Differentially Expressed Autophagy-related Genes Research Articles

Exploring the autophagy-related pathogenesis of active ulcerative colitis.

The pathogenesis of ulcerative colitis (UC) is complex, and recent therapeutic advances remain unable to fully alleviate the condition. To inform the development of novel UC treatments, bioinformatics was used to explore the autophagy-related pathogenesis associated with the active phase of UC. The GEO database was searched for UC-related datasets that included healthy controls who met the screening criteria. Differential analysis was conducted to obtain differentially expressed genes (DEGs). Autophagy-related targets were collected and intersected with the DEGs to identiy differentially expressed autophagy-related genes (DEARGs) associated with active UC. DEARGs were then subjected to KEGG, GO, and DisGeNET disease enrichment analyses using R software. Differential analysis of immune infiltrating cells was performed using the CiberSort algorithm. The least absolute shrinkage and selection operator algorithm and protein-protein interaction network were used to narrow down the DEARGs, and the top five targets in the Dgree ranking were designated as core targets. A total of 4822 DEGs were obtained, of which 58 were classified as DEARGs. SERPINA1, BAG3, HSPA5, CASP1, and CX3CL1 were identified as core targets. GO enrichment analysis revealed that DEARGs were primarily enriched in processes related to autophagy regulation and macroautophagy. KEGG enrichment analysis showed that DEARGs were predominantly associated with NOD-like receptor signaling and other signaling pathways. Disease enrichment analysis indicated that DEARGs were significantly linked to diseases such as malignant glioma and middle cerebral artery occlusion. Immune infiltration analysis demonstrated a higher presence of immune cells like activated memory CD4 T cells and follicular helper T cells in active UC patients than in healthy controls. Autophagy is closely related to the active phase of UC and the potential targets obtained from the analysis in this study may provide new insight into the treatment of active UC patients.

Investigation of autophagy‑related genes and immune infiltration in calcific aortic valve disease: A bioinformatics analysis and experimental validation.

The present study aimed to elucidate the role of autophagy-related genes (ARGs) in calcific aortic valve disease (CAVD) and their potential interactions with immune infiltration via experimental verification and bioinformatics analysis. A total of three microarray datasets (GSE12644, GSE51472 and GSE77287) were obtained from the Gene Expression Omnibus database, and gene set enrichment analysis was performed to identify the relationship between autophagy and CAVD. After differentially expressed genes and differentially expressed ARGs (DEARGs) were identified using CAVD samples and normal aortic valve samples, a functional analysis was performed, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, protein-protein interaction network construction, hub gene identification and validation, immune infiltration and drug prediction. The results of the present study indicated a significant relationship between autophagy and CAVD. A total of 46 DEARGs were identified. GO and pathway enrichment analyses revealed the complex roles of DEARGs in regulating CAVD, including multiple gene functions and pathways. A total of 10 hub genes were identified, with three (SPP1, CXCL12 and CXCR4) consistently upregulated in CAVD samples compared with normal aortic valve samples in multiple datasets and experimental validation. Immune infiltration analyses demonstrated significant differences in immune cell proportions between CAVD samples and normal aortic valve samples, thus showing the crucial role of immune infiltration in CAVD development. Furthermore, therapeutic drugs were predicted that could target the identified hub genes, including bisphenol A, resveratrol, progesterone and estradiol. In summary, the present study illuminated the crucial role of autophagy in CAVD development and identified key ARGs as potential therapeutic targets. In addition, the observed immune cell infiltration and predicted autophagy-related drugs suggest promising avenues for future research and novel CAVD treatments.

Identification of differentially expressed autophagy-related genes in cases of intracranial aneurysm: Bioinformatics analysis

ObjectiveRecent research indicates that autophagy is essential for the rupture of intracranial aneurysm (IA). This study aimed to examine and validate potential autophagy-related genes (ARGs) in cases of IA using bioinformatics analysis. MethodsTwo expression profiles (GSE54083 and GSE75436) were obtained from the Gene Expression Omnibus database. Differentially expressed ARGs (DEARGs) in cases of IA were screened using GSE75436, and enrichment analysis and Protein–Protein Interaction (PPI) networks were used to identify the hub genes and related pathways. Furthermore, a novel predictive diagnostic signature for IA based on the hub genes was constructed. The area under the Receiver Operating Characteristic curve (AUC) was used to evaluate the signature performance in GSE75436. ResultsIn total, 75 co-expressed DEARGs were identified in the GSE75436 and GSE54083 dataset (28 upregulated and 47 downregulated genes). Enrichment analysis of DEARGs revealed several enriched terms associated with proteoglycans in cancer and human immunodeficiency virus 1 infection. PPI analysis revealed interactions between these genes. Hub DEARGs included insulin-like growth factor 1, clusters of differentiation 4, cysteine-aspartic acid protease 8, Bcl-2-like protein 11, mouse double mutant 2 homolog, toll-like receptor 4, growth factor receptor-bound protein 2, Jun proto-oncogene, AP-1 transcription factor subunit, hypoxia inducible factor 1 alpha, and erythroblastic oncogene B-2. Notably, the signature showed good performance in distinguishing IA (AUC = 0.87). The sig calibration curves showed good calibration. ConclusionBioinformatic analysis identified 75 potential DEARGs in cases of IA. This study revealed that IA is affected by autophagy, which could explain the pathogenesis of IA and aid in its diagnosis and treatment. However, future research with experimental validation is necessary to identify potential DEARGs in cases of IA.

