Tumor suppressor gene mutations correlate with prognosis and immunotherapy benefit in hepatocellular carcinoma

Integrated Exploration of Epigenetic Dysregulation Reveals a Stemness/EMT Subtype and MMP12 Linked to the Progression and Prognosis in Hepatocellular Carcinoma

Epigenetic dysregulation drives aberrant transcriptional programs playing a critical role in hepatocellular carcinoma (HCC), which may provide novel insights into the heterogeneity of HCC. This study performed an integrated exploration on the epigenetic dysregulation of miRNA and methylation. We discovered and validated three patterns endowed with gene-related transcriptional traits and clinical outcomes. Specially, a stemness/epithelial-mesenchymal transition (EMT) subtype was featured by immune exhaustion and the worst prognosis. Besides, MMP12, a characteristic gene, was highly expressed in the stemness/EMT subtype, which was verified as a pivotal regulator linked to the unfavorable prognosis and further proven to promote tumor proliferation, invasion, and metastasis in vitro experiments. Proteomic analysis by mass spectrometry sequencing also indicated that the overexpression of MMP12 was significantly associated with cell proliferation and adhesion. Taken together, this study unveils innovative insights into epigenetic dysregulation and identifies a stemness/EMT subtype-specific gene, MMP12, correlated with the progression and prognosis of HCC.

Engaging stimulatory immune checkpoint interactions in the tumour immune microenvironment of primary liver cancers - how to push the gas after having released the brake

Hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA) are the first and second most common primary liver cancer (PLC). For decades, systemic therapies consisting of tyrosine kinase inhibitors (TKIs) or chemotherapy have formed the cornerstone of treating advanced-stage HCC and CCA, respectively. More recently, immunotherapy using immune checkpoint inhibition (ICI) has shown anti-tumour reactivity in some patients. The combination regimen of anti-PD-L1 and anti-VEGF antibodies has been approved as new first-line treatment of advanced-stage HCC. Furthermore, gemcibatine plus cisplatin (GEMCIS) with an anti-PD-L1 antibody is awaiting global approval for the treatment of advanced-stage CCA. As effective anti-tumour reactivity using ICI is achieved in a minor subset of both HCC and CCA patients only, alternative immune strategies to sensitise the tumour microenvironment of PLC are waited for. Here we discuss immune checkpoint stimulation (ICS) as additional tool to enhance anti-tumour reactivity. Up-to-date information on the clinical application of ICS in onco-immunology is provided. This review provides a rationale of the application of next-generation ICS either alone or in combination regimen to potentially enhance anti-tumour reactivity in PLC patients.

Comprehensive analysis of single cell and bulk data develops a promising prognostic signature for improving immunotherapy responses in ovarian cancer

The tumor heterogeneity is an important cause of clinical therapy failure and yields distinct prognosis in ovarian cancer (OV). Using the advantages of integrated single cell RNA sequencing (scRNA-seq) and bulk data to decode tumor heterogeneity remains largely unexplored. Four public datasets were enrolled in this study, including E-MTAB-8107, TCGA-OV, GSE63885, and GSE26193 cohorts. Random forest algorithm was employed to construct a multi-gene prognostic panel and further evaluated by receiver operator characteristic (ROC), calibration curve, and Cox regression. Subsequently, molecular characteristics were deciphered, and treatments strategies were explored to deliver precise therapy. The landscape of cell subpopulations and functional characteristics, as well as the dynamic of macrophage cells were detailly depicted at single cell level, and then screened prognostic candidate genes. Based on the expression of candidate genes, a stable and robust cell characterized gene associated prognosis signature (CCIS) was developed, which harbored excellent performance at prognosis assessment and patient stratification. The ROC and calibration curves, and Cox regression analysis elucidated CCIS could serve as serve as an independent factor for predicting prognosis. Moreover, a promising clinical tool nomogram was also constructed according to stage and CCIS. Through comprehensive investigations, patients in low-risk group were charactered by favorable prognosis, elevated genomic variations, higher immune cell infiltrations, and superior antigen presentation. For individualized treatment, patients in low-risk group were inclined to better immunotherapy responses. This study dissected tumor heterogeneity and afforded a promising prognostic signature, which was conducive to facilitating clinical outcomes for patients with OV.

