Identification of immune infiltration-related genes as prognostic indicators for hepatocellular carcinoma

Identification and validation of a novel predictive signature based on hepatocyte-specific genes in hepatocellular carcinoma by integrated analysis of single-cell and bulk RNA sequencing

BACKGROUND

Hepatocellular carcinoma represents a significant global burden in terms of cancer-related mortality, posing a substantial risk to human health. Despite the availability of various treatment modalities, the overall survival rates for patients with hepatocellular carcinoma remain suboptimal. The objective of this study was to explore the potential of novel biomarkers and to establish a novel predictive signature utilizing multiple transcriptome profiles.

METHODS

The GSE115469 and CNP0000650 cohorts were utilized for single cell analysis and gene identification. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets were utilized in the development and evaluation of a predictive signature. The expressions of hepatocyte-specific genes were further validated using the GSE135631 cohort. Furthermore, immune infiltration results, immunotherapy response prediction, somatic mutation frequency, tumor mutation burden, and anticancer drug sensitivity were analyzed based on various risk scores. Subsequently, functional enrichment analysis was performed on the differential genes identified in the risk model. Moreover, we investigated the expression of particular genes in chronic liver diseases utilizing datasets GSE135251 and GSE142530.

RESULTS

Our findings revealed hepatocyte-specific genes (ADH4, LCAT) with notable alterations during cell maturation and differentiation, leading to the development of a novel predictive signature. The analysis demonstrated the efficacy of the model in predicting outcomes, as evidenced by higher risk scores and poorer prognoses in the high-risk group. Additionally, a nomogram was devised to forecast the survival rates of patients at 1, 3, and 5 years. Our study demonstrated that the predictive model may play a role in modulating the immune microenvironment and impacting the anti-tumor immune response in hepatocellular carcinoma. The high-risk group exhibited a higher frequency of mutations and was more likely to benefit from immunotherapy as a treatment option. Additionally, we confirmed that the downregulation of hepatocyte-specific genes may indicate the progression of hepatocellular carcinoma and aid in the early diagnosis of the disease.

CONCLUSION

Our research findings indicate that ADH4 and LCAT are genes that undergo significant changes during the differentiation of hepatocytes into cancer cells. Additionally, we have created a unique predictive signature based on genes specific to hepatocytes.

Screening immune-related blood biomarkers for DKD-related HCC using machine learning

Background

Diabetes mellitus is a significant health problem worldwide, often leading to diabetic kidney disease (DKD), which may also influence the occurrence of hepatocellular carcinoma (HCC). However, the relationship and diagnostic biomarkers between DKD and HCC are unclear.

Methods

Using public database data, we screened DKD secretory RNAs and HCC essential genes by limma and WGCNA. Potential mechanisms, drugs, and biomarkers for DKD-associated HCC were identified using PPI, functional enrichment, cMAP, and machine learning algorithms, and a diagnostic nomogram was constructed. Then, ROC, calibration, and decision curves were used to evaluate the diagnostic performance of the nomograms. In addition, immune cell infiltration in HCC was explored using CIBERSORT. Finally, the detectability of critical genes in blood was verified by qPCR.

Results

104 DEGs associated with HCC using WGCNA were identified. 101 DEGs from DKD were predicated on secreting into the bloodstream with Exorbase datasets. PPI analysis identified three critical modules considered causative genes for DKD-associated HCC, primarily involved in inflammation and immune regulation. Using lasso and RM, four hub genes associated with DKD-associated HCC were identified, and a diagnostic nomogram confirmed by DCA curves was established. The results of immune cell infiltration showed immune dysregulation in HCC, which was associated with the expression of four essential genes. PLVAP was validated by qPCR as a possible blood-based diagnostic marker for DKD-related HCC.

Conclusion

We revealed the inflammatory immune pathways of DKD-related HCC and developed a diagnostic nomogram for HCC based on PLVAP, C7, COL15A1, and MS4A6A. We confirmed with qPCR that PLVAP can be used as a blood marker to assess the risk of HCC in DKD patients.

