Identification and validation of an individualized autophagy-clinical prognostic index in bladder cancer patients

FGFR3 mutation characterization identifies prognostic and immune-related gene signatures in bladder cancer

BACKGROUND

Immunotherapy and FGFR3-targeted therapy play an important role in the management of locally advanced and metastatic bladder cancer (BLCA). Previous studies indicated that FGFR3 mutation (mFGFR3) may be involved in the alterations of immune infiltration, which may affect the priority or combination of these two treatment regimes. However, the specific impact of mFGFR3 on the immunity and how FGFR3 regulates the immune response in BLCA to affect prognosis remain unclear. In this study, we aimed to elucidate the immune landscape associated with mFGFR3 status in BLCA, screen immune-related gene signatures with prognostic value, and construct and validate a prognostic model.

METHODS

ESTIMATE and TIMER were used to assess the immune infiltration within tumors in the TCGA BLCA cohort based on transcriptome data. Further, the mFGFR3 status and mRNA expression profiles were analyzed to identify immune-related genes that were differentially expressed between patients with BLCA with wild-type FGFR3 or mFGFR3 in the TCGA training cohort. An FGFR3-related immune prognostic score (FIPS) model was established in the TCGA training cohort. Furthermore, we validated the prognostic value of FIPS with microarray data in the GEO database and tissue microarray from our center. Multiple fluorescence immunohistochemical analysis was performed to confirm the relationship between FIPS and immune infiltration.

RESULTS

mFGFR3 resulted in differential immunity in BLCA. In total, 359 immune-related biological processes were enriched in the wild-type FGFR3 group, whereas none were enriched in the mFGFR3 group. FIPS could effectively distinguish high-risk patients with poor prognosis from low-risk patients. The high-risk group was characterized by a higher abundance of neutrophils; macrophages; and follicular helper, CD4, and CD8 T-cells than the low-risk group. In addition, the high-risk group exhibited higher expression of PD-L1, PD-1, CTLA-4, LAG-3, and TIM-3 than the low-risk group, indicating an immune-infiltrated but functionally suppressed immune microenvironment. Furthermore, patients in the high-risk group exhibited a lower mutation rate of FGFR3 than those in the low-risk group.

CONCLUSIONS

FIPS effectively predicted survival in BLCA. Patients with different FIPS exhibited diverse immune infiltration and mFGFR3 status. FIPS might be a promising tool for selecting targeted therapy and immunotherapy for patients with BLCA.

Down-regulation and clinical significance of Sorbin and SH3 domain-containing protein 1 in bladder cancer tissues

Bladder cancer (BC) is a common cancer worldwide with a high prevalence. This study was conducted to elucidate the expression and clinical significance of Sorbin and SH3 domain-containing protein 1 (SORBS1) in BC as well as to explore its molecular mechanism in BC tumourigenesis. RNA-sequencing data, microarray, and Immunohistochemistry (IHC) were applied to elucidated the SORBS1 expression at multiple levels. After that, the relationship between tumour-immune infiltration and SORBS1 was also explored. Finally, SORBS1-related genes in BC were identified to perform functional enrichment analyses. The expression integration revealed that the comprehensive expression of SORBS1 at the mRNA level was -1.02 and that at the protein level was -3.73, based on 12 platforms, including 1221 BC and 187 non-BC samples. SORBS1 was negatively correlated with tumour purity (correlation = -0.342, p < 0.001) and positively correlated with macrophage (correlation = 0.358, p < 0.001). The results of enrichment analyses revealed that the most significant biological pathways of SORBS1-related genes were epithelial-mesenchymal transition. SORBS1 was significantly down-regulated in BC and may play a role as tumour suppressor. This study provides new directions and biomarkers for future BC diagnosis.

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

BACKGROUND: Autophagy-related genes and immune-related genes contribute significantly to the initiation and prognosis of bladder cancer (BLCA). OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Identification of Core Genes and Prognostic Models of Laryngeal Cancer by Autophagy Related Biomarkers

Background: The aims of our article were to identify the core genes of the autophagy-related genes (ARGs) which abnormally expressed in laryngeal cancer (LC) and constructed a risk prognostic models with these genes. Methods: In this study, we identified genes with abnormally expressed in LC, and they were mainly involved in some cancer-related gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes pathways (KEGG). Statistical analysis were conducted to identify the powerful independent prognostic factors associated with clinical factors and survival. Results: A total of 35 DEGs were identified in our research. The risk prediction model was constructed with three potential prognostic genes (VEGFA, SPNS1 and CCL2) of autophagy by lasso regression analysis that can successfully predict the prognosis in LC. We applied ROC curve to evaluate the effectiveness of the risk prognostic model, and found that AUC was 0.693 below the curve. Risk prediction model was only related to survival status (P < 0.01), and was not related to clinicopathological factors. Furthermore, the genes (VEGFA and CCL2) were considered as core genes not only because they were the highly connected genes but also they were the composed genes of risk prognostic model. Conclusions: Taken together, ARGs were considered as important roles in the progression of LC and the prognostic model can help to identification of new targets to guide the diagnosis and therapy.

Integrated Analysis and Identification of Critical RNA-Binding Proteins in Bladder Cancer

Simple Summary

The role of RNA-binding proteins (RBPs) in bladder cancer (BC) remains unclear. Therefore, we analyzed the clinical information and RNA sequencing data from patients with BC and identified RBPs that may be promising predictors of BC.

Abstract

RBPs in the development and progression of BC remains unclear. Here, we elucidated the role of RBPs in predicting the survival of patients with BC. Clinical information and RNA sequencing data of the training and validation cohorts were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. Survival-related differentially expressed RBPs were identified using Cox regression analyses. A total of 113 upregulated and 54 downregulated RBPs were observed, with six showing prognostic values (AHNAK, MAP1B, LAMA2, P4HB, FASN, and GSDMB). In both the GSE32548 and GSE31684 datasets, patients with low-risk scores in survival-related six RBPs-based prognostic model showed longer overall survival than those with high-risk scores. AHNAK, MAP1B, P4HB, and FASN expression were significantly upregulated in both BC tissues and cell lines. BC tissues from high-risk group showed higher proportions of naive CD4+ T cells, M0 and M2 macrophages, and neutrophils and lower proportions of plasma cells, CD8+ T cells, and T-cell follicular helper compared to low-risk group. AHNAK knockdown significantly inhibited the proliferation, invasion, and migration of BC cells in vitro and inhibited the growth of subcutaneous tumors in vivo. We thus developed and functionally validated a novel six RBPs-based prognostic model for BC.

