A Prognostic Model for Colon Cancer Patients Based on Eight Signature Autophagy Genes

Preoperative blood markers and intra-abdominal infection after colorectal cancer resection

BACKGROUND

Colorectal cancer (CRC) has one of the highest morbidity and mortality rates among digestive tract tumors. Intra-abdominal infection (IAI) is a common postoperative complication that affects the clinical outcomes of patients with CRC and hinders their rehabilitation process. However, the factors influencing abdominal infection after CRC surgery remain unclear; further, prediction models are rarely used to analyze preoperative laboratory indicators and postoperative complications.

AIM

To explore the predictive value of preoperative blood markers for IAI after radical resection of CRC.

METHODS

The data of 80 patients who underwent radical resection of CRC in the Anorectal Surgery Department of Suzhou Hospital affiliated with Anhui Medical University were analyzed. These patients were categorized into IAI (n = 15) and non-IAI groups (n = 65) based on whether IAI occurred. Influencing factors were compared; general data and laboratory indices of both groups were identified. The relationship between the indicators was assessed. Further, a nomogram prediction model was developed and evaluated; its utility and clinical applicability were assessed.

RESULTS

The risk factors for IAI after radical resection of CRC were neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and carcinoembryonic antigen (CEA) levels. NLR was correlated with PLR and SII (r = 0.604, 0.925, and 0.305, respectively), while PLR was correlated with SII (r = 0.787). The nomogram prediction model demonstrated an area under the curve of 0.968 [95% confidence interval (CI): 0.948-0.988] in the training set (n = 60) and 0.926 (95%CI: 0.906-0.980) in the validation set (n = 20). The average absolute errors of the calibration curves for the training and validation sets were 0.032 and 0.048, respectively, indicating a good model fit. The decision curve analysis curves demonstrated high net income above the 5% threshold, indicating the clinical practicality of the model.

CONCLUSION

The nomogram model constructed using NLR, PLR, SII, and CEA levels had good accuracy and reliability in predicting IAI after radical resection of CRC, potentially aiding clinical treatment decision-making.

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

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

Artificial neural network-based gene screening and immune cell infiltration analysis of osteosarcoma feature

BACKGROUND

The present study aimed to construct an artificial neural network (ANN) model that leverages characteristic genes associated with osteosarcoma (OS) to enable accurate prognostication for OS patients.

METHODS

Our research revealed 467 differentially expressed genes (DEGs) via gene expression contrast analysis, consisting of 345 downregulated genes and 122 upregulated genes. Gene Ontology (GO) enrichment analysis illuminated functions primarily encompassing T-cell activation, secretory granule lumen and antioxidant activity, among others. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, we discovered significant correlations between the DEGs and certain pathways, including phagosome, Staphylococcus aureus infection and human T-cell leukemia virus 1 infection. We then screened out 30 characteristic DEGs (CDEGs) based on random forest analysis and constructed the ANN model using the gene score matrix. To verify the credibility and accuracy of the ANN model, we performed internal and external validation processes, which affirmed our model's predictive capabilities.

RESULTS

The study further delved into the analysis of immune cell infiltration and its correlation with the target CDEGs, revealing disparities in the infiltration of 22 types of immune cells across different groups and their interrelationships. Moreover, we probed the expression of the two foremost CDEGs (YES1 and MFNG) in OS and normal tissues. We noted a positive relationship between the expression of YES1 and MFNG in OS tissues and the clinicopathological characteristics of OS patients.

CONCLUSIONS

Collectively, the findings of the present study validate the effectiveness of the CDEGs-based ANN model in predicting OS patients, which might facilitate early diagnosis and treatment of OS.

Identification of a 5-Gene Cuproptosis Signature Predicting the Prognosis for Colon Adenocarcinoma Based on WGCNA

Background: Colorectal cancer is a highly aggressive malignant tumor that primarily affects the digestive system. It is frequently diagnosed at an advanced stage. Cuproptosis is a copper-dependent form cell death mechanism, distinct from all other known pathways underlying cell death, tumor progression, prognosis, and immune response. Although the role of cuproptosis in colorectal cancer has been investigated over time, there is still an urgent need to explore new methods and insights to understand its potential function. Methods: The Gene Expression Omnibus and The Cancer Genome Atlas gene expression data were systematically explored to investigate the role of cuproptosis in colon adenocarcinoma. The weighted gene coexpression network analysis was used to construct a gene coexpression network and identify the critical module and cuproptosis-related genes correlated with colon adenocarcinoma prognosis. A cuproptosis-related genes prognostic signature for colon adenocarcinoma was identified and validated. To validate the identified gene signature, quantitative reverse transcription-polymerase chain reaction was performed. Cell proliferation assays were analyzed by CCK8 and cell cycle detection. In addition, reactive oxygen species assay was also analyzed. Results: Five hub cuproptosis-related genes (Dihydrolipoamide S-acetyltransferase, Cyclin-dependent kinase inhibitor 2A, ATOX1, VEGFA, and ULK1) were screened and a prognostic risk model for predicting overall survival was established based on these genes. The model was successfully tested in the validation cohort and the GEPIA database. Colon adenocarcinoma patients were categorized into high-risk and low-risk groups based on risk scores. The study revealed that patients with higher risk scores were more likely to have a poor prognosis. Moreover, Dihydrolipoamide S-acetyltransferase was a tumor suppressor gene that can induce cell death and affected the redox reactions in the colon cancer cell line. Conclusions: These findings suggest that the newly identified 5-gene signature may serve as a more reliable prognostic factor than clinical factors such as age and stage of disease. These findings offer a theoretical foundation for further investigation into potential cuproptosis-related biomarkers for predicting colon adenocarcinoma prognosis in the future.

