Autophagy-related gene expression classification defines three molecular subtypes with distinct clinical and microenvironment cell infiltration characteristics in colon cancer

Mitophagy-related gene signature for predicting the prognosis of multiple myeloma

Aims

The aims of this study were to explore the molecular mechanism of mitophagy in multiple myeloma (MM) and to develop an effective prognostic signature for the disease based on mitophagy-related genes (MRGs).

Methods

Three gene sets from the Reactome database were used to explore MRGs, following which those that were differentially expressed between MM and normal samples were investigated using the data from the Genomic Data Commons-Multiple Myeloma Research Foundation-CoMMpass Study. Mitophagy-related molecular subtypes of MM were identified and their immune infiltration, associated patient survival rates, immune checkpoint genes, and mitophagy scores were compared. Prognostic genes for MM were identified, and a prognostic model was constructed. Additionally, a nomogram was constructed using the prognostic model and prognosis-related clinical features. Finally, the drug sensitivity and correlation analyses of the subtypes were performed between the two risk groups.

Results

We identified two MM molecular subtypes that exhibited significant differences in mitophagy scores, associated patient survival rates, immune infiltration, and immune checkpoint genes. An MRG-based prognostic signature was constructed using six genes (TRIP13, KIF7, GPR63, CRIP2, DNTT, and HSPB8), which had high predictive prognostic value. A nomogram was constructed by screening five indicators (risk score, subtype, age, sex, and stage) that could predict the 1-, 3-, and 5-year survival probabilities of patients with MM. The two risk groups displayed significant differences in their IC50 values of 33 drugs, such as bleomycin. Patients in the high-risk group tended to fall within Mitophagy_cluster_A.

Conclusion

Our MRG-based signature is a promising prognostic biomarker for MM.

Experimental prognostic model integrating N6-methyladenosine-related programmed cell death genes in colorectal cancer

Colorectal cancer (CRC) intricacies, involving dysregulated cellular processes and programmed cell death (PCD), are explored in the context of N6-methyladenosine (m6A) RNA modification. Utilizing the TCGA-COADREAD/CRC cohort, 854 m6A-related PCD genes are identified, forming the basis for a robust 10-gene risk model (CDRS) established through LASSO Cox regression. qPCR experiments using CRC cell lines and fresh tissues was performed for validation. The CDRS served as an independent risk factor for CRC and showed significant associations with clinical features, molecular subtypes, and overall survival in multiple datasets. Moreover, CDRS surpasses other predictors, unveiling distinct genomic profiles, pathway activations, and associations with the tumor microenvironment. Notably, CDRS exhibits predictive potential for drug sensitivity, presenting a novel paradigm for CRC risk stratification and personalized treatment avenues.

Gene body hypomethylation of pyroptosis-related genes NLRP7, NLRP2, and NLRP3 facilitate non-invasive surveillance of hepatocellular carcinoma

Programmed cell death (PCD) resistance is a key driver of cancer occurrence and development. The prognostic relevance of PCD-related genes in hepatocellular carcinoma (HCC) has attracted considerable attention in recent years. However, there is still a lack of efforts to compare the methylation status of different types of PCD genes in HCC and their roles in its surveillance. The methylation status of genes related to pyroptosis, apoptosis, autophagy, necroptosis, ferroptosis, and cuproptosis was analyzed in tumor and non-tumor tissues from TCGA. Whole-genome bisulfite sequencing (WGBS) data of paired tumor tissue and buffy coat samples were used to filter the potential interference of blood leukocytes in cell-free DNA (cfDNA). The WGBS data of healthy individuals' and early-stage HCC patients' cfDNA were analyzed to evaluate the distinguishing ability. The average gene body methylation (gbDNAme) of pyroptosis-related genes (PRGs) was significantly altered in HCC tissues relative to normal tissues, and their distinguishing ability was higher compared to the other types of PCD-related genes. The gbDNAme of NLRP7, NLRP2, and NLRP3 was reflective of the hypomethylation in HCC tissues, and methylation levels of NLRP3 correlated positively with its expression level (r=0.51). The candidate hypomethylated PRGs could discriminate between early HCC patients and healthy controls in cfDNA analysis with high accuracy (area under the receiver operation curve, AUC=0.94). Furthermore, the hypomethylation of PRGs was associated with poor prognosis of HCC. Gene body hypomethylation of PRGs is a promising biomarker for early HCC detection, monitoring of tumor recurrence, and prognosis prediction.