Integrated bioinformatics analysis identifies autophagy-associated genes as candidate biomarkers and reveals the immune infiltration landscape in psoriasis

Autophagy is implicated in the pathogenesis of psoriasis. We aimed to identify autophagy-related biomarkers in psoriasis via an integrated bioinformatics approach. We downloaded the gene expression profiles of GSE30999 dataset, and the “limma” package was applied to identify differentially expressed genes (DEGs). Then, differentially expressed autophagy-related genes (DEARGs) were identified via integrating autophagy-related genes with DEGs. CytoHubba plugin was used for the identification of hub genes and verified by the GSE41662 dataset. Subsequently, a series of bioinformatics analyses were employed, including protein–protein interaction network, functional enrichment, spearman correlation, receiver operating characteristic, and immune infiltration analyses. One hundred and one DEARGs were identified, and seven DEARGs were identified as hub genes and verified using the GSE41662 dataset. These validated genes had good diagnostic value in distinguishing psoriasis lesions. Immune infiltration analysis indicated that ATG5, SQSTM1, EGFR, MAPK8, MAPK3, MYC, and PIK3C3 were correlated with infiltration of immune cells. Seven DEARGs, namely ATG5, SQSTM1, EGFR, MAPK8, MAPK3, MYC, and PIK3C3, may be involved in the pathogenesis of psoriasis, which expanded the understanding of the development of psoriasis and provided important clinical significance for treatment of this disease.

Identification of m6A-associated autophagy genes in non-alcoholic fatty liver.

Studies had shown that autophagy was closely related to nonalcoholic fat liver disease (NAFLD), while N6-methyladenosine (m6A) was involved in the regulation of autophagy. However, the mechanism of m6A related autophagy in NAFLD was unclear. The NAFLD related datasets were gained via the Gene Expression Omnibus (GEO) database, and we also extracted 232 autophagy-related genes (ARGs) and 37 m6A. First, differentially expressed ARGs (DE-ARGs) and differentially expressed m6A (DE-m6A) were screened out by differential expression analysis. DE-ARGs associated with m6A were sifted out by Pearson correlation analysis, and the m6A-ARGs relationship pairs were acquired. Then, autophagic genes in m6A-ARGs pairs were analyzed for machine learning algorithms to obtain feature genes. Further, we validated the relationship between feature genes and NAFLD through quantitative real-time polymerase chain reaction (qRT-PCR), Western blot (WB). Finally, the immuno-infiltration analysis was implement, and we also constructed the TF-mRNA and drug-gene networks. There were 19 DE-ARGs and four DE-m6A between NAFLD and normal samples. The three m6A genes and five AGRs formed the m6A-ARGs relationship pairs. Afterwards, genes obtained from machine learning algorithms were intersected to yield three feature genes (TBK1, RAB1A, and GOPC), which showed significant positive correlation with astrocytes, macrophages, smooth muscle, and showed significant negative correlation with epithelial cells, and endothelial cells. Besides, qRT-PCR and WB indicate that TBK1, RAB1A and GOPC significantly upregulated in NAFLD. Ultimately, we found that the TF-mRNA network included FOXP1-GOPC, ATF1-RAB1A and other relationship pairs, and eight therapeutic agents such as R-406 and adavosertib were predicted based on the TBK1. The study investigated the potential molecular mechanisms of m6A related autophagy feature genes (TBK1, RAB1A, and GOPC) in NAFLD through bioinformatic analyses and animal model validation. However, it is critical to note that these findings, although consequential, demonstrate correlations rather than cause-and-effect relationships. As such, more research is required to fully elucidate the underlying mechanisms and validate the clinical relevance of these feature genes.

