell communication among these immune cells was elucidated. Finally, fifteen hub genes that may be potential therapeutic targets for the prevention of recurrence were identified.

Conclusions

We constructed and verified a useful model for the prediction of DFS time of HCC after surgery. In addition, fifteen hub genes were identified as candidates for the prevention of recurrence, and a preliminarily investigation of potential drugs targeting these hub genes was carried out.

A Novel Multi-Omics Analysis Model for Diagnosis and Survival Prediction of Lower-Grade Glioma Patients

Background

Lower-grade gliomas (LGGs) are characterized by remarkable genetic heterogeneity and different clinical outcomes. Classification of LGGs is improved by the development of molecular stratification markers including IDH mutation and 1p/19q chromosomal integrity, which are used as a hallmark of survival and therapy sensitivity of LGG patients. However, the reproducibility and sensitivity of the current classification remain ambiguous. This study aimed to construct more accurate risk-stratification approaches.

Methods

According to bioinformatics, the sequencing profiles of methylation and transcription and imaging data derived from LGG patients were analyzed and developed predictable risk score and radiomics score. Moreover, the performance of predictable models was further validated.

Results

In this study, we determined a cluster of 6 genes that were correlated with IDH mutation/1p19q co-deletion status. Risk score model was calculated based on 6 genes and showed gratifying sensitivity and specificity for survival prediction and therapy response of LGG patients. Furthermore, a radiomics risk score model was established to noninvasively assist judgment of risk score in pre-surgery. Taken together, a predictable nomogram that combined transcriptional signatures and clinical characteristics was established and validated to be preferable to the histopathological classification. Our novel multi-omics nomograms showed a satisfying performance. To establish a user-friendly application, the nomogram was further developed into a web-based platform: https://drw576223193.shinyapps.io/Nomo/, which could be used as a supporting method in addition to the current histopathological-based classification of gliomas.

Conclusions

Our novel multi-omics nomograms showed the satisfying performance of LGG patients and assisted clinicians to draw up individualized clinical management.

A methylation-driven genes prognostic signature and the immune microenvironment in epithelial ovarian cancer

Aberrant gene methylation has been implicated in the development and progression of tumors. In this study, we aimed to identity methylation driven genes involved in epithelial ovarian cancer (EOC) to establish a prognostic signature for patients with EOC. We identified and verified 6 MDGs that are closely related to the prognosis of ovarian cancer. A prognostic risk score model and nomogram for predicting the prognosis of ovarian cancer were constructed based on the six MDGs. It can also effectively reflect the immune environment and immunotherapy response of ovarian cancer. These MDGs have great significance to the implementation of individualized treatment and disease monitoring of ovarian cancer patients.

A novel ferroptosis-related gene signature for clinically predicting recurrence after hepatectomy of hepatocellular carcinoma patients

High recurrence rate in HCC is the primary cause of the poor prognosis after hepatectomy. Therefore, in this study, we aimed to construct a gene signature for predicting the recurrence rate in HCC. The mRNA expression profiles and clinical information of HCC patients from GEO and TCGA databases were used, and ferroptosis-related gene list was obtained from the FerrDb database. We identified 39 ferroptosis-related genes (FDEGs) that were differentially expressed between HCC samples and normal tissues from the GSE14520 dataset. The univariate and multivariate Cox regression analyses were employed to construct a prognostic signature. Seven FDEGs (MAPK9, SLC1A4, PCK2, ACSL3, STMN1, CDO1, and CXCL2) were included to construct a risk model, which was validated in the TCGA dataset. Patients in high-risk groups exhibited a significantly poor prognosis compared with patients in low-risk groups in both the training set (GSE14520 cohort) and the validation set (TCGA cohort). Multivariate cox regression analyses demonstrated that the 7-gene signature was an independent risk factor for RFS in HCC patients. KEGG analysis showed that FDEGs were mainly enriched in Ferroptosis, Hepatocellular carcinoma pathway, and MAPK signaling pathway. GSEA analysis suggested that the high-risk group was correlated with multiple oncogenic signatures and invasive-related pathways. These results indicated that this risk model can accurately predict recurrence after hepatectomy and offer novel research directions for personalized treatment in HCC patients.

