Construction prognosis model based on autophagy-related gene signatures in hepatocellular carcinoma

Role of autophagy-related genes in liver cancer prognosis

Autophagy, a highly conserved process of protein and organelle degradation, has emerged as a critical regulator in various diseases, including cancer progression. In the context of liver cancer, the predictive value of autophagy-related genes remains ambiguous. Leveraging chip datasets from the TCGA and GTEx databases, we identified 23 differentially expressed autophagy-related genes in liver cancer. Notably, five key autophagy genes, PRKAA2, BIRC5, MAPT, IGF1, and SPNS1, were highlighted as potential prognostic markers, with MAPT showing significant overexpression in clinical samples. In vitro cellular assays further demonstrated that MAPT promotes liver cancer cell proliferation, migration, and invasion by inhibiting autophagy and suppressing apoptosis. Subsequent in vivo studies further corroborated the pro-tumorigenic role of MAPT by suppressing autophagy. Collectively, our model based on the five key genes provides a promising tool for predicting liver cancer prognosis, with MAPT emerging as a pivotal factor in tumor progression through autophagy modulation.

Macrophage-specific autophagy-related gene HSPB8 is involved in the macrophage polarization in atherosclerosis

BACKGROUND

Atherosclerosis (AS) is a chronic inflammatory disease, as a main cause leading to vascular diseases worldwide. Although increasing studies have focused on macrophages in AS, the exact relating mechanism is still largely unclear. Our study aimed to explore the pathogenic role and diagnostic role of macrophage autophagy related genes (MARGs) in AS.

METHODS

All datasets were downloaded from Gene Expression Omnibus database and Human Autophagy Database. The differential expression analysis and cross analysis were performed to identify candidate MARGs. GO and KEGG enrichment analyses were conducted to obtain the functional information. Moreover, we analyzed the correlation between target gene and macrophage polarization in AS. The correlation between target gene and plaque instability, different stages of AS were also analyzed.

RESULTS

Compared with normal samples, a total of 575 differentially expressed genes (DEGs) were identified in AS samples. A total of 12 overlapped genes were obtained after cross-analysis of the above 575 DEGs and autophagy related genes (ARGs). Then, 10 MARGs were identified in AS samples, which were significantly enriched in 22 KEGG pathways and 61 GO terms. The expression of HSPB8 was significantly down-regulated in atherosclerotic samples compared with normal samples (with largest fold change). Meanwhile, the proportion of M-CSF in low HSPB8 expression AS group was higher than high expression AS group. Furthermore, the expression of HSPB8 was negatively correlated with most inflammatory factors.

CONCLUSION

The downregulation of MARG HSPB8 probably involves in the M2 macrophage polarization in AS samples. HSPB8 is a promising diagnostic marker for AS patients.

HSPB8 facilitates prostate cancer progression via activating the JAK/STAT3 signaling pathway

Prostate cancer (PC) is a clinically and biologically heterogeneous disease that lacks effective treatment. Heat shock protein B8 (HSPB8) is an important factor in the progression of various types of cancer. However, the clinical significance and biological role of HSPB8 in PC are still unclear. In this study, we determined HSPB8 expression in PC tissues by immunohistochemical staining and explored the in vitro functions of HSPB8 using HSPB8 knockdown DU145 and LNcap PC cell lines. The in vivo effect of HSPB8 was explored by a subcutaneous xenograft mice model. The human phospho-kinase array and signal transducer and activator of transcription (STAT) 3 activator were utilized to explore the potential mechanism of HSPB8-induced PC progression. As a result, we found that HSPB8 was abundantly expressed in PC tissues and cell lines. HSPB8 knockdown inhibited cell proliferation and migration, promoted apoptosis and cycle repression, as well as weakened tumorigenesis ability. Mechanistically, we demonstrated that HSPB8 facilitates the malignant phenotypes of PC by activating the Janus kinase/STAT3 signaling pathway. These results proposed that HSPB8 seems to be an attractive therapeutic target for PC patients.

HSPB8 counteracts tumor activity of BRAF- and NRAS-mutant melanoma cells by modulation of RAS-prenylation and autophagy