Comprehensive Bioinformatic Analysis Reveals an Autophagy-related Gene Signature for Predicting Outcome, Immune Status, and Drug Sensitivity in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world, but molecular complexity and tumor heterogeneity make predictive models for HCC prognosis ineffective. Many recent studies have suggested that autophagy is important in tumor progression. Using autophagy-related genes (ARGs), we attempted to create a novel signature for individual prognosis prediction in patients with HCC. Differentially expressed ARGs (DE-ARGs) in HCC and normal samples were screened using TCGA datasets. Univariate Cox and multivariate Cox regression analyses were performed to determine ARGs related to survival in HCC. An autophagy-based signature was constructed using LASSO, and its correlation with the prognosis and the immune infiltration of HCC patients was explored. In this study, we screened 32 survival-related DE-ARGs by analyzing TCGA datasets. Functional enrichment indicated that the 32 DE-ARGs may play important functional and regulatory roles in cellular autophagy, the regulation of multiple signaling pathways, as well as in the context of cancer and neurological diseases. Based on PPI Network, we identified several hub genes. LASSO Cox regression analysis selected five genes (CASP8, FOXO1, PRKCD, SPHK1, and SQSTM1) for a novel prognostic model. AUCs of 0.752, 0.686, and 0.665 in the TCGA whole set indicated that the model accurately predicted 1-, 3-, and 5-year overall survival, respectively. Cox regression analysis showed that the five-gene signature is an independent and robust predictor in patients with HCC. The high-risk group demonstrated higher levels of immune cell infiltration and exhibited a strong correlation with the immune microenvironment and tumor stem cells. In addition, we further identified PRKCD and SQSTM1 as critical regulators involved in HCC progression. The expression levels of PRKCD and SQSTM1 genes play a crucial role in chemotherapy drug sensitivity and resistance. We introduce here a novel ARG-based predictive feature for HCC patients. Effective use of this signature will aid in determining a patient's prognosis and may lead to novel approaches to clinical decision-making and therapy.

Autophagy-related risk signature based on CDNK2A to facilitate survival prediction of patients with endometrial cancer.

Autophagy plays an important role in immunity and inflammation. The present study aimed to explore the prognostic significance of autophagy-related genes (ARGs) in endometrial cancer (EC) using bioinformatics. The list of ARGs was obtained from the Human Autophagy Database. The differentially expressed ARGs (DEARGs) between the EC and normal endometrial tissue samples were screened from The Cancer Genome Atlas database. Cox regression analysis was performed on the DEARGs to screen the prognostic ARGs and construct risk signatures for overall survival (OS) and progression-free survival (PFS). The hub ARGs were identified from a protein-protein interaction network, and CDKN2A was obtained from the intersection of prognostic ARGs and hub ARGs. The association of CDKN2A expression with clinical characteristics and immune infiltration were analyzed. Finally, the role of CDKN2A in autophagy was confirmed in EC cell lines. CDKN2A, PTK6 and DLC1 were used to establish risk signatures for predicting the survival of EC patients. Receiver operating characteristic curve analysis indicated that the risk signatures can accurately predict both OS and PFS. CDKN2A was the only hub prognostic ARG, and showed significant association with the age, survival status, grade, histological type, body mass index and FIGO (i.e. International Federation of Gynecology and Obstetrics) stage (p < 0.05). Furthermore, CDKN2A expression was also correlated with the infiltration of immune cells, indicating that CDKN2A might play a critical role in regulating the immune microenvironment and immune responses in EC. In addition, silencing of CDKN2A gene promoted autophagy in the HEC-1A cell line and upregulated the expression levels of autophagy-related proteins. CDKN2A is a prognostic factor and therapeutic target in EC, and is likely associated with the tumor immune landscape and autophagy.

Identification and verification of three autophagy-related genes as potential biomarkers for the diagnosis of psoriasis

Psoriasis vulgaris is the most common form of the four clinical types. However, early diagnosis of psoriasis vulgaris is difficult due to the lack of effective biomarkers. The aim of this study was to screen potential biomarkers for the diagnosis of psoriasis. In our study, we downloaded the original data from GSE30999 and GSE41664, and the autophagy-related genes list from human autophagy database to identify differentially expressed autophagy-related genes (DERAGs) by R software. Then Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for DERAGs. DERAGs were validated by the other four databases (GSE13355, GSE14905, GSE6710, and GSE55201) to screen biomarkers with high diagnostic value for the early diagnosis of psoriasis vulgaris. Finally, DERAGs were verified in our clinical blood samples by ELISA. A total of 12 DERAGs were identified between 123 paired non-lesional and lesional skin samples from patients with psoriasis vulgaris. GO and KEGG enrichment analysis indicated the TORC2 complex was more enriched and the NOD-like receptor signaling pathway was mostly enriched. Three autophagy-related genes (BIRC5, NAMPT and BCL2) were identified through bioinformatics analysis and verified by ELISA in clinical blood samples. And these genes showed high diagnostic value for the early diagnosis of psoriasis vulgaris. We identified three autophagy-related genes (BIRC5, NAMPT and BCL2) with high diagnostic value for the early diagnosis of psoriasis vulgaris through bioinformatics analysis and clinical samples. Therefore, we proposed that BIRC5, NAMPT and BCL2 may be as potential biomarkers for the early diagnosis of psoriasis vulgaris. In addition, BIRC5, NAMPT and BCL2 may affect the development of psoriasis by regulating autophagy.