Identification of an autophagy-related gene signature for predicting prognosis and immune activity in pancreatic adenocarcinoma

Adenocarcinoma of the pancreas (PAAD) is a cancerous growth that deteriorates rapidly and has a poor prognosis. Researchers are investigating autophagy in PAAD to identify a new biomarker and treatment target. An autophagy-related gene (ARG) model for overall survival (OS) was constructed using multivariate Cox regression analyses. A cohort of the Cancer Genome Atlas (TCGA)-PAAD was used as the training group as a basis for model construction. This prediction model was validated with several external datasets. To evaluate model performance, the analysis with receiver operating characteristic curves (ROC) was performed. The Human Protein Atlas (HPA) and Cancer Cell Line Encyclopedia (CCLE) were investigated to validate the effects of ARGs expression on cancer cells. Comparing the levels of immune infiltration between high-risk and low-risk groups was finished through the use of CIBERSORT. The differentially expressed genes (DEGs) between the low-/high-risk groups were analyzed further via Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, which were used to identify potential small-molecule compounds in Connectivity Map (CMap), followed by half-maximal inhibitory concentration (IC50) examination with PANC-1 cells. The risk score was finally calculated as follows: BAK1 × 0.34 + ITGA3 × 0.38 + BAG3 × 0.35 + APOL1 × 0.26-RAB24 × 0.67519. ITGA3 and RAB24 both emerged as independent prognostic factors in multivariate Cox regression. Each PAAD cohort had a significantly shorter OS in the high-risk group than in the low-risk group. The high-risk group exhibited infiltration of several immune cell types, including naive B cells (p = 0.003), plasma cells (p = 0.044), and CD8 T cells (nearly significant, p = 0.080). Higher infiltration levels of NK cells (p = 0.025), resting macrophages (p = 0.020), and mast cells (p = 0.007) were found in the high-risk group than the low-risk group. The in vitro and in vivo expression of signature ARGs was consistent in the CCLE and HPA databases. The top 3 enriched Gene Ontology biological processes (GO-BPs) were signal release, regulation of transsynaptic signaling, and modulation of chemical synaptic transmission, and the top 3 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were MAPK, cAMP, and cell adhesion molecules. Four potential small-molecule compounds (piperacetazine, vinburnine, withaferin A and hecogenin) that target ARGs were also identified. Taking the results together, our research shows that the ARG signature may serve as a useful prognostic indicator and reveal potential therapeutic targets in patients with PAAD.

Bioinformatics approach to identify common gene signatures of patients with coronavirus 2019 and lung adenocarcinoma

Coronavirus disease 2019 (COVID-19) continues as a global pandemic. Patients with lung cancer infected with COVID-19 may develop severe disease or die. Treating such patients severely burdens overwhelmed healthcare systems. Here, we identified potential pathological mechanisms shared between patients with COVID-19 and lung adenocarcinoma (LUAD). Co-expressed, differentially expressed genes (DEGs) in patients with COVID-19 and LUAD were identified and used to construct a protein-protein interaction (PPI) network and to perform enrichment analysis. We used the NetworkAnalyst platform to establish a co-regulatory of the co-expressed DEGs, and we used Spearman's correlation to evaluate the significance of associations of hub genes with immune infiltration and immune checkpoints. Analysis of three datasets identified 112 shared DEGs, which were used to construct a protein-PPI network. Subsequent enrichment analysis revealed co-expressed genes related to biological process (BP), molecular function (MF), and cellular component (CC) as well as to pathways, specific organs, cells, and diseases. Ten co-expressed hub genes were employed to construct a gene-miRNA, transcription factor (TF)-gene, and TF-miRNA network. Hub genes were significantly associated with immune infiltration and immune checkpoints. Finally, methylation level of hub genes in LUAD was obtained via UALCAN database. The present multi-dimensional study reveals commonality in specific gene expression by patients with COVID-19 and LUAD. These findings provide insights into developing strategies for optimising the management and treatment of patients with LUAD with COVID-19.

Development and Verification of a Prostate Cancer Prognostic Signature Based on an Immunogenomic Landscape Analysis

Purpose

Prostate cancer (PCa) has a high incidence among older men. Until now, there are no immunological markers available to predict PCa patients’ survival. Therefore, it is necessary to explore the immunological characteristics of PCa.

Methods

First, we retrieved RNA-seq and clinical data of 499 PCa and 52 normal prostate tissue samples from the Cancer Genome Atlas (TCGA). We identified 193 differentially expressed immune-related genes (IRGs) between PCa and normal prostate tissues. Functional enrichment analyses showed that the immune system can participate in PCa initiation. Then, we constructed a correlation network between transcription factors (TFs) and IRGs. We performed univariate and multivariate Cox regression analyses and identified five key prognostic IRGs (S100A2, NOX1, IGHV7-81, AMH, and AGTR1). Finally, a predictive nomogram was established and verified by the C-index.

Results

We successfully constructed and validated an immune-related PCa prediction model. The signature could independently predict PCa patients’ survival. Results showed that high-immune-risk patients were correlated with advanced stage. We also validated the S100A2 expression in vitro using PCa and normal prostate tissues. We found that higher S100A2 expressions were related to lower biochemical recurrences. Additionally, higher AMH expressions were related to higher Gleason score, lymph node metastasis and positive rate, and tumor stages, and higher ATGR1 expressions were related to lower PSA value.

Conclusion

Overall, we detected five IRGs (S100A2, NOX1, IGHV7-81, AMH, and AGTR1) that can be used as independent PCa prognostic factors.

Autophagy-Related Genes and Long Noncoding RNAs Signatures as Predictive Biomarkers for Osteosarcoma Survival

Osteosarcoma is a common malignant tumor that seriously threatens the lives of teenagers and children. Autophagy is an intracellular metabolic process mediated by autophagy-related genes (ARGs), which is known to be associated with the progression and drug resistance of osteosarcoma. In this study, RNA sequence data from TARGET and genotype-tissue expression (GTEx) databases were analyzed. A six autophagy-related long noncoding RNAs (ARLs) signature that accurately predicted the clinical outcomes of osteosarcoma patients was identified, and the relations between immune response and the ARLs prognostic signature were examined. In addition, we obtained 30 ARGs differentially expressed among osteosarcoma tissue and healthy tissue, and performed functional enrichment analysis on them. To screen for prognostic-related ARGs, univariate and LASSO Cox regression analyses were successively applied. Then, multivariate regression analysis was used to complete construction of the prognostic signature of ARGs. Based on the risk coefficient, we calculated the risk score and grouped the patients. Survival analysis showed that high-risk patients evolve with poor prognosis. And we verified the prognosis model in the GSE21257 cohort. Finally, verification was conducted by qRT-PCR and western blot to measure the expression of genes. The results show that autophagy-related marker models may provide a new therapeutic and diagnostic target for osteosarcoma.