Molecular subtypes and tumor microenvironment infiltration signatures based on cuproptosis-related genes in colon cancer

Background

Colon cancer is one of the common cancers, and its prognosis remains to be improved. The role of cuproptosis as a newly discovered form of cell death in the development of colon cancer has not been determined.

Methods

Based on 983 colon cancer samples in the TCGA database and the GEO database, we performed a comprehensive genomic analysis to explore the molecular subtypes mediated by cuproptosis-related genes. Single-sample gene set enrichment analysis (ssGSEA) was utilized to quantify the relative abundance of each cell infiltrate in the TME. A risk score was established using least absolute shrinkage and selection operator regression (LASSO), and its predictive ability for colon cancer patients was verified to explore its guiding value for treatment.

Results

We identified two distinct cuproptosis-related molecular subtypes in colon cancer. These two distinct molecular subtypes can predict clinicopathological features, prognosis, TME activity, and immune-infiltrating cells. A risk model was developed and its predictive ability was verified. Compared with patients in the high-risk score group, patients in the low-risk score group were characterized by lower tumor microenvironment score, higher stem cell activity, lower tumor mutational burden, lower microsatellite instability, higher sensitivity to chemotherapeutics, and better immunotherapy efficacy.

Conclusion

This study contributes to understanding the molecular characteristics of cuproptosis-related subtypes. We demonstrate a critical role for cuproptosis genes in colon cancer s in the TME. Our study contributes to the development of individualized treatment regimens for colon cancer.

Autophagy and the Insulin-like Growth Factor (IGF) System in Colonic Cells: Implications for Colorectal Neoplasia

Colorectal cancer (CRC) is one of the most common human malignancies worldwide. Along with apoptosis and inflammation, autophagy is one of three important mechanisms in CRC. The presence of autophagy/mitophagy in most normal mature intestinal epithelial cells has been confirmed, where it has mainly protective functions against reactive oxygen species (ROS)-induced DNA and protein damage. Autophagy regulates cell proliferation, metabolism, differentiation, secretion of mucins and/or anti-microbial peptides. Abnormal autophagy in intestinal epithelial cells leads to dysbiosis, a decline in local immunity and a decrease in cell secretory function. The insulin-like growth factor (IGF) signaling pathway plays an important role in colorectal carcinogenesis. This is evidenced by the biological activities of IGFs (IGF-1 and IGF-2), IGF-1 receptor type 1 (IGF-1R) and IGF-binding proteins (IGF BPs), which have been reported to regulate cell survival, proliferation, differentiation and apoptosis. Defects in autophagy are found in patients with metabolic syndrome (MetS), inflammatory bowel diseases (IBD) and CRC. In neoplastic cells, the IGF system modulates the autophagy process bidirectionally. In the current era of improving CRC therapies, it seems important to investigate the exact mechanisms not only of apoptosis, but also of autophagy in different populations of tumor microenvironment (TME) cells. The role of the IGF system in autophagy in normal as well as transformed colorectal cells still seems poorly understood. Hence, the aim of the review was to summarize the latest knowledge on the role of the IGF system in the molecular mechanisms of autophagy in the normal colon mucosa and in CRC, taking into account the cellular heterogeneity of the colonic and rectal epithelium.