A novel investigation into an E2F transcription factor-related prognostic model with seven signatures for colon cancer patients

The pathogenesis of colon cancer, a common gastrointestinal tumour, involves complicated factors, especially a series of cell cycle-related genes. E2F transcription factors during the cell cycle play an essential role in the occurrence of colon cancer. It is meaningful to establish an efficient prognostic model of colon cancer targeting cellular E2F-associated genes. This has not been reported previously. The authors first aimed to explore the links of E2F genes with the clinical outcomes of colon cancer patients by integrating data from the TCGA-COAD (n = 521), GSE17536 (n = 177) and GSE39582 (n = 585) cohorts. The Cox regression and Lasso modelling approach to identify a novel colon cancer prognostic model involving several hub genes (CDKN2A, GSPT1, PNN, POLD3, PPP1R8, PTTG1 and RFC1) were utilised. Moreover, an E2F-related nomogram that efficiently predicted the survival rates of colon cancer patients was created. Additionally, the authors first identified two E2F tumour clusters, which showed distinct prognostic features. Interestingly, the potential links of E2F-based classification and 'protein secretion' issues of multiorgans and tumour infiltration of 'T-cell regulatory (Tregs)' and 'CD56dim natural killer cell' were detected. The authors' findings are of potential clinical significance for the prognosis assessment and mechanistic exploration of colon cancer.

Genome-wide RNA-sequencing dataset reveals AC096751.1 sever as a novel prognostic long non-coding RNA and its potential molecular mechanisms in patients with colon adenocarcinoma

Objective: Through data analysis, we observed that AC096751.1 is markedly imbalance between colon adenocarcinoma (COAD) cancer and paracancerous tissues. However, the prognostic value and potential molecular mechanism of AC096751.1 in COAD are still unclear. Methods: Whole genome RNA-sequencing datasets of The Cancer Genome Atlas (TCGA) COAD cohort were collected into current study, comprehensive survival analysis and bioinformatics function enrichment analysis approaches were apply to explore the clinical outcome and molecular mechanisms of AC096751.1 in COAD. Results: In current study, we found that AC096751.1 is markedly down-regulated in COAD cancer tissues (log2 fold change =2.303, P<0.0001, false discovery rate <0.0001), and can be serve as a biomarker to distinguish COAD cancer and paracancerous tissues [area under curve=0.9518, 95% confidence interval (CI)=0.9261-0.9776]. Survival analysis suggests that low expression of AC096751.1 is connected with poor clinical outcome of COAD, and can serve as a novel prognostic indicator (log-rank P=0.016, adjusted P=0.005, hazard ratio=0.548, 95% CI=0.360-0.836). Bioinformatics function enrichment analysis suggests that the molecular mechanism of AC096751.1 in COAD may include participation in cell adhesion, cell proliferation, mitogen-activated protein kinase kinase (MAPKK), MAPK, janus-activated kinase-singal transducers and activators of transcriprion cascade, Erk1 and Erk 2 cascade, and nuclear factor-kappa B pathway. Tumor microenvironment and immune infiltration analysis indicates that COAD patients with different AC096751.1 expression have significant variation in tumor immune background. Conclusion: The present study found that AC096751.1 is significantly differentially expressed in COAD and can be serve as a novel prognostic biomarker.

Exploring Cancer Dependency Map genes and immune subtypes in colon cancer, in which TIGD1 contributes to colon cancer progression

BACKGROUND

Tumour-dependent genes identified in CRISPR-Cas9 screens have been widely reported in Cancer Dependency Maps (CDMs). CDM-derived tumour-dependent genes play an important role in tumorigenesis and progression; however, they have not been investigated in colon cancer (CC).

METHODS

CDM genes overexpressed in CC were identified from the TCGA-COAD dataset and CDM platform. A CDM signature and prognostic nomogram were constructed by Lasso Cox regression and multivariate Cox analyses. A weighted correlation network analysis (WGCNA) and consensus clustering were used to define coexpressed genes with CDM risk scores and to determine two new immune subtypes. A comprehensive investigation was performed between the two subtypes and immune regulation, the immune microenvironment and the impact of immunotherapy.

RESULTS

First, 1304 overexpressed CDM genes were identified. Then, a CDM signature with five cancer-dependent genes (MMS19, NOP14, POLRMT, SNAPC5 and TIGD1) and a prognostic nomogram were constructed, and they demonstrated robust predictive performance and a close relationship with clinical characteristics in different CC datasets. Patients with high CDM risk scores showed worse survival outcome and weaker response to chemotherapy. Additionally, TIGD1 genes were oncogenes that affected the CC cell cycle, according to cell functional experiments that involved the suppression of the TIGD1 gene. Furthermore, WGCNA and consensus clustering were used to define coexpressed genes with CDM risk scores and to determine two new immune subtypes. Finally, systematic investigations were conducted with the relationship between the CDM subtypes and immune regulation.

