Exploring the prognostic analysis of autophagy and tumor microenvironment based on monocyte cells in lung cancer.

A deep understanding of the biological mechanisms of lung cancer offers more precise treatment options for patients. In our study, we integrated data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) to investigate lung adenocarcinoma. Analyzing 538 lung cancer samples and 31 normal samples, we focused on 3076 autophagy-related genes. Using Seurat, dplyr, tidyverse, and ggplot2, we conducted single-cell data analysis, assessing the quality and performing Principal Component Analysis (PCA) and t-SNE analyses. Differential analysis of TCGA data using the "Limma" package, followed by immune infiltration analysis using the CIBERSORT algorithm, led us to identify seven key genes. These genes underwent further scrutiny through consensus clustering and gene set variation analysis (GSVA). We developed a prognostic model using Lasso Cox regression and multivariable Cox analysis, which was then validated with a nomogram, predicting survival rates for lung adenocarcinoma. The model's accuracy and universality were corroborated by ROC curves. Additionally, we explored the relationship between immune checkpoint genes and immune cell infiltration and identified two key genes, HLA-DQB1 and OLR1. This highlighted their potential as therapeutic targets. Our comprehensive approach sheds light on the molecular landscape of lung adenocarcinoma and offers insights into potential treatment strategies, emphasizing the importance of integrating single-cell and genomic data in cancer research.

The new kids on the block: RNA-binding proteins regulate autophagy in disease.

Mammalian autophagy is a highly regulated and conserved cellular homeostatic process. Its existence allows the degradation of self-components to mediate cell survival in different stress conditions. Autophagy is involved in the regulation of cellular metabolic needs, protecting the cell or tissue from starvation through the degradation and recycling of cytoplasmic materials and organelles to basic molecular building blocks. It also plays a critical role in eliminating damaged or harmful proteins, organelles, and intracellular pathogens. Thus, a deterioration of the process may result in pathological conditions, such as aging-associated disorders and cancer. Understanding the crucial role of autophagy in maintaining the normal physiological function of cells, tissue, or organs has led to copious and expansive research regarding the regulation of this process. So far, most of the research has revolved around transcriptional and post-translational regulation. Here, we discuss the regulation of autophagy-related (ATG) mRNA transcripts by RNA-binding proteins (RBPs). This analysis focuses on how RBPs modulate autophagy in disease. A deeper understanding of the involvement of RBPs in autophagy can facilitate further research and treatment of a variety of human diseases.

Bioinformatics proved the existence of potential hub genes activating autophagy to participate in cartilage degeneration in osteonecrosis of the femoral head.

The obvious degeneration of articular cartilage occurs in the late stage of osteonecrosis of the femoral head (ONFH), which aggravates the condition of ONFH. This study aimed to demonstrate aberrant activation of autophagy processes in ONFH chondrocytes through bioinformatics and to predict and identify relevant hub genes and pathways. Differentially expressed genes (DEGs) were identified using R software in the GSE74089 dataset from the GEO database. DEGs were crossed with the Human Autophagy Database (HADb) autophagy genes to screen out autophagy-related differential genes (AT-DEGs). GSEA, GSVA, GO, and KEGG pathway enrichment analyses of AT-DEGs were performed. The STRING database was used to analyze the protein-protein interaction (PPI) of the AT-DEGs network, and the MCODE and CytoHubba plugin in the Cytoscape software was used to analyze the key gene cluster module and screen the hub genes. The PPI network of hub genes was constructed using the GeneMANIA database, and functional enrichment and gene connectivity categories were analyzed. The expression levels of hub genes of related genes in the ONFH patients were verified in the dataset GSE123568, and the protein expression was verified by immunohistochemistry in tissues. The analysis of DEGs revealed abnormal autophagy in ONFH cartilage. AT-DEGs in ONFH have special enrichment in macroautophagy, autophagosome membrane, and phosphatidylinositol-3-phosphate binding. In the GSE123568 dataset, it was also found that ATG2B, ATG4B, and UVRAG were all significantly upregulated in ONFH patients. By immunohistochemistry, it was verified that ATG2B, ATG4B, and UVRAG were significantly overexpressed. These three genes regulate the occurrence and extension of autophagosomes through the PI3KC3C pathway. Finally, we determined that chondrocytes in ONFH undergo positive regulation of autophagy through the corresponding pathways involved in three genes: ATG2B, ATG4B, and UVRAG.