A New Inflammation-Related Risk Model for Predicting Hepatocellular Carcinoma Prognosis

Background

Hepatocellular carcinoma (HCC) is characterized by a poor prognosis. Inflammation has a vital role in the formation and development of HCC. However, the prediction of HCC prognosis using inflammation-related genes (IRGs) remains elusive. In this study, we constructed a new IRG risk model to predict the HCC prognosis.

Results

HCC-related RNA expression profiles and their corresponding clinical data were downloaded from TCGA and ICGC databases to explore the IRGs' predicting ability. Seven hundred thirty-seven IRGs from GeneCards were used as candidate genes to construct the model. The associations of overall survival (OS) with IRGs were evaluated using the log-rank test and univariate Cox analysis, and 32 out of 737 IRGs showed predicting the potential for HCC prognosis. These IRGs were further analyzed using the least absolute shrinkage and selection operator (LASSO) and multivariate Cox analyses. Finally, 6 IRGs were included in an IRG risk model. Based on the cut-off of the risk score calculated according to the IRG risk model, HCC samples were divided into the high-risk and the low-risk groups. The OS of patients was lower in the high-risk group than in the low-risk group (P < 0.05). The area under the receiver operating characteristic curve (AUC) of the risk score was 0.78 for 3-year survival. Univariate Cox and multivariate Cox analyses revealed that the risk score was an independent risk factor for HCC prognosis. The KEGG and GO enrichment analysis results further showed that the risk scores were closely related to inflammatory and immune pathways. In addition, the ssGSEA demonstrated that several immune cells and some immune-related pathways were negatively correlated with the risk score.

Conclusions

The new IRG risk score was an independent risk factor for HCC prognosis and could be used to assess the immune status of the HCC microenvironment.

Comprehensive analysis to identify noncoding RNAs mediated upregulation of maternal embryonic leucine zipper kinase (MELK) correlated with poor prognosis in hepatocellular

Object: Maternal embryonic leucine zipper kinase (MELK) is involved in the development and progression of various cancers. This work investigated the usefulness of MELK in the prediction of hepatocellular carcinoma (HCC) prognosis.

Methods: Information on MELK expression was obtained by pan-cancer analysis using The Cancer Genome Atlas (TCGA) database. The TCGA-liver hepatic cancer (TCGA-LIHC), Oncomine datasets, International Cancer Genome Consortium (ICGC) datasets were used to investigate MELK expression in HCC. The prognostic roles of MELK in HCC were assessed by univariate and multivariate survival analyses. The underlying mechanism for noncoding RNAs (ncRNAs) involved in MELK expression was investigated by in silico studies, correlation, methylation, and survival analyses. The relationships between MELK expression and immune cells, immune markers, and checkpoint markers were also analyzed.

Results: (1) MELK was identified as an independent predictor of overall survival (OS) in HCC patients (MELK high vs. low expression, HR 2.469; 95% CI 1.217–5.008; p = 0.012) in a multivariate Cox analysis, with a concordance index (C-index) value of 0.727 (95% CI 0.750–0.704). (2) The noncoding RNA miR3142HG and the LINC00265/has-miR-101-3p axis were found to regulate MELK expression in HCC tissue. (3) MELK levels were linked to various immune functions, including tumor infiltration and the expression of immune checkpoints and biomarkers in HCC.

Conclusion: MELK may have an oncogenic function in HCC and was found to be up-regulated by ncRNAs and associated with immune cell infiltration and unfavorable prognosis.