Construction and validation of a bladder cancer risk model based on autophagy-related genes

Autophagy has an important association with tumorigenesis, progression, and prognosis. However, the mechanism of autophagy-regulated genes on the risk prognosis of bladder cancer (BC) patients has not been fully elucidated yet. In this study, we created a prognostic model of BC risk based on autophagy-related genes, which further illustrates the value of genes associated with autophagy in the treatment of BC. We first downloaded human autophagy-associated genes and BC datasets from Human Autophagy Database and The Cancer Genome Atlas (TCGA) database, and finally obtained differential prognosis-associated genes for autophagy by univariate regression analysis and differential analysis of cancer versus normal tissues. Subsequently, we downloaded two datasets from Gene Expression Omnibus (GEO), GSE31684 and GSE15307, to expand the total number of samples. Based on these genes, we distinguished the molecular subtypes (C1, C2) and gene classes (A, B) of BC by consistent clustering analysis. Using the genes merged from TCGA and the two GEO datasets, we conducted least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to obtain risk genes and construct autophagy-related risk prediction models. The accuracy of this risk prediction model was assessed by receiver operating characteristic (ROC) and calibration curves, and then nomograms were constructed to predict the survival of bladder cancer patients at 1, 3, and 5 years, respectively. According to the median value of the risk score, we divided BC samples into the high- and low-risk groups. Kaplan-Meier (K-M) survival analysis was performed to compare survival differences between subgroups. Then, we used single sample gene set enrichment analysis (ssGSEA) for immune cell infiltration abundance, immune checkpoint genes, immunotherapy response, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, and tumor mutation burden (TMB) analysis for different subgroups. We also applied quantitative real-time polymerase chain reaction (PCR) and immunohistochemistry (IHC) techniques to verify the expression of these six genes in the model. Finally, we chose the IMvigor210 dataset for external validation. Six risk genes associated with autophagy (SPOCD1, FKBP10, NAT8B, LDLR, STMN3, and ANXA2) were finally screened by LASSO regression algorithm and multivariate Cox regression analysis. ROC and calibration curves showed that the model established was accurate and reliable. Univariate and multivariate regression analyses were used to verify that the risk model was an independent predictor. K-M survival analysis indicated that patients in the high-risk group had significantly worse overall survival than those in the low-risk group. Analysis by algorithms such as correlation analysis, gene set variation analysis (GSVA), and ssGSEA showed that differences in immune microenvironment, enrichment of multiple biologically active pathways, TMB, immune checkpoint genes, and human leukocyte antigens (HLAs) were observed in the different risk groups. Then, we constructed nomograms that predicted the 1-, 3-, and 5-year survival rates of different BC patients. In addition, we screened nine sensitive chemotherapeutic drugs using the correlation between the obtained expression status of risk genes and drug sensitivity results. Finally, the external dataset IMvigor210 verified that the model is reliable and efficient. We established an autophagy-related risk prognostic model that is accurate and reliable, which lays the foundation for future personalized treatment of bladder cancer.

NXPH4 Used as a New Prognostic and Immunotherapeutic Marker for Muscle-Invasive Bladder Cancer

Background

One of the most common malignant tumors of the urinary system is muscle-invasive bladder cancer (MIBC). With the increased use of immunotherapy, its importance in the field of cancer is becoming abundantly evident. This study classifies MIBC according to GSVA score from the perspective of the GSEA immune gene set.

Methods

This study integrated the sequencing and clinical data of MIBC patients in TCGA and GEO databases, then scored the data using the GSVA algorithm, the CNMF algorithm was implemented to divide the subtypes of GEO and TCGA datasets, respectively, and finally screened and determined the key pathways in combination with clinical data. Simultaneously, LASSO Cox regression model was constructed based on key pathway genes to assess the model's predictive ability (ROC) and describe the immune landscape differences between high- and low-risk groups; key genes were further analyzed and verified in patient tissues.

Results

404 TCGA and 297 GEO datasets were divided into C1-3 groups (TCGA-C1:120/C2:152/C3:132; GEO- C1:112/C2:101/C3:84), of which TCGA-C2 (n = 152) subtype and GEO-C1 (n = 112) subtype had the worst prognosis. LASSO Cox regression model with ROC (train set = 0.718, test set = 0.667) could be constructed. When combined with the Cancer Immunome Atlas database, it was found that patients with high-risk scores were more sensitive to PD-1 inhibitor and PD-1 inhibitor combined with CTLA-4. NXPH4, as a key gene, plays a role in MIBC with tissue validation results show that nxph4 is highly expressed in tumor.

