Multi-omics analysis reveals prognostic value of tumor mutation burden in hepatocellular carcinoma

Autophagy in Multiple Sclerosis: Phagocytosis and Autophagy of Oligodendrocyte Precursor Cells

Multiple sclerosis (MS) is a leading cause of chronic neurological dysfunction in young to middle-aged adults, affecting approximately 2.5 million people worldwide. It is characterized by inflammation, multifocal demyelination, axonal loss, and white and gray matter gliosis. Autophagy is a highly conserved protein degradation pathway. Polymorphisms in autophagy-related genes have been implicated in a variety of autoimmune diseases, including systemic lupus erythematosus, rheumatoid arthritis, psoriasis and MS. However, the significance of autophagy in MS remains to be elucidated. This paper aims to explore the potential role of autophagy-related genes in MS diseases by using bioinformatics combined with machine learning methods. Finally, we obtained 9 autophagy genes with the highest correlation with MS, and further changes in these autophagy genes were verified in the experimental autoimmune encephalomyelitis (EAE) model and oligodendrocyte precursor cells (OPCs) engulfed myelin debris (MD). Combined with bioinformatic analysis and experimental data, Becn1 showed obvious expression abnormalities suggesting that this gene has vital functions in autophagy and MD engulfed by OPCs. This work will be of great significance for the further exploration of autophagy-related genes in demyelinating diseases.

Whole transcriptome sequencing identifies key lncRNAs,circRNAs, and mRNAs for exploring the pathogenesis and therapeutic target of mouse pneumoconiosis

BACKGROUND

Pneumoconiosis is a kind of lung dysfunction caused by the inhalation of mineral dust. However, the potential molecular mechanism of pneumoconiosis have not been fully elucidated.

METHODS

In this study, the silica-treated pneumoconiosis mice model was constructed and the transcriptome sequencing data including lncRNA, circRNA, and mRNA were obtained. Firstly, differentially expressed lncRNA, circRNA, and mRNA (DElncRNA, DEcircRNA, DEGs) between control and pneumoconiosis/silicosis samples were screened, the target miRNAs (co-pre-miRNAs) were obtained by intersecting the miRNAs predicted by DElncRNA and DEcircRNA, respectively, and the target mRNAs (co-mRNA) were obtained by intersecting the mRNAs predicted by target miRNA and DEGs. Then, the lncRNA/circRNA-miRNA-mRNA networks were constructed by Cytoscape. Next, the key mRNAs were obtained by protein-protein interaction (PPI) analysis, and the key lncRNAs/circRNAs were selected by correlation analysis. Moreover, the expression of the key lncRNAs, circRNAs and mRNAs on chromosome were studied by the "circlize" package. Furthermore, the TFs-miRNA-mRNA network was constructed and the function of DEGs were explored by Ingenuity Pathway Analysis (IPA). To demonstrate the feasibility and value of the constructed ceRNA networks, we validated key genes and mmu-miR-682 pathway. Finally, We used the Drug-Gene Interaction database to predict potential drugs that could interfere with key genes,which may help to find promising treatment.

RESULTS

There were 427 DElncRNAs, 107 DEcircRNAs and 1,597 DEGs between silicosis and control groups. Totals of 77 co-pre-miRNAs and 96 co-mRNA were screened, and the lncRNA/circRNA-miRNA-mRNA networks were constructed with 27 lncRNA/25 circRNAs, 74 miRNAs and 96 mRNAs. Then, 6 key mRNAs including Igf1, Klf4, Ptgs2, Epas1, Gnao1, and Il1a were obtained by PPI, and all of these key mRNAs and 10 key lncRNAs and 8 circRNAs were significantly different between the pneumoconiosis and normal groups, in which 10 lncRNAs and 9 circRNA that have not been previously studied in pneumoconiosis/silicosis can be used as new potential therapeutic targets. Moreover, the TFs-miRNA-mRNA network were constructed with 11 TFs, 1 key miRNA (mmu-miR-682) and 3 key mRNAs (Igf1, Epas1, Ptgs2). And the validation of key genes revealing by RNA-seq through experimental approaches shows the the predictive power of this study. Finally, IPA results indicated that 41 pathways were activated and 2 pathways were suppressed in pneumoconiosis/silicosis groups, and Pathogen Induced Cytokine Storm Signaling Pathway was the most significant pathway affected by pneumoconiosis/silicosis. In addition, 93 drugs were screened out by Drug-Gene Interaction database. Among them, Hydroxychloroquine was a kind of drug which associated with Il1a and Ptgs2, may be a promising treatment.