Diagnosis and prognosis prediction model for digestive system tumors based on immunologic gene sets

According to 2020 global cancer statistics, digestive system tumors (DST) are ranked first in both incidence and mortality. This study systematically investigated the immunologic gene set (IGS) to discover effective diagnostic and prognostic biomarkers. Gene set variation (GSVA) analysis was used to calculate enrichment scores for 4,872 IGSs in patients with digestive system tumors. Using the machine learning algorithm XGBoost to build a classifier that distinguishes between normal samples and cancer samples, it shows high specificity and sensitivity on both the validation set and the overall dataset (area under the receptor operating characteristic curve [AUC]: validation set = 0.993, overall dataset = 0.999). IGS-based digestive system tumor subtypes (IGTS) were constructed using a consistent clustering approach. A risk prediction model was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) method. DST is divided into three subtypes: subtype 1 has the best prognosis, subtype 3 is the second, and subtype 2 is the worst. The prognosis model constructed using nine gene sets can effectively predict prognosis. Prognostic models were significantly associated with tumor mutational burden (TMB), tumor immune microenvironment (TIME), immune checkpoints, and somatic mutations. A composite nomogram was constructed based on the risk score and the patient's clinical information, with a well-fitted calibration curve (AUC = 0.762). We further confirmed the reliability and validity of the diagnostic and prognostic models using other cohorts from the Gene Expression Omnibus database. We identified diagnostic and prognostic models based on IGS that provide a strong basis for early diagnosis and effective treatment of digestive system tumors.

A prognostic signature of pyroptosis-related lncRNAs verified in gastric cancer samples to predict the immunotherapy and chemotherapy drug sensitivity

Background: Pyroptosis is a recently identified mode of programmed inflammatory cell death that has remarkable implications for cancer development. lncRNAs can be involved in cellular regulation through various pathways and play a critical role in gastric cancer (GC). However, pyroptosis -related lncRNAs (PRlncRNAs) have been rarely studied in GC.

Methods: Pyroptosis-related gene were abstracted from the literature and GSEA Molecular Signatures data resource. PRlncRNAs were obtained using co-expression analysis. LASSO Cox regression assessment was employed to build a risk model. Kaplan-Meier (KM), univariate along with multivariate Cox regression analysis were adopted to verify the predictive efficiency of the risk model in terms of prognosis. qRT-PCR was adopted to validate the expression of PRlncRNAs in GC tissues. In addition, immune cell infiltration assessment and ESTIMATE score evaluation were adopted for assessing the relationship of the risk model with the tumor immune microenvironment (TME). Finally, immune checkpoint gene association analysis and chemotherapy drug sensitivity analysis were implemented to assess the worthiness of our risk model in immunotherapy and chemotherapy of GC.

Results: We identified 3 key PRlncRNAs (PVT1, CYMP-AS1 and AC017076.1) and testified the difference of their expression levels in GC tumor tissues and neighboring non-malignant tissues (p < 0.05). PRlncRNAs risk model was able to successfully estimate the prognosis of GC patients, and lower rate of survival was seen in the high-GC risk group relative to the low-GC risk group (p < 0.001). Other digestive system tumors such as pancreatic cancer further validated our risk model. There was a dramatic difference in TMB level between high-GC and low-GC risk groups (p < 0.001). Immune cell infiltration analysis and ESTIMATE score evaluation demonstrated that the risk model can be adopted as an indicator of TME status. Besides, the expressions of immunodetection site genes in different risk groups were remarkably different (CTLA-4 (r = −0.14, p = 0.010), VISTA (r = 0.15, p = 0.005), and B7-H3 (r = 0.14, p = 0.009)). PRlncRNAs risk model was able to effectively establish a connection with the sensitivity of chemotherapeutic agents.

Conclusion: The 3 PRlncRNAs identified in this study could be utilized to predict disease outcome in GC patients. It may also be a potential therapeutic target in GC therapy, including immunotherapy and chemotherapy.

Necroptosis throws novel insights on patient classification and treatment strategies for hepatocellular carcinoma

Introduction

Necroptosis is a novel pattern of immunogenic cell death and has triggered an emerging wave in antitumor therapy. More evidence has suggested the potential associations between necroptosis and intra-tumoral heterogeneity. Currently, the underlying role of necroptosis remains elusive in hepatocellular carcinoma (HCC) at antitumor immunity and inter-tumoral heterogeneity.

Methods

This study enrolled a total of 728 HCC patients and 139 immunotherapy patients from eight public datasets. The consensus clustering approach was employed to depict tumor heterogeneity of cancer necroptosis. Subsequently, our study further decoded the heterogeneous clinical outcomes, genomic landscape, biological behaviors, and immune characteristics in necroptosis subtypes. For each patient, providing curative clinical recommendations and developing potential therapeutic drugs were used to promote precise medicine.

Results

With the use of the weighted gene coexpression network analysis (WGCNA) algorithm, necroptosis-associated long non-coding RNAs (lncRNAs) (NALRs) were identified in HCC. Based on the NALR expression, two heterogeneous subtypes were decoded with distinct clinical outcomes. Compared to patients in C1, patients in C2 harbored superior pathological stage and presented more unfavorable overall survival and recurrence-free survival. Then, the robustness and reproducibility of necroptosis subtypes were further validated via the nearest template prediction (NTP) approach and classical immune phenotypes. Through comprehensive explorations, C1 was characterized by enriched immune-inflammatory and abundant immune infiltration, while C2 possessed elevated proliferative and metabolic activities and highly genomic instability. Moreover, our results indicated that C1 was more prone to obtain desirable benefits from immunotherapy. For patients in C2, numerous underlying therapeutic agents were developed, which might produce significant efficacy.