Cutaneous melanoma is one of the most aggressive and lethal forms of skin cancer. Some specific driver mutations have been described in multiple oncogenes including BRAF and NRAS that are mutated in 60-70% and 15-20% of melanoma, respectively. The aim of this study was to evaluate the role of Small Heat Shock Protein B8 (HSPB8) on cell growth and migration of both BLM (BRAF^wt/NRAS^Q61R) and A375 (BRAF^V600E/NRAS^wt) human melanoma cell lines. HSPB8 is a member of the HSPB family of chaperones involved in protein quality control (PQC) system and contributes to chaperone assisted selective autophagy (CASA) as well as in the regulation of mitotic spindle. In cancer, HSPB8 has anti- or pro-tumoral action depending on tumor type. In melanoma cell lines characterized by low HSPB8 levels, we demonstrated that the restoration of HSPB8 expression causes cell growth arrest, reversion of EMT (Epithelial-Mesenchymal Transition)-like phenotype switching and antimigratory effect, independently from the cell mutational status. We demonstrated that HSPB8 regulates the levels of the active prenylated form of NRAS in NRAS-mutant and NRAS-wild-type melanoma cell lines. Consequently, the inhibition of NRAS impairs the activation of Akt/mTOR pathway inducing autophagy activation. Autophagy can play a dual role in regulating cell death and survival. We have therefore demonstrated that HSPB8-induced autophagy is a crucial event that counteracts cell growth in melanoma. Collectively, our results suggest that HSPB8 has an antitumoral action in melanoma cells characterized by BRAF and NRAS mutations.

Cuproptosis-Related Signature Predicts the Prognosis, Tumor Microenvironment, and Drug Sensitivity of Hepatocellular Carcinoma

Background

Copper (Cu) metabolism is strongly associated with liver disease. Cuproptosis is a novel format of cell death, and cuproptosis-related genes (CRGs) were identified. However, the role of CRGs in Hepatocellular Carcinoma (HCC) remains unknown.

Method

The mRNA transcriptome profiling data, somatic mutation data, and copy number gene level data of The Cancer Genome Atlas-Liver Hepatocellular Carcinoma project (TCGA-LIHC) were downloaded for subsequent analysis. Molecular characterization analysis of CRGs, including differential gene expression analysis, mutation analysis, copy number variation (CNV) analysis, Kaplan-Meier analysis, and immune regulator prioritization analysis, was implemented. The nonnegative matrix factorization (NMF) approach was used to identify the CRG-related molecular subtypes. Principal component analysis was adopted to verify the robustness and reliability of the molecular subtype. The least absolute shrinkage and selection operator regression analysis was performed to construct the prognostic signature based on differentially expressed genes between molecular subtypes. The survival characteristics of the molecular subtype and the signature were analyzed. The Gene Set Variation Analysis was performed for functional annotation. The immune landscape analysis, including immune checkpoint gene analysis, single sample gene set enrichment analysis, tumor immune dysfunction and exclusion (TIDE) analysis, immune infiltration cell, and tumor mutation burden analysis (TMB), was conducted. The ability of the signature to predict conventional anti-HCC agent responses was evaluated. The signature was validated in the LIRI-JP cohort and the IMvigor210 cohort.

Result

A total of 13 CRGs are differentially expressed between the tumor and normal samples, while the mutation of CRGs in HCC is infrequent. The expression of CRGs is associated with the CNV level. Fourteen CRGs are associated with the prognosis of HCC. Two clusters were identified and HCC patients were divided into 2 groups with a cutoff risk score value of 1.570. HCC patients in the C1 cluster and high-risk have a worse prognosis. The area under the receiver operating characteristic curve for predicting 1-, 2-, and 3-year overall survival is 0.775, 0.768, and 0.757 in the TCGA-LIHC cohort, and 0.811, 0.741, and 0.775 in the LIRI-JP cohort. Multivariate Cox regression analysis indicates that the signature is an independent prognostic factor. Pathways involved in metabolism and gene stability and immune infiltration cells are significantly enriched. Immune checkpoint genes are highly expressed in the C1 cluster. TMB is positively correlated with the risk score. HCC patients in the high-risk group are more likely to benefit from conventional anti-HCC agents and immune checkpoint inhibitor therapies.

Conclusion

The molecular characterization of CRGs in HCC is presented in this study, and a successful prognostic signature for HCC based on the cuproptosis-related molecular subtype was constructed.

HSPB8 is a Potential Prognostic Biomarker that Correlates With Immune Cell Infiltration in Bladder Cancer

Background: Heat shock protein B8 (HSPB8) is expressed in various cancers. However, the functional and clinicopathological significance of HSPB8 expression in bladder cancer (BC) remains unclear. The present study sought to elucidate the clinicopathological features and prognostic value of HSPB8 in BC.

Methods: A BC RNA-seq data set was obtained from The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA) database, and the external validation dataset GSE130598 was downloaded from the GEO database. Samples in the TCGA-BLCA were categorized into two groups based on HSPB8 expression. Differentially expressed genes (DEGs) between the two groups were defined as HSPB8 co-expressed genes. Gene set enrichment analysis (GSEA), protein-protein interaction networks, and mRNA-microRNA (miRNA) interaction networks were generated to predict the function and interactions of genes that are co-expressed with HSPB8. Finally, we examined immune cell infiltration and constructed a survival prediction model for BC patients.