Crucial Roles of microRNA-Mediated Autophagy in Urologic Malignancies

Urologic oncologies are major public health problems worldwide. Both microRNA and autophagy, separately or concurrently, are involved in a variety of the cellular and molecular processes of multiple cancers, including urologic malignancies. In this review, we have summarized the related studies and found that microRNA-mediated autophagy acted as carcinogenic factors or suppressors in prostate cancer, kidney cancer, and bladder cancer. MiRNAs, targeted genes, and the different signaling pathways constitute a complex network that orchestrates autophagy regulation, militating the oncogenic and tumor-suppressive effects in urologic malignancies. Aberrant expression of miRNAs may induce the dysregulation of the autophagy process, resulting in tumorigenesis, progression, and resistance to anticancer therapies. Targeting specific miRNAs for autophagy modulation may present as reliable diagnostic and prognostic biomarkers or promising therapeutic strategies for urologic oncologies.

Autophagy-Related Three-Gene Prognostic Signature for Predicting Survival in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in China, and its prognosis remains poor. Autophagy is an evolutionarily conserved catabolic process involved in the occurrence and development of ESCC. In this study, we described the expression profile of autophagy-related genes (ARGs) in ESCC and developed a prognostic prediction model for ESCC patients based on the expression pattern of ARGs. We used four ESCC cohorts, GSE53624 (119 samples) set as the discovery cohort, The Cancer Genome Atlas (TCGA) ESCC set (95 samples) as the validation cohort, 155 ESCC cohort, and Oncomine cohort were used to screen and verify differentially expressed ARGs. We identified 34 differentially expressed genes out of 222 ARGs. In the discovery cohort, we divided ESCC patients into three groups that showed significant differences in prognosis. Then, we analyzed the prognosis of 34 differentially expressed ARGs. Three genes [poly (ADP-ribose) polymerase 1 (PARP1), integrin alpha-6 (ITGA6), and Fas-associated death domain (FADD)] were ultimately obtained through random forest feature selection and were constructed as an ARG-related prognostic model. This model was further validated in TCGA ESCC set. Cox regression analysis confirmed that the three-gene signature was an independent prognostic factor for ESCC patients. This signature effectively stratified patients in both discovery and validation cohorts by overall survival (P = 5.162E-8 and P = 0.052, respectively). We also constructed a clinical nomogram with a concordance index of 0.713 to predict the survival possibility of ESCC patients by integrating clinical characteristics and the ARG signature. The calibration curves substantiated fine concordance between nomogram prediction and actual observation. In conclusion, we constructed a new ARG-related prognostic model, which shows the potential to improve the ability of individualized prognosis prediction in ESCC.

Identification of autophagy-related risk signatures for the prognosis, diagnosis, and targeted therapy in cervical cancer

Background

To rummage autophagy-related prognostic, diagnostic, and therapeutic biomarkers in cervical cancer (CC).

Methods

The RNA-sequence and clinical information were from the TCGA and GTEx databases. We operated Cox regression to determine signatures related to overall survival (OS) and recurrence-free survival (RFS) respectively. The diagnostic and therapeutic effectiveness of prognostic biomarkers were further explored.

Results

We identified nine (VAMP7, MTMR14, ATG4D, KLHL24, TP73, NAMPT, CD46, HGS, ATG4C) and three risk signatures (SERPINA1, HSPB8, SUPT20H) with prognostic values for OS and RFS respectively. Six risk signatures (ATG4C, ATG4D, CD46, TP73, SERPINA1, HSPB8) were selected for qPCR. We screened five prognostic signatures(ATG4C, CD46, HSPB8, MTMR14, NAMPT) with diagnostic function through the GEO database. Correlation between our models and treatment targets certificated the prognostic score provided a reference for precision medicine.

Conclusions

We constructed OS and RFS prognostic models in CC. Autophagy-related risk signatures might serve as diagnostic and therapeutic biomarkers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02073-w.

Construction and Validation of an Autophagy-Related Prognostic Signature and a Nomogram for Bladder Cancer

Objective

Bladder cancer (BC) is one of the top ten cancers endangering human health but we still lack accurate tools for BC patients’ risk stratification. This study aimed to develop an autophagy-related signature that could predict the prognosis of BC. In order to provide clinical doctors with a visual tool that could precisely predict the survival probability of BC patients, we also attempted to establish a nomogram based on the risk signature.

Methods

We screened out autophagy-related genes (ARGs) combining weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) in BC. Based on the screened ARGs, we performed survival analysis and Cox regression analysis to identify potential prognostic biomarkers. A risk signature based on the prognostic ARGs by multivariate Cox regression analysis was established, which was validated by using seven datasets. To provide clinical doctors with a useful tool for survival possibility prediction, a nomogram assessed by the ARG-based signature and clinicopathological features was constructed, verified using four independent datasets.

Results

Three prognostic biomarkers including BOC (P = 0.008, HR = 1.104), FGF7(P = 0.030, HR = 1.066), and MAP1A (P = 0.001, HR = 1.173) were identified and validated. An autophagy-related risk signature was established and validated. This signature could act as an independent prognostic feature in patients with BC (P = 0.047, HR = 1.419). We then constructed two nomograms with and without ARG-based signature and subsequent analysis indicated that the nomogram with ARG signature showed high accuracy for overall survival probability prediction of patients with BC (C-index = 0.732, AUC = 0.816). These results proved that the ARG signature improved the clinical net benefit of the standard model based on clinicopathological features (age, pathologic stage).

Conclusions

Three ARGs were identified as prognosis biomarkers in BC. An ARG-based signature was established for the first time, showing strong potential for prognosis prediction in BC. This signature was proven to improve the clinical net benefit of the standard model. A nomogram was established using this signature, which could lead to more effective prognosis prediction for BC patients.

Correlation of autophagy-related genes for predicting clinical prognosis in colorectal cancer