CONCLUSION

This study constructed the lncRNA/circRNA-miRNA-mRNA and TFs-miRNA-mRNA networks, which could deepen the potential molecular regulatory mechanism of pneumoconiosis/silicosis.

Exploring the mechanism of LncRNA CASC15 affecting hepatocellular carcinoma through miRNA

This study aimed to determine the potential mechanisms through which long noncoding (Lnc) RNA cancer susceptibility candidate 15 (CASC15) affects hepatocellular carcinoma (HCC). We retrieved HCC RNA-seq and clinical information from the UCSC Xena database. The differential expression (DE) of CASC15 was detected. Overall survival was analyzed using Kaplan-Meier (K-M) curves. Molecular function and signaling pathways affected by CASC15 were determined using Gene Set Enrichment Analysis. Associations between CASC15 and the HCC microenvironment were investigated using immuno-infiltration assays. A differential CASC15-miRNA-mRNA network and HCC-specific CASC15-miRNA-mRNA ceRNA network were constructed. The overexpression of CASC15 in HCC tissues was associated with histological grade, clinical stage, pathological T stage, poor survival, more complex immune cell components, and 12 immune checkpoints. We identified 27 DE miRNAs and 270 DE mRNAs in the differential CASC15-miRNA-mRNA network, and 10 key genes that were enriched in 12 cancer-related signaling pathways. Extraction of the HCC-specific CASC15-miRNA-mRNA network revealed that IGF1R, MET, and KRAS were associated with HCC progression and occurrence. Our bioinformatic findings confirmed that CASC15 is a promising prognostic biomarker for HCC, and elevated levels in HCC are associated with the tumor microenvironment. We also constructed a disease-specific CASC15-miRNA-mRNA regulatory ceRNA network that provides a new perspective for the precise indexing of patients with elevated levels of CASC15.

Screening of Immune-related lncRNAs in Lung Adenocarcinoma and Establishing a Survival Prognostic Risk Prediction Model

OBJECTIVE

This study aimed to improve lung adenocarcinoma (LUAD) prognosis prediction based on a signature of immune-related long non-coding RNAs (lncRNAs).

METHODS

LUAD samples from the TCGA database were divided into the immunity_H group and the immunity_L group. Differentially expressed RNAs (DERs) between the two groups were identified. Optimized immune-related lncRNAs combination was obtained using LASSO Cox regression. A prognostic risk prediction (RS) model was built and further validated in the training and validation datasets. A network among lncRNAs in the RS model, their co-expressed DERs, and the related KEGG pathways were established. Critical lncRNAs were validated in LUAD tissue samples.

RESULTS

In total, 255 DERs were obtained, and 11 immune-related lncRNAs were significantly related to prognosis. Six lncRNAs were demonstrated as an optimal combination for building the RS model, including LINC00944, LINC00930, LINC00607, LINC00582, LINC00543, and LINC00319. The KM curve and ROC curve revealed the RS model to be a reliable indicator for LUAD prognosis. LINC00944 and LINC00582 showed a co-expression relationship with the MS4A1. LINC00944, LINC00582, and MS4A1 were successfully validated in LUAD samples.

CONCLUSION

We have established a promising LUAD patient survival prediction model based on six immune-related lncRNAs. For LUAD patients, this prognostic model could guide personalized treatment.

Comprehensive evaluation of cell death-related genes as novel diagnostic biomarkers for breast cancer

Background

Breast cancer (BRCA) ranks first among cancers in terms of incidence and mortality rates in women, primarily owing to metastasis, chemo-resistance, and heterogeneity. To predict long-term prognosis and design novel therapies for BRCA, more sensitive markers need to be explored.

Methods

Data from 1089 BRCA patients were downloaded from TCGA database. Pearson's correlation analysis and univariate and multivariate Cox regression analyses were performed to assess the role of cell death-related genes (CDGs) in predicting BRCA prognosis. Kaplan-Meier survival curves were generated to compare the overall survival in the two subgroups. A nomogram was constructed using risk scores based on the five CDGs and other clinicopathological features. CCK-8, EdU incorporation, and colony formation assays were performed to verify the inhibitory effect of NFKBIA on BRCA cell proliferation. Transwell assay, flow cytometry, and immunohistochemistry analyses were performed to ascertain the biological function of NFKBIA.