Identification of Autophagy-Related Genes Involved in Intervertebral Disc Degeneration by Microarray Data Analysis.

BACKGROUND: Nucleus pulposus cells survive in a hypoxic, acidic, nutrient-poor, and hypotonic microenvironment. Consequently, they maintain low proliferation and undergo autophagy to protect themselves from cellular stress. Therefore, we aimed to identify autophagy-related biomarkers involved in intervertebral disc degeneration pathogenesis. METHODS: Autophagy-related differentially expressed genes were derived from the intersection between the public GSE147383 microarray data set to identify differentially expressed genes and online databases to identify autophagy-related genes. Furthermore, we assessed their biological functions with gene annotation and enrichment analysis in the Metscape portal. Then, the STRING database and Cytoscape software allowed inferring a protein-protein interaction (PPI) network and identifying hub genes. In addition, to predict transcription factors that may regulate the hub genes, we used the GeneMANIA website. Finally, the competing endogenous RNA prediction tools and Cytoscape were also used to construct an mRNA-miRNA-lncRNA network. RESULTS: A total of 123 autophagy-related differentially expressed genes were identified, they were mainly involved in phosphoinositide 3-kinase-Akt signaling, autophagy animal, and apoptosis pathways. Nine were identified as hub genes (PTEN, MYC, CTNNB1, JUN, BECN1, ERBB2, FOXO3, ATM, and FN1) and 36 transcription factors were associated with them. Finally, an autophagy-associated competing endogenous RNA network was constructed based on the 9 hub genes. CONCLUSIONS: Nine hub genes were identified and a network of competing endogenous RNA associated with autophagy was established. They can be used as autophagy-related biomarkers of intervertebral disc degeneration and for further exploration.

Investigation of autophagy­related 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.

OBJECTIVE: Recent 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. METHODS: Two 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. RESULTS: In 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. CONCLUSION: Bioinformatic 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.

General autophagy-dependent and -independent lipophagic processes collaborate to regulate the overall level of lipophagy in yeast.

ABBREVIATION: AP: autophagosome; ATG: autophagy related; CMA: chaperone-mediated autophagy; ESCRT: endosomal sorting complex required for transport; FA: fatty acid; LD: lipid droplet; Ld microdomains: liquid-disordered microdomains; NL: neutral lipid.

Autophagy Genes and Otitis Media Outcomes.

Otitis media (OM) is a common cause of hearing loss in children that requires corrective surgery. Various studies have investigated the pathomechanisms and treatment of OM. Autophagy, an essential cellular recycling and elimination mechanism implicated in various diseases, is known to play an important role in the pathogenesis of OM. Here, we conducted a literature review on autophagy in OM, highlighting the relationship between expression patterns of autophagy-related factors and pathophysiological and clinical aspects of OM. We summarized the existing research results on the expression of autophagy-related factors in acute OM (AOM), OM with effusion (OME), chronic OM (COM) with cholesteatoma, and COM without cholesteatoma (CholeOM) in animals and humans. Autophagy-related factors are expressed in the middle ear mucosa or fluid of AOM, effusion of OME, granulation tissue of COM, and cholesteatoma of CholeOM. Among ATGs and other autophagy-related factors, the most extensively studied in relation to the pathogenesis of OM are mTOR, LC3II/I, PI3K, Beclin-1, FLIP, Akt, and Rubicon. Expression of autophagy-related factors is associated with AOM, OME, COM, and CholeOM. Inadequate expression of these factors or a decrease/increase in autophagy responses can result in OM, underscoring the critical role of ATGs and related factors in the pathogenesis of OM.

Natural products targeting macroautophagy signaling in hepatocellular carcinoma therapy: Recent evidence and perspectives.