Identification of autophagy-related genes in osteoarthritis articular cartilage and their roles in immune infiltration.

Autophagy plays a critical role in the progression of osteoarthritis (OA), mainly by regulating inflammatory and immune responses. However, the underlying mechanisms remain unclear. This study aimed to investigate the potential relevance of autophagy-related genes (ARGs) associated with infiltrating immune cells in OA. GSE114007, GSE169077, and ARGs were obtained from the Gene Expression Omnibus (GEO) database and the Human Autophagy database. R software was used to identify the differentially expressed autophagy-related genes (DEARGs) in OA. Functional enrichment and protein-protein interaction (PPI) analyses were performed to explore the role of DEARGs in OA cartilage, and then Cytoscape was utilized to screen hub ARGs. Single-sample gene set enrichment analysis (ssGSEA) was used to conduct immune infiltration analysis and evaluate the potential correlation of key ARGs and immune cell infiltration. Then, the expression levels of hub ARGs in OA were further verified by the GSE169077 and qRT-PCR. Finally, Western blotting and immunohistochemistry were used to validate the final hub ARGs. A total of 24 downregulated genes and five upregulated genes were identified, and these genes were enriched in autophagy, mitophagy, and inflammation-related pathways. The intersection results identified nine hub genes, namely, CDKN1A, DDIT3, FOS, VEGFA, RELA, MAP1LC3B, MYC, HSPA5, and HSPA8. GSE169077 and qRT-PCR validation results showed that only four genes, CDKN1A, DDT3, MAP1LC3B, and MYC, were consistent with the bioinformatics analysis results. Western blotting and immunohistochemical (IHC) showed that the expression of these four genes was significantly downregulated in the OA group, which is consistent with the qPCR results. Immune infiltration correlation analysis indicated that DDIT3 was negatively correlated with immature dendritic cells in OA, and FOS was positively correlated with eosinophils. CDKN1A, DDIT3, MAP1LC3B, and MYC were identified as ARGs that were closely associated with immune infiltration in OA cartilage. Among them, DDIT3 showed a strong negative correlation with immature dendritic cells. This study found that the interaction between ARGs and immune cell infiltration may play a crucial role in the pathogenesis of OA; however, the specific interaction mechanism needs further research to be clarified. This study provides new insights to further understand the molecular mechanisms of immunity involved in the process of OA by autophagy.

Identification and validation of autophagy-related genes in primary open-angle glaucoma

BackgroundAs the most common type of glaucoma, the etiology of primary open-angle glaucoma (POAG) has not been unified. Autophagy may affect the occurrence and development of POAG, while the specific mechanism and target need to be further explored.MethodsThe GSE27276 dataset from the Gene Expression Omnibus (GEO) database and the autophagy gene set from the GeneCards database were selected to screen differentially expressed autophagy-related genes (DEARGs) of POAG. Hub DEARGs were selected by constructing protein-protein interaction (PPI) networks and utilizing GSE138125 dataset. Subsequently, immune cell infiltration analysis, genome-wide association study (GWAS) analysis, gene set enrichment analysis (GSEA) and other analyses were performed on the hub genes. Eventually, animal experiments were performed to verify the mRNA levels of the hub genes by quantitative real time polymerase chain reaction (qRT-PCR).ResultsA total of 67 DEARGs and 2 hub DEARGs, HSPA8 and RPL15, were selected. The hub genes were closely related to the level of immune cell infiltration. GWAS analysis confirmed that the causative regions of the 2 hub genes in glaucoma were on chromosome 11 and chromosome 3, respectively. GSEA illustrated that pathways enriched for highly expressed HSPA8 and RPL15 contained immunity, autophagy, gene expression and energy metabolism-related pathways. qRT-PCR confirmed that the expression of Hspa8 and Rpl15 in the rat POAG model was consistent with the results of bioinformatics analysis.ConclusionsThis study indicated that HSPA8 and RPL15 may affect the progression of POAG by regulating autophagy and provided new ideas for the pathogenesis and treatment of POAG.