CONCLUSIONS

This study constructed a CDM signature consisting of five risk genes that predict survival in CC patients. In addition, the immune subtypes provided valuable insights into immunotherapy for CC patients. TIGD1, as an oncogene, is independent prognostic factors for CC, and contributes to CC progression.

Integration of bulk RNA sequencing data and single-cell RNA sequencing analysis on the heterogeneity in patients with colorectal cancer

The cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway has emerged as a critical innate immune pathway that could virtually impact nearly all aspects of tumorigenesis including colorectal cancer. This work aimed to develop and validate molecular subtypes related to cGAS-STING pathways for colorectal cancer using Bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data. Bulk RNA-seq data were acquired from The Cancer Genome Atlas dataset (training dataset) and Gene Expression Omnibus dataset (validation dataset). Univariate COX survival analysis was utilized to identify prognostic differentially expressed genes (DEGs) from 6 immune pathways related to cGAS-STING. ConsensusClusterPlus package was used to classify different subtypes based on DEGs. scRNA-seq data were used to validate differences in immune status between different subtypes. Two clusters with distinct prognosis were identified based on 27 DEGs. The six cGAS-STING-related pathways had different levels of significance between the two clusters. Clust1 had most number of amplified CNVs and clust2 had the most number of loss CNVs. TP53 was the top mutated gene of which missense mutations contributed the most of single-nucleotide variants. Immune score of clust1 was higher than that in clust2, as reflected in macrophages, T cells, and natural killer cells. Three unfavorable genes and 31 protection factors were screened between the two clusters in three datasets. ScRNA-seq data analysis demonstrated that macrophages were more enriched in clust1, and tumor cells and immune cells had close interaction. We classified two distinct subtypes with different prognosis, mutation landscape, and immune characteristics.

Construction of cuproptosis‑associated prognostic signature in colon adenocarcinoma based on bioinformatics and RT‑qPCR analysis

Colon adenocarcinoma (COAD) is the most common pathological subtype of colon cancer with a high degree of malignancy. Cuproptosis is a newly discovered copper-dependent cell death pattern distinguished from all the other known programmed cell death. Hence, it can be used as a potential therapeutic target for cancer. The present study aimed to clarify the relationship between cuproptosis and prognosis of COAD. The variations of 12 cuproptosis-associated genes based on 623 patients with COAD were comprehensively identified. It was found that 8 out of 12 were differentially expressed in tumors and normal tissues and CDKN2A showed a higher prognostic value. Therefore, two molecular subtypes were explored and the subtype A, with higher expression of cuproptosis-associated genes, showed more enrichment of immune pathways and survival advantage over those with lower cuproptosis-associated genes expression. The risk score and a nomogram predicting pattern were constructed to quantify a single patient and the risk score could serve as an independent prognostic factor by multivariate Cox regression analysis (P<0.001, HR: 1.350, 95% CI: 1.189-1.534). The expression levels of key prognostic genes (PMM2, ACOX1, KDM3A, HSPB1, PPARGC1A, UPK3B and EPHB2) was analyzed by HCT-116 colon cancer cells and HT-29 colorectal cancer cells using reverse transcription-quantitative PCR. The high-risk group, characterized by higher immune infiltration, increased microsatellite instability-high, high tumor mutation burden and high expression level of immune checkpoints, indicated higher drug sensitivity. In conclusion, our analysis confirms the potential role of cuproptosis-associated genes in the prognosis of COAD and it will provide new ideas for immunotherapy.

Identification of necroptosis-related subtypes, development of a novel signature, and characterization of immune infiltration in colorectal cancer

Introduction

Necroptosis, a type of programmed cell death, has recently been extensively studied as an important pathway regulating tumor development, metastasis, and immunity. However, the expression patterns of necroptosis-related genes (NRGs) in colorectal cancer (CRC) and their potential roles in the tumor microenvironment (TME) have not been elucidated.

Methods

We explored the expression patterns of NRGs in 1247 colorectal cancer samples from genetics and transcriptional perspective. Based on a consensus clustering algorithm, we identified NRG molecular subtypes and gene subtypes, respectively. Furthermore, we constructed a necroptosis-related signature for predicting overall survival time and verified the predictive ability of the model. Using the ESTIMATE, CIBERSORT, and ssGSEA algorithms, we assessed the association between the above subtypes, scores and immune infiltration.

Results

Most NRGs were differentially expressed between CRC tissues and normal tissues. We found that distinct subtypes exhibited different NRGs expression, patients' prognosis, immune checkpoint gene expression, and immune infiltration characteristics. The scores calculated from the necroptosis-related signature can be used to classify patients into high-risk and low-risk groups, with the high-risk group corresponding to reduced immune cell infiltration and immune function, and a greater risk of immune dysfunction and immune escape.