Results: The expression level of HSBP8 has a significant difference between cancer samples and normal samples, and its diagnosis effect was validated by the ROC curve. 446 differential expressed genes between HSBP8 high-expression and HSBP8 low expression groups were identified. Gene enrichment analysis and GSEA analysis show that these differential gene functions are closely related to the occurrence and development of BC and the metabolic pathways of BC. The cancer-related pathways included Cytokine-cytokine receptor Interaction, Focal adhesion, and Proteoglycans in cancer. PPI and protein-coding gene-miRNA network visualized the landscape for these tightly bounded gene interactions. Immune cell infiltration shows that B cells, CD4+T cells, and CD8+T cells have strongly different infiltration levels between the HSBP8 high exp group and low exp group. The survival prediction model shows that HSBP8 has strong prognosis power in the BLCA cohort.

Conclusion: Identifying DEGs may enhance understanding of BC development’s causes and molecular mechanisms. HSPB8 may play an essential role in BC progression and prognosis and serve as a potential biomarker for BC treatment.

Construction and validation of m6 A RNA methylation regulators associated prognostic model for gastrointestinal cancer

N6-methyladenosine (m⁶ A) RNA methylation is correlated with carcinogenesis and dynamically possessed through the m⁶ A RNA methylation regulators. This paper aimed to explore 13 m⁶ A RNA methylation regulators' role in gastrointestinal cancer (GIC) and determine the risk model and prognosis value of m⁶ A RNA methylation regulators in GIC. We used several bioinformatics methods to identify the differential expression of m⁶ A RNA methylation regulators in GIC, constructed a prognostic model, and carried out functional enrichment analysis. Eleven of 13 m⁶ A RNA methylation regulators were differentially expressed in different clinicopathological characteristics of GIC, and m⁶ A RNA methylation regulators were nearly associated with GIC. We constructed a risk model based on five m⁶ A RNA methylation regulators (METTL3, FTO, YTHDF1, ZC3H13, and WTAP); the risk score is an independent prognosis biomarker. Moreover, the five m⁶ A RNA methylation regulators can also forecast the 1-, 3- and 5-year overall survival through a nomogram. Furthermore, four hallmarks of oxidative phosphorylation, glycolysis, fatty acid metabolism, and cholesterol homoeostasis gene sets were significantly enriched in GIC. m⁶ A RNA methylation regulators were related to the malignant clinicopathological characteristics of GIC and may be used for prognostic stratification and development of therapeutic strategies.

An Autophagy-Related Gene-Based Prognostic Risk Signature for Hepatocellular Carcinoma: Construction and Validation

BACKGROUND

Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer. Treatment is dramatically difficult due to its high complexity and poor prognosis. Due to the disclosed dual functions of autophagy in cancer development, understanding autophagy-related genes devotes into novel biomarkers for HCC.

METHODS

Differential expression of genes in normal and tumor groups was analyzed to acquire autophagy-related genes in HCC. These genes were subjected to GO and KEGG pathway analyses. Genes were then screened by univariate regression analysis. The screened genes were subjected to multivariate Cox regression analysis to build a prognostic model. The model was validated by the ICGC validation set.

RESULTS

To sum up, 42 differential genes relevant to autophagy were screened by differential expression analysis. Enrichment analysis showed that they were mainly enriched in pathways including regulation of autophagy and cell apoptosis. Genes were screened by univariate analysis and multivariate Cox regression analysis to build a prognostic model. The model constituted 6 feature genes: EIF2S1, BIRC5, SQSTM1, ATG7, HDAC1, and FKBP1A. Validation confirmed the accuracy and independence of this model in predicting the HCC patient's prognosis.

CONCLUSION

A total of 6 feature genes were identified to build a prognostic risk model. This model is conducive to investigating interplay between autophagy-related genes and HCC prognosis.

Large-Scale Transcriptome Analysis Identified a Novel Cancer Driver Genes Signature for Predicting the Prognostic of Patients With Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is a common malignant tumor with high mortality and heterogeneity. Genetic mutations caused by driver genes are important contributors to the formation of the tumor microenvironment. The purpose of this study is to discuss the expression of cancer driver genes in tumor tissues and their clinical value in predicting the prognosis of HCC.

Methods: All data were sourced from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) public databases. Differentially expressed and prognostic genes were screened by the expression distribution of the cancer driver genes and their relationship with survival. Candidate genes were subjected to functional enrichment and transcription factor regulatory network. We further constructed a prognostic signature and analyzed the survival outcomes and immune status between different risk groups.

Results: Most cancer driver genes are specifically expressed in cancer tissues. Driver genes may influence HCC progression through processes such as transcription, cell cycle, and T-cell receptor-related pathways. Patients in different risk groups had significant survival differences (p < 0.05), and risk scores showed high predictive efficacy (AUC>0.69). Besides, risk subgroups were also associated with multiple immune functions and immune cell content.