Targeting LRRC41 as a potential therapeutic approach for hepatocellular carcinoma

Introduction: Hepatocellular carcinoma (HCC) is the most common primary liver cancer, characterized by high mortality rate. In clinical practice, several makers of liver cancer, such as VEGFR1, FGFR1 and PDGFRα, were identified and their potentials as a therapeutic target were explored. However, the unsatisfied treatment results emphasized the needs of new therapeutic targets. Methods: 112 HCC patients samples were obtained to evaluate the expression of LRRC41, SOX9, CD44, and EPCAM in HCC, combined with prognosis analysis. A DEN-induced HCC rat model was constructed to verify the expression of LRRC41 and SOX9 in HCC and lung metastasis tissues. Immune score evaluation was analysized by bioinformatics methods. Network pharmacology was performed to explored the potential FDA-approved drugs targeting LRRC41. Results: Through analysis of the Timer database and tissue micro-array, we confirmed that LRRC41 was over-expressed in HCC and exhibited a significant positive correlation with recurrence and metastasis. Immunohistochemistry staining of human HCC tissue samples revealed significant upregulation of LRRC41, SOX9, CD44, and EPCAM, with LRRC41 showing a positive correlation with SOX9, CD44, and EPCAM expression. UALCAN database analysis indicated that LRRC41 and SOX9 contribute to poor prognosis whereas CD44 and EPCAM did not demonstrate the same significance. Furthermore, analysis of a DEN-induced HCC rat model confirmed the significantly elevated expression of LRRC41 and SOX9 in HCC and lung metastasis tissues. Drug sensitivity analysis and molecular docking targeting LRRC41 identified several FDA-approved drugs, which may have potential antitumor effects on HCC by targeting LRRC41. Conclusion: Our findings highlight the role of LRRC41 overexpression in promoting HCC progression and its association with a poor prognosis. Drug sensitivity analysis and molecular docking shows several FDA-approved drugs may be potential therapeutic targets for HCC. Targeting LRRC41 may hold promise as a potential therapeutic strategy for HCC.

Establishment and Clinical Application of the Nomogram Related to Risk or Prognosis of Hepatocellular Carcinoma: A Review

Hepatocellular carcinoma (HCC) is the most prevalent primary liver malignancy, accounting for approximately 90% of all primary liver cancers, with high mortality and a poor prognosis. A large number of predictive models have been applied that integrate multiple clinical factors and biomarkers to predict the prognosis of HCC. Nomograms, as easy-to-use prognostic predictive models, are widely used to predict the probability of clinical outcomes. We searched PubMed with the keywords "hepatocellular carcinoma" and "nomogram", and 974 relative literatures were retrieved. According to the construction methodology and the real validity of the nomograms, in this study, 97 nomograms for HCC were selected in 77 publications. These 97 nomograms were established based on more than 100,000 patients, covering seven main prognostic outcomes. The research data of 56 articles are from hospital-based HCC patients, and 13 articles provided external validation results of the nomogram. In addition to AFP, tumor size, tumor number, stage, vascular invasion, age, and other common prognostic risk factors are included in the HCC-related nomogram, more and more biomarkers, including gene mRNA expression, gene polymorphisms, and gene signature, etc. were also included in the nomograms. The establishment, assessment and validation of these nomograms are also discussed in depth. This study would help clinicians construct and select appropriate nomograms to guide precise judgment and appropriate treatments.

A prognostic risk model, tumor immune environment modulation, and drug prediction of ferroptosis and amino acid metabolism-related genes in hepatocellular carcinoma