Discussion

Our comprehensive analysis of NRGs in CRC demonstrated their potential role in clinicopathological features, prognosis, and immune infiltration in the TME. These findings help us deepen our understanding of NRGs and the tumor microenvironment landscape, and lay a foundation for effectively assessing patient outcomes and promoting more effective immunotherapy.

Establishment and validation of individualized clinical prognostic markers for LUAD patients based on autophagy-related genes

There is considerable heterogeneity in the genomic drivers of lung adenocarcinoma, which has a dismal prognosis. Bioinformatics analysis was performed on lung adenocarcinoma (LUAD) datasets to establish a multi-autophagy gene model to predict patient prognosis. LUAD data were downloaded from The Cancer Genome Atlas (TCGA) database as a training set to construct a LUAD prognostic model. According to the risk score, a Kaplan-Meier cumulative curve was plotted to evaluate the prognostic value. Furthermore, a nomogram was established to predict the three-year and five-year survival of patients with LUAD based on their prognostic characteristics. Two genes (ITGB1 and EIF2AK3) were identified in the autophagy-related prognostic model, and the multivariate Cox proportional risk model showed that risk score was an independent predictor of prognosis in LUAD patients (HR=3.3, 95%CI= 2.3 to 4.6, P< 0.0001). The Kaplan-Meier cumulative curve showed that low-risk patients had significantly better overall (P<0.0001). The validation dataset GSE68465 further confirmed the nomogram’s robust ability to assess the prognosis of LUAD patients. A prognosis model of autophagy-related genes based on a LUAD dataset was constructed and exhibited diagnostic value in the prognosis of LUAD patients. Moreover, real-time qPCR confirmed the expression patterns of EIF2AK3 and ITGB1 in LUAD cell lines. Two key autophagy-related genes have been suggested as prognostic markers for lung adenocarcinoma.

Identification of Necroptosis-Related miRNA Signature as a Potential Predictive Biomarker for Prognosis and Immune Status in Colon Adenocarcinoma

Objective

Increasing studies suggest that necroptosis is correlated with tumor progression. And aberrant microRNA (miRNA) expression plays a vital role in various tumors. Thus, we are committed to exploring a necroptosis-associated miRNA signature to serve as a prognostic biomarker in colon adenocarcinoma (COAD). Data Sources and Methods. In the current study, The Cancer Genome Atlas (TCGA) database was used to download the miRNA and mRNA expression profiles and clinical information of samples. All patients were stochastically assigned to TCGA-train and TCGA-test clusters. Subsequently, we established a prognostic signature comprised of necroptosis-related miRNAs (NR-mis) via LASSO-Cox regression and then developed a nomogram signature composed of the prognostic signature and clinical factors. Corresponding prognostic values were evaluated. Functional analysis, tumor microenvironment (TME), and chemosensitivity of risk subgroups were also identified.

Results

The prognostic signature based on miR-141-3p, miR-148a-3p, miR-16-5p, and miR-200a-5p was closely associated with overall survival (OS) of samples and tumor metastasis in COAD. The Area Under Curve (AUC) was 0.605, 0.721, and 0.752 in TCGA-train cluster , 0.661, 0.613, and 0.695 in the TCGA-test cluster at 1, 3, and 5 years, respectively. The C-index for nomogram signature was 0.754. Functional analysis showed the remarkable enrichment of the signature-dependent miRNAs in tumor progression and immune response. And two risk subgroups were correlated with the distinct immune infiltration and immune checkpoints. In addition, the high-risk subgroup is more sensitive to cisplatin, doxorubicin, etoposide, and gemcitabine.

Conclusions

Necroptosis-related miRNAs play a crucial role in the prognosis, metastasis, immune status, and drug sensitivity in COAD.

A DCS-related lncRNA signature predicts the prognosis and chemotherapeutic response of patients with gastric cancer

The combination of docetaxel, cisplatin, and S-1 (DCS) is a common chemotherapy regimen for patients with gastric cancer (GC). However, studies on long noncoding RNAs (lncRNAs) associated with the chemotherapeutic response to and prognosis after DCS remain lacking. The aim of the present study was to identify DCS mRNAs-lncRNAs associated with chemotherapy response and prognosis in GC patients. In the present study, we identified 548 lncRNAs associated with these 16 mRNAs in the TCGA and GSE31811 datasets. Eleven lncRNAs were used to construct a prognostic signature by least absolute shrinkage and selection operator (LASSO) regression. A model including the 11 lncRNAs (LINC02532, AC007277.1, AC005324.4, AL512506.1, AC068790.7, AC022509.2, AC113139.1, LINC00106, AC005165.1, MIR100HG, and UBE2R2-AS1) associated with the prognosis of GC was constructed. The signature was validated in the TCGA database, model comparison, and qRT-PCR experiments. The results showed that the risk signature was a more effective prognostic factor for GC patients. Furthermore, the results showed that this model can well predicting chemotherapy drug response and immune infiltration of GC patients. In addition, our experimental results indicated that lower expression levels of LINC00106 and UBE2R2-AS1 predicted worse drug resistance in AGS/DDP cells. The experimental results agreed with the predictions. Furthermore, knockdown of LINC00106 or UBE2R2-AS1 can significantly enhanced the proliferation and migration of GC AGS cells in vitro. In conclusion, a novel DCS therapy-related lncRNA signature may become a new strategy to predict chemotherapy response and prognosis in GC patients. LINC00106 and UBE2R2-AS1 may exhibit a tumor suppressive function in GC.