Hepatocellular carcinoma (HCC), presently the second leading cause of global cancer-related mortality, continues to pose significant challenges in the realm of medical oncology, impacting both clinical drug selection and mechanistic research. Recent investigations have unveiled autophagy-related signaling as a promising avenue for HCC treatment. A growing body of research has highlighted the pivotal role of autophagy-modulating natural products in inhibiting HCC progression. In this context, we provide a concise overview of the fundamental autophagy mechanism and delineate the involvement of autophagic signaling pathways in HCC development. Additionally, we review pertinent studies demonstrating how natural products regulate autophagy to mitigate HCC. Our findings indicate that natural products exhibit cytotoxic effects through the induction of excessive autophagy, simultaneously impeding HCC cell proliferation by autophagy inhibition, thereby depriving HCC cells of essential energy. These effects have been associated with various signaling pathways, including PI3K/AKT, MAPK, AMPK, Wnt/ß-catenin, Beclin-1, and ferroautophagy. These results underscore the considerable therapeutic potential of natural products in HCC treatment. However, it is important to note that the present study did not establish definitive thresholds for autophagy induction or inhibition by natural products. Further research in this domain is imperative to gain comprehensive insights into the dual role of autophagy, equipping us with a better understanding of this double-edged sword in HCC management.

Identification and verification of a prognostic autophagy-related gene signature in hepatocellular carcinoma.

This study aimed to investigate the potential of autophagy-related genes (ATGs) as a prognostic signature for HCC and explore their relationships with immune cells and immune checkpoint molecules. A total of 483 samples were collected from the GEO database (n = 115) and The Cancer Genome Atlas (TCGA) database (n = 368). The GEO dataset was used as the training set, while the TCGA dataset was used for validation. The list of ATGs was obtained from the human autophagy database (HADB). Using Cox regression and LASSO regression methods, a prognostic signature based on ATGs was established. The independent use of this prognostic signature was tested through subgroup analysis. Additionally, the predictive value of this signature for immune-related profiles was explored. Following selection through univariate Cox regression analysis and iterative LASSO Cox analysis, a total of 11 ATGs were used in the GEO dataset to establish a prognostic signature that stratified patients into high- and low-risk groups based on survival. The robustness of this prognostic signature was validated using an external dataset. This signature remained a prognostic factor even in subgroups with different clinical features. Analysis of immune profiles revealed that patients in the high-risk group exhibited immunosuppressive states characterized by lower immune scores and ESTIMATE scores, greater tumour purity, and increased expression of immune checkpoint molecules. Furthermore, this signature was found to be correlated with the infiltration of different immune cell subpopulations. The results suggest that the ATG-based signature can be utilized to evaluate the prognosis of HCC patients and predict the immune status within the tumour microenvironment (TME). However, it is important to note that this study represents a preliminary attempt to use ATGs as prognostic indicators for HCC, and further validation is necessary to determine the predictive power of this signature.

Investigating the Association between the Autophagy Markers LC3B, SQSTM1/p62, and DRAM and Autophagy-Related Genes in Glioma.

High-grade gliomas are extremely fatal tumors, marked by severe hypoxia and therapeutic resistance. Autophagy is a cellular degradative process that can be activated by hypoxia, ultimately resulting in tumor advancement and chemo-resistance. Our study aimed to examine the link between autophagy markers' expression in low-grade gliomas (LGGs) and high-grade gliomas (HGGs). In 39 glioma cases, we assessed the protein expression of autophagy markers LC3B, SQSTM1/p62, and DRAM by immunohistochemistry (IHC) and the mRNA expression of the autophagy genes PTEN, PI3K, AKT, mTOR, ULK1, ULK2, UVRAG, Beclin 1, and VPS34 using RT-qPCR. LC3B, SQSTM1/p62, and DRAM expression were positive in 64.1%, 51.3%, and 28.2% of glioma cases, respectively. The expression of LC3B and SQSTM1/p62 was notably higher in HGGs compared to LGGs. VPS34 exhibited a significant differential expression, displaying increased fold change in HGGs compared to LGGs. Additionally, it exhibited robust positive associations with Beclin1 (rs = 0.768), UVRAG (rs = 0.802), and ULK2 (rs = 0.786) in HGGs. This underscores a potential association between autophagy and the progression of gliomas. We provide preliminary data for the functional analysis of autophagy using a cell culture model and to identify potential targets for therapeutic interventions.