The prognosis of hepatocellular carcinoma (HCC) is challenging due to its heterogeneity. Ferroptosis and amino acid metabolism have been shown to be closely related to HCC. We obtained HCC-related expression data from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. We then crossed differentially expressed genes (DEGs), amino acid metabolism genes, and ferroptosis-related genes (FRGs) to obtain amino acid metabolism-ferroptosis-related differentially expressed genes (AAM-FR DEGs). Moreover, we developed a prognostic model using Cox analysis, followed by a correlation analysis of risk scores with clinical characteristics. We also performed an immune microenvironment analysis and drug sensitivity analysis. Finally, the expression levels of model genes were verified by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemical assays. We found that the 18 AAM-FR DEGs were mainly enriched to the alpha-amino acid metabolic process and amino acid biosynthesis pathways. Cox analysis identified CBS, GPT2, SUV39H1, and TXNRD1 as prognostic biomarkers for the risk model construction. Our results showed that the risk scores differed between pathology stage, pathology T stage, and HBV, and the number of HCC patients in the two groups. In addition, the expression of PD-L1 and CTLA-4 was high in the high-risk group, and the half-maximal inhibitory concentration (IC50) of sorafenib also differed between the two groups. Finally, the experimental validation demonstrated that the expression of biomarkers was consistent with the study analysis. Therefore, in this study, we constructed and validated a prognostic model (CBS, GPT2, SUV39H1, and TXNRD1) related to ferroptosis and amino acid metabolism and examined their prognostic value for HCC.

Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods

Aim: As the most common cardiomyopathy, dilated cardiomyopathy (DCM) often leads to progressive heart failure and sudden cardiac death. This study was designed to investigate the molecular subgroups of DCM. Methods: Three datasets of DCM were downloaded from GEO database (GSE17800, GSE79962 and GSE3585). After log2-transformation and background correction with "limma" package in R software, the three datasets were merged into a metadata cohort. The consensus clustering was conducted by the "Consensus Cluster Plus" package to uncover the molecular subgroups of DCM. Moreover, clinical characteristics of different molecular subgroups were compared in detail. We also adopted Weighted gene co-expression network analysis (WGCNA) analysis based on subgroup-specific signatures of gene expression profiles to further explore the specific gene modules of each molecular subgroup and its biological function. Two machine learning methods of LASSO regression algorithm and SVM-RFE algorithm was used to screen out the genetic biomarkers, of which the discriminative ability of molecular subgroups was evaluated by receiver operating characteristic (ROC) curve. Results: Based on the gene expression profiles, heart tissue samples from patients with DCM were clustered into three molecular subgroups. No statistical difference was found in age, body mass index (BMI) and left ventricular internal diameter at end-diastole (LVIDD) among three molecular subgroups. However, the results of left ventricular ejection fraction (LVEF) statistics showed that patients from subgroup 2 had a worse condition than the other group. We found that some of the gene modules (pink, black and grey) in WGCNA analysis were significantly related to cardiac function, and each molecular subgroup had its specific gene modules functions in modulating occurrence and progression of DCM. LASSO regression algorithm and SVM-RFE algorithm was used to further screen out genetic biomarkers of molecular subgroup 2, including TCEAL4, ISG15, RWDD1, ALG5, MRPL20, JTB and LITAF. The results of ROC curves showed that all of the genetic biomarkers had favorable discriminative effectiveness. Conclusion: Patients from different molecular subgroups have their unique gene expression patterns and different clinical characteristics. More personalized treatment under the guidance of gene expression patterns should be realized.

Identification and Validation of an m6A Modification of JAK-STAT Signaling Pathway–Related Prognostic Prediction Model in Gastric Cancer

Background: Gastric cancer (GC) is one of the malignant tumors worldwide. Janus (JAK)–signal transduction and activator of transcription (STAT) signaling pathway is involved in cellular biological process and immune function. However, the association between them is still not systematically described. Therefore, in this study, we aimed to identify key genes involved in JAK-STAT signaling pathway and GC, as well as the potential mechanism.

Methods: The Cancer Genome Atlas (TCGA) database was the source of RNA-sequencing data of GC patients. Gene Expression Omnibus (GEO) database was used as the validation set. The predictive value of the JAK-STAT signaling pathway-related prognostic prediction model was examined using least absolute shrinkage and selection operator (LASSO); survival, univariate, and multivariate Cox regression analyses; and receiver operating characteristic curve (ROC) analyses to examine the predictive value of the model. Quantitative real-time polymerase chain reaction (qRT-PCR) and chi-square test were used to verify the expression of genes in the model and assess the association between the genes and clinicopathological parameters of GC patients, respectively. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis, version 3.0 (GSEA), sequence-based RNA adenosine methylation site predictor (SRAMP) online websites, and RNA immunoprecipitation (RIP) experiments were used to predict the model-related potential pathways, m6A modifications, and the association between model genes and m6A.