Identification and validation of a novel 16-gene prognostic signature for patients with breast cancer

Despite increased early diagnosis and improved treatment in breast cancer (BRCA) patients, prognosis prediction is still a challenging task due to the disease heterogeneity. This study was to identify a novel gene signature that can accurately evaluate BRCA patient survival. The gene expression and clinical data of BRCA patients were collected from The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of BRCA International Consortium (METABRIC) databases. Genes associated with prognosis were determined by Kaplan–Meier survival analysis and multivariate Cox regression analysis. A prognostic 16-gene score was established with linear combination of 16 genes. The prognostic value of the signature was validated in the METABRIC and GSE202203 datasets. Gene expression analysis was performed to investigate the diagnostic values of 16 genes. The 16-gene score was associated with shortened overall survival in BRCA patients independently of clinicopathological characteristics. The signalling pathways of cell cycle, oocyte meiosis, RNA degradation, progesterone mediated oocyte maturation and DNA replication were the top five most enriched pathways in the high 16-gene score group. The 16-gene nomogram incorporating the survival‐related clinical factors showed improved prediction accuracies for 1-year, 3-year and 5‐year survival (area under curve [AUC] = 0.91, 0.79 and 0.77 respectively). MORN3, IGJ, DERL1 exhibited high accuracy in differentiating BRCA tissues from normal breast tissues (AUC > 0.80 for all cases). The 16-gene profile provides novel insights into the identification of BRCA with a high risk of death, which eventually guides treatment decision making.

Exploring immune-related signatures for predicting immunotherapeutic responsiveness, prognosis, and diagnosis of patients with colon cancer

The present study focused on identifying the immune-related signatures and exploring their performance in predicting the prognosis, immunotherapeutic responsiveness, and diagnosis of patients with colon cancer. Firstly, the immunotherapeutic response-related differential expressed genes (DEGs) were identified by comparing responders and non-responders from an anti-PD-L1 cohort using the edgeR R package. Then, the immunotherapeutic response related DEGs was intersected with immune-related genes (IRGs) to obtain the immunotherapeutic response and immune-related genes (IRIGs). Then, an immunotherapeutic response and immune-related risk score (IRIRScore) model consisting of 6 IRIGs was constructed using the univariable Cox regression analysis and multivariate Cox regression analysis based on the COAD cohort from the cancer genome atlas (TCGA) database, which was further validated in two independent gene expression omnibus database (GEO) datasets (GSE39582 and GSE17536) and anti-PD-L1 cohort. A nomogram with good accuracy was established based on the immune-related signatures and clinical factors (C-index = 0.75). In the training dataset and GSE39582, higher IRIRScore was significantly associated with higher TMN and advanced pathological stages. Based on the anti-PD-L1 cohort, patients who were sensitive to immunotherapy had significantly lower risk score than non-responders. Furthermore, we explored the immunotherapy-related signatures based on the training dataset. Kaplan-Meier curve revealed a high level of T cells regulatory (Tregs) was significantly related to poor overall survival (OS), while a high level of T cells CD4 memory resting was significantly related to better OS. Besides, the TMB value of patients in the high-risk group was significantly higher than those in a low-risk group. Moreover, patients in the high-risk group had significantly higher expression levels of immune checkpoint inhibitors. In addition, the immune-related signatures were applied to establish prediction models using the random forest algorithm. Among them, TDGF1 and NRG1 revealed excellent diagnostic predictive performance (AUC >0.8). In conclusion, the current findings provide new insights into immune-related immunotherapeutic responsiveness, prognosis, and diagnosis of colon cancer.

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.

Identification and validation of EMT-immune-related prognostic biomarkers CDKN2A, CMTM8 and ILK in colon cancer

Colon cancer (CC) is a disease with high incidence and mortality rate. The interaction between epithelial-mesenchymal transition (EMT) and immune status has important clinical significance. We aim to identify EMT-immune-related prognostic biomarkers in colon cancer. The GEO2R and GEPIA 2.0 were utilized to calculate the differential expression genes between CC and normal mucosa. Immport, InnateDB and EMTome databases were used to define EMT-immune-related genes. We conducted batch prognostic analysis by TCGA data. The expression patterns were verified by multiple datasets and lab experiments. GEPIA 2.0 and TIMER 2.0 were utilized to analyze the correlation of the hub genes with EMT markers and immune infiltration. GeneMANIA, STRING, and Metascape were used for co-expression and pathway enrichment analysis. Finally, we established a signature by the method of multivariate Cox regression analysis. CDKN2A, CMTM8 and ILK were filtered out as prognostic genes. CDKN2A and CMTM8 were up-regulated, while ILK was down-regulated in CC. CDKN2A was positively correlated with infiltration of macrophages, Th2 cells, Treg cells, and negatively correlated with NK cells. CMTM8 was negatively correlated with CD8+ T cells, dendritic cells, and NK cells. ILK was positively correlated with CD8+ T cells and dendritic cells. Moreover, CDKN2A, CMTM8 and ILK were significantly correlated with EMT markers. The three genes could participate in the TGF-β pathway. The prognosis model established by the three hub genes was an independent prognosis factor, which can better predict the prognosis. CDKN2A, CMTM8 and ILK are promising prognostic biomarkers and may be potential therapeutic targets in colon cancer.