Distinct roles of core autophagy-related genes in zebrafish definitive hematopoiesis.

Despite the well-described discrepancy between ATG (macroautophagy/autophagy-related) genes in the regulation of hematopoiesis, varying essentiality of core ATG proteins in vertebrate definitive hematopoiesis remains largely unclear. Here, we employed zebrafish (Danio rerio) to compare the functions of six core atg genes, including atg13, becn1 (beclin1), atg9a, atg2a, atg5, and atg3, in vertebrate definitive hematopoiesis via clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 ribonucleoprotein and morpholino targeting. Zebrafish with various atg mutations showed autophagic deficiency and presented partially consistent hematopoietic abnormalities during early development. All six atg mutations led to a declined number of spi1b+ (Spi-1 proto-oncogene b) myeloid progenitor cells. However, only becn1 mutation resulted in the expansion of myb+ (v-myb avian myeloblastosis viral oncogene homolog) hematopoietic stem and progenitor cells (HSPCs) and transiently increased coro1a+ (coronin, actin binding protein, 1A) leukocytes, whereas atg3 mutation decreased the number of HSPCs and leukocytes. Proteomic analysis of caudal hematopoietic tissue identified sin3aa (SIN3 transcription regulator family member Aa) as a potential modulator of atg13- and becn1-regulated definitive hematopoiesis. Disruption of sin3aa rescued the expansion of HSPCs and leukocytes in becn1 mutants and exacerbated the decrease of HSPCs in atg13 mutants. Double mutations were also performed to examine alternative functions of various atg genes in definitive hematopoiesis. Notably, becn1 mutation failed to induce HSPCs expansion with one of the other five atg mutations. These findings demonstrated the distinct roles of atg genes and their interplays in zebrafish definitive hematopoiesis, thereby suggesting that the vertebrate definitive hematopoiesis is regulated in an atg gene-dependent manner.Abbreviations: AGM: aorta-gonad-mesonephros; AO: acridine orange; atg: autophagy related; becn1: beclin 1, autophagy related; CHT: caudal hematopoietic tissue; CKO: conditional knockout; coro1a: coronin, actin binding protein, 1A; CQ: chloroquine; CRISPR: clustered regularly interspaced short palindromic repeats; dpf: days post fertilization; FACS: fluorescence-activated cell sorting; hbae1.1: hemoglobin, alpha embryonic 1.1; HSCs: hematopoietic stem cells; HSPCs: hematopoietic stem and progenitor cells; KD: knockdown; KO: knockout; map1lc3/lc3: microtubule-associated protein 1 light chain 3; MO: morpholino; mpeg1.1: macrophage expressed 1, tandem duplicate 1; mpx: myeloid-specific peroxidase; myb: v-myb avian myeloblastosis viral oncogene homolog; PE: phosphatidylethanolamine; p-H3: phospho-H3 histone; PtdIns3K: class 3 phosphatidylinositol 3-kinase; rag1: recombination activating 1; rb1cc1/fip200: RB1-inducible coiled-coil 1; RFLP: restriction fragment length polymorphism; RNP: ribonucleoprotein; sin3aa: SIN3 transcription regulator family member Aa; spi1b: Spi-1 proto-oncogene b; ulk: unc-51 like autophagy activating kinase; vtg1: vitellogenin 1; WISH: whole-mount in situ hybridization.

Characteristics of Autophagy-Related Genes, Diagnostic Models, and Their Correlation with Immune Infiltration in Keratoconus.