Aim: Autophagy plays a controversial role in cancer. The role of autophagy-related genes (ARGs) in colorectal cancer (CRC) was evaluated based on publicly available data from The Cancer Genome Atlas and the Human Autophagy Database. Materials & methods: After collecting CRC-related transcript and clinical data and a list of ARGs from public databases, the Wilcoxon test was used to identify the differentially expressed ARGs between CRC and paired normal tissues. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were used to identify the major biological properties and pathways associated with these genes. Univariate Cox regression was used to identify the prognosis-associated ARGs, and a forest plot was used to visualize the results. Kaplan-Meier analysis of the 5-year survival rate was performed. Univariate and multivariate Cox analyses were used to verify the impact of the prognosis-associated ARGs. Results: A total of 36 differentially expressed genes (16 upregulated and 20 downregulated in CRC) were obtained from among 206 ARGs. There were 53 enriched pathways, including the p53 signaling pathway, platinum drug resistance, apoptosis, EGFR tyrosine kinase inhibitor resistance and ErbB signaling pathway (p- and q-values <0.05). Kaplan-Meier analysis showed that the 5-year survival rate was 46.0% (95% CI: 0.335-0.631) and 76.0% (95% CI: 0.651-0.886) in the high- and low-risk groups, respectively. The high-risk patients had worse survival probability (p = 6.256 × 10-5). Independent-samples t-tests revealed that MAP1LC3C expression was higher in patients aged ≤65 than >65 (p = 0.022); RAB7A expression was higher in patients aged ≤65 than >65 (p = 7.31 × 10-4), higher in M1 than M0 (p = 0.042), higher in N1-3 than N0 (p = 0.002) and higher in stage III and IV than I and II (p = 0.042); risk score was higher in N1-3 than N0 (p = 0.001) and in stage III and IV than I and II (p = 0.002); and WIPI2 expression was higher in M1 than M0 (p = 0.002), higher in N1-3 than N0 (p = 2.059 × 10-7) and higher in stage III and IV than I and II (p = 2.299 × 10-7). There were no differences in risk score between males and females (p = 0.593), T1-2 and T3-4 (p = 0.082) or M0 and M1 (p = 0.072). Univariate and multivariate Cox analyses showed that RAB7A was a lower-risk gene, while MAP1LC3C, WIPI2, DAPK1, ULK3 and PELP1 were high-risk genes. Conclusion: Certain ARGs are potential prognostic molecular markers of poor prognosis in CRC. Additionally, the p53 signaling pathway, platinum drug resistance, apoptosis, EGFR tyrosine kinase inhibitor resistance and ErbB signaling pathway may be critical pathways regulated by ARGs in CRC.

Identification of autophagy-related genes signature predicts chemotherapeutic and immunotherapeutic efficiency in bladder cancer (BLCA)

Autophagy maintains cellular homeostasis by degrading and recycling cytoplasmic components under stress conditions, which is identified to be involved in tumorigenesis and now has been recognized as novel target in cancer treatment. In present study, we gathered total autophagy‐related genes and established an autophagy‐related genes signature (ATGRS) through LASSO cox regression analysis in BLCA. Kaplan‐Meier survival and multivariate cox regression analyses both showed the ATGRS was a robust independent prognostic factor with high accuracy. Subsequently, integrated analyses indicated that ATGRS had a strong correlation with molecular subtypes, clinicopathological characteristics and somatic mutation alteration. Moreover, ATGRS was found to be positively correlated with the infiltration of immune cells in tumour microenvironment (TME) and immune checkpoint expression, indicating the potent role of autophagy by regulating the TME. In addition, ATGRS was proved to be efficient in predicting the clinical benefit of immune checkpoint inhibitors (ICIs) based immunotherapy and chemotherapy in BLCA. Furthermore, we observed abnormal expression levels of autophagy‐related genes and found the different behaviour of ATGRS in pancancer by LASSO cox regression analysis. Therefore, construction of ATGRS in BLCA could help us to interpret the underlying mechanism of autophagy and sheds a light on the clinical application for a combination of autophagy modification with targeted immunotherapy and chemotherapy in BLCA.

Colibactin-Producing Escherichia coli Induce the Formation of Invasive Carcinomas in a Chronic Inflammation-Associated Mouse Model

Simple Summary

Changes in the composition of the intestinal flora have been reported in patients with colorectal cancer, the second leading cause of cancer death in the world, with an increase in so-called "harmful" bacteria. Among these, Escherichia coli producing colibactin, a toxin that causes DNA damage, has attracted the interest of many research groups. Here, we showed that infection of wild-type mice with a colibactin-producing E. coli (CoPEC) strain, isolated from a patient with colorectal cancer, combined with chronic inflammation induced the formation of invasive colonic tumors, i.e., tumors that spread beyond epithelial layer and grow into surrounding tissues. We also showed that autophagy, a cell defense process, is necessary to inhibit the tumorigenesis induced by CoPEC. Thus, this work highlights the role of CoPEC as a driver of colorectal cancer development, and suggests that targeting autophagy could be a promising strategy to inhibit the protumoral effects of these bacteria.

Abstract

Background: Escherichia coli producing the genotoxin colibactin (CoPEC or colibactin-producing E. coli) abnormally colonize the colonic mucosa of colorectal cancer (CRC) patients. We previously showed that deficiency of autophagy in intestinal epithelial cells (IECs) enhances CoPEC-induced colorectal carcinogenesis in Apc^Min/+ mice. Here, we tested if CoPEC trigger tumorigenesis in a mouse model lacking genetic susceptibility or the use of carcinogen. Methods: Mice with autophagy deficiency in IECs (Atg16l1^∆IEC) or wild-type mice (Atg16l1^flox/flox) were infected with the CoPEC 11G5 strain or the mutant 11G5∆clbQ incapable of producing colibactin and subjected to 12 cycles of DSS treatment to induce chronic colitis. Mouse colons were used for histological assessment, immunohistochemical and immunoblot analyses for DNA damage marker. Results: 11G5 or 11G5∆clbQ infection increased clinical and histological inflammation scores, and these were further enhanced by IEC-specific autophagy deficiency. 11G5 infection, but not 11G5∆clbQ infection, triggered the formation of invasive carcinomas, and this was further increased by autophagy deficiency. The increase in invasive carcinomas was correlated with enhanced DNA damage and independent of inflammation. Conclusions: CoPEC induce colorectal carcinogenesis in a CRC mouse model lacking genetic susceptibility and carcinogen. This work highlights the role of (i) CoPEC as a driver of CRC development, and (ii) autophagy in inhibiting the carcinogenic properties of CoPEC.

An autophagy-related long non-coding RNA prognostic signature accurately predicts survival outcomes in bladder urothelial carcinoma patients

In this study, we analyzed the prediction accuracy of an autophagy-related long non-coding RNA (lncRNA) prognostic signature using bladder urothelial carcinoma (BLCA) patient data from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analyses showed significant correlations between five autophagy-related lncRNAs, LINC02178, AC108449.2, Z83843.1, FAM13A-AS1 and USP30-AS1, and overall survival (OS) among BCLA patients. The risk scores based on the autophagy-related lncRNA prognostic signature accurately distinguished high- and low-risk BCLA patients that were stratified according to age; gender; grade; and AJCC, T, and N stages. The autophagy-related lncRNA signature was an independent prognostic predictor with an AUC value of 0.710. The clinical nomogram with the autophagy-related lncRNA prognostic signature showed a high concordance index of 0.73 and accurately predicted 1-, 3-, and 5-year survival times among BCLA patients in the high- and low-risk groups. The lncRNA-mRNA co-expression network contained 77 lncRNA-mRNA links among 5 lncRNAs and 49 related mRNAs. Gene set enrichment analysis showed that cancer- and autophagy-related pathways were significantly enriched in the high-risk group, and immunoregulatory pathways were enriched in the low-risk group. These findings demonstrate that an autophagy-related lncRNA signature accurately predicts the prognosis of BCLA patients.