Results

Five differentially expressed CDGs were detected among 156 CDGs. The risk score for each patient was then calculated based on the expression levels of the five CDGs. Distinct differences in immune infiltration, expression of immune-oncological targets, mutation status, and half-maximal inhibitory concentration values of some targeted drugs were observed between the high- and low-risk groups. Finally, in vitro cell experiments verified that NFKBIA overexpression suppresses the proliferation and migration of BRCA cells.

Conclusions

Our study revealed that some CDGs, especially NFKBIA, could serve as sensitive markers for predicting the prognosis of patients with BRCA and designing more personalized clinical therapies.

A cuproptosis-related lncRNA signature for predicting prognosis and immune response in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) has a high incidence and poor prognosis. Cuproptosis is a novel type of cell death, which differs from previously reported types of cell death such as apoptosis, autophagy, proptosis, ferroptosis, necroptosis, etc. Long non-coding RNAs (lncRNAs) play multiple roles in HCC.

Methods

We downloaded information from The Cancer Genome Atlas (TCGA) database, and obtained cuproptosis-related genes from published studies. The cuproptosis-related lncRNAs were obtained by correlation analysis, and subsequently used to construct a prognostic cuproptosis-related lncRNA signature. Analyses of overall survival (OS), progression-free survival (PFS), receiver operating characteristic (ROC) curve with the area under the curve (AUC) values and the index of concordance (c-index) curve were used to evaluate the signature. The tumor microenvironment (TME) was analyzed by ESTIMATE algorithm. The immune cell data was downloaded from the Tumor Immune Estimation Resource (TIMER) 2.0 database. Immune-related pathways were analyzed by single-sample gene set enrichment analysis (ssGSEA) algorithm. Immunophenoscore (IPS) scores from The Cancer Immunome (TCIA) database were used to evaluate immunotherapy response. The "pRRophetic" was employed to screen drugs for high-risk patients. The candidate lncRNA expression levels were detected by Real Time Quantitative PCR.

Results

We constructed a cuproptosis-related lncRNA signature containing seven lncRNAs: AC125437.1, PCED1B-AS1, PICSAR, AP001372.2, AC027097.1, LINC00479, and SLC6A1-AS1. This signature had excellent accuracy, and was independent of the stratification of clinicopathological features. Further study showed that high-risk tumors under this signature had higher TMB, fewer TME components and higher tumor purity. The tumors with high risk were not enriched in immune cell infiltration or immune process pathways, and high-risk patients had a poor response to immunotherapy. Moreover, 29 drugs such as sorafenib, dasatinib and paclitaxel were screened for high-risk HCC patients to improve their prognosis. The expression levels of the candidate lncRNAs in HCC tissue were significantly increased (except PCED1B-AS1).

Conclusions

Our prognostic cuproptosis-related lncRNA signature was accurate and effective for predicting the prognosis of HCC. The immunotherapy was unsuitable for high-risk HCC patients with this signature.

A novel defined pyroptosis-related gene signature predicts prognosis and correlates with the tumour immune microenvironment in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the most common causes of cancer-related death. The role of pyroptosis in LUAD remains unclear. Our study aimed to identify a prognostic signature of pyroptosis-related genes (PRGs) and explore the connection of PRGs with the tumour microenvironment in LUAD. Gene expression and clinical information were obtained from The Cancer Genome Atlas database. Consensus clustering was applied to classify LUAD patients. The least absolute shrinkage and selection operator Cox and multivariate Cox regression models were used to generate a PRG-related prognostic signature. The correlations between PRGs and tumour-infiltrating immune cells or the tumour mutational burden were analysed by Spearman's correlation analysis. In this study, 44 PRGs significantly differed in expression between LUAD and normal tissues. Based on these genes, patients were clustered into three clusters with significantly different distributions of tumour-infiltrating immune cells and immune checkpoint regulators. A total of four PRGs (NLRP1, HMGB1, CYCS, and BAK1) were used to construct a prognostic model. Significant correlations were observed between these prognostic PRGs and immune cell infiltration or the tumour mutational burden. Predictive nomogram results showed that BAK1 could be an independent prognostic biomarker in LUAD. Additionally, the expression level of BAK1 was validated in two independent Gene Expression Omnibus cohorts. Our identified prognostic PRG signature may provide insight for future studies targeting pyroptosis and the tumour microenvironment in LUAD. Future studies are needed to verify our current findings.