Purpose: Keratoconus (KTCN) is one of the most common degenerative keratopathies, significantly affecting vision and even leading to blindness. This study identifies potential biomarkers of KTCN based on the characterization of autophagy-related genes (ARGs) and the construction of a diagnostic model; and explores their relevance to immune infiltrating cells in KTCN. Methods: Gene Expression Omnibus (GEO) data were downloaded and ARGs were acquired from GeneCards and Molecular Signatures Database (MSigDB). Autophagy-related differential expression genes (ARDEGs) were discovered through the integration of differentially expressed genes (DEGs) with ARGs, while hub genes of KTCN were discovered by protein-protein interaction (PPI) network analysis. The probable biological roles of these hub ARDEGs were examined using functional enrichment analysis, and a KTCN diagnostic model was generated using the least absolute shrinkage and selection operator (LASSO) regression analysis. We also employed the CIBERSORTx and ssGSEA algorithms to identify potential regulatory pathways to compare the abundance of immune cell infiltrates and their association with hub genes. Finally, the hub gene expression levels were confirmed using validation datasets as well as blood samples from KTCN and healthy individuals. Results: In this study, we identified 12 hub ARDEGs, of which 9 genes were substantially distinct between KTCN patients and normal groups. The LASSO risk score was used to generate the nomogram, and the calibration curve evaluated the model's effective diagnostic performance (C index of 0.961). Patients with KTCN had greater percentages of M2 Macrophages and Gamma delta T cells, according to CIBERSORTx and ssGSEA. The outcomes of the bioinformatics analysis were supported by the DDIT3 and BINP3 expression levels in KTCN patients and healthy controls, according to the qRT-PCR data. Conclusion: Five biomarkers (CFTR, PLIN2, DDIT3, BAG3, and BNIP3) and diagnostic models offer fresh perspectives on identifying and managing KTCN.

Increased expression of autophagy-related gene 5 indicates poor prognosis in patients with hepatocellular carcinoma.

OBJECTIVES: As a critical component of the autophagic machinery, autophagy-related gene 5 (ATG5) is essential for autophagosome formation. Autophagy participates in the transformation and progression of various malignant tumors, but the role of ATG5 in hepatocellular carcinoma (HCC) remains to be illustrated. In this study we aimed to investigate the prognostic significance of ATG5 in HCC. METHODS: ATG5 expression was evaluated in 89 pairs of HCC tissues and adjacent non-tumor tissues. The relationship between ATG5 expression and patients' clinicopathological characteristics and prognosis were evaluated. Moreover, subgroup analyses were performed regarding patients' age and number of tumors. Nomograms estimating overall survival (OS) and disease-free survival (DFS) were conducted. RESULTS: ATG5 expression was increased in HCC specimens rather than adjacent non-tumor tissues. The upregulated ATG5 expression was positively associated with serum α-fetoprotein (AFP) level. Moreover, cases with a strong ATG5 expression had a poorer disease-free survival (DFS) and overall survival (OS) than those with a weak ATG5 expression. Multivariate analysis showed that a strong expression of ATG5 was related to a poor OS and DFS in patients with HCC. Further analysis indicated that cases with a higher ATG5 expression had a poorer OS and DFS in the young patients (≤55 years) and those with solitary tumor. The nomogram suggested that there was a coherence between nomogram prediction and the actual situation of patient survival related to ATG5. CONCLUSION: ATG5 promotes tumor progression in HCC, making it a potential biomarker in the diagnosis and a therapeutic target of HCC.

Autophagy signaling in hypertrophied muscles of diabetic and control rats.

Autophagy plays a vital role in cell homeostasis by eliminating nonfunctional components and promoting cell survival. Here, we examined the levels of autophagy signaling proteins after 7 days of overload hypertrophy in the extensor digitorum longus (EDL) and soleus muscles of control and diabetic rats. We compared control and 3-day streptozotocin-induced diabetic rats, an experimental model for type 1 diabetes mellitus (T1DM). EDL muscles showed increased levels of basal autophagy signaling proteins. The diabetic state did not affect the extent of overload-induced hypertrophy or the levels of autophagy signaling proteins (p-ULK1, Beclin-1, Atg5, Atg12-5, Atg7, Atg3, LC3-I and II, and p62) in either muscle. The p-ULK-1, Beclin-1, and p62 protein expression levels were higher in the EDL muscle than in the soleus before the hypertrophic stimulus. On the contrary, the soleus muscle exhibited increased autophagic signaling after overload-induced hypertrophy, with increases in Beclin-1, Atg5, Atg12-5, Atg7, Atg3, and LC3-I expression in the control and diabetic groups, in addition to p-ULK-1 in the control groups. After hypertrophy, Beclin-1 and Atg5 levels increased in the EDL muscle of both groups, while p-ULK1 and LC3-I increased in the control group. In conclusion, the baseline EDL muscle exhibited higher autophagy than the soleus muscle. Although TDM1 promotes skeletal muscle mass loss and strength reduction, it did not significantly alter the extent of overload-induced hypertrophy and autophagy signaling proteins in EDL and soleus muscles, with the two groups exhibiting different patterns of autophagy activation.