Identification and Validation of a Prognostic Model Based on Three Autophagy-Related Genes in Hepatocellular Carcinoma

Background

Accumulating studies have demonstrated that autophagy plays an important role in hepatocellular carcinoma (HCC). We aimed to construct a prognostic model based on autophagy-related genes (ARGs) to predict the survival of HCC patients.

Methods

Differentially expressed ARGs were identified based on the expression data from The Cancer Genome Atlas and ARGs of the Human Autophagy Database. Univariate Cox regression analysis was used to identify the prognosis-related ARGs. Multivariate Cox regression analysis was performed to construct the prognostic model. Receiver operating characteristic (ROC), Kaplan-Meier curve, and multivariate Cox regression analyses were performed to test the prognostic value of the model. The prognostic value of the model was further confirmed by an independent data cohort obtained from the International Cancer Genome Consortium (ICGC) database.

Results

A total of 34 prognosis-related ARGs were selected from 62 differentially expressed ARGs identified in HCC compared with noncancer tissues. After analysis, a novel prognostic model based on ARGs (PRKCD, BIRC5, and ATIC) was constructed. The risk score divided patients into high- or low-risk groups, which had significantly different survival rates. Multivariate Cox analysis indicated that the risk score was an independent risk factor for survival of HCC after adjusting for other conventional clinical parameters. ROC analysis showed that the predictive value of this model was better than that of other conventional clinical parameters. Moreover, the prognostic value of the model was further confirmed in an independent cohort from ICGC patients.

Conclusion

The prognosis-related ARGs could provide new perspectives on HCC, and the model should be helpful for predicting the prognosis of HCC patients.

Development of an Autophagy-Related Gene Prognostic Model and Nomogram for Estimating Renal Clear Cell Carcinoma Survival

Background

Kidney renal clear cell carcinoma (KIRC) is a fatal malignancy of the urinary system. Autophagy is implicated in KIRC occurrence and development. Here, we evaluated the prognostic value of autophagy-related genes (ARGs) in kidney renal clear cell carcinoma.

Materials and Methods

We analyzed RNA sequencing and clinical KIRC patient data obtained from TCGA and ICGC to develop an ARG prognostic signature. Differentially expressed ARGs were further evaluated by functional assessment and bioinformatic analysis. Next, ARG score was determined in 215 KIRC patients using univariable Cox and LASSO regression analyses. An ARG nomogram was built based on multivariable Cox analysis. The prognosis nomogram model based on the ARG signatures and clinicopathological information was evaluated for discrimination, calibration, and clinical usefulness.

Results

A total of 47 differentially expressed ARGs were identified. Of these, 8 candidates that significantly correlated with KIRC overall survival were subjected to LASSO analysis and an ARG score built. Functional enrichment and bioinformatic analysis were used to reveal the differentially expressed ARGs in cancer-related biological processes and pathways. Multivariate Cox analysis was used to integrate the ARG nomogram with the ARG signature and clinicopathological information. The nomogram exhibited proper calibration and discrimination (C-index = 0.75, AUC = >0.7). Decision curve analysis also showed that the nomogram was clinically useful.

Conclusions

KIRC patients and doctors could benefit from ARG nomogram use in clinical practice.

An autophagic gene-based signature to predict the survival of patients with low-grade gliomas

Background

Since autophagy remains an important topic of investigation, the RNA‐sequence profiles of autophagy‐related genes (ARGs) can provide insights into predicting low‐grade gliomas (LGG) prognosis.

Methods

The RNA‐seq profiles of autophagic genes and prognosis data of LGG were integrated from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression model were carried out to identify the differentially expressed prognostic autophagy‐related genes. Then, the autophagic‐gene signature was formed and verified in TCGA test set and external CGGA cohorts. Time‐dependent receiver operating characteristic (ROC) was examined to test the accuracy of this signature feature. A nomogram was conducted to meet the needs of clinicians. Sankey diagrams were performed to visualize the relationship between the multigene signatures and clinic‐pathological features.

Results

Twenty‐four ARGs were finally identified most relevant to LGG prognosis. According to the specific prediction index formula, the patients were classified into low‐risk or high‐risk groups. Prognostic accuracy was proved by time‐dependent ROC analysis, with AUC 0.9, 0.93, and 0.876 at the survival time of 2‐, 3‐, and 5‐year, respectively, which was superior to the AUC of the isocitrate dehydrogenase (IDH) mutation. The result was confirmed while validated in the TCGA test set and external validation CGGA cohort. A nomogram was constructed to meet individual needs. With a visualization approach, Sankey diagrams show the relationship of the histological type, IDH status, and predict index. In TCGA and CGGA cohorts, both low‐risk groups displayed better survival rate in LGG while histological type and IDH status did not show consistency results.

Conclusions

24‐ARGs may play crucial roles in the progression of LGG, and LGG patients were effectively divided into low‐risk and high‐risk groups according to prognostic prediction. Overall, our study will provide novel strategies for clinical applications.

Identification and Validation of Autophagy-Related Genes in Chronic Obstructive Pulmonary Disease

Purpose

Autophagy plays essential roles in the development of COPD. We aim to identify and validate the potential autophagy-related genes of COPD through bioinformatics analysis and experiment validation.

Methods

The mRNA expression profile dataset GSE38974 was obtained from GEO database. The potential differentially expressed autophagy-related genes of COPD were screened by R software. Then, protein–protein interactions (PPI), correlation analysis, gene-ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied for the differentially expressed autophagy-related genes. Finally, RNA expression of top five differentially expressed autophagy-related genes was validated in blood samples from COPD patients and healthy controls by qRT-PCR.

Results

A total of 40 differentially expressed autophagy-related genes (14 up-regulated genes and 26 down-regulated genes) were identified between 23 COPD patients and 9 healthy controls. The PPI results demonstrated that these autophagy-related genes interacted with each other. The GO and KEGG enrichment analysis of differentially expressed autophagy-related genes indicated several enriched terms related to autophagy and mitophagy. The results of qRT-PCR showed that the expression levels of HIF1A, CDKN1A, BAG3, ERBB2 and ATG16L1 in COPD patients and healthy controls were consistent with the bioinformatics analysis results from mRNA microarray.

Conclusion

We identified 40 potential autophagy-related genes of COPD through bioinformatics analysis. HIF1A, CDKN1A, BAG3, ERBB2 and ATG16L1 may affect the development of COPD by regulating autophagy. These results may expand our understanding of COPD and might be useful in the treatment of COPD.

Autophagy-related genes are potential diagnostic and prognostic biomarkers in prostate cancer

Background

Recently, autophagy was found related to several malignances.