Conclusion

The immune gene score of MIBC data in TCGA and GEO databases was successfully evaluated using GSVA in this research. The lasso Cox expression model was successfully constructed by screening immune genes, the high-risk group had a worse prognosis and higher sensitivity to immunotherapy, PD-1 inhibitors or PD-1 combined with CTLA-4 inhibitors can be preferentially used in high-risk patients who are sensitive to immunotherapy, and NXPH4 may be a molecular target to adjust the effect of immunotherapy.

Transforming growth factor-β in tumour development

Transforming growth factor-β (TGFβ) is a ubiquitous cytokine essential for embryonic development and postnatal tissue homeostasis. TGFβ signalling regulates several biological processes including cell growth, proliferation, apoptosis, immune function, and tissue repair following injury. Aberrant TGFβ signalling has been implicated in tumour progression and metastasis. Tumour cells, in conjunction with their microenvironment, may augment tumourigenesis using TGFβ to induce epithelial-mesenchymal transition, angiogenesis, lymphangiogenesis, immune suppression, and autophagy. Therapies that target TGFβ synthesis, TGFβ-TGFβ receptor complexes or TGFβ receptor kinase activity have proven successful in tissue culture and in animal models, yet, due to limited understanding of TGFβ biology, the outcomes of clinical trials are poor. Here, we review TGFβ signalling pathways, the biology of TGFβ during tumourigenesis, and how protein quality control pathways contribute to the tumour-promoting outcomes of TGFβ signalling.

Characteristic of Molecular Subtypes in Lung Squamous Cell Carcinoma Based on Autophagy-Related Genes and Tumor Microenvironment Infiltration

Background

Recently, a large number of studies have sought personalized treatment for lung squamous cell carcinoma (LUSC) by dividing patients into different molecular subtypes. Autophagy plays an important role in maintaining the tumor microenvironment and immune-related biological processes. However, the molecular subtypes mediated by autophagy in LUSC are not clear.

Methods

Based on 490 LUSC samples, we systematically analyzed the molecular subtype modification patterns mediated by autophagy-related genes. The ssGSEA and CIBERSORT algorithm were utilized to quantify the relative abundance of TME cell infiltration. Principal component analysis was used to construct autophagy prognostic score (APS) model.

Results

We identified three autophagy subtypes in LUSC, and their clinical outcomes and TME cell infiltration had significant heterogeneity. Cluster A was rich in immune cell infiltration. The enrichment of EMT stromal pathways and immune checkpoint molecules were significantly enhanced, which may lead to its immunosuppression. Cluster B was characterized by relative immunosuppression and relative stromal activation. Cluster C was activated in biological processes related to repair. Patients with high APS were significantly positively correlated with TME stromal activity and poor survival. Meanwhile, high APS showed an advantage in response to anti-PD1 and anti-CTLA4 immunotherapy.

Conclusion

This study explored the autophagy molecular subtypes in LUSC. We also discovered the heterogeneity of TME cell infiltration driven by autophagy-related genes. The established APS model is of great significance for evaluating the prognosis of LUSC patients, the infiltration of TME cells, and the effect of immunotherapy.

