Construction and Validation of an Autophagy-Related Prognostic Risk Signature for Survival Predicting in Clear Cell Renal Cell Carcinoma Patients

Deciphering the tumour microenvironment of clear cell renal cell carcinoma: Prognostic insights from programmed death genes using machine learning

Clear cell renal cell carcinoma (ccRCC), a prevalent kidney cancer form characterised by its invasiveness and heterogeneity, presents challenges in late-stage prognosis and treatment outcomes. Programmed cell death mechanisms, crucial in eliminating cancer cells, offer substantial insights into malignant tumour diagnosis, treatment and prognosis. This study aims to provide a model based on 15 types of Programmed Cell Death-Related Genes (PCDRGs) for evaluating immune microenvironment and prognosis in ccRCC patients. ccRCC patients from the TCGA and arrayexpress cohorts were grouped based on PCDRGs. A combination model using Lasso and SuperPC was constructed to identify prognostic gene features. The arrayexpress cohort validated the model, confirming its robustness. Immune microenvironment analysis, facilitated by PCDRGs, employed various methods, including CIBERSORT. Drug sensitivity analysis guided clinical treatment decisions. Single-cell data enabled Programmed Cell Death-Related scoring, subsequent pseudo-temporal and cell-cell communication analyses. A PCDRGs signature was established using TCGA-KIRC data. External validation in the arrayexpress cohort underscored the model's superiority over traditional clinical features. Furthermore, our single-cell analysis unveiled the roles of PCDRG-based single-cell subgroups in ccRCC, both in pseudo-temporal progression and intercellular communication. Finally, we performed CCK-8 assay and other experiments to investigate csf2. In conclusion, these findings reveal that csf2 inhibit the growth, infiltration and movement of cells associated with renal clear cell carcinoma. This study introduces a PCDRGs prognostic model benefiting ccRCC patients while shedding light on the pivotal role of programmed cell death genes in shaping the immune microenvironment of ccRCC patients.

SPAG9 Expression Predicts Good Prognosis in Patients with Clear-Cell Renal Cell Carcinoma: A Bioinformatics Analysis with Experimental Validation

Clear-cell renal cell carcinoma (ccRCC) is the most common and aggressive type of renal-cell carcinoma (RCC). Sperm-associated antigen 9 (SPAG9) has been reported to promote the progression of a variety of tumors and is thus a potential prognostic marker. This study combined a bioinformatics analysis with an experimental validation, exploring the prognostic value of SPAG9 expression in ccRCC patients and the possible underlying mechanisms. The SPAG9 expression was associated with a poor prognosis in pan-cancer patients, but with a good prognosis and slow tumor progression in ccRCC patients. To explore the underlying mechanism, we investigated the roles of SPAG9 in ccRCC and bladder urothelial carcinoma (BLCA). The latter was chosen for comparison with ccRCC to represent the tumor types in which SPAG9 expression suggests a poor prognosis. The overexpression of SPAG9 increased the expression of autophagy-related genes in 786-O cells but not in HTB-9 cells, and SPAG9 expression was significantly correlated with a weaker inflammatory response in ccRCC but not in BLCA. Through an integrated bioinformatics analysis, we screened out seven key genes (AKT3, MAPK8, PIK3CA, PIK3R3, SOS1, SOS2, and STAT5B) in this study. The correlation between SPAG9 expression and ccRCC prognosis depends on the expression of key genes. Since most of the key genes were PI3K-AKT-pathway members, we used the PI3K agonist 740Y-P to stimulate the 786-O cells, to mimic the effect of key-gene overexpression. Compared with the Ov-SPAG9 786-O cells, the 740Y-P further increased the expression of autophagy-related genes by more than twofold. Moreover, we constructed a nomogram based on SPAG9/key genes and other clinical features, which was proven to have some predictive value. Our study found that SPAG9 expression predicted opposite clinical outcomes in pan-cancer and ccRCC patients, and we speculated that SPAG9 suppresses tumor progression by promoting autophagy and inhibiting inflammatory responses in ccRCC. We further found that some genes might cooperate with SPAG9 to promote autophagy, and that these were highly expressed in the tumor stroma and could be represented by key genes. The SPAG9-based nomogram can help to estimate the long-term prognosis of ccRCC patients, indicating that SPAG9 is a potential prognostic marker for ccRCC.