Conclusion

This study identified two necroptosis subtypes with distinct characteristics, decoding the tumor heterogeneity. For an individualized patient, our work tailored corresponding treatment strategies to improve clinical management.

Pyroptosis-Related Signature as Potential Biomarkers for Predicting Prognosis and Therapy Response in Colorectal Cancer Patients

Background: Abnormal mucosal inflammation is a critical risk factor for pathogenesis and progression of colorectal cancer (CRC). As a type of proinflammatory death, pyroptosis can recast a suitable microenvironment to promote tumor growth. However, the potential role of pyroptosis in CRC remains unclear.

Methods: A total of 38 pyroptosis-related gene (PRG) expression profiles and clinical information were collected from multiple public datasets. Bioinformatics methods were used to analyze the clinical significance, functional status, immune infiltration, genomic alteration, and drug sensitivity in different subgroups. Whole-genome microarray analysis was performed to analyze the regulation of gut microbiota on the expression of PRGs.

Results: Two distinct molecular subtypes were identified and suggested that multilayer PRG alterations were associated with patient clinicopathological features, prognosis, and tumor microenvironment (TME) infiltrating characteristics. Furthermore, we obtained eight PRG signatures by applying differential expression analysis and univariate Cox and Lasso regression analyses. A risk prognosis model was constructed for predicting overall survival (OS) and recurrence-free survival (RFS) based on the PRG signature. There were significant differences in clinical characteristics, 22 immune cells, and immune functions between the high- and low-risk groups. In addition, the PRG signature was significantly associated with the microsatellite instability (MSI), tumor mutation burden (TMB), cancer stem cell (CSC) index, immunotherapeutic characteristics, and chemotherapeutic drug sensitivity. Moreover, the in vitro experiments had shown that Fusobacterium nucleatum (F.n) could affect the CASP6 expression, which was associated with the chemoresistance to 5-fluorouracil (5-Fu) in CRC.

Conclusion: Our findings provided a foundation for future research targeting pyroptosis and a new insight into the prognosis and immune cell infiltration of CRC, and they suggested that F.n might influence CRC progression through pyroptosis.

Comprehensive Analysis Identifies and Validates the Tumor Microenvironment Subtypes to Predict Anti-Tumor Therapy Efficacy in Hepatocellular Carcinoma

Objective

The objective of this study was to explore and verify the subtypes in hepatocellular carcinoma based on the immune (lymphocyte and myeloid cells), stem, and stromal cells in the tumor microenvironment and analyze the biological characteristics and potential relevance of each cluster.

Methods

We used the xCell algorithm to calculate cell scores and got subtypes by k-means clustering. In the external validation sets, we verified the conclusion stability by a neural network model. Simultaneously, we speculated the inner connection between clusters by pseudotime trajectory analysis and confirmed it by pathway enrichment, TMB, CNV, etc., analysis.

Result

According to the results of the consensus cluster, we chose k = 4 as the optimal value and got four different subtypes (C1, C2, C3, and C4) with different biological characteristics based on infiltrating levels of 48 cells in TME. In univariable Cox regression, the hazard ratio (HR) value of C3 versus C1 was 2.881 (95% CI: 1.572–5.279); in multivariable Cox regression, we corrected the age and TNM stage, and the HR value of C3 versus C1 was 2.510 (95% CI: 1.339–4.706). C1 and C2 belonged to the immune-active type, C3 and C4 related to the immune-insensitive type and the potential conversion relationships between clusters. We established a neural network model, and the area under the curves of the neural network model was 0.949 in the testing cohort; the same survival results were also observed in the external validation set. We compared the differences in cell infiltration, immune function, pathway enrichment, TMB, and CNV of four clusters and speculated that C1 and C2 were more likely to benefit from immunotherapy and C3 may benefit from FGF inhibitors.

Discussion

Our analysis provides a new approach for the identification of four tumor microenvironment clusters in patients with liver cancer and identifies the biological differences and predicts the immunotherapy efficacy between the four subtypes.

Sortilin 1 Promotes Hepatocellular Carcinoma Cell Proliferation and Migration by Regulating Immune Cell Infiltration

Objectives

Recent evidence suggests that Sort1 promotes carcinogenesis and tumor progression in multiple types of cancers. This study investigates the role of Sort1 in hepatocellular carcinoma (HCC).

Methods

The differentially expressed gene was screened through GEO and TCGA databases. The Sort1 gene was identified and its expression was then verified by TCGA and HCCDB (a database of hepatocellular carcinoma expression atlas) databases. The Human Protein Atlas database was used to assess the gene expression in tissues. The TCGA and KM-plotter databases were used to study the relationship between Sort1 and HCC. The correlation between Sort1 and immune cells was evaluated through the TIMER database. GO and KEGG enrichment analysis was used to investigate the possible mechanism. The role of Sort1 in cell proliferation and invasion of HCC was further explored through in vitro experiments.