Identification and experimental validation of autophagy-related genes in abdominal aortic aneurysm

AimAutophagy plays essential roles in abdominal aortic aneurysm (AAA) development and progression. The objective of this study was to verify the autophagy-related genes (ARGs) underlying AAA empirically and using bioinformatics analysis.MethodsTwo gene expression profile datasets GSE98278 and GSE57691 were downloaded from the Gene Expression Omnibus (GEO) database, and principal component analysis was performed. Following, the R software (version 4.0.0) was employed to analyze potentially differentially expressed genes related with AAA and autophagy. Subsequently, the candidate genes were screened using protein–protein interaction (PPI), gene ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Finally, quantitative real-time polymerase chain reaction (RT-qPCR) was performed to detect the RNA expression levels of the top five selected abnormal ARGs in clinical samples obtained from the normal and AAA patients.ResultsAccording to the information contained (97 AAA patients and 10 healthy controls) in the two datasets, a total of 44 differentially expressed autophagy-related genes (6 up-regulated genes and 38 down-regulated genes) were screened. GO enrichment analysis of differentially expressed autophagy-related genes (DEARGs) demonstrated that some enrichment items were associated with inflammation, and PPI analysis indicated interaction between these genes. RT-qPCR results presented that the expression levels of IL6, PPARG, SOD1, and MAP1LC3B were in accordance with the bioinformatics prediction results acquired from the mRNA chip.ConclusionBioinformatics analysis identified 44 potential autophagy-related differentially expressed genes in AAA. Further verification by RT- qPCR presented that IL6, PPARG, SOD1, and MAP1LC3B may affect the development of AAA by regulating autophagy. These findings might help explain the pathogenesis of AAA and be helpful in its diagnosis and treatment.

Machine learning and bioinformatics analysis to identify autophagy-related biomarkers in peripheral blood for rheumatoid arthritis.

Background: Although rheumatoid arthritis (RA) is a common autoimmune disease, the precise pathogenesis of the disease remains unclear. Recent research has unraveled the role of autophagy in the development of RA. This research aims to explore autophagy-related diagnostic biomarkers in the peripheral blood of RA patients. Methods: The gene expression profiles of GSE17755 were retrieved from the gene expression ontology (GEO) database. Differentially expressed autophagy-related genes (DE-ARGs) were identified for the subsequent research by inserting autophagy-related genes and differentially expressed genes (DEGs). Three machine learning algorithms, including random forest, support vector machine recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO), were employed to identify diagnostic biomarkers. A nomogram model was constructed to assess the diagnostic value of the biomarkers. The CIBERSORT algorithm was performed to investigate the correlation of the diagnostic biomarkers with immune cells and immune factors. Finally, the diagnostic efficacy and differential expression trend of diagnostic biomarkers were validated in multiple cohorts containing different tissues and diseases. Results: In this study, 25 DE-ARGs were identified between RA and healthy individuals. In addition to "macroautophagy" and "autophagy-animal," DE-ARGs were also associated with several types of programmed cell death and immune-related pathways according to GO and KEGG analysis. Three diagnostic biomarkers, EEF2, HSP90AB1 and TNFSF10, were identified by the random forest, SVM-RFE, and LASSO. The nomogram model demonstrated excellent diagnostic value in GSE17755 (AUC = 0.995, 95% CI: 0.988-0.999). Furthermore, immune infiltration analysis showed a remarkable association between EEF2, HSP90AB1, and TNFSF10 expression with various immune cells and immune factors. The three diagnostic biomarkers also exhibited good diagnostic efficacy and demonstrated the same trend of differential expression in multiple validation cohorts. Conclusion: This study identified autophagy-related diagnostic biomarkers based on three machine learning algorithms, providing promising targets for the diagnosis and treatment of RA.

MAPK8 and CAPN1 as potential biomarkers of intervertebral disc degeneration overlapping immune infiltration, autophagy, and ceRNA.