Prognostic Model and Nomogram Construction and Validation With an Autophagy-Related Gene Signature in Low-Grade Gliomas

Background: Autophagy plays a vital role in cancer development. However, the prognostic value of autophagy-related genes (ARGs) in low-grade gliomas (LGG) is unclear. This research aimed to investigate whether ARGs correlated with overall survival (OS) in LGG patients.

Methods: RNA-sequencing data were obtained from The Cancer Genome Atlas (TCGA) TARGET GTEx database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of ARGs were performed by the “clusterprofile” R package. Cox regression with the wald χ² test was employed to identify prognostic significant ARGs. Next, the receiver operator characteristic curves were established to evaluate the feasibility of risk score (riskscore=h0(t)exp(∑j=1nCoefj×Xj)) and other clinical risk factors to predict prognosis. A nomogram was constructed. Correlations between clinical features and ARGs were further verified by a t-test or Kruskal–Wallis test. In addition, the correlations between autophagy and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and tumor immune estimation resource database. Last, the prediction model was verified by LGG data downloaded from the Chinese Glioma Genome Atlas (CGGA) database.

Results: Overall, 35 DE-ARGs were identified. Functional enrichment analysis showed that these genes were mainly related to oxidative stress and regulation of autophagy. Nine ARGs (BAX, BIRC5, CFLAR, DIRAS3, GRID2, MAPK9, MYC, PTK6, and TP53) were significantly associated with OS. Age (Hazard ratio (HR) = 1.063, 95% CI: 1.046–1.080), grade (HR = 3.412, 95% CI: 2.164–5.379), histological type (HR = 0.556, 95% CI: 0.346–0.893), and risk score (HR = 1.135, 95% CI: 1.104–1.167) were independent prognostic risk factors (all p < 0.05). In addition, BIRC5, CFLAR, DIRAS3, TP53, and risk scores were found to correlate significantly with age and tumor grade (all p < 0.05). Immune cell enrichment analysis demonstrated that the types of immune cells and their expression levels in the high-risk group were significantly different from those in the low-risk group (all p < 0.05). A prognostic nomogram was constructed to predict 1-, 3-, and 5-year survival, and the prognostic value of sorted ARGs were verified in the CGGA database and clinical samples.

Conclusion: Our findings suggest that the 9 DE-ARGs’ risk score model could serve as diagnostic and prognostic biomarkers. The prognostic nomograms could be useful for individualized survival prediction and improved treatment strategies.

Construction of an individualized clinical prognostic index based on ubiquitination-associated lncRNA in clear cell renal cell carcinoma patients

BACKGROUND

ccRCC is considered as the main subtype of RCC, which accounted for sixth deadliest cancer worldwide. Recently, ubiquitination has been reported to be closely involved in the progression of tumore. The purpose of this study was to identify the ubiquitination-associated genes and co-expressed lncRNAs on the prognosis of clear cell renal cell carcinoma (ccRCC) patients.

METHODS AND PATIENTS

We downloaded 530 cases and the corresponding transcriptome profiling from The Cancer Genome Atlas (TCGA) database. We distinguished mRNA and lncRNA expression data from the transcriptome profiling and then extracted the expression of mRNAs that regulate protein ubiquitination. We obtained lncRNAs associated with protein ubiquitination regulation from the lncRNA data by gene co-expression analysis. Cox regression analysis of survival time, survival status, and lncRNA expression level was carried out, and a prognostic index (PI) was constructed.

RESULTS

The PI was established based on 8 prognostic lncRNAs that regulate protein ubiquitination and distinguish the high-risk group patients from all patients. Multivariate analysis indicated that this PI was an individualized clinical prognostic factor for patients with ccRCC. Regarding clinical characteristics, a ubiquitination-associated clinical-prognostic index (UCPI), containing 8 ubiquitination-related lncRNAs and age, was established and tested with AUC of 0.80.

CONCLUSION

We established a UCPI containing 8 lncRNAs related to protein ubiquitination. This UCPI may become an appropriate model to predict the prognosis in ccRCC patients and guide clinicians to adjust the follow-up regimen.

Prognostic Implications of an Autophagy-related Gene Signature in Pancreatic Ductal Adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is difficult to diagnose and resistant to therapy and has a poor prognosis. Autophagy plays a vital role in PDAC development and progression. This study aimed to establish an autophagy-related gene (ARG) signature to predict the prognosis of patients with PDAC.

MATERIALS AND METHODS

The expression profiles of PDAC and healthy pancreatic tissues were obtained from The Cancer Genome of Atlas (TCGA) and GTEx (Genotype-Tissue Expression) databases, respectively. Univariate and multivariate Cox regression analyses were performed on differentially expressed ARGs to identify the optimal prognosis-related genes.