Result

The differentially expressed molecule obtained from database screening was Sort1. Its expression was higher in cancer tissues than in paracancerous ones, and it was mainly located in the cytoplasm. The TCGA, KM-plotter databases, and our study data showed that low expression of Sort1 in HCC patients had better overall survival (OS), progression-free survival (PFI), and disease-specific survival (DSS). Further analysis indicated a significant correlation between Sort1 expression and immune cell infiltration. The gene set enrichment analysis (GSEA) analysis showed that Sort1 affected the biological events of HCC by participating in the WNT, TGF-BETA, JAK, STAT, and CALCIUM signaling pathways. In vitro, cytological experiments demonstrated reduced expression of PCNA, Ki-67, Vimentin, N-cadherin, and MMP-9 mRNA after knocking down Sort1, although E-cadherin expression was promoted. Overall, these processes reduced the ability of proliferation and invasion of HCC cells.

Conclusion

Downregulation of Sort1 can prolong the OS, PFI, and DSS of HCC patients. Furthermore, due to its link with immune cell infiltration, the Sort1 gene represents a potentially novel predictive biomarker of HCC. The growth of HCC can be significantly inhibited by interfering with Sort1; therefore, these results provide a potential target for developing anticancer strategies for HCC.

Modulation of Lymphocyte Functions in the Microenvironment by Tumor Oncogenic Pathways

Despite the broad application of different immunotherapeutic strategies for the treatment of solid as well as hematopoietic cancers, the efficacy of these therapies is still limited, with only a minority of patients having a long-term benefit resulting in an improved survival rate. In order to increase the response rates of patients to the currently available immunotherapies, a better understanding of the molecular mechanisms underlying the intrinsic and/or extrinsic resistance to treatment is required. There exist increasing evidences that activation of different oncogenic pathways as well as inactivation of tumor suppressor genes (TSG) in tumor cells inhibit the immune cell recognition and influegnce the composition of the tumor microenvironment (TME), thus leading to an impaired anti-tumoral immune response. A deeper understanding of the link between the tumor milieu and genomic alterations of TSGs and oncogenes is indispensable for the optimization of immunotherapies and to predict the patients’ response to these treatments. This review summarizes the role of different cancer-related, oncogene- and TSG-controlled pathways in the context of anti-tumoral immunity and response to different immunotherapies.

Title: Multi-Omics and Immune Landscape of Proliferative LncRNA Signatures: Implications for Risk Stratification and Immunotherapy in Hepatocellular Carcinoma

Background: Long noncoding RNAs (lncRNAs) are significantly implicated in tumor proliferation. Nevertheless, proliferation-derived lncRNAs and their latent clinical significance remain largely unrevealed in hepatocellular carcinoma (HCC).

Methods: This research enrolled 658 HCC patients from five independent cohorts. We retrieved 50 Hallmark gene sets from the MSigDB portal. Consensus clustering was applied to identify heterogeneous proliferative subtypes, and the nearest template prediction (NTP) was utilized to validate the subtypes. We introduced an integrative framework (termed “ProLnc”) to identify proliferation-derived lncRNAs. Moreover, a proliferation-related signature was developed and verified in four independent cohorts.

Results: In 50 Hallmarks, seven proliferation pathways were significantly upregulated and correlated with a worse prognosis. Subsequently, we deciphered two heterogeneous proliferative subtypes in TCGA-LIHC. Subtype 2 displayed enhanced proliferative activities and a worse prognosis, whereas subtype 1 was associated with hyperproliferative HCC and a favorable prognosis. The NTP further verified the robustness and reproducibility of two subtypes in four cohorts derived from different platforms. Combining the differentially expressed lncRNAs from two subtypes with proliferative lncRNA modulators from our ProLnc pipeline, we determined 230 proliferation-associated lncRNAs. Based on the bootstrapping channel and the verification of multiple cohorts, we further identified ten lncRNAs that stably correlated with prognosis. Subsequently, we developed and validated a proliferative lncRNA signature (ProLncS) that could independently and accurately assess the overall survival (OS) and relapse-free survival (RFS) of HCC patients in the four cohorts. Patients with high ProLncS score displayed significantly genomic alterations (e.g., TP53 mutation, 8p23-8p24 copy number variation) and higher abundances of immune cells and immune checkpoint molecules, which suggested immunotherapy was more suitable for patients with high ProLncS score.