Identification of a seven autophagy-related gene pairs signature for the diagnosis of colorectal cancer using the RankComp algorithm.

Based on the colorectal cancer microarray sets gene expression data series (GSE) GSE10972 and GSE74602 in colon cancer and 222 autophagy-related genes, the differential signature in colorectal cancer and paracancerous tissues was analyzed by RankComp algorithm, and a signature consisting of seven autophagy-related reversal gene pairs with stable relative expression orderings (REOs) was obtained. Scoring based on these gene pairs could significantly distinguish colorectal cancer samples from adjacent noncancerous samples, with an average accuracy of 97.5% in two training sets and 90.25% in four independent validation GSE21510, GSE37182, GSE33126, and GSE18105. Scoring based on these gene pairs also accurately identifies 99.85% of colorectal cancer samples in seven other independent datasets containing a total of 1406 colorectal cancer samples.

Polymorphism in autophagy-related genes LRP1 and CAPZA1 may promote gastric mucosal atrophy.

BACKGROUND: Helicobacter pylori secretes cytotoxin-associated gene A (CagA) into the gastric epithelium, causing gastric mucosal atrophy (GMA) and gastric cancer. In contrast, host cells degrade CagA via autophagy. However, the association between polymorphisms in autophagy-related genes and GMA must be fully elucidated. RESULTS: We evaluated the association between single nucleotide polymorphisms (SNPs) in autophagy-related genes (low-density lipoprotein receptor-related protein 1, LRP1; capping actin protein of muscle Z-line alpha subunit 1, CAPAZ1; and lysosomal-associated membrane protein 1, LAMP1) and GMA in 200 H. pylori-positive individuals. The frequency of the T/T genotype at rs1800137 in LRP1 was significantly lower in the GMA group than in the non-GMA group (p = 0.018, odds ratio [OR] = 0.188). The frequencies of the G/A or A/A genotype at rs4423118 and T/A or A/A genotype at rs58618380 of CAPAZ1 in the GMA group were significantly higher than those in the non-GMA group (p = 0.029 and p = 0.027, respectively). Multivariate analysis revealed that C/C or C/T genotype at rs1800137, T/A or A/A genotype at rs58618380, and age were independent risk factors for GMA (p = 0.038, p = 0.023, and p = 0.006, respectively). Furthermore, individuals with the rs1800137 C/C or C/T genotype of LRP1 had a 5.3-fold higher susceptibility to GMA. These genetic tests may provide future directions for precision medicine for individuals more likely to develop GMA. CONCLUSION: LRP1 and CAPZA1 polymorphisms may be associated with the development of GMA.

Prognostic value of autophagy-related genes based on single-cell RNA-sequencing in colorectal cancer.