RESULTS

A total of 73 ARGs demonstrated significant differences in expression levels between PDAC and healthy pancreatic tissues. Several pathways that play crucial roles in biological processes were identified via enrichment analyses. Furthermore, an ARG signature was established based on overall survival-related ARGs (CASP4, BAK1, PIK3R4, CASP8, BIRC5, RPTOR, and CAPN1) using least absolute shrinkage and selection operator (LASSO) regression. Cox regression analysis confirmed that the 7-gene signature was an independent prognostic factor for patients with PDAC (P<0.001). In addition, the GSE21501 and GSE28735 datasets were used to validate the predictive value of the prognostic model for PDAC. We also constructed a clinical nomogram with a concordance index of 0.712 to predict the overall survival of patients by integrating clinical characteristics and the ARG signature. Calibration curves substantiated fine concordance between nomogram prediction and actual observation.

CONCLUSION

We constructed a new ARG-related prognostic model, which can be a prognostic biomarker and offers insights into identifying potential therapeutic targets for PDAC.

Gene signatures with therapeutic value: emerging perspective for personalized immunotherapy in renal cancer

Renal cancer is one of the deadliest urogenital diseases. In recent years, the advent of immunotherapy has led to significant improvement in the management of patients with renal cancer. Although cancer immunotherapy and its combinations had benefited numerous patients, several challenges need to be addressed. Apart from the high costs of treatment, the lack of predictive biomarkers and toxic side-effects have impeded its wider applicability. To address these issues, new biomarkers are required to predict responsiveness and design personalized treatment strategies. Recent advances in the field of single-cell sequencing and multi-dimensional spatial transcriptomics have identified clinically relevant subtypes of renal cancer. Furthermore, there is emerging potential for gene signatures based on immune cells, non-coding RNAs, and pathways such as metabolism and RNA modification. In this review article, we have discussed recent progress in the identification of gene signatures with predictive and prognostic potential in renal cancer.

A Novel Prognostic Prediction Model for Colorectal Cancer Based on Nine Autophagy-Related Long Noncoding RNAs

Introduction

Colorectal cancer (CRC) is the most common gastrointestinal cancer and has a low overall survival rate. Tumor–node–metastasis staging alone is insufficient to predict patient prognosis. Autophagy and long noncoding RNAs play important roles in regulating the biological behavior of CRC. Therefore, establishing an autophagy-related lncRNA (ARlncRNA)-based bioinformatics model is important for predicting survival and facilitating clinical treatment.

Methods

CRC data were retrieved from The Cancer Genome Atlas. The database was randomly divided into train set and validation set; then, univariate and multivariate Cox regression analyses were performed to screen prognosis-related ARlncRNAs for prediction model construction. Interactive network and Sankey diagrams of ARlncRNAs and messenger RNAs were plotted. We analyzed the survival rate of high- and low-risk patients and plotted survival curves and determined whether the risk score was an independent predictor of CRC. Receiver operating characteristic curves were used to evaluate model sensitivity and specificity. Then, the expression level of lncRNA was detected by quantitative real-time polymerase chain reaction, and the location of lncRNA was observed by fluorescence in situ hybridization. Additionally, the protein expression was detected by Western blot.

Results

A prognostic prediction model of CRC was built based on nine ARlncRNAs (NKILA, LINC00174, AC008760.1, LINC02041, PCAT6, AC156455.1, LINC01503, LINC00957, and CD27-AS1). The 5-year overall survival rate was significantly lower in the high-risk group than in the low-risk group among train set, validation set, and all patients (all p < 0.001). The model had high sensitivity and accuracy in predicting the 1-year overall survival rate (area under the curve = 0.717). The prediction model risk score was an independent predictor of CRC. LINC00174 and NKILA were expressed in the nucleus and cytoplasm of normal colonic epithelial cell line NCM460 and colorectal cancer cell lines HT29. Additionally, LINC00174 and NKILA were overexpressed in HT29 compared with NCM460. After autophagy activation, LINCC00174 expression was significantly downregulated both in NCM460 and HT29, while NKILA expression was significantly increased.

Conclusion

The new ARlncRNA-based model predicts CRC patient prognosis and provides new research ideas regarding potential mechanisms regulating the biological behavior of CRC. ARlncRNAs may play important roles in personalized cancer treatment.