Prognostic, Immunological, and Mutational Analysis of MTA2 in Pan-Cancer and Drug Screening for Hepatocellular Carcinoma

BACKGROUND

Metastasis-associated protein 2 (MTA2) is a member of the metastasis-associated transcriptional regulator family and is a core component of the nucleosome remodeling and histone deacetylation complex. Despite growing evidence that MTA2 plays a crucial role in the tumorigenesis of certain cancers, no systematic pan-cancer analysis of MTA2 is available to date. Therefore, the aim of our study is to explore the prognostic value of MTA2 in 33 cancer types and to investigate its potential immune function.

METHODS

by comprehensive use of databases from TCGA, GTEx, GEO, UCSC xena, cBioPortal, comPPI, GeneMANIA, TCIA, MSigDB, and PDB, we applied various bioinformatics approaches to investigate the potential role of MTA2, including analyzing the association of MTA2 with MSI, prognosis, gene mutation, and immune cell infiltration in different tumors. We constructed a nomogram in TCGA-LIHC, performed single-cell sequencing (scRNA-seq) analysis of MTA2 in hepatocellular carcinoma (HCC), and screened drugs for the treatment of HCC. Finally, immunohistochemical experiments were performed to verify the expression and prognostic value of MTA2 in HCC. In vitro experiments were employed to observe the growth inhibition effects of MK-886 on the HCC cell line HepG2.

RESULTS

The results suggested that MTA2 was highly expressed in most cancers, and MTA2 expression was associated with the prognosis of different cancers. In addition, MTA2 expression was associated with Tumor Mutation Burden (TMB) in 12 cancer types and MSI in 8 cancer types. Immunoassays indicated that MTA2 positively correlated with activated memory CD4 T cells and M0 macrophage infiltration levels in HCC. ScRNA-seq analysis based on the GEO dataset discovered that MTA2 was significantly expressed in T cells in HCC. Finally, the eXtreme Sum (Xsum) algorithm was used to screen the antitumor drug MK-886, and the molecular docking technique was utilized to reveal the binding capacity between MK-886 and the MTA2 protein. The results demonstrated excellent binding sites between them, which bind to each other through Π-alkyl and alkyl interaction forces. An immunohistochemistry experiment showed that MTA2 protein was highly expressed in HCC, and high MTA2 expression was associated with poor survival in HCC patients. MK-886 significantly inhibited the proliferation and induced cell death of HepG2 cells in a dose-dependent manner.

CONCLUSIONS

Our study demonstrated that MTA2 plays crucial roles in tumor progression and tumor immunity, and it could be used as a prognostic marker for various malignancies. MK-886 might be a powerful drug for HCC.

Comprehensive Analysis of Prognosis and Immune Landscapes Based on Lipid-Metabolism- and Ferroptosis-Associated Signature in Uterine Corpus Endometrial Carcinoma

(1) Background: The effect of tumor immunotherapy is influenced by the immune microenvironment, and it is unclear how lipid metabolism and ferroptosis regulate the immune microenvironment of uterine corpus endometrial carcinoma (UCEC). (2) Methods: Genes associated with lipid metabolism and ferroptosis (LMRGs-FARs) were extracted from the MSigDB and FerrDb databases, respectively. Five hundred and forty-four UCEC samples were obtained from the TCGA database. The risk prognostic signature was constructed by consensus clustering, univariate cox, and LASSO analyses. The accuracy of the risk modes was assessed through receiver operating characteristic (ROC) curve, nomogram, calibration,, and C-index analyses. The relationship between the risk signature and immune microenvironment was detected by the ESTIMATE, EPIC, TIMER, xCELL, quan-TIseq, and TCIA databases. The function of a potential gene, PSAT1, was measured by in vitro experiments. (3) Results: A six-gene (CDKN1A, ESR1, PGR, CDKN2A, PSAT1, and RSAD2) risk signature based on MRGs-FARs was constructed and evaluated with high accuracy in UCEC. The signature was identified as an independent prognostic parameter and it divided the samples into high- and low-risk groups. The low-risk group was positively associated with good prognosis, high mutational status, upregulated immune infiltration status, high expression of CTLA4, GZMA and PDCD1, anti-PD-1 treatment sensitivity, and chemoresistance. (4) Conclusions: We constructed a risk prognostic model based on both lipid metabolism and ferroptosis and evaluated the relationship between the risk score and tumor immune microenvironment in UCEC. Our study has provided new ideas and potential targets for UCEC individualized diagnosis and immunotherapy.