Conclusion: We confirmed the critical role of cancer driver genes in mediating HCC progression and the immune microenvironment. Risk subgroups contribute to the assessment of prognostic value in different patients and explain the heterogeneity of HCC.

Autophagy and Digestive Disorders: Advances in Understanding and Therapeutic Approaches

The gastrointestinal (GI) tract is a series of hollow organs that is responsible for the digestion and absorption of ingested foods and the excretion of waste. Any changes in the GI tract can lead to GI disorders. GI disorders are highly prevalent in the population and account for substantial morbidity, mortality, and healthcare utilization. GI disorders can be functional, or organic with structural changes. Functional GI disorders include functional dyspepsia and irritable bowel syndrome. Organic GI disorders include inflammation of the GI tract due to chronic infection, drugs, trauma, and other causes. Recent studies have highlighted a new explanatory mechanism for GI disorders. It has been suggested that autophagy, an intracellular homeostatic mechanism, also plays an important role in the pathogenesis of GI disorders. Autophagy has three primary forms: macroautophagy, microautophagy, and chaperone-mediated autophagy. It may affect intestinal homeostasis, host defense against intestinal pathogens, regulation of the gut microbiota, and innate and adaptive immunity. Drugs targeting autophagy could, therefore, have therapeutic potential for treating GI disorders. In this review, we provide an overview of current understanding regarding the evidence for autophagy in GI diseases and updates on potential treatments, including drugs and complementary and alternative medicines.

Identification of a Novel Four-Gene Signature Correlated With the Prognosis of Patients With Hepatocellular Carcinoma: A Comprehensive Analysis

Purpose

Hepatocellular carcinoma (HCC) is a common solid-tumor malignancy with high heterogeneity, and accurate prognostic prediction in HCC remains difficult. This analysis was performed to find a novel prognostic multigene signature.

Methods

The TCGA-LIHC dataset was analyzed for differentially coexpressed genes through weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. A protein-protein interaction (PPI) network and univariate Cox regression analysis of overall survival (OS) were utilized to identify their prognostic value. Next, we used least absolute shrinkage and selection operator (LASSO) Cox regression to establish a prognostic module. Subsequently, the ICGC-LIRI-JP dataset was applied for further validation. Based on this module, HCC cases were stratified into high-risk and low-risk groups, and differentially expressed genes (DEGs) were identified. Functional enrichment analyses of these DEGs were conducted. Finally, single-sample gene set enrichment analysis (ssGSEA) was performed to explore the correlation between the prognostic signature and immune status.

Results

A total of 393 differentially coexpressed genes were obtained. Forty differentially coexpressed hub genes were identified using the CytoHubba plugin, and 38 of them were closely correlated with OS. Afterward, we established the four-gene prognostic signature with an acceptable accuracy (area under the curve [AUC] of 1-year survival: 0.739). The ICGC-LIRI-JP dataset also supported the acceptable accuracy (AUC of 1-year survival:0.752). Compared with low-risk cohort, HCC cases in the high-risk cohort had shorter OS, higher tumor grades, and higher T stages. The risk scores of this signature still act as independent predictors of OS (P<0.001). Functional enrichment analyses suggest that it was mainly organelle fission and nuclear division that were enriched. Finally, ssGSEA revealed that this signature is strongly associated with the immune status of HCC patients.

Conclusions

The proposed prognostic signature of four differentially coexpressed hub genes has satisfactory prognostic ability, providing important insight into the prediction of HCC prognosis.

The Role of HSPB8, a Component of the Chaperone-Assisted Selective Autophagy Machinery, in Cancer

The cellular response to cancer-induced stress is one of the major aspects regulating cancer development and progression. The Heat Shock Protein B8 (HSPB8) is a small chaperone involved in chaperone-assisted selective autophagy (CASA). CASA promotes the selective degradation of proteins to counteract cell stress such as tumor-induced stress. HSPB8 is also involved in (i) the cell division machinery regulating chromosome segregation and cell cycle arrest in the G0/G1 phase and (ii) inflammation regulating dendritic cell maturation and cytokine production. HSPB8 expression and role are tumor-specific, showing a dual and opposite role. Interestingly, HSPB8 may be involved in the acquisition of chemoresistance to drugs. Despite the fact the mechanisms of HSPB8-mediated CASA activation in tumors need further studies, HSPB8 could represent an important factor in cancer induction and progression and it may be a potential target for anticancer treatment in specific types of cancer. In this review, we will discuss the molecular mechanism underlying HSPB8 roles in normal and cancer conditions. The basic mechanisms involved in anti- and pro-tumoral activities of HSPB8 are deeply discussed together with the pathways that modulate HSPB8 expression, in order to outline molecules with a beneficial effect for cancer cell growth, migration, and death.