The Prognostic and Molecular Landscape of Autophagy-Related Long Noncoding RNA in Colorectal Cancer

Current evidence suggests that autophagy is closely correlated with the pathogenesis and development of malignant tumors. This study is aimed at assessing the potential prognostic significance of autophagy-related long noncoding RNA (ARlncRNA) in colorectal cancer (CRC). 3145 ARlncRNAs were obtained from autophagy-related genes (ARGs) by Pearson correlation analysis, and we established a competing endogenous RNA (ceRNA) network mediated by ARlncRNAs. A novel six-ARlncRNA prognostic signature was constructed based on TCGA samples used as the training group. Kaplan–Meier survival analysis and independent prognosis analysis were performed on the internal (training and test groups) and external validations (GEO datasets) to assess the accuracy and clinical practicability. Moreover, the nomogram combining the two independent prognostic factors (age and ARlncRNA-risk score (ARlncRNA-RS)) intuitively displayed overall survival. Gene set enrichment analysis (GSEA) conducted on the prognostic signature revealed that the gene set of the high-risk group was significantly enriched in the hallmark gene set “hypoxia” and the gene set of the low-risk group was enriched in KEGG pathways, including “peroxisome,” “the citrate cycle (TCA cycle),” and “other glycan degradation.” Assessment of antineoplastic therapy susceptibility and microsatellite instability (MSI) analysis were performed on CRC samples based on the prognostic signature. Moreover, Spearman correlation analysis was conducted on the expression of six ARlncRNAs of the prognostic signature and cancer stem cell (CSC) index as well as the tumor microenvironment (TME). In conclusion, this study established a six-ARlncRNA prognostic signature, which yielded favorable prognostic significance and demonstrated the correlation between ARlncRNAs and CRC progression.

Development of a novel necroptosis-associated miRNA risk signature to evaluate the prognosis of colon cancer patients

BACKGROUND

Necroptosis is a recently discovered caspase-independent form of cell death which plays an important role in the occurrence and development of cancer. As an important regulatory factor in necroptosis, microRNAs (miRNAs) are important for the development of colon cancer. This study established a novel necroptosis-related miRNA risk signature to evaluate the prognosis of patients with colon adenocarcinoma (COAD).

METHODS

The necroptosis-related miRNAs were selected by assessing the differential expression of miRNAs in 459 COAD patient samples and 8 control samples from The Cancer Genome Atlas (TCGA). Selection operator Cox analyses and survival analyses were used to establish the risk signature of 7 miRNAs related to necroptosis. Functional enrichment analysis and nomograms were used to explore the potential effects of necroptosis-related miRNAs on prognosis and metastasis. The target genes of the necroptosis-related miRNAs were predicted using online databases and the genes related to overall survival (OS) were screened.

RESULTS

The risk signature was based on 7 necroptosis-related miRNAs. Nomograms showed that the risk signature was effective at predicting the prognosis and TNM stage of COAD patients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses demonstrated that these miRNAs play an important role in cancer development, metastasis, and prognosis. A total of 38 target genes for these miRNAs were found to be associated with the OS in COAD patients.

CONCLUSIONS

This study provided novel evidence that necroptosis-related miRNAs are associated with the prognosis of COAD patients. A risk signature established based on these miRNAs could effectively predict the prognosis and metastasis of COAD in patients.

Identification of Transcription Factor-Related Gene Signature and Risk Score Model for Colon Adenocarcinoma

The prognosis of colon adenocarcinoma (COAD) remains poor. However, the specific and sensitive biomarkers for diagnosis and prognosis of COAD are absent. Transcription factors (TFs) are involved in many biological processes in cells. As the molecule of the signal pathway of the terminal effectors, TFs play important roles in tumorigenesis and development. A growing body of research suggests that aberrant TFs contribute to the development of COAD, as well as to its clinicopathological features and prognosis. In consequence, a few studies have investigated the relationship between the TF-related risk model and the prognosis of COAD. Therefore, in this article, we hope to develop a prognostic risk model based on TFs to predict the prognosis of patients with COAD. The mRNA transcription data and corresponding clinical data were downloaded from TCGA and GEO. Then, 141 differentially expressed genes, validated by the GEPIA2 database, were identified by differential expression analysis between normal and tumor samples. Univariate, multivariate and Lasso Cox regression analysis were performed to identify seven prognostic genes (E2F3, ETS2, HLF, HSF4, KLF4, MEIS2, and TCF7L1). The Kaplan-Meier curve and the receiver operating characteristic curve (ROC, 1-year AUC: 0.723, 3-year AUC: 0.775, 5-year AUC: 0.786) showed that our model could be used to predict the prognosis of patients with COAD. Multivariate Cox analysis also reported that the risk model is an independent prognostic factor of COAD. The external cohort (GSE17536 and GSE39582) was used to validate our risk model, which indicated that our risk model may be a reliable predictive model for COAD patients. Finally, based on the model and the clinicopathological factors, we constructed a nomogram with a C-index of 0.802. In conclusion, we emphasize the clinical significance of TFs in COAD and construct a prognostic model of TFs, which could provide a novel and reliable model for the prognosis of COAD.