Intervertebral disc degeneration (IDD) is one of the most common health problems in the elderly and a major causative factor in low back pain (LBP). An increasing number of studies have shown that IDD is closely associated with autophagy and immune dysregulation. Therefore, the aim of this study was to identify autophagy-related biomarkers and gene regulatory networks in IDD and potential therapeutic targets. We obtained the gene expression profiles of IDD by downloading the datasets GSE176205 and GSE167931 from the Gene Expression Omnibus (GEO) public database. Subsequently, differentially expressed genes (DEGs) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, gene ontology (GO), and gene set enrichment analysis (GSEA) were performed to explore the biological functions of DEGs. Differentially expressed autophagy-related genes (DE-ARGs) were then crossed with the autophagy gene database. The hub genes were screened using the DE-ARGs protein-protein interaction (PPI) network. The correlation between the hub genes and immune infiltration and the construction of the gene regulatory network of the hub genes were confirmed. Finally, quantitative PCR (qPCR) was used to validate the correlation of hub genes in a rat IDD model. We obtained 636 DEGs enriched in the autophagy pathway. Our analysis revealed 30 DE-ARGs, of which six hub genes (MAPK8, CTSB, PRKCD, SNCA, CAPN1, and EGFR) were identified using the MCODE plugin. Immune cell infiltration analysis revealed that there was an increased proportion of CD8+ T cells and M0 macrophages in IDD, whereas CD4+ memory T cells, neutrophils, resting dendritic cells, follicular helper T cells, and monocytes were much less abundant. Subsequently, the competitive endogenous RNA (ceRNA) network was constructed using 15 long non-coding RNAs (lncRNAs) and 21 microRNAs (miRNAs). In quantitative PCR (qPCR) validation, two hub genes, MAPK8 and CAPN1, were shown to be consistent with the bioinformatic analysis results. Our study identified MAPK8 and CAPN1 as key biomarkers of IDD. These key hub genes may be potential therapeutic targets for IDD.

Integrated Bioinformatics Approach for Disclosing Autophagy Pathway as a Therapeutic Target in Advanced KRAS Mutated/Positive Lung Adenocarcinoma

Background: Lung cancer is the leading cause of cancer-related deaths, accounting for 1.8 million deaths (18%). Nearly 80%-85% of lung cancer cases are non-small cell lung cancers (NSCLC). One of the most frequent genetic mutations in NSCLC is the Kirsten Rat Sarcoma Oncogene Homolog (KRAS) gene mutation. In recent years, autophagy has drawn substantial attention as a potential pathway that can be targeted in cancer driven by KRAS gene mutation to efficiently improve the therapeutic profile of different treatments. Methods: In this study, we have investigated the potential of targeting the autophagy pathway as a treatment approach in advanced KRAS-mutated lung adenocarcinoma using gene expression data from The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) project. Results: Compared to KRAS wild-type lung adenocarcinoma, there were found 11 differentially expressed autophagy-related genes (DEARGs), with 5 upregulated and 6 downregulated DEARGs (threshold of adjusted p-value &lt;0.05). Conclusion: These DEARGs can be investigated as potential genes that can be targeted by different autophagy inhibitors.

Establishment and validation of the autophagy-related ceRNA network in irreversible pulpitis

BackgroundThe molecular mechanisms underlying the onset and progression of irreversible pulpitis have been studied for decades. Many studies have indicated a potential correlation between autophagy and this disease. Against the background of the competing endogenous RNA (ceRNA) theory, protein-coding RNA functions are linked with long noncoding RNAs (lncRNAs) and microRNAs (miRNAs). This mechanism has been widely studied in various fields but has rarely been reported in the context of irreversible pulpitis. The hub genes selected under this theory may represent the key to the interaction between autophagy and irreversible pulpitis.ResultsFiltering and differential expression analyses of the GSE92681 dataset, which contains data from 7 inflamed and 5 healthy pulp tissue samples, were conducted. The results were intersected with autophagy-related genes (ARGs), and 36 differentially expressed ARGs (DE-ARGs) were identified. Functional enrichment analysis and construction of the protein‒protein interaction (PPI) network of DE-ARGs were performed. Coexpression analysis was conducted between differentially expressed lncRNAs (DElncRNAs) and DE-ARGs, and 151 downregulated and 59 upregulated autophagy-related DElncRNAs (AR-DElncRNAs) were identified. StarBase and multiMiR were then used to predict related microRNAs of AR-DElncRNAs and DE-ARGs, respectively. We established ceRNA networks including 9 hub lncRNAs (HCP5 and AC112496.1 ↑; FENDRR, AC099850.1, ZSWIM8-AS1, DLX6-AS1, LAMTOR5-AS1, TMEM161B-AS1 and AC145207.5 ↓), which were validated by a qRT‒PCR analysis of pulp tissue from patients with irreversible pulpitis.ConclusionWe constructed two networks consisting of 9 hub lncRNAs based on the comprehensive identification of autophagy-related ceRNAs. This study may provide novel insights into the interactive relationship between autophagy and irreversible pulpitis and identifies several lncRNAs that may serve as potential biological markers.