Conclusion: Our work provided new insights into the heterogeneity of tumor proliferation, and ProLncS could be a prospective tool for tailoring the clinical decision and management of HCC.

m6A Methylation Patterns and Tumor Microenvironment Infiltration Characterization in Clear-Cell Renal Cell Carcinoma

Increasing evidence suggests the essential regulation of RNA N6-methyladenosine (m6A) modification in carcinogenesis and immune response. Nevertheless, the potential impacts of these modifications on the tumor microenvironment (TME) immune cell infiltration characteristics in clear-cell renal cell carcinoma (ccRCC) remain unclear. Utilizing a consensus clustering algorithm, we determined three m6A modification patterns and identified three m6A-related gene clusters among 569 ccRCC samples, which were associated with different biological functions and clinical outcomes. Thereafter, the m6A score was constructed using m6A-associated signature genes to accurately exploit the m6A modification patterns within individual tumors. The m6A score was further demonstrated to be noticeably related to ccRCC prognosis. In addition, the m6A score was found to be strongly correlated with tumor mutational burden (TMB), microsatellite instability, immune infiltration, immune checkpoint expression, and immunotherapy response, which was also validated in the pan-cancer analyses. Our findings thoroughly elucidated that m6A modification contributes to tumor microenvironment immune-infiltrating characteristics and prognosis in ccRCC. Assessing the m6A modification patterns of individual patients with ccRCC will offer novel insights into TME infiltration and help develop more effective treatment strategies.

Construction of a Novel LncRNA Signature Related to Genomic Instability to Predict the Prognosis and Immune Activity of Patients With Hepatocellular Carcinoma

Background

Genomic instability (GI) plays a crucial role in the development of various cancers including hepatocellular carcinoma. Hence, it is meaningful for us to use long non-coding RNAs related to genomic instability to construct a prognostic signature for patients with HCC.

Methods

Combining the lncRNA expression profiles and somatic mutation profiles in The Cancer Genome Atlas database, we identified GI-related lncRNAs (GILncRNAs) and obtained the prognosis-related GILncRNAs through univariate regression analysis. These lncRNAs obtained risk coefficients through multivariate regression analysis for constructing GI-associated lncRNA signature (GILncSig). ROC curves were used to evaluate signature performance. The International Cancer Genomics Consortium (ICGC) cohort, and in vitro experiments were used for signature external validation. Immunotherapy efficacy, tumor microenvironments, the half-maximal inhibitory concentration (IC50), and immune infiltration were compared between the high- and low-risk groups with TIDE, ESTIMATE, pRRophetic, and ssGSEA program.

Results

Five GILncRNAs were used to construct a GILncSig. It was confirmed that the GILncSig has good prognostic evaluation performance for patients with HCC by drawing a time-dependent ROC curve. Patients were divided into high- and low-risk groups according to the GILncSig risk score. The prognosis of the low-risk group was significantly better than that of the high-risk group. Independent prognostic analysis showed that the GILncSig could independently predict the prognosis of patients with HCC. In addition, the GILncSig was correlated with the mutation rate of the HCC genome, indicating that it has the potential to measure the degree of genome instability. In GILncSig, LUCAT1 with the highest risk factor was further validated as a risk factor for HCC in vitro. The ESTIMATE analysis showed a significant difference in stromal scores and ESTIMATE scores between the two groups. Multiple immune checkpoints had higher expression levels in the high-risk group. The ssGSEA results showed higher levels of tumor-antagonizing immune cells in the low-risk group compared with the high-risk group. Finally, the GILncSig score was associated with chemotherapeutic drug sensitivity and immunotherapy efficacy of patients with HCC.

Conclusion

Our research indicates that GILncSig can be used for prognostic evaluation of patients with HCC and provide new insights for clinical decision-making and potential therapeutic strategies.

Immunologic Gene Sets Reveal Features of the Tumor Immune Microenvironment and Predict Prognosis and Immunotherapy Response: A Pan-Cancer Analysis

In the treatment of cancer, anti-programmed cell death-1 (PD-1)/programmed cell death-ligand 1 (PD-L1) immunotherapy has achieved unprecedented clinical success. However, the significant response to these therapies is limited to a small number of patients. This study aimed to predict immunotherapy response and prognosis using immunologic gene sets (IGSs). The enrichment scores of 4,872 IGSs in 348 patients with metastatic urothelial cancer treated with anti-PD-L1 therapy were computed using gene set variation analysis (GSVA). An IGS-based classification (IGSC) was constructed using a nonnegative matrix factorization (NMF) approach. An IGS-based risk prediction model (RPM) was developed using the least absolute shrinkage and selection operator (LASSO) method. The IMvigor210 cohort was divided into three distinct subtypes, among which subtype 2 had the best prognosis and the highest immunotherapy response rate. Subtype 2 also had significantly higher PD-L1 expression, a higher proportion of the immune-inflamed phenotype, and a higher tumor mutational burden (TMB). An RPM was constructed using four gene sets, and it could effectively predict prognosis and immunotherapy response in patients receiving anti-PD-L1 immunotherapy. Pan-cancer analyses also demonstrated that the RPM was capable of accurate risk stratification across multiple cancer types, and RPM score was significantly associated with TMB, microsatellite instability (MSI), CD8+ T-cell infiltration, and the expression of cytokines interferon-γ (IFN-γ), transforming growth factor-β (TGF-β) and tumor necrosis factor-α (TNF-α), which are key predictors of immunotherapy response. The IGSC strengthens our understanding of the diverse biological processes in tumor immune microenvironment, and the RPM can be a promising biomarker for predicting the prognosis and response in cancer immunotherapy.