Results: A four-gene prognostic model (GHR, PIM1, IFNA8, and IFNB1) was constructed, namely, riskScore. The Kaplan–Meier curves suggested that patients with high riskScore expression had a poorer prognosis than those with low riskScore expression (p = 0.006). Multivariate Cox regression analyses showed that the model could be an independent predictor (p < 0.001; HR = 3.342, 95%, CI = 1.834–6.088). The 5-year area under time-dependent ROC curve (AUC) reached 0.655. The training test set verified these results. Further analyses unveiled an enrichment of cancer-related pathways, m6A modifications, and the direct interaction between m6A and the four genes.

Conclusion: This four-gene prognostic model could be applied to predict the prognosis of GC patients and might be a promising therapeutic target in GC.

A Novel Purine and Uric Metabolism Signature Predicting the Prognosis of Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is regarded as one of the most common cancers in the world with a poor prognosis. Patients with HCC often have abnormal purine and uric acid metabolism, but their relationship with prognosis is unclear.

Methods: Here, we collected the data of peripheral blood uric acid and clinical data in 50 patients with HCC and analyzed the relationship with prognosis. At the same time, the transcriptome sequencing data of TCGA and GEO databases were collected to analyze the changes in purine metabolic pathway activity and construct a prognosis prediction model. Based on the prognosis prediction model related to purine metabolism, we further looked for the differences in the immune microenvironment and molecular level and provided possible drug targets.

Results: We found that the level of serum uric acid was positively correlated with the prognosis of HCC. At the same time, purine metabolism and purine biosynthesis pathway activities were significantly activated in patients with a poor prognosis of HCC. The prognosis prediction model of HCC based on purine metabolism and purine biosynthesis pathway can accurately evaluate the prognosis of patients with HCC. Meanwhile, we found that there were significant changes in tumor immune infiltration microenvironment and biological function at the molecular level in patients with over-activation of purine metabolism and purine biosynthesis pathway. In addition, we found that uric acid level was positively correlated with peripheral blood leukocytes in HCC patients.

Conclusion: In this study, we found that the level of peripheral blood uric acid in patients with HCC is correlated with their prognosis. The prognosis of patients with HCC can be accurately predicted through the metabolic process of uric acid and purine.

DHW-208, A Novel Phosphatidylinositol 3-Kinase (PI3K) Inhibitor, Has Anti-Hepatocellular Carcinoma Activity Through Promoting Apoptosis and Inhibiting Angiogenesis

Hepatocellular carcinoma (HCC) is one of the most common tumors worldwide with high prevalence and lethality. Due to insidious onset and lack of early symptoms, most HCC patients are diagnosed at advanced stages without adequate methods but systemic therapies. PI3K/AKT/mTOR signaling pathway plays a crucial role in the progression and development of HCC. Aberrant activation of PI3K/AKT/mTOR pathway is involved in diverse biological processes, including cell proliferation, apoptosis, migration, invasion and angiogenesis. Therefore, the development of PI3K-targeted inhibitors is of great significance for the treatment of HCC. DHW-208 is a novel 4-aminoquinazoline derivative pan-PI3K inhibitor. This study aimed to assess the therapeutic efficacy of DHW-208 in HCC and investigate its underlying mechanism. DHW-208 could inhibit the proliferation, migration, invasion and angiogenesis of HCC through the PI3K/AKT/mTOR signaling pathway in vitro. Consistent with the in vitro results, in vivo studies demonstrated that DHW-208 elicits an antitumor effect by inhibiting the PI3K/AKT/mTOR-signaling pathway with a high degree of safety in HCC. Therefore, DHW-208 is a candidate compound to be developed as a small molecule PI3K inhibitor for the treatment of HCC, and our study provides a certain theoretical basis for the treatment of HCC and the development of PI3K inhibitors.