Methods

To explore the diagnostic and prognostic values of autophagy in prostate cancer (PCa), we first identified differentially expressed autophagy-related genes (DEARGs) based on The Cancer Genome Atlas (TCGA) Prostate Adenocarcinoma (PRAD) dataset. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were applied to perform gene functional annotation. Then, multivariate logistic regression analysis was applied to construct the risk index (RI). Receiver operating characteristic (ROC), calibration and decision curve analysis (DCA) curves were performed to identify the effectiveness of RI. Next, multivariate Cox regression analyses were performed to construct the prognostic index (PI) and autophagy-clinical prognostic index (ACPI).

Results

We identified 16 DEARGs and functional annotation demonstrated the relevance of these genes to autophagy and revealed the association of these DEARGs with digestive system, drug resistance and apoptosis. Then, the RI was constructed based on 5 DEARGs and the area under the ROC curve (AUC) was 0.9858. Validation based on Gene Expression Omnibus (GEO) datasets suggested that the RI was effective. Next, 7 ARGs were identified associated with overall survival (OS) and the PI was developed composed of 3 ARGs. Finally, ACPI was constructed based on PI and the M stage.

Conclusions

This study provided potential models for predicting the risk and prognosis of PCa and indicated the molecular insights of autophagy in PCa. While no other dataset was applied to test the effectiveness of the PI and ACPI models attribute to the well prognosis of PCa.

Upregulation of ATIC in multiple myeloma tissues based on tissue microarray and gene microarrays

PURPOSE

Multiple myeloma (MM) is characterized by the malignant proliferation of plasma cells, which produce a monoclonal immunoglobulin protein. The role of 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase (ATIC) has not yet been well studied in the area of MM. Thus, in the current study, we sought to examine the expression levels, including mRNA and protein levels of ATIC in MM.

METHODS

Multiple myeloma microarray and RNA-seq data were screened from the SRA, GEO, ArrayExpress, and Oncomine databases. The mRNA level of ATIC was extracted from the high throughput data, and the prognostic value was studied. The protein level of ATIC was also detected by in-house immunohistochemistry on a tissue microarray. Potential signaling pathways were enriched with ATIC-related genes in MM.

RESULTS

Both the mRNA and protein levels of ATIC were significantly upregulated in MM samples as compared to normal samples. Furthermore, the summarized Standardized Mean Difference was 1.66 with 674 cases of MM based on 10 independent studies including the in-house tissue microarray. The overall hazard ratio of ATIC in MM was 1.7 with 1631 cases of MM based on five microarrays. In the KEGG pathway analysis, the ATIC-related genes were mainly enriched in the pathway of complement and coagulation cascades.

CONCLUSION

We provided the first evidence supporting the upregulation of ATIC may play an essential part in the tumorigenesis and development of MM. The promoting cancer capacity may be related to the pathway of complement and coagulation cascades.

Prognostic autophagy model based on CASP4 and BIRC5 expression in patients with renal cancer: independent datasets-based study

The purpose of this study was to identify key autophagy-related genes (ARGs) in patients with renal cancer (RC) by bioinformatics analysis, and to clarify their potential prognostic value. Thirty-eight differentially expressed ARGs were identified between RC and normal tissues based on The Cancer Genome Atlas database. Functional enrichment analysis suggested that autophagy may play a tumor-promoting role in the initiation of RC. We established a prognostic model with two ARGs (CASP4 and BIRC5) demonstrating significant correlations in expression levels with patient overall survival (OS). Multivariate Cox regression analysis showed that age and the autophagy genes prognostic model were independent prognostic factors for patients with RC. Considering the known prognostic significance of clinical stage in RC, we constructed a nomogram based on age, clinical stage, and the prognostic model. The prognostic model was verified in a separate validation set and external cohort of patients from Beijing Hospital. Patients of low and high risk were defined based on the median risk value calculated by the model and the high risk appeared associated with a significant shorter OS (P < 0.01). Overall, our findings reveal that ARGs have potential prognostic value in patients with RC, providing new directions for targeted therapy.

BRDT is a novel regulator of eIF4EBP1 in renal cell carcinoma

Among all types of kidney diseases, renal cell carcinoma (RCC) has the highest mortality, recurrence and metastasis rates, which results in high numbers of tumor-associated mortalities in China. Identifying a novel therapeutic target has attracted increasing attention. Bromodomain and extraterminal domain (BET) proteins have the ability to read the epigenome, leading to regulation of gene transcription. As an important member of the BET family, bromodomain testis-specific protein (BRDT) has been well studied; however, the mechanism underlying BRDT in the regulation of RCC has not been fully investigated. Eukaryotic translation initiation factor 4E-binding protein 1 (eIF4EBP1) is a binding partner of eIF4E that is involved in affecting the progression of various cancer types via regulating gene transcription. To identify novel regulators of eIF4EBP1, an immunoprecipitation assay and mass spectrometry analysis was performed in RCC cells. It was revealed that eIF4EBP1 interacted with BRDT, a novel interacting protein. In addition, the present study further demonstrated that BRDT inhibitors PLX51107 and INCB054329 blocked the progression of RCC cells, along with suppressing eIF4EBP1 and c-myc expression. Small interfering (si) RNAs were used to knock down BRDT expression, which suppressed RCC cell proliferation and eIF4EBP1 protein expression. In addition, overexpression of eIF4EBP1 partially abolished the inhibited growth function of PLX51107 but knocking down eIF4EBP1 improved the inhibitory effects of PLX51107. Furthermore, treatment with PLX51107 or knockdown of BRDT expression decreased c-myc expression at both the mRNA and protein levels, and attenuated its promoter activity, as determined by luciferase reporter assays. PLX51107 also significantly altered the interaction between the c-myc promoter with eIF4EBP1 and significantly attenuated the increase of RCC tumors, accompanied by decreased c-myc mRNA and protein levels in vivo. Taken together, these data suggested that blocking of BRDT by PLX51107, INCB054329 or BRDT knockdown suppressed the growth of RCC via decreasing eIF4EBP1, thereby leading to decreased c-myc transcription levels. Considering the regulatory function of BET proteins in gene transcription, the present study suggested that there is a novel mechanism underlying eIF4EBP1 regulation by BRDT, and subsequently decreased c-myc in RCC, and further identified a new approach by regulating eIF4EBP1 or c-myc for enhancing BRDT-targeting RCC therapy.

Roles of Reactive Oxygen Species in Biological Behaviors of Prostate Cancer

Prostate cancer (PCa), known as a heterogenous disease, has a high incidence and mortality rate around the world and seriously threatens public health. As an inevitable by-product of cellular metabolism, reactive oxygen species (ROS) exhibit beneficial effects by regulating signaling cascades and homeostasis. More and more evidence highlights that PCa is closely associated with age, and high levels of ROS are driven through activation of several signaling pathways with age, which facilitate the initiation, development, and progression of PCa. Nevertheless, excessive amounts of ROS result in harmful effects, such as genotoxicity and cell death. On the other hand, PCa cells adaptively upregulate antioxidant genes to detoxify from ROS, suggesting that a subtle balance of intracellular ROS levels is required for cancer cell functions. The current review discusses the generation and biological roles of ROS in PCa and provides new strategies based on the regulation of ROS for the treatment of PCa.