Identification of autophagy-related long non-coding RNA prognostic and immune signature for clear cell renal cell carcinoma

BACKGROUND

Studies over the past decade have shown that long non-coding RNAs (lncRNAs) play an essential role in the tumorigenesis and progression of kidney renal clear cell carcinoma (KIRC). Meanwhile, autophagy has been demonstrated to regulate KIRC pathogenesis and targeting therapy resistance. However, the prognostic value of autophagy-related lncRNAs in KIRC patients has not been reported before.

METHODS

In this study, we obtained transcriptome data of 611 KIRC cases from the TCGA database and 258 autophagy-related mRNAs from the HADb database to identify autophagy-related lncRNAs by co-expression network. A prognostic model was then established basing on these autophagy-related lncRNAs, dividing patients into high-risk and low-risk groups. Survival analysis, clinical variables dependent receiver operating characteristic (ROC) analyses, univariate/multivariate Cox analyses, and clinical correlation analysis were performed based on risk signature with R language. Gene set enrichment analysis (GSEA) was then performed to investigate the potential mechanism of the risk signature promoting KIRC progression with GSEA software. CIBERSORT algorithm was performed to assess the impact of these lncRNAs on the infiltration of immune cells.

RESULTS

A total of 17 lncRNAs were screened out and all these lncRNAs were found significantly related to KIRC patients' overall survival in subsequent survival analyses. Besides, the overall survival time in the high-risk group was much poorer than in the low-risk group. The ROC analysis revealed that the prognostic value of risk signature was better than age, gender, grade, and N stage. Univariate/multivariate analyses suggested that the risk signature was an independent predictive factor for KIRC patients. Immune and autophagy related pathways were dramatically enriched in high-risk and low-risk groups, respectively, and lncRNAs related immune cells were identified by CIBERSORT.

CONCLUSIONS

In summary, our identified 17 autophagy-related lncRNAs had prognostic value for KIRC patients which may function in immunomodulation.

Development and Interpretation of a Genomic Instability Derived lncRNAs Based Risk Signature as a Predictor of Prognosis for Clear Cell Renal Cell Carcinoma Patients

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is a kind of frequently diagnosed cancer, leading to high death rate in patients. Genomic instability (GI) is regarded as playing indispensable roles in tumorigenesis and impacting the prognosis of patients. The aberrant regulation of long non-coding RNAs (lncRNAs) is a main cause of GI. We combined the somatic mutation profiles and expression profiles to identify GI derived lncRNAs (GID-lncRNAs) in ccRCC and developed a GID-lncRNAs based risk signature for prognosis prediction and medication guidance.

METHODS

We decided cases with top 25% cumulative number of somatic mutations as genomically unstable (GU) group and last 25% as genomically stable (GS) group, and identified differentially expressed lncRNAs (GID-lncRNAs) between two groups. Then we developed the risk signature with all overall survival related GID-lncRNAs with least absolute shrinkage and selection operator (LASSO) Cox regression. The functions of the GID-lncRNAs were partly interpreted by enrichment analysis. We finally validated the effectiveness of the risk signature in prognosis prediction and medication guidance.

RESULTS

We developed a seven-lncRNAs (LINC00460, AL139351.1, AC156455.1, AL035446.1, LINC02471, AC022509.2, and LINC01606) risk signature and divided all samples into high-risk and low-risk groups. Patients in high-risk group were in more severe clinicopathologic status (higher tumor grade, pathological stage, T stage, and more metastasis) and were deemed to have less survival time and lower survival rate. The efficacy of prognosis prediction was validated by receiver operating characteristic analysis. Enrichment analysis revealed that the lncRNAs in the risk signature mainly participate in regulation of cell cycle, DNA replication, material metabolism, and other vital biological processes in the tumorigenesis of ccRCC. Moreover, the risk signature could help assess the possibility of response to precise treatments.

CONCLUSION

Our study combined the somatic mutation profiles and the expression profiles of ccRCC for the first time and developed a GID-lncRNAs based risk signature for prognosis predicting and therapeutic scheme deciding. We validated the efficacy of the risk signature and partly interpreted the roles of the seven lncRNAs composing the risk signature in ccRCC. Our study provides novel insights into the roles of genomic instability derived lncRNAs in ccRCC.

Construction and validation of an autophagy-related long noncoding RNA signature for prognosis prediction in kidney renal clear cell carcinoma patients

Purpose

The purpose of this study was to identify autophagy‐associated long noncoding RNAs (ARlncRNAs) using the kidney renal clear cell carcinoma (KIRC) patient data from The Cancer Genome Atlas (TCGA) database and to construct a prognostic risk‐related ARlncRNAs signature to accurately predict the prognosis of KIRC patients.