Identification and analysis of type 2 diabetes-mellitus-associated autophagy-related genes.

Autophagy, an innate safeguard mechanism for protecting the organism against harmful agents, is implicated in the survival of pancreatic â cells and the development of type 2 diabetes mellitus (T2DM). Potential autophagy-related genes (ARGs) may serve as potential biomarkers for T2DM treatment. The GSE25724 dataset was downloaded from the Gene Expression Omnibus (GEO) database, and ARGs were obtained from the Human Autophagy Database. The differentially expressed autophagy-related genes (DEARGs) were screened at the intersection of ARGs and differentially expressed genes (DEGs) between T2DM and non-diabetic islet samples, which were subjected to functional enrichment analyses. A protein-protein interaction (PPI) network was constructed to identify hub DEARGs. Expressions of top 10 DEARGs were validated in human pancreatic â-cell line NES2Y and rat pancreatic INS-1 cells using quantitative reverse transcription polymerase chain reaction (qRT-PCR). Cell viability and insulin secretion were measured after cell transfection with lentiviral vector EIF2AK3 or RB1CC1 into islet cells. In total, we discovered 1,270 DEGs (266 upregulated and 1,004 downregulated genes) and 30 DEARGs enriched in autophagy- and mitophagy-related pathways. In addition, we identified GAPDH, ITPR1, EIF2AK3, FOXO3, HSPA5, RB1CC1, LAMP2, GABARAPL2, RAB7A, and WIPI1 genes as the hub ARGs. Next, qRT-PCR analysis revealed that expressions of hub DEARGs were consistent with findings from bioinformatics analysis. EIF2AK3, GABARAPL2, HSPA5, LAMP2, and RB1CC1 were both differentially expressed in the two cell types. Overexpression of EIF2AK3 or RB1CC1 promoted cell viability of islet cells and increased the insulin secretion. This study provides potential biomarkers as therapeutic targets for T2DM.

Comprehensive analysis of autophagy-related gene expression profiles identified five gene biomarkers associated with immune infiltration and advanced plaques in carotid atherosclerosis

BackgroundAutophagy plays an important role in the progression of carotid atherosclerosis (CAS). This study aimed to identify hub autophagy-related genes (ATGs) associated with CAS.MethodsGSE43292 and GSE28829 datasets of early and advanced CAS plaques were enrolled from the Gene Expression Omnibus (GEO) database. A comprehensive analysis of differentially expressed ATGs (DE-ATGs) was conducted. Functional enrichment assay was used to explore biological functions of DE-ATGs. The hub ATGs were identified by protein–protein interaction (PPI) network. Immunohistochemistry (IHC) and Real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR) were used to validate hub ATGs at the protein level and mRNA level. Correlation analysis of hub ATGs with immune cells was also conducted. In addition, a competitive endogenous RNA (ceRNA) network was constructed, and diagnostic value of hub ATGs was evaluated.ResultsA total of 19 DE-ATGs were identified in early and advanced CAS plaques. Functional enrichment analysis of DE-ATGs suggested that they were closely correlated to autophagy, apoptosis, and lipid regulation. Moreover, 5 hub ATGs, including TNFSF10, ITGA6, CTSD, CCL2, and CASP1, were identified and further verified by IHC. The area under the curve (AUC) values of the 5 hub ATGs were 0.818, 0.732, 0.792, 0.814, and 0.812, respectively. Competing endogenous RNA (ceRNA) networks targeting the hub ATGs were also constructed. In addition, the 5 hub ATGs were found to be closely associated with immune cell infiltration in CAS.ConclusionIn this study, we identified 5 hub ATGs including CASP1, CCL2, CTSD, ITGA6 and TNFSF10, which could serve as candidate diagnostic biomarkers and therapeutic targets.

TNFSF10, an autophagy related gene, was a prognostic and immune infiltration marker in skin cutaneous melanoma.