Contrast effects of autophagy in the treatment of bladder cancer

Bladder cancer is a disease that negatively affects patients' quality of life, but treatment options have remained unchanged for a long time. Although promising results have been achieved with current bladder cancer treatments, cancer recurrence, progression, and therapy resistance are the most severe problems preventing the efficiency of bladder cancer treatments. Autophagy refers to an evolutionarily conserved catabolic process in which proteins, damaged organelles, and cytoplasmic components are degraded by lysosomal enzymes. Autophagy regulates the therapeutic response to the chemotherapy drugs, thus determining the effect of therapy on cancer cells. Autophagy is a stress-induced cell survival mechanism and its excessive stimulation can cause resistance of tumor cells to therapeutic agents. Depending on the conditions, an increase in autophagy may cause treatment resistance or autophagic cell death, and it is related to important anti-cancer mechanisms, such as apoptosis. Therefore, understanding the roles of autophagy under different conditions is important for designing effective anti-cancer agents. The dual role of autophagy in cancer has attracted considerable attention in respect of bladder cancer treatment. In this review, we summarize the basic characteristics of autophagy, including its mechanisms, regulation, and functions, and we present examples from current studies concerning the dual role of autophagy in bladder cancer progression and therapy.

Identification of small molecule drugs and development of a novel autophagy-related prognostic signature for kidney renal clear cell carcinoma

Abnormal autophagic levels have been implicated in the pathogenesis of multiple cancers, however, its role in tumors is complex and has not yet been explored clearly. Hence, we aimed to explore the prognostic values of autophagy-related genes (ARGs) for kidney renal clear cell carcinoma (KIRC). Differentially expressed ARGs and transcription factors (TFs) were identified in KIRC patients obtaining from the The Cancer Genome Atlas (TCGA) database. Then, networks between TFs and ARGs, gene ontology functional annotations and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were conducted. Next, we performed consensus clustering, COX regression analysis and Lasso regression analysis to identify the prognostic ARGs. Finally, an individual prognostic index (PI, riskScore) was established. Based on TCGA cohort and ArrayExpress cohort, Survival analysis, ROC curve, independent prognostic analysis, and clinical correlation analysis were also performed to evaluate this PI. Based on differentially expressed ARGs, KIRC patients were successfully divided into two clusters (P = 5.916e-04). AS for PI, it was constructed based on 11 ARGs and significantly classified KIRC patients into high-risk group and low-risk group in terms of OS (P = 4.885e-15 for TCGA cohort, P = 6.366e-03 for ArrayExpress cohort). AUC of its ROC curve reached 0.747 for TCGA cohort and 0.779 for ArrayExpress cohort. What's more, this PI was proven to be a valuable independent prognostic factor in both univariate and multivariate COX regression analysis (P < .001). Prognostic nomograms were also performed to visualize the relationship between individual predictors and survival rates in patients with KIRC. By means of connectivity map database, emetine, cephaeline and co-dergocrine mesilate related to ARGs were found to be negatively correlated with KIRC. This study provided an effective PI for KIRC and also displayed networks between TFs and ARGs. KIRC patients were successfully divided into two clusters based on differentially expressed ARGs. Besides, small molecule drugs related to ARGs were also identified for KIRC.

An autophagy-related model of 4 key genes for predicting prognosis of patients with laryngeal cancer

Autophagy, a major cause of cancer-related death, is correlated with the pathogenesis of various diseases including cancers. Our study aimed to develop an autophagy-related model for predicting prognosis of patients with laryngeal cancer.We analyzed the correlation between expression profiles of autophagy-related genes (ARGs) and clinical outcomes in 111 laryngeal cancer patients from The Cancer Genome Atlas (TCGA). Afterward, gene functional enrichment analyses of gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to find the major biological attributes. Univariate Cox regression analyses and multivariate Cox regression analyses were performed to screen ARGs whose expression profiles were significantly associated with laryngeal cancer patients overall survival (OS). Furthermore, to provide the doctors and patients with a quantitative method to perform an individualized survival prediction, we constructed a prognostic nomogram.Thirty eight differentially expressed ARGs were screened out in laryngeal cancer patients through the TCGA database. Related functional enrichments may act as tumor-suppressive roles in the tumorigenesis of laryngeal cancer. Subsequently, 4 key prognostic ARGs (IKBKB, ST13, TSC2, and MAP2K7) were identified from all ARGs by the Cox regression model, which significantly correlated with OS in laryngeal cancer. Furthermore, the risk score was constructed, which significantly divided laryngeal cancer patients into high- and low-risk groups. Integrated with clinical characteristics, gender, N and the risk score are very likely associated with patients OS. A prognostic nomogram of ARGs was constructed using the Cox regression model.Our study could provide a valuable prognostic model for predicting the prognosis of laryngeal cancer patients and a new understanding of autophagy in laryngeal cancer.

Prognostic value of an autophagy-related gene expression signature for endometrial cancer patients

Background

Autophagy is associated with cancer development. Autophagy-related genes play significant roles in endometrial cancer (EC), a major gynecological malignancy worldwide, but little was known about their value as prognostic markers. Here we evaluated the value of a prognostic signature based on autophagy-related genes for EC.

Methods

First, various autophagy-related genes were obtained via the Human Autophagy Database and their expression profiles were downloaded from The Cancer Genome Atlas. Second, key prognostic autophagy-related genes were identified via univariate, LASSO and multivariate Cox regression analyses. Finally, a risk score to predict the prognosis of EC was calculated and validated by using the test and the entire data sets. Besides, the key genes mRNA expression were validated using quantitative real-time PCR in clinical tissue samples.

Results

A total of 40 differentially expressed autophagy-related genes in EC were screened and five of them were prognosis-related (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1). A prognostic signature was constructed based on these five genes using the train set, which stratified EC patients into high-risk and low-risk groups (p < 0.05). In terms of overall survival, the analyses of the test set and the entire set yielded consistent results (test set: p < 0.05; entire set: p < 0.05). Time-dependent ROC analysis suggested that the risk score predicted EC prognosis accurately and independently (0.674 at 1 year, 0.712 at 3 years and 0.659 at 5 years). A nomogram with clinical utility was built. Patients in the high-risk group displayed distinct mutation signatures compared with those in the low-risk group. For clinical sample validation, we found that EIF4EBP1and ERBB2 had higher level in EC than that in normal tissues while CDKN1B, DLC1 and GRID1 had lower level, which was consistent with the results predicted.

Conclusions

Based on five autophagy-related genes (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1), our model can independently predict the OS of EC patients by combining molecular signature and clinical characteristics.