Construction of a Prognostic Model for Hepatocellular Carcinoma Based on Immunoautophagy-Related Genes and Tumor Microenvironment

Background

The aim of this study was to screen and identify immunoautophagy-related genes (IARGs) in HCC patients and clarify their potential prognostic value in HCC patients.

Methods

Immune-related genes and autophagy-related gene were downloaded from public databases. Cox regression analysis was used to selected several immunoautophagy-related genes to establish a prognostic model, and patients were divided into high- and low-risk groups based on median risk score. We analyzed the overall survival and clinicopathological characteristics between two groups. Meanwhile, internal validation dataset and external ICGC dataset were used to verify robustness of the model. Associations between six immune cells infiltrates and risk score were analyzed.

Results

A prognostic model was established based on CANX and HDAC1. The prognoses of the high-risk group were worse than low-risk group in both TCGA and ICGC datasets. Multivariate Cox regression analysis showed that risk score was an independent prognostic factor for HCC patients. Results showed that the risk score in young group was higher than elderly group. Patients with poorly differentiated tumor may have high risk score and poor survival. The score was positively correlated with immune cells.

Conclusion

Our study shows that immunoautophagy-related genes have potential prognostic value for patients with HCC and may provide new information and direction for targeted therapy.

Autophagy and Digestive Disorders: Advances in Understanding and Therapeutic Approaches

The gastrointestinal (GI) tract is a series of hollow organs that is responsible for the digestion and absorption of ingested foods and the excretion of waste. Any changes in the GI tract can lead to GI disorders. GI disorders are highly prevalent in the population and account for substantial morbidity, mortality, and healthcare utilization. GI disorders can be functional, or organic with structural changes. Functional GI disorders include functional dyspepsia and irritable bowel syndrome. Organic GI disorders include inflammation of the GI tract due to chronic infection, drugs, trauma, and other causes. Recent studies have highlighted a new explanatory mechanism for GI disorders. It has been suggested that autophagy, an intracellular homeostatic mechanism, also plays an important role in the pathogenesis of GI disorders. Autophagy has three primary forms: macroautophagy, microautophagy, and chaperone-mediated autophagy. It may affect intestinal homeostasis, host defense against intestinal pathogens, regulation of the gut microbiota, and innate and adaptive immunity. Drugs targeting autophagy could, therefore, have therapeutic potential for treating GI disorders. In this review, we provide an overview of current understanding regarding the evidence for autophagy in GI diseases and updates on potential treatments, including drugs and complementary and alternative medicines.

Identification and Validation of Autophagy-Related Gene Nomograms to Predict the Prognostic Value of Patients with Cervical Cancer

Autophagy is a process of engulfing one's own cytoplasmic proteins or organelles and coating them into vesicles, fusing with lysosomes to form autophagic lysosomes, and degrading the contents it encapsulates. Increasing studies have shown that autophagy disorders are closely related to the occurrence of tumors. However, the prognostic role of autophagy genes in cervical cancer is still unclear. In this study, we constructed risk signatures of autophagy-related genes (ARGs) to predict the prognosis of cervical cancer. The expression profiles and clinical information of autophagy gene sets were downloaded from TCGA and GSE52903 queues as training and validation sets. The normal cervical tissue expression profile data from the UCSC XENA website (obtained from GTEx) were used as a supplement to the TCGA normal cervical tissue. Univariate COX regression analysis of 17 different autophagy genes was performed with the consensus approach. Tumor samples from TCGA were divided into six subtypes, and the clinical traits of the six subtypes had different distributions. Further absolute shrinkage and selection operator (LASSO) and multivariable COX regression yielded an autophagy genetic risk model consisting of eight genes. In the training set, the survival rate of the high-risk group was lower than that of the low-risk group (p < 0.0001). In the validation set, the AUC area of the receiver operating characteristic (ROC) curve was 0.772 for the training set and 0.889 for the verification set. We found that high and low risk scores were closely related to TNM stage (p < 0.05). The nomogram shows that the risk score combined with other indicators, such as G, T, M, and N, better predicts 1-, 3-, and 5-year survival rates. Decline curve analysis (DCA) shows that the risk model combined with other indicators produces better clinical efficacy. Immune cells with an enrichment score of 28 showed statistically significant differences related to high and low risk. GSEA enrichment analysis showed the main enrichment being in KRAS activation, genes defining epithelial and mesenchymal transition (EMT), raised in response to the low oxygen level (hypoxia) gene and NF-kB in response to TNF. These pathways are closely related to the occurrence of tumors. Our constructed autophagy risk signature may be a prognostic tool for cervical cancer.