Identification of the Tumor Immune Microenvironment and Therapeutic Biomarkers by a Novel Molecular Subtype Based on Aging-Related Genes in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors with poor prognosis. Increasing evidence has revealed that immune cells and checkpoints in the tumor microenvironment (TME) and aging are associated with the prognosis of HCC. However, the association between aging and the tumor immune microenvironment (TIME) in HCC is still unclear.

Methods

RNA expression profiles and clinical data concerning HCC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Based on differentially expressed aging-related genes (DEAGs), unsupervised clustering was used to identify a novel molecular subtype in HCC. The features of immune cell infiltration and checkpoints were further explored through CIBERSORTx. Enrichment analysis and both univariate and multivariate Cox analyses were conducted to construct a 3-gene model for predicting prognosis and chemosensitivity. Finally, the mRNA and protein expression levels of the 3 genes were verified in HCC and other cancers through database searches and experiments.

Results

Eleven differentially expressed AGs (GHR, APOC3, FOXM1, PON1, TOP2A, FEN1, HELLS, BUB1B, PPARGC1A, PRKDC, and H2AFX) correlated with the prognosis of HCC were used to divide HCC into two subtypes in which the prognosis was different. In cluster 2, which had a poorer prognosis, the infiltration of naive B cells and monocytes was lower in the TCGA and GEO cohorts, while the infiltration of M0 macrophages was higher. In addition, the TCGA cohort indicated that the microenvironment of cluster 2 had more immunosuppression through immune checkpoints. Enrichment analysis suggested that the MYC and E2F targets were positively associated with cluster 2 in the TCGA and GEO cohorts. Additionally, 3 genes (HMGCS2, SLC22A1, and G6PD) were screened to construct the prognostic model through univariate/multivariate Cox analysis. Then, the model was validated through the TCGA validation set and GEO dataset (GSE54236). Cox analysis indicated that the risk score was an independent prognostic factor and that patients in the high-risk group were sensitive to multiple targeted drugs (sorafenib, gemcitabine, rapamycin, etc.). Finally, significantly differential expression of the 3 genes was detected across cancers.

Conclusion

We systematically described the immune differences in the TME between the molecular subtypes based on AGs and constructed a novel three-gene signature to predict prognosis and chemosensitivity in patients with HCC.

Development and Validation of Ischemic Events Related Signature After Carotid Endarterectomy

Background: Ischemic events after carotid endarterectomy (CEA) in carotid artery stenosis patients are unforeseeable and alarming. Therefore, we aimed to establish a novel model to prevent recurrent ischemic events after CEA.

Methods: Ninety-eight peripheral blood mononuclear cell samples were collected from carotid artery stenosis patients. Based on weighted gene co-expression network analysis, we performed whole transcriptome correlation analysis and extracted the key module related to ischemic events. The biological functions of the 292 genes in the key module were annotated via GO and KEGG enrichment analysis, and the protein-protein interaction (PPI) network was constructed via the STRING database and Cytoscape software. The enrolled samples were divided into train (n = 66), validation (n = 28), and total sets (n = 94). In the train set, the random forest algorithm was used to identify critical genes for predicting ischemic events after CEA, and further dimension reduction was performed by LASSO logistic regression. A diagnosis model was established in the train set and verified in the validation and total sets. Furthermore, fifty peripheral venous blood samples from patients with carotid stenosis in our hospital were used as an independent cohort to validation the model by RT-qPCR. Meanwhile, GSEA, ssGSEA, CIBERSORT, and MCP-counter were used to enrichment analysis in high- and low-risk groups, which were divided by the median risk score.

Results: We established an eight-gene model consisting of PLSCR1, ECRP, CASP5, SPTSSA, MSRB1, BCL6, FBP1, and LST1. The ROC-AUCs and PR-AUCs of the train, validation, total, and independent cohort were 0.891 and 0.725, 0.826 and 0.364, 0.869 and 0.654, 0.792 and 0.372, respectively. GSEA, ssGSEA, CIBERSORT, and MCP-counter analyses further revealed that high-risk patients presented enhanced immune signatures, which indicated that immunotherapy may improve clinical outcomes in these patients.

Conclusion: An eight-gene model with high accuracy for predicting ischemic events after CEA was constructed. This model might be a promising tool to facilitate the clinical management and postoperative surveillance of carotid artery stenosis patients.

Molecular Characterization Clinical and Immunotherapeutic Characteristics of m5C Regulator NOP2 Across 33 Cancer Types

Background: Recent studies have identified that RNA 5-methylcytosine (m5C) is a wide-spread epigenetic modification in tumorigenesis. However, the clinical and immunotherapeutic values of m5C regulator NOP2 in 33 cancers remain unclear.