Tumor Mutational Burden for Predicting Prognosis and Therapy Outcome of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), the primary hepatic malignancy, represents the second-highest cause of cancer-related death worldwide. Many efforts have been devoted to finding novel biomarkers for predicting both patients' survival and the outcome of pharmacological treatments, with a particular focus on immunotherapy. In this regard, recent studies have focused on unravelling the role of tumor mutational burden (TMB), i.e., the total number of mutations per coding area of a tumor genome, to ascertain whether it can be considered a reliable biomarker to be used either for the stratification of HCC patients in subgroups with different responsiveness to immunotherapy, or for the prediction of disease progression, particularly in relation to the different HCC etiologies. In this review, we summarize the recent advances on the study of TMB and TMB-related biomarkers in the HCC landscape, focusing on their feasibility as guides for therapy decisions and/or predictors of clinical outcome.

A hypoxia risk score for prognosis prediction and tumor microenvironment in adrenocortical carcinoma

Background: Adrenocortical carcinoma (ACC) is a rare malignant endocrine tumor derived from the adrenal cortex. Because of its highly aggressive nature, the prognosis of patients with adrenocortical carcinoma is not impressive. Hypoxia exists in the vast majority of solid tumors and contributes to invasion, metastasis, and drug resistance. This study aimed to reveal the role of hypoxia in Adrenocortical carcinoma and develop a hypoxia risk score (HRS) for Adrenocortical carcinoma prognostic prediction. Methods: Hypoxia-related genes were obtained from the Molecular Signatures Database. The training cohorts of patients with adrenocortical carcinoma were downloaded from The Cancer Genome Atlas, while another three validation cohorts with comprehensive survival data were collected from the Gene Expression Omnibus. In addition, we constructed a hypoxia classifier using a random survival forest model. Moreover, we explored the relationship between the hypoxia risk score and immunophenotype in adrenocortical carcinoma to evaluate the efficacy of immune check inhibitors (ICI) therapy and prognosis of patients. Results: HRS and tumor stage were identified as independent prognostic factors. HRS was negatively correlated with immune cycle activity, immune cell infiltration, and the T cell inflammatory score. Therefore, we considered the low hypoxia risk score group as the inflammatory immunophenotype, whereas the high HRS group was a non-inflammatory immunophenotype. In addition, the HRS was negatively related to the expression of common immune checkpoint molecules such as PD-L1, CD200, CTLA-4, and TIGIT, suggesting that patients with a lower hypoxia risk score respond better to immunotherapy. Conclusion: We developed and validated a novel hypoxia risk score to predict the immunophenotype and response of patients with adrenocortical carcinoma to immune check inhibitors therapy. These findings not only provide fresh prognostic indicators for adrenocortical carcinoma but also offer several promising treatment targets for this disease.

Comprehensive analysis of molecular features, prognostic values, and immune landscape association of m6A-regulated immune-related lncRNAs in smoking-associated lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) is the second most common histopathological subtype of lung cancer, and smoking is the leading cause of this type of cancer. However, the critical factors that directly affect the survival rate and sensitivity to immunotherapy of smoking LUSC patients are still unknown. Previous studies have highlighted the role of N6-methyladenosine (m6A) RNA modification, the most common epigenetic modification in eukaryotic species, together with immune-related long non-coding RNAs (lncRNAs) in promoting the development and progression of tumors. Thus, elucidating m6A-modified immune lncRNAs in LUSC patients with smoking history is vital. In this study, we described the expression and mutation features of the 24 m6A-related regulators in the smoking-associated LUSC cohort from The Cancer Genome Atlas (TCGA) database. Then, two distinct subtypes based on the expression levels of the prognostic m6A-regulated immune lncRNAs were defined, and differentially expressed genes (DEGs) between the subtypes were identified. The distributions of clinical characteristics and the tumor microenvironment (TME) between clusters were analyzed. Finally, we established a lncRNA-associated risk model and exhaustively clarified the clinical features, prognosis, immune landscape, and drug sensitivity on the basis of this scoring system. Our findings give insight into potential mechanisms of LUSC tumorigenesis and development and provide new ideas in offering LUSC patients with individual and effective immunotherapies.

Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer

Backgrounds

Pyroptosis, a newly pattern of specific programmed cell death, has been reported to participate in several cancers. However, the value of pyroptosis in breast cancer (BRCA) is still not clear.

Methods

Herein, we analyzed the data of BRCA from both The Cancer Genome Atlas (TCGA) and GSEA MSigDB database. Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. After that, we performed clustering analysis by ConsensusClusterPlus. The PRGs with significant prognostic value were then screened through univariate cox regression and further evaluate by constructing a risk model by least absolute shrinkage and selection operator (LASSO) Cox regression. The immune and sensitivity to drugs were also predicted by comprehensive algorithms. Finally, real-time quantitative PCR (qPCR) was performed on two of the screened signature PRGs.

Results

A total of 49 PRGs were obtained from public database and 35 of them were significantly differentially expressed genes (DEGs). Cluster analysis was then performed to explore the relationship between DEGs with overall survival. After that, 6 optimal PRGs (GSDMC, IL-18, CHMP3, TP63, GZMB and CHMP6) were screened out to construct a prognostic signature, which divide BRCA patients into two risk groups. Risk scores were then confirmed to be independent prognostic factors in BRCA. Functional enrichment analyses showed that the signature were obviously associated with tumor-related and immune-associated pathways. 79 microenvironmental cells and 11 immune checkpoint genes were found disparate in two groups. Besides, tumor immune dysfunction and exclusion (TIDE) scores revealed that patients with higher risk scores are more sensitive to immune checkpoint blockade treatment. Patients in the low-risk group were more sensitive to Cytarabine, Docetaxel, Gefitinib, Paclitaxel, and Vinblastine. Inversely, patients in the high-risk group were more sensitive to Lapatinib. Finally, we found that, CHMP3 were down-regulated in both BRCA tissues and cell lines, while IL-18 were up-regulated.

Conclusion

PRGs play important roles in BRCA. Our study fills the gaps of 6 selected PRGs in BRCA, which were worthy for the further study as predict potential biomarkers and therapeutic targets.

LTBP2 inhibits prostate cancer progression and metastasis via the PI3K/AKT signaling pathway

Biochemical recurrence (BCR) is a cause of concern in advanced prostate cancer (PCa). Thus, novel diagnostic biomarkers are required to improve clinical care. However, research on PCa immunotherapy is also scarce. Hence, the present study aimed to explore promising BCR-related diagnostic biomarkers, and their expression pattern, prognostic value, immune response effects, biological functions, and possible molecular mechanisms were evaluated. GEO datasets (GSE46602, GSE70768, and GSE116918) were downloaded and merged as the training cohort, and differential expression analysis was performed. Lasso regression and SVM-RFE algorithm, as well as PPI analysis and MCODE algorithm, were then applied to filter BCR-related biomarker genes. The CIBERSORT and estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) methods were used to calculate the fractions of tumor-infiltrating immune cells. GO/DO enrichment analyses were used to identify the biological functions. The expression of latent transforming growth factor β-binding protein 2 (LTBP2) was determined by RT-qPCR and western blotting. The role of LTBP2 in PCa was determined by CCK-8, Transwell, and the potential mechanism was investigated by KEGG and GSEA and confirmed by western blotting. In total, 44 BCR-related differentially expressed genes (DEGs) in the training cohort were screened. LTBP2 was found to be a diagnostic biomarker of BCR in PCa and was associated with CD4⁺ T-cell infiltration and response to anti-PD-1/PD-L1 immunotherapy. Subsequently, using the ESTIMATE algorithm, it was identified that LTBP2 was associated with the tumor microenvironment and could be a predictor of the clinical benefit of immune checkpoint blockade. Finally, the expression and biological function of LTBP2 were evaluated via cellular experiments. The results showed that LTBP2 was downregulated in PCa cells and inhibited PCa proliferation and metastasis via the PI3K/AKT signaling pathway in vitro. In conclusion, LTBP2 was a promising diagnostic biomarker of BCR of PCa and had an important role in CD4⁺ T-cell recruitment. Moreover, it was associated with immunotherapy in patients with PCa who developed BCR, and it inhibited PCa proliferation and metastasis via the PI3K/AKT signaling pathway in vitro.