Methods

The KIRC patient data were originated from TCGA database and were classified into a training set and testing set. Seven prognostic risk‐related ARlncRNAs, identified using univariate, lasso, and multivariate Cox regression analysis, were used to construct prognostic risk‐related signatures. Kaplan–Meier curves and receiver operating characteristic (ROC) curves as well as independent prognostic factor analysis and correlation analysis with clinical characteristics were utilized to evaluate and verify the specificity and sensitivity of the signature in training set and testing set, respectively. Two nomograms were established to predict the probable 1‐, 3‐, and 5‐year survival of the KIRC patients. In addition, the lncRNA‐mRNA co‐expression network was constructed and Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to identify biological functions of ARlncRNAs.

Results

We constructed and verified a prognostic risk‐related ARlncRNAs signature in training set and testing set, respectively. We found the survival time of KIRC patients with low‐risk scores was significantly better than those with high‐risk scores in training set and testing set. ROC curves suggested that the area under the ROC (AUC) value for prognostic risk score signature was 0.81 in training set and 0.705 in testing set. And AUC values corresponding to 1‐, 3‐, and 5 years of OS were 0.809, 0.753, and 0.794 in training set and 0.698, 0.682, and 0.754 in testing set, respectively. We established the two nomograms that confirmed high C‐index and accomplished good prediction accuracy.

Conclusions

We constructed a prognostic risk‐related ARlncRNAs signature that could accurately predict the prognosis of KIRC patients.

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.

BIRC3 and BIRC5: multi-faceted inhibitors in cancer

BACKGROUND

The evasion from apoptosis is a common strategy adopted by most tumors, and inhibitors of apoptosis proteins (IAPs) are among the most studied molecular and therapeutic targets. BIRC3 (cellular IAP2) and BIRC5 (survivin) are two of the eight members of the human IAPs family. This family is characterized by the presence of the baculoviral IAP repeat (BIR) domains, involved in protein-protein interactions. In addition to the BIR domains, IAPs also contain other important domains like the C-terminal ubiquitin-conjugating (UBC) domain, the caspase recruitment (CARD) domain and the C-terminal Ring zinc-finger (RING) domain.

MAIN BODY

BIRC3 and BIRC5 have been characterized in some solid and hematological tumors and are therapeutic targets for the family of drugs called "Smac mimetics". Many evidences point to the pro-survival and antiapoptotic role of BIRC3 in cancer cells, however, not all the data are consistent and the resulting picture is heterogeneous. For instance, BIRC3 genetic inactivation due to deletions or point mutations is consistently associated to shorter progression free survival and poor prognosis in chronic lymphocytic leukemia patients. BIRC3 inactivation has also been associated to chemoimmunotherapy resistance. On the contrary, the progression from low grade gliomas to high grade gliomas is accompanied by BIRC3 expression increase, which bears relevant prognostic consequences. Due to the relationship between BIRC3, MAP3K14 and the non-canonical NF-kB pathway, BIRC3 inactivation bears consequences also on the tumor cells relying on NF-kB pathway to survive. BIRC5, on the contrary, is commonly considered an anti-apoptotic molecule, promoting cell division and tumor progression and it is widely regarded as potential therapeutic target.

CONCLUSIONS

The present manuscript collects and reviews the most recent literature concerning the role played by BIRC3 and BIRC5 in cancer cells, providing useful information for the choice of the best therapeutic targets.

Pathogenic Single Nucleotide Polymorphisms on Autophagy-Related Genes

In recent years, the study of single nucleotide polymorphisms (SNPs) has gained increasing importance in biomedical research, as they can either be at the molecular origin of a determined disorder or directly affect the efficiency of a given treatment. In this regard, sequence variations in genes involved in pro-survival cellular pathways are commonly associated with pathologies, as the alteration of these routes compromises cellular homeostasis. This is the case of autophagy, an evolutionarily conserved pathway that counteracts extracellular and intracellular stressors by mediating the turnover of cytosolic components through lysosomal degradation. Accordingly, autophagy dysregulation has been extensively described in a wide range of human pathologies, including cancer, neurodegeneration, or inflammatory alterations. Thus, it is not surprising that pathogenic gene variants in genes encoding crucial effectors of the autophagosome/lysosome axis are increasingly being identified. In this review, we present a comprehensive list of clinically relevant SNPs in autophagy-related genes, highlighting the scope and relevance of autophagy alterations in human disease.