Molecular subtypes, prognostic and immunotherapeutic relevant gene signatures mediated by DNA methylation regulators in hepatocellular carcinoma

Growing evidence has revealed the crucial role of epigenetics in tumor progression and immune response. However, the molecular subtypes and their microenvironment characterization mediated by DNA methylation regulators in hepatocellular carcinoma remain little known. In this study, we comprehensively integrated the transcriptome profiling of twenty DNA methylation regulators in hepatocellular carcinoma. Consensus clustering was used to identify distinct methylation regulator-related molecular subtypes. The prognostic DMS signature was constructed using principal components analysis. Most regulators experienced a low genomic variation, but we found a remarkably difference in mRNA expression of these regulators between normal and tumor tissues. Three distinct methylation regulator-related molecular subtypes were successfully identified according to the expression of 20 regulators, which had substantially different biological characteristics and prognosis. The classic carcinogenic pathways and stromal activity including TGF-beta, p53 and WNT signaling pathway were significantly activated in subtype B, leading to a survival inferiority in subtype B compared to other two subtypes. Further analysis demonstrated the constructed DMS signature was an independent predictive biomarker in patient prognosis. Two anti-checkpoint immunotherapy cohorts demonstrated patients with high DMS presented significantly improved treatment advantages and enhanced responses especially the survival prolonged. Generally, the high DMS groups improved more than 15% clinical response to immunotherapy than low DMS groups. In conclusion, this study identified three DNA methylation regulator-related subtypes with distinct clinical, molecular and biological characteristics, and constructed a prognostic and immunotherapeutic relevant gene signature. It might help to promote individualized immunotherapy for hepatocellular carcinoma from the perspective DNA methylation regulators.

Pyroptosis-Related Gene Signature and Expression Patterns in the Deterioration of Atherosclerosis

Background

Pyroptosis has been shown to be involved in the overall process of atherosclerosis. This study was aimed at investigating pyroptosis-related gene expression patterns in atherosclerosis and their diagnostic significance.

Methods and Results

In GSE100927, fifty-four pyroptosis-related genes were identified. Between atherosclerotic plaques and normal samples, the expression patterns of pyroptosis-related genes were significantly different. In order to construct a pyroptosis-related risk score signature (PRSS), the least absolute shrinkage and selection operator (LASSO) was combined with multivariate logistic regression to screen twelve genes. The diagnostic efficiency of the PRSS performed well in GSE43292, as shown by the results of receiver-operating characteristics (ROCs). Consensus clustering identified two expression patterns of pyroptosis-related genes in different statuses of atherosclerotic plaque in GSE163154. The biological behavior of the different clusters was examined by the gene set variation analysis (GSVA). The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses revealed that the differentially expressed genes (DEGs) in the two clusters were enriched in the immune response. The Cytoscape software was used to construct protein-protein interaction (PPI) networks for hub gene screening. Following that, the Drug Gene Interaction Database (DGIdb) was utilized to find 47 possible medicines and chemical compounds that interact with hub genes in atherosclerotic plaques.

Conclusion

The results of this study showed that pyroptosis-related genes contribute to the progression of atherosclerosis and may serve as biomarkers in clinical diagnosis as well as novel therapeutic targets for the treatment of AS.

Characterization of an Autophagy-Immune Related Genes Score Signature and Prognostic Model and its Correlation with Immune Response for Bladder Cancer

Purpose

The study aimed to identify an autophagy-related molecular subtype and characterize a novel defined autophagy-immune related genes score (AI-score) signature and prognosis model in bladder cancer (BLCA) patients using public databases.

Methods

The transcriptome cohorts downloaded from TCGA and GEO database were carried out with genomic analysis and unsupervised methods to obtain autophagy-related molecular subtypes. The single-sample gene-set enrichment analysis (ssGSEA) was utilized to perform immune subtype clustering. We defined a novel autophagy subtype and evaluated the role in TME cell infiltration. Then, the principal-component analysis (PCA) was applied to construct an AI-score signature. Subsequently, two immunotherapeutic cohorts were used to evaluate the predictive value in immunotherapeutic benefits and immune response. Finally, univariate, Lasso and multivariate Cox regression algorithm were used to construct and evaluate an autophagy-immune-related genes prognosis model. Also, qRT-PCR and IHC was applied to validate the expression of the 6 genes in the model.