Novel γδ T cell-based prognostic signature to estimate risk and aid therapy in hepatocellular carcinoma

BACKGROUND

Numerous studies have revealed that gamma delta (γδ) T cell infiltration plays a crucial regulatory role in hepatocellular carcinoma (HCC) development. Nonetheless, a comprehensive analysis of γδ T cell infiltration in prognosis evaluation and therapeutic prediction remains unclear.

METHODS

Multi-omic data on HCC patients were obtained from public databases. The CIBERSORT algorithm was applied to decipher the tumor immune microenvironment (TIME) of HCC. Weighted gene co-expression network analysis (WGCNA) was performed to determine significant modules with γδ T cell-specific genes. Kaplan-Meier survival curves and receiver operating characteristic analyses were used to validate prognostic capability. Additionally, the potential role of RFESD inhibition by si-RFESD in vitro was investigated using EdU and CCK-8 assays.

RESULTS

A total of 16,421 genes from 746 HCC samples (616 cancer and 130 normal) were identified based on three distinct cohorts. Using WGCNA, candidate modules (brown) with 1755 significant corresponding genes were extracted as γδ T cell-specific genes. Next, a novel risk signature consisting of 11 hub genes was constructed using multiple bioinformatic analyses, which presented great prognosis prediction reliability. The risk score exhibited a significant correlation with ICI and chemotherapeutic targets. HCC samples with different risks experienced diverse signalling pathway activities. The possible interaction of risk score with tumor mutation burden (TMB) was further analyzed. Subsequently, the potential functions of the RFESD gene were explored in HCC, and knockdown of RFESD inhibited cell proliferation in HCC cells. Finally, a robust prognostic risk-clinical nomogram was developed and validated to quantify clinical outcomes.

CONCLUSIONS

Collectively, comprehensive analyses focusing on γδ T cell patterns will provide insights into prognosis prediction, the mechanisms of immune infiltration, and advanced therapy strategies in HCC.

Neurexophilin 4 is a prognostic biomarker correlated with immune infiltration in bladder cancer

Recent studies have shown that NXPH family member 4 (NXPH4) plays an important role in the progression of cancer. However, the potential role of NXPH4 in bladder cancer (BCa) remains to be explored. The purpose of the present study was to identify whether NXPH4 could be used as a biomarker to predict the prognosis of BCa. We first examined the expression of NXPH4 in pan-cancer, and then focused on BCa. Univariate and multivariate Cox regression analysis were used to investigate whether NXPH4 could be used as an independent prognostic indicator. Gene set enrichment analysis (GSEA) was used for functional analysis of NXPH4-related genes. CIBERSORT algorithm was used to calculate immune cell infiltration levels with different NXPH4 expression. Finally, the expression of NXPH4 was validated in clinical tissue specimens and bladder cancer cell lines by immunohistochemistry and qRT-PCR. The tumor-promoting effects of NXPH4 were further investigated using counting kit-8 (CCK-8), colony formation, EdU assays, and tumor xenograft model. Our results showed that NXPH4 was highly expressed in BCa tissues. Patients in the high NXPH4 expression group had shorter overall survival (OS) and progression-free survival (PFS). We found that immune-related pathways were enriched in NXPH4-related genes. Immune cell infiltrations in BCa were also associated with different NXPH4 expression. NXPH4 was further found to be highly expressed in our validation specimens. The proliferative effect of NXPH4 was confirmed in BCa in vivo and in vitro. Overall, NXPH4 is a biomarker for predicting BCa prognosis and associated with immune infiltration.Abbreviations: NXPH4: Neurexophilin 4; BCa: Bladder cancer; TCGA-BLCA: The Cancer Genome Atlas Urothelial Bladder Carcinoma; shRNA: short hairpin RNA; NC: Negative control; OS: Overall survival; PFS: Progression-free survival; TME: Tumor microenvironment; IPS: immunophenoscore; ICIs: Immune checkpoint inhibitors; DEGs: Differential expression genes.

Prognostic Value and Correlation With Tumor Immune Infiltration of a Novel Metabolism-Related Gene Signature in Pancreatic Cancer

Background

Energy metabolism has been considered as one of the novel features of neoplasms. This study aimed to establish the prognostic signature for pancreatic cancer (PC) based on metabolism-related genes (MRGs).