Methods: The mRNA expression data and clinical data of 33 cancers were downloaded from The Cancer Genome Atlas (TCGA) database. The immunotherapy data including GSE67501, GSE78220, GSE35640, and IMvigor210 were downloaded from the Gene Expression Omnibus (GEO) database and the website based on the Creative Commons 3.0 license (http://research-pub.Gene.com/imvigor210corebiologies). The expression, survival, clinical parameters, tumor mutation burden (TMB), microsatellite instability (MSI), and tumor microenvironment (TME) were evaluated. Finally, the relationship between NOP2 and immunotherapy response was further explored.

Results: NOP2 was significantly upregulated in most cancers, and high NOP2 expression was associated with poor prognosis. TMB, MSI, and NOP2 activities were involved in the dysregulation of NOP2. NOP2 was closely associated with immune cell infiltration, immune modulators, and immunotherapeutic inactivation.

Conclusions: We comprehensively explored the clinical and immunotherapeutic values of NOP2 in cancers, providing evidence regarding the function of NOP2 and its role in clinical treatment.

Pan-Cancer Analysis Identified CD93 as a Valuable Biomarker for Predicting Patient Prognosis and Immunotherapy Response

Background: The rapid development of immunotherapy has significantly improved patient outcomes in recent years. CD93, a novel biomarker expressed on vascular endothelial cells, is essential for tumor angiogenesis. Recent studies have shown that CD93 is closely related to immune cell infiltration and immunotherapy. However, its role in pan-cancer has not been reported.

Methods: The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA), cbioportal, Gene Expression Omnibus (GEO), Tumor Immune Estimation Resource (TIMER2.0), and the Tumor–Immune System Interactions and Drug Bank (TISIDB) databases were used to analyze CD93 in pan-cancers. R software was used for statistical analysis and mapping.

Results: There were significant differences in the expression of CD93 between tumor tissues and adjacent normal tissues in pan-cancer. The high expression of CD93 was associated with poor prognosis and high TNM stage in multiple tumor types. However, a high expression of CD93 was a protective factor in kidney renal clear cell carcinoma (KIRC). In addition, CD93 was closely related to immune cell infiltration in tumor tissues. Moreover, CD93 presented a robust correlation with immune modulators and immunotherapeutic markers [e.g., tumor mutation burden (TMB) and microsatellite instability (MSI)]. The results of gene set enrichment analysis (GSEA) showed that CD93 was correlated with tumor angiogenesis. Importantly, patients with a low expression of CD93 were more sensitive to immunotherapy in urothelial cancer.

Conclusion: CD93, which is involved in various immune responses, controls immune cell infiltration and impacts on the malignant properties of various cancer types. Therefore, CD93 has potential value to be biomarker for determining the prognosis and immune infiltration in multiple cancers.

ASF1B Serves as a Potential Therapeutic Target by Influencing Cell Cycle and Proliferation in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with high morbidity and mortality. Therefore, it is very important to find potential biomarkers that can effectively predict the prognosis and progression of HCC. Recent studies have shown that anti-silencing function 1B (ASF1B) may be a new proliferative marker for tumor diagnosis and prognosis. However, the expression and function of ASF1B in hepatocellular carcinoma remain to be determined. In this study, integrated analysis of the Cancer Genome Atlas (TCGA), genotypic tissue expression (GTEx), and Gene Expression Omnibus (GEO) databases revealed that ASF1B was highly expressed in HCC. Kaplan-Meier survival curve showed that elevated ASF1B expression was associated with poor survival in patients with liver cancer. Correlation analysis of immune infiltration suggested that ASF1B expression was significantly correlated with immune cell infiltration in HCC patients. Gene set enrichment analysis (GSEA) indicated that ASF1B regulated the cell cycle, DNA Replication and oocyte meiosis signaling. Our experiments confirmed that ASF1B was highly expressed in HCC tissues and HCC cell lines. Silence of ASF1B inhibited hepatocellular carcinoma cell growth in vitro. Furthermore, ASF1B deficiency induced apoptosis and cell cycle arrest. Mechanistically, ASF1B knockdown reduced the expression of proliferating cell nuclear antigen (PCNA), cyclinB1, cyclinE2 and CDK9.Moreover, ASF1B interacted with CDK9 in HCC cells. Taken together, these results suggest that the oncogenic gene ASF1B could be a target for inhibiting hepatocellular carcinoma cell growth.