Results

Three distinct autophagy clusters and immune-related clusters were identified, and a novel autophagy-related molecular subtypes were defined. Furthermore, the roles in TME cell infiltration and clinical traits for the autophagy subtypes were characterized. Meanwhile, we constructed an AI-score signature and demonstrated it could predict genetic mutation, clinicopathological traits, prognosis, and TME stromal activity. We found that it could accurately predict the clinicopathological characteristics and immune response of individual BLCA patients and provide guidance for selecting immunotherapy. Ultimately, we constructed and verified an autophagy-immune-related prognostic model of BLCA patients and established a prognostic nomogram with a good prediction accuracy.

Conclusion

We constructed AI-score signatures and prognosis risk model to characterize their role in clinical features and TME immune cell infiltration. It revealed that the AI-score signature and prognosis model could be a valid predictive tool, which could accurately predict the prognosis of BLCA patients and contribute to choosing effective personalized immunotherapy strategies.

Analysis of Multi-Layer RNA Modification Patterns for the Characterization of Tumor Immune Microenvironment in Hepatocellular Carcinoma

Background: RNA modifications have emerged as important posttranscriptional changes in multiple tumor cellular processes and tumorigenesis, including hepatocellular carcinoma (HCC). However, the potential roles and the interaction between regulators of RNA modifications and the tumor microenvironment (TME) are unclear in HCC. Methods: The gene expression profiles of 26 RNA modification "writers" were investigated in the TCGA cohort. The unsupervised clustering approach was used to class these RNA modification regulators. The characteristics of immune cell infiltration from TME for each cluster was tested by the CIBERSORT method. Additionally, we established a scoring model to evaluate the RNA modification characteristics of individual tumors. The associations between the scoring model and genetic as well as clinical characteristics, drug sensitivity, and response to immunotherapy were also analyzed. Results: We mapped the somatic mutations and somatic copy number variation of the RNA modification regulators. The expression of all selected regulators was detected, and two modification patterns were identified that featured distinct immune cell infiltration characteristics. Subsequently, we developed a score model (termed as WM-Score model). Furthermore, the survival analysis showed that the WM-Score value was associated with HCC patient prognosis. The results of the ROC curves analysis and multivariate analysis all confirmed that the WM-Score value was strongly associated with anti-cancer drug resistance and therapeutic efficacy of immunotherapy, thus could be used as an independent risk factor in HCC. Conclusion: Our research identified two RNA modification patterns characterized by distinct TME, and the WM-Score model was developed that might serve as reliable prognostic and immunotherapeutic effect predictor of HCC.

Down-Regulation of an Autophagy-Related Gene SERPINA1 as a Superior Prognosis Biomarker Associates with Relapse and Distant Metastasis in Colon Adenocarcinoma

Background

The relapse and distant metastasis in colon adenocarcinoma (COAD) patients with a poor prognosis. Autophagy has gained increasing attention recently.

Methods

This study utilized univariate Cox analysis from the TCGA database to obtain 10 prognostic autophagy-related genes (ARGs). GO and KEGG functional annotation analysis suggested that the ARGs were significantly enriched in tumor metabolic processes. We verified the autophagy-related genes screened by TCGA clinical data. Then, we compared the expression of SERPINA1 in primary and metastatic tumor cells in the GEO database, and finally verified the relationship between SERPINA1 protein expression and prognosis with the CPTAC database.

Results

The ROC curves showed SERPINA1 had robust prediction capability in judging the prognosis and disease process compared with the other 4 ARGs and risk score in COAD. Clinical relationship analysis further indicated SERPINA1 was related to TMN stage, clinical-stage, OS, RFS, and DMFS in COAD. Besides, survival analysis presented that higher expression of SERPINA1 was significantly associated with the longer OS, RFS, or DMFS. Moreover, SERPINA1 protein was validated to be associated with OS, RFS, and DMFS through our own IHC and CPTAC database. Finally, we exploratoryly combined the SERPINA1 mRNA and SERPINA1 protein as a new index for prognostics.

Conclusion

This new combined index showed the highest prognostic value for OS, RFS, and DMFS, and had the potential to become a practical biomarker for prognosis.