Background: Colorectal cancer (CRC) is the second most common cancer in China. Autophagy plays an important role in the initiation and development of CRC. Here, we assessed the prognostic value and potential functions of autophagy-related genes (ARGs) using integrated analysis using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) and RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA). Methods: We analyzed GEO-scRNA-seq data from GEO using various single-cell technologies, including cell clustering, and identification of differentially expressed genes (DEGs) in different cell types. Additionally, we performed gene set variation analysis (GSVA). The differentially expressed ARGs among different cell types and those between CRC and normal tissues were identified using TCGA-RNA-seq data, and the hub ARGs were screened. Finally, a prognostic model based on the hub ARGs was constructed and validated, and patients with CRC in TCGA datasets were divided into high- and low-risk groups based on their risk-score, and immune cells infiltration and drug sensitivity analyses between the two groups were performed. Results: We obtained single-cell expression profiles of 16,270 cells, and clustered them into seven types of cells. GSVA revealed that the DEGs among the seven types of cells were enriched in many signaling pathways associated with cancer development. We screened 55 differentially expressed ARGs, and identified 11 hub ARGs. Our prognostic model revealed that the 11 hub ARGs including CTSB, ITGA6, and S100A8, had a good predictive ability. Moreover, the immune cell infiltrations in CRC tissues were different between the two groups, and the hub ARGs were significantly correlated with the enrichment of immune cell infiltration. The drug sensitivity analysis revealed that the patients in the two risk groups had difference in their response to anti-cancer drugs. Conclusion: We developed a novel prognostic 11-hub ARG risk model, and these hubs may act as potential therapeutic targets for CRC.

Comprehensive analysis of autophagy-related clusters and individual risk model for immunotherapy response prediction in gastric cancer.

Introduction: Autophagy can be triggered by oxidative stress and is a double-edged sword involved in the progression of multiple malignancies. However, the precise roles of autophagy on immune response in gastric cancer (GC) remain clarified. Methods: We endeavor to explore the novel autophagy-related clusters and develop a multi-gene signature for predicting the prognosis and the response to immunotherapy in GC. A total of 1505 patients from eight GC cohorts were categorized into two subtypes using consensus clustering. We compare the differences between clusters by the multi-omics approach. Cox and LASSO regression models were used to construct the prognostic signature. Results: Two distinct clusters were identified. Compared with cluster 2, the patients in cluster 1 have favorable survival outcomes and lower scores for epithelial-mesenchymal transition (EMT). The two subtypes are further characterized by high heterogeneity concerning immune cell infiltration, somatic mutation pattern, and pathway activity by gene set enrichment analysis (GSEA). We obtained 21 autophagy-related differential expression genes (DEGs), in which PTK6 amplification and BCL2/CDKN2A deletion were highly prevalent. The four-gene (PEA15, HSPB8, BNIP3, and GABARAPL1) risk signature was further constructed with good predictive performance and validated in 3 independent datasets including our local Tianjin cohort. The risk score was proved to be independent prognostic factor. A prognostic nomogram showed robust validity of GC survival. The risk score was significantly associated with immune cell infiltration status, tumor mutation burden (TMB), microsatellite instability (MSI), and immune checkpoint molecules. Furthermore, the model was efficient for predicting the response to tumor-targeted agent and immunotherapy and verified by the IMvigor210 cohort. This model is also capable of discriminating between low and high-risk patients receiving chemotherapy. Conclusion: Altogether, our exploratory research on the landscape of autophagy-related patterns may shed light on individualized therapies and prognosis in GC.

Differential Expression of Autophagy-Related Long Non-Coding RNA in Melanoma.

To explore the role of autophagy-related differential long non-coding RNA (lncRNA) in the pathogenesis of melanoma, we established a prognostic prediction model for patients with melanoma based on the expression profiles of autophagy-related gene. Based on The Cancer Genome Atlas and GeneCard database, we used single-sample gene set enrichment analysis (ssGSEA), weighted gene co-expression network analysis (WGCNA), uniCOX in R software for COX proportional hazard regression analysis, and enrichment analysis to get an idea of biological processes with autophagy-related genes, which evaluates the relationship between autophagy-related genes and immune cell infiltration in patients with melanoma. The roles of identified lncRNA were evaluated by the risk score based on the results of single factor regression analysis for each lncRNA and on the prognosis for patients obtained from the database. Then, the whole sample was divided into high- and low-risk groups. Survival curve analysis showed that low-risk group had a better prognosis. Enrichment analysis revealed multiple key pathways enriched with lncRNA-associated genes. Analysis of immune cell infiltration revealed differences between high- and low-risk groups. Finally, 3 datasets verified the effect of our model on prognosis. There are important autophagy-related lncRNA in patients with melanoma. Top 6 lncRNA are significantly related to the overall survival rate of patients with melanoma and provide the basis for predicting the prognostic survival of patients.