Comprehensive analysis of autophagy-related prognostic genes in breast cancer

Accumulating evidence revealed that autophagy played vital roles in breast cancer (BC) progression. Thus, the aim of this study was to investigate the prognostic value of autophagy-related genes (ARGs) and develop a ARG-based model to evaluate 5-year overall survival (OS) in BC patients. We acquired ARG expression profiling in a large BC cohort (N = 1007) from The Cancer Genome Atlas (TCGA) database. The correlation between ARGs and OS was confirmed by the LASSO and Cox regression analyses. A predictive model was established based on independent prognostic variables. Thus, time-dependent receiver operating curve (ROC), calibration plot, decision curve and subgroup analysis were conducted to determine the predictive performance of ARG-based model. Four ARGs (ATG4A, IFNG, NRG1 and SERPINA1) were identified using the LASSO and multivariate Cox regression analyses. A ARG-based model was constructed based on the four ARGs and two clinicopathological risk factors (age and TNM stage), dividing patients into high-risk and low-risk groups. The 5-year OS of patients in the low-risk group was higher than that in the high-risk group (P < 0.0001). Time-dependent ROC at 5 years indicated that the four ARG-based tool had better prognostic accuracy than TNM stage in the training cohort (AUC: 0.731 vs 0.640, P < 0.01) and validation cohort (AUC: 0.804 vs 0.671, P < 0.01). The mutation frequencies of the four ARGs (ATG4A, IFNG, NRG1 and SERPINA1) were 0.9%, 2.8%, 8% and 1.3%, respectively. We built and verified a novel four ARG-based nomogram, a credible approach to predict 5-year OS in BC, which can assist oncologists in determining effective therapeutic strategies.

KEGG-expressed genes and pathways in triple negative breast cancer: Protocol for a systematic review and data mining

BACKGROUND

The incidence of triple negative breast cancer (TNBC) is at a relatively high level, and our study aimed to identify differentially expressed genes (DEGs) in TNBC and explore the key pathways and genes of TNBC.

METHODS

The gene expression profiling (GSE86945, GSE86946 and GSE102088) data were obtained from Gene Expression Omnibus Datasets, DEGs were identified by using R software, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed by the Database for Annotation, Visualization and Integrated Discovery (DAVID) tools, and the protein-protein interaction (PPI) network of the DEGs was constructed by the STRING database and visualized by Cytoscape software. Finally, the survival value of hub DEGs in breast cancer patients were performed by the Kaplan-Meier plotter online tool.

RESULTS

A total of 2998 DEGs were identified between TNBC and health breast tissue, including 411 up-regulated DEGs and 2587 down-regulated DEGs. GO analysis results showed that down-regulated DEGs were enriched in gene expression (BP), extracellular exosome (CC), and nucleic acid binding, and up-regulated were enriched in chromatin assembly (BP), nucleosome (CC), and DNA binding (MF). KEGG pathway results showed that DEGs were mainly enriched in Pathways in cancer and Systemic lupus erythematosus and so on. Top 10 hub genes were picked out from PPI network by connective degree, and 7 of top 10 hub genes were significantly related with adverse overall survival in breast cancer patients (P < .05). Further analysis found that only EGFR had a significant association with the prognosis of triple-negative breast cancer (P < .05).

CONCLUSIONS

Our study showed that DEGs were enriched in pathways in cancer, top 10 DEGs belong to up-regulated DEGs, and 7 gene connected with poor prognosis in breast cancer, including HSP90AA1, SRC, HSPA8, ESR1, ACTB, PPP2CA, and RPL4. These can provide some guidance for our research on the diagnosis and prognosis of TNBC, and further research is needed to evaluate their value in the targeted therapy of TNBC.

Development and validation of a survival model for lung adenocarcinoma based on autophagy-associated genes

BACKGROUND

Given that abnormal autophagy is involved in the pathogenesis of cancers, we sought to explore the potential value of autophagy-associated genes in lung adenocarcinoma (LUAD).

METHODS

RNA sequencing and clinical data on tumour and normal samples were acquired from The Cancer Genome Atlas (TCGA) database and randomly assigned to training and testing groups. Differentially expressed autophagy-associated genes (AAGs) were screened. Within the training group, Cox regression and Lasso regression analyses were conducted to screen five prognostic AAGs, which were used to develop a model. Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves were plotted to determine the performance of the model in both groups. Immunohistochemistry was used to demonstrate the differential expression of AAGs in tumour and normal tissues at the protein level. Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were utilized to further elucidate the roles of AAGs in LUAD.

RESULTS

The data from the TCGA database included 497 tumour and 54 normal samples, within which 30 differentially expressed AAGs were screened. Using Cox regression and Lasso regression analyses for the training group, 5 prognostic AAGs were identified and the prognostic model was constructed. Patients with low risk had better overall survival (OS) in the training group (3-year OS, 73.0% vs 48.0%; 5-year OS, 45.0% vs 33.8%; P = 1.305E-04) and in the testing group (3-year OS, 66.8% vs 41.2%; 5-year OS, 31.7% vs 25.8%; P = 1.027E-03). The areas under the ROC curves (AUC) were significant for both the training and testing groups (3-year AUC, 0.810 vs 0.894; 5-year AUC, 0.792 vs 0.749).

CONCLUSIONS

We developed a survival model for LUAD and validated the performance of the model, which may provide superior outcomes for the patients.

Profiles of Immune Infiltration in Bladder Cancer and its Clinical Significance: an Integrative Genomic Analysis

Tumor-infiltrating immune cells are closely related to the prognosis of bladder cancer. Analysis of tumor infiltrating immune cells is usually based on immunohistochemical analysis. Since many immune cell marker proteins are not specific for different immune cells, which may induce misleading or incomplete. CIBERSORT is an algorithm to estimate specific cell types in a mixed cell population using gene expression data. In this study, the CIBERSORT algorithm was used to identify the immune cell infiltration signatures. The gene expression profiles, mutation data, and clinical data were collected from The Cancer Genome Atlas (TCGA) database. Unsupervised consensus clustering was used to acquire the immune cell infiltration subtypes of bladder cancer based on the fractions of 22 immune cell types. Four immune cell clusters with different immune infiltrate and mutation characteristics were identified. In addition, this stratification has a prognostic relevance, with cluster 2 having the best outcome, cluster 1 the worst. These clusters showed distinct mRNA expression patterns. The characteristic genes in subtype cluster 1 were mainly involved in cell division, those in subtype cluster 2 were mainly related in antigen processing and presentation, those in subtype cluster 3 were mainly involved in epidermal cell differentiation, and those in subtype cluster 4 were mainly related in the humoral immune response. These differences may affect the development of the bladder cancer, the sensitivity to treatment as well as the prognosis. Through further validation, this study may contribute to the development of personalized therapy and precision medical treatments.