Immune-related biomarkers shared by inflammatory bowel disease and liver cancer

It has been indicated that there is an association between inflammatory bowel disease (IBD) and hepatocellular carcinoma (HCC). However, the molecular mechanism underlying the risk of developing HCC among patients with IBD is not well understood. The current study aimed to identify shared genes and potential pathways and regulators between IBD and HCC using a system biology approach. By performing the different gene expression analyses, we identified 871 common differentially expressed genes (DEGs) between IBD and HCC. Of these, 112 genes overlapped with immune genes were subjected to subsequent bioinformatics analyses. The results revealed four hub genes (CXCL2, MMP9, SPP1 and SRC) and several other key regulators including six transcription factors (FOXC1, FOXL1, GATA2, YY1, ZNF354C and TP53) and five microRNAs (miR-124-3p, miR-34a-5p, miR-1-3p, miR-7-5p and miR-99b-5p) for these disease networks. Protein-drug interaction analysis discovered the interaction of the hub genes with 46 SRC-related and 11 MMP9- related drugs that may have a therapeutic effect on IBD and HCC. In conclusion, this study sheds light on the potential connecting mechanisms of HCC and IBD.

Screening of key methylation-driven genes CDO1 in breast cancer based on WGCNA

BACKGROUND

With the rapid development of genomics and molecular biology, not only have biochemical indicators been used as tumour markers, but many new molecular markers have emerged. Epigenetic abnormalities are a new type of molecular marker, and DNA methylation is an important part of epigenetics.

OBJECTIVE

This study used weighted gene coexpression network analysis (WGCNA) to analyse key methylation-driven genes in breast cancer.

METHODS

The RNA-seq transcriptome data, DNA methylation data, and clinical information data of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database, and the MethylMix R package was used to screen methylation-driven genes in breast cancer. The ClusterProfiler package and enrichplot package in R software were used to further analyse the function and signalling pathway of methylation-driven genes. Through univariate and multivariate Cox regression analyses, methylation-driver genes related to prognostic were obtained, a prognostic model was constructed and prognostic characteristics were analysed.

RESULTS

The 17 methylation-driven genes related to prognosis were obtained by the WGCNA method in breast cancer, and the prognostic significance of these methylation-driven genes was determined by transcriptome and methylation combined survival analysis. Analysis of functions and signalling pathways showed that these genes were mainly enriched in biological processes and signalling pathway. Through univariate and multivariate Cox regression analyses, a prognostic model of 5 methylation-driven genes was constructed.

CONCLUSIONS

The AUC of the receiver operating characteristic (ROC) curve of this model was 0.784, showing that the model had a good prediction effect. Based on WGCNA screening, it was found that only CDO1 was the key methylation-driven gene for prognosis in breast cancer, indicating that CDO1 may be an important indicator of the prognosis of breast cancer patients.

THRSP identified as a potential hepatocellular carcinoma marker by integrated bioinformatics analysis and experimental validation

Hepatocellular carcinoma (HCC) is the most common malignant liver tumor with high mortality and poor prognosis worldwide. This study aimed to identify hub genes and investigate the underlying molecular mechanisms in HCC progression by integrated bioinformatics analysis and experimental validation. Based on the Gene Expression Omnibus (GEO) databases and The Cancer Genome Atlas (TCGA), 12 critical differential co-expression genes were identified between tumor and normal tissues. Via survival analysis, we found higher expression of LCAT, ACSM3, IGF1, SRD5A2, THRSP and ACADS was associated with better prognoses in HCC patients. Among which, THRSP was selected for the next investigations. We found that THRSP mRNA expression was negatively correlated with its methylation and closely associated with clinical characteristics in HCC patients. Moreover, THRSP expression had a negative correlation with the infiltration levels of several immune cells (e.g., B cells and CD4+ T cells). qRT-PCR verified that THRSP was lower expressed in HCC tissues and cell lines compared with control. Silencing of THRSP promoted the migration, invasion, proliferation, and inhibited cell apoptosis of HCCLM and Huh7 cell lines. Decreased expression of THRSP promoted HCC progression by NF-κB, ERK1/2, and p38 MAPK signaling pathways. In conclusion, THRSP might serve as a novel biomarker and therapeutic target of HCC.