Autophagy exerts a pivotal effect on skin cutaneous melanoma (SKCM). This study was aimed to investigate the expression of autophagy related genes (ARGs) in SKCM as well as its clinical value. Differentially expressed (DE) ARGs were downloaded from the intersection of SKCM data in GEPIA2 database and ARGs in Human Autophagy Database (HADB) database, and were verified in SKCM datasets GSE46517 and GSE15605. DE ARGs were enriched by Metascape online tools. According to GEPIA2 database, tumor necrosis factor-related apoptosis-inducing ligand (TNFSF10) was identified as a closely related factor and prognostic marker of SKCM. Then the correlation analysis of clinicopathological characteristics between TNFSF10 and SKCM was completed by several online tools such as TISCH, HPA, BEST and qRT-PCR. Subsequently, we investigated TNFSF10 related functions and signal pathways with LinkedOmics online tool, and immune infiltration using Assistant for Clinical Bioinformatics online tool. Furthermore, correlation analysis between TNFSF10 expression and immunotherapy response was performed by TIDE algorithm and BEST online tool. And Kaplan-Meier Plotter was used to assessing the prognosis of SKCM patients receiving immunotherapy. Finally, the correlation analysis among TNFSF10 methylation, TNFSF10 expression and patient prognosis was completed by the DiseaseMeth version 2.0, UCSC XENA and qRT-PCR. ARGs are DE in SKCM and participate in the ERBB signaling pathway, as well as the processing and presentation of antigens. Moreover, TNFSF10's expression along with methylation expression were significantly associated with the prognosis. Low expression of TNFSF10 was associated with malignant clinicopathological features, lower immune signal activity and lower immunocytes abundance in patients with SKCM. As an ARG, TNFSF10 has a potential capacity in predicting the prognosis of SKCM patients, meanwhile, may be a novel immunotherapy marker for SKCM.

Expression and Regulatory Mechanism of Autophagy-related Genes in Synovial Tissues of Patients with Rheumatoid Arthritis

Objective To investigate the expression regulation of autophagy-related genes(ATG)and the mechanism of autophagy in rheumatoid arthritis(RA).Methods The differentially expressed genes(DEG)of RA were identified from GSE55235 and GSE55457,on the basis of which the differentially expressed autophagy-related genes(DE-ATG)were selected from the Human Autophagy Database.STRING 11.0 and GeneMANIA were used to establish protein-protein interaction networks.Further,the transcription factor-gene-miRNA co-expression network was established via NetworkAnalyst and Cytoscape.Finally,receiver operating characteristic(ROC)curve and DrugBank were employed to evaluate the efficacy of the predicted biomarkers and the performance of drugs targeting DE-ATG.GraphPad Prism 8.2.1 and R 4.0.3 were used for statistical analysis and graphics.Results A total of 485 DEG were enriched in signaling pathways such as T cell activation,hormone regulation,osteoclast differentiation,RA,and chemokines.Eleven DE-ATG regulated the expression of RUNX1,TP53,SOX2,and hsa-mir-155-5p in synovial tissues of RA patients and were involved in the response to environmental factors such as 2,3,7,8-tetrachlorodibenzodioxin and silicon dioxide.The ROC curve analysis identified the DE-ATG with good sensitivity and specificity,such as MYC,MAPK8,CDKN1A,and TNFSF10,which can be used to distinguish certain phenotypes and serve as novel biomarkers for RA.Conclusions In RA,down-regulated DE-ATG expression may promote apoptosis and lysis of chondrocytes.The identified novel biomarkers provides new ideas and methods for diagnosing and treating RA.The establishment of transcription factor-miRNA-gene co-expression network provides direct evidence for dissecting synovial inflammation and articular cartilage destruction.

The tumor microenvironment and prognostic role of autophagy- and immune-related genes in bladder cancer.

Autophagy-related genes and immune-related genes contribute significantly to the initiation and prognosis of bladder cancer (BLCA). We aimed to explore differentially expressed autophagy-related genes (DEARGs) and immune-related genes (DEIRGs) in BLCA to create a prognostic risk assessment model and gain some insights into BLCA's molecular underpinnings. The prognostic DEARGs and DEIRGs were evaluated for BLCA through The Cancer Genome Atlas (TCGA) database (n= 399) and GSE13507 dataset (n= 165). The BLCA risk model was constructed and verified. The immune score, stromal score, and estimate score in different risk groups were calculated by the ESTIMATE algorithm. Immune infiltration levels were assessed by a single sample gene set enrichment analysis (GSEA) algorithm. In the risk model, AURKA, ACTC1, MYLK, PDGFD, PDGFRA and TNC were significantly associated with the overall survival. The pathways in cancer, T cell receptor signaling pathway and B cell receptor signaling pathway were significantly gathered in the high-risk group. Moreover, the risk score was significantly correlated with infiltrating immune cells, expression of critical immune checkpoints and mismatch repair genes including MSH6, MLH1, and MSH2. In this study, three DEARGs (AURKA, ACTC1, MYLK) and three DEIRGs (PDGFD, PDGFRA, TNC) were demonstrated to be potential prognostic biomarkers for BLCA patients through bioinformatics methods, which might be novel therapeutic targets and prognostic markers for BLCA, in follow up studies, we will combine experiments to verify this.