Positive Correlation Between LTA Expression and Overall Immune Activity Suggests an Increased Probability of Survival in Uterine Corpus Endometrial Carcinoma

Mounting evidence indicates that immune status plays a crucial role in tumor progress and metastasis, while there are no effective and easily assayed biomarkers to reflect it in uterine corpus endometrial carcinoma (UCEC) patients. Here, we attempted to identify the potential biomarkers that were differentially expressed between normal and tumor tissues and involved in prognosis and immune microenvironment of UCEC patients. RNA-seq data with relevant clinical information were obtained from The Cancer Genome Atlas (TCGA). ssGSEA algorithm was applied to calculate the enrichment scores of every tumor infiltration lymphocyte (TIL) set in each sample, and patients were then divided into three clusters using multiple R packages. Cox analysis, ESTIMATE, and CIBERSORT were utilized to determine the differentially expressed immune-related genes (DEIGs) with overall survival, and to explore their roles in prognosis, immune microenvironment, and immunotherapeutic response. The TIMER and TISIDB databases were utilized to predict the effectiveness of immunotherapy in UCEC patients. LTA was finally identified to be significantly upregulated in tumor tissues and closely associated with prognosis and immunological status, which was then verified in GSE17025. In multivariate analysis, the hazard ratio of LTA was 0.42 with 95% CI (0.22–0.80) (p = 0.008). Patients with high LTA expression had better survival and apparently immune-activated phenotypes, such as more tumor mutation burden (TMB), stronger immune cell infiltrations, higher expression of immunosuppressive points, and higher immunophenoscore, meaning they had an immunotherapeutic advantage over those with low LTA expression. TIMER and TISIDB indicated that LTA was highly expressed in UCEC, and its expression was negatively correlated with stages and positively related to prognosis. Additionally, we found that LTA ectopic expression weakened the proliferation ability of RL95-2 cells. All these findings indicated that LTA could act as a novel and easily assayed biomarker to predict immunological status and clinical outcomes and even as an antioncogene to explore UCEC in depth.

Histone Acetylation Regulator-Mediated Acetylation Patterns Define Tumor Malignant Pathways and Tumor Microenvironment in Hepatocellular Carcinoma

Background

Histone acetylation modification is one of the most common epigenetic methods used to regulate chromatin structure, DNA repair, and gene expression. Existing research has focused on the importance of histone acetylation in regulating tumorigenicity, tumor progression, and tumor microenvironment (TME) but has not explored the potential roles and interactions of histone acetylation regulators in TME cell infiltration, drug sensitivity, and immunotherapy.

Methods

The mRNA expression and genetic alterations of 36 histone acetylation regulators were analyzed in 1599 hepatocellular carcinoma (HCC) samples. The unsupervised clustering method was used to identify the histone acetylation patterns. Then, based on their differentially expressed genes (DEGs), an HAscore model was constructed to quantify the histone acetylation patterns and related subtypes of individual samples. Lastly, the relationship between HAscore and transcription background, tumor clinical features, characteristics of TME, drug response, and efficacy of immunotherapy were analyzed.

Results

We identified three histone acetylation patterns characterized by high, medium, and low HAscore. Patients with HCC in the high HAscore group experienced worse overall survival time, and the cancer-related malignant pathways were more active in the high HAscore group, comparing to the low HAscore group. The high HAscore group was characterized by an immunosuppressive subtype because of the high infiltration of immunosuppressive cells, such as regulatory T cells and myeloid-derived suppressor cells. Following validation, the HAscore was highly correlated with the sensitivity of anti-tumor drugs; 116 therapeutic agents were found to be associated with it. The HAscore was also correlated with the therapeutic efficacy of the PD-L1 and PD-1 blockade, and the response ratio was significantly higher in the low HAscore group.

Conclusion

To the best of our knowledge, our study is the first to provide a comprehensive analysis of 36 histone acetylation regulators in HCC. We found close correlations between histone acetylation patterns and tumor malignant pathways and TME. We also analyzed the therapeutic value of the HAscore in targeted therapy and immunotherapy. This work highlights the interactions and potential clinical utility of histone acetylation regulators in treatment of HCC and improving patient outcomes.

The Prognostic Significance and Potential Mechanism of Ferroptosis-Related Genes in Hepatocellular Carcinoma

Ferroptosis exerts a pivotal role in the formation and dissemination processes of hepatocellular carcinoma (HCC). The heterogeneity of ferroptosis and the link between ferroptosis and immune responses have remained elusive. Based on ferroptosis-related genes (FRGs) and HCC patients from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) cohorts, we comprehensively explored the heterogeneous ferroptosis subtypes. The genetic alterations, consensus clustering and survival analysis, immune infiltration, pathway enrichment analysis, integrated signature development, and nomogram building were further investigated. Kaplan-Meier plotter confirmed statistically differential probabilities of survival among the three subclusters. Immune infiltration analysis showed there were clear differences among the types of immune cell infiltration, the expression of PD-L1, and the distribution of TP53 mutations among the three clusters. Univariate Cox regression analysis, random survival forest, and multivariate Cox analysis were used to identify the prognostic integrated signature, including MED8, PIGU, PPM1G, RAN, and SNRPB. Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) curves revealed the satisfactory predictive potential of the five-gene model. Subsequently, a nomogram was established, which combined the signature with clinical factors. The nomogram including the ferroptosis-based signature was conducted and showed some clinical net benefits. These results facilitated an understanding of ferroptosis and immune responses for HCC.