Methods

We obtained MRGs from the Molecular Signatures Database (MSigDB) and gene sequence data in the Cancer Genome Atlas (TCGA) databases. Then, differentially expressed MRGs (DE-MRGs) were identified utilizing the R software. We built the prognostic model via multivariate Cox regression. Moreover, external validation of the prognostic signature was also performed. Nomogram was created to predict the overall survival (OS). Next, this study analyzed the prognostic value, clinical relationship, and metabolism-related signaling pathways of the prognostic signature. The role in tumor infiltration was further evaluated. Eventually, the expression level of the three MRGs along with the function of NT5E was validated.

Results

Twenty-two MRGs were chosen, eight of which were identified to be most significantly correlated with the prognosis of PC. Meanwhile, a 3-MRG prognostic signature was established, and we verified this prognostic model in two separate external cohorts. What is more, the nomogram was used to predict 1-/2-/3-year OS of PC patients. In addition, the immune cell infiltration and expression of immune checkpoint were significantly influenced by the risk score. Finally, three MRGs were highly expressed in PC cell lines, and NT5E was associated with the proliferation and migration ability of PC.

Conclusion

To sum up, the study established and validated a 3-MRG prognostic signature for PC, and the signature could be utilized to predict the prognosis and assist the individualized clinical management of patients with PC.

Signature Construction and Molecular Subtype Identification Based on Pyroptosis-Related Genes for Better Prediction of Prognosis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. However, there is a lack of adequate means of treatment prognostication for HCC. Pyroptosis is a newly discovered way of programmed cell death. However, the prognostic role of pyroptosis in HCC has not been thoroughly investigated. Here, we generated a novel prognostic signature to evaluate the prognostic value of pyroptosis-related genes (PRGs) using the data from The Cancer Genome Atlas (TCGA) database. The accuracy of the signature was validated using survival analysis through the International Cancer Genome Consortium cohort (n = 231) and the First Affiliated Hospital of Wenzhou Medical University cohort (n = 180). Compared with other clinical factors, the risk score of the signature was found to be associated with better patient outcomes. The enrichment analysis identified multiple pathways related with pyroptosis in HCC. Furthermore, drug sensitivity testing identified six potential chemotherapeutic agents to provide possible treatment avenues. Interestingly, patients with low risk were confirmed to be associated with lower tumor mutation burden (TMB). However, patients at high risk were found to have a higher count of immune cells. Consensus clustering was performed to identify two main molecular subtypes (named clusters A and B) based on the signature. It was found that compared with cluster B, better survival outcomes and lower TMB were observed in cluster A. In conclusion, signature construction and molecular subtype identification of PRGs could be used to predict the prognosis of HCC, which may provide a specific reference for the development of novel biomarkers for HCC treatment.

Akt inhibitor augments anti-proliferative efficacy of a dual mTORC1/2 inhibitor by FOXO3a activation in p53 mutated hepatocarcinoma cells

Hepatocellular carcinoma (HCC) is one of the most common malignancy-related deaths. p53 mutation in HCC associates with worse clinicopathologic features including therapeutic limitation. A combination of targeted therapy may have some advantages. Akt/mTOR signaling contributes to the regulation of cell proliferation and cell death. Akt inhibitor (AZD5363) and mTORC1/2 dual inhibitor (AZD8055) are in a clinical trial for HCC and other cancers. In this study, we examined whether these inhibitors successfully induce antiproliferative activity in p53 mutant HCC cells, and the underlying mechanisms. We observed that a combination of AZD5363 and AZD8055 treatment synergizes antiproliferative activity on p53 mutated or wild-type HCC cell lines and induces apoptotic cell death. Mechanistic insights indicate that a combination of AZD5363 and AZD8055 activated FOXO3a to induce Bim-associated apoptosis in p53 mutated HCC cells, whereas cells retaining functional p53 enhanced Bax. siRNA-mediated knock-down of Bim or Bax prevented apoptosis in inhibitor-treated cells. We further observed a combination of treatment inhibits phosphorylation of FOXO3a and protects FOXO3a from MDM2 mediated degradation by preventing the phosphorylation of Akt and SGK1. FOXO3a accumulates in the nucleus under these conditions and induces Bim transcription in p53 mutant HCC cells. Combination treatment in the HCC cells expressing wild-type p53 causes interference of FOXO3a function for direct interaction with functional p53 and unable to induce Bim-associated cell death. On the other hand, Bim-associated cell death occurs in p53 mutant cells due to uninterrupted FOXO3a function. Overall, our findings suggested that a combined regimen of dual mTORC1/2 and Akt inhibitors may be an effective therapeutic strategy for HCC patients harboring p53 mutation.