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

Background

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

Methods

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

Results

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

Conclusion

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

A Prognostic Ferroptosis-Related lncRNAs Signature Associated With Immune Landscape and Radiotherapy Response in Glioma

Recent studies have demonstrated that long non-coding RNAs (lncRNAs) are implicated in the regulation of tumor cell ferroptosis. However, the prognostic value of ferroptosis-related lncRNAs has never been comprehensively explored in glioma. In this study, the transcriptomic data and clinical information of glioma patients were downloaded from TCGA, CGGA and Rembrandt databases. We identified 24 prognostic ferroptosis-related lncRNAs, 15 of which (SNAI3-AS1, GDNF-AS1, WDFY3-AS2, CPB2-AS1, WAC-AS1, SLC25A21-AS1, ARHGEF26-AS1, LINC00641, LINC00844, MIR155HG, MIR22HG, PVT1, SNHG18, PAXIP1-AS2, and SBF2-AS1) were used to construct a ferroptosis-related lncRNAs signature (FRLS) according to the least absolute shrinkage and selection operator (LASSO) regression. The validity of this FRLS was verified in training (TCGA) and validation (CGGA and Rembrandt) cohorts, respectively. The Kaplan-Meier curves revealed a significant distinction of overall survival (OS) between the high- and low-risk groups. The receiver operating characteristic (ROC) curves exhibited robust prognostic capacity of this FRLS. A nomogram with improved accuracy for predicting OS was established based on independent prognostic factors (FRLS, age, and WHO grade). Besides, patients in the high-risk group had higher immune, stroma, and ESTIMATE scores, lower tumor purity, higher infiltration of immunosuppressive cells, and higher expression of immune checkpoints. Patients in the low-risk group benefited significantly from radiotherapy, while no survival benefit of radiotherapy was observed for those in the high-risk group. In conclusion, we identified the prognostic ferroptosis-related lncRNAs in glioma, and constructed a prognostic signature which was associated with the immune landscape of glioma microenvironment and radiotherapy response.

Prediction of Overall Survival Rate in Patients With Hepatocellular Carcinoma Using an Integrated Model Based on Autophagy Gene Marker

The autophagy cell, which can inhibit the formation of tumor in the early stage and can promote the development of tumor in the late stage, plays an important role in the development of tumor. Therefore, it has potential significance to explore the influence of autophagy-related genes (AAGs) on the prognosis of hepatocellular carcinoma (HCC). The differentially expressed AAGs are selected from HCC gene expression profile data and clinical data downloaded from the TCGA database, and human autophagy database (HADB). The role of AAGs in HCC is elucidated by GO functional annotation and KEGG pathway enrichment analysis. Combining with clinical data, we selected age, gender, grade, stage, T state, M state, and N state as Cox model indexes to construct the multivariate Cox model and survival curve of Kaplan Meier (KM) was drawn to estimate patients’ survival between high- and low-risk groups. Through an ROC curve drawn by univariate and multivariate Cox regression analysis, we found that seven genes with high expression levels, including HSP90AB1, SQSTM1, RHEB, HDAC1, ATIC, HSPB8, and BIRC5 were associated with poor prognosis of HCC patients. Then the ICGC database is used to verify the reliability and robustness of the model. Therefore, the prognosis model of HCC constructed by autophagy genes might effectively predict the overall survival rate and help to find the best personalized targeted therapy of patients with HCC, which can provide better prognosis for patients.

Identification of an Autophagy-Related Pair Signature for Predicting Prognoses and Immune Activity in Pancreatic Adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PAAD) spreads quickly and has a poor prognosis. Autophagy research on PAAD could reveal new biomarkers and targets for diagnosis and treatment.

METHODS

Autophagy-related genes were translated into autophagy-related gene pairs, and univariate Cox regression was performed to obtain overall survival (OS)-related IRGPs (P<0.001). LASSO Cox regression analyses were performed to construct an autophagy-related gene pair (ARGP) model for predicting OS. The Cancer Genome Atlas (TCGA)-PAAD cohort was set as the training group for model construction. The model predictive value was validated in multiple external datasets. Receiver operating characteristic (ROC) curves were used to evaluate model performance. Tumor microenvironments and immune infiltration were compared between low- and high-risk groups with ESTIMATE and CIBERSORT. Differentially expressed genes (DEGs) between the groups were further analyzed by Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and used to identify potential small-molecule compounds in L1000FWD.

RESULTS

Risk scores were calculated as follows: ATG4B|CHMP4C×(-0.31) + CHMP2B|MAP1LC3B×(0.30) + CHMP6|RIPK2 ×(-0.33) + LRSAM1|TRIM5×(-0.26) + MAP1LC3A|PAFAH1B2×(-0.15) + MAP1LC3A|TRIM21×(-0.08) + MET|MFN2×(0.38) + MET|MTDH×(0.47) + RASIP1|TRIM5×(-0.23) + RB1CC1|TPCN1×(0.22). OS was significantly shorter in the high-risk group than the low-risk group in each PAAD cohort. The ESTIMATE analysis showed no difference in stromal scores but a significant difference in immune scores (p=0.0045) and ESTIMATE scores (p=0.014) between the groups. CIBERSORT analysis showed higher naive B cell, Treg cell, CD8 T cell, and plasma cell levels in the low-risk group and higher M1 and M2 macrophage levels in the high-risk group. In addition, the results showed that naive B cells (r=-0.32, p<0.001), Treg cells (r=-0.31, p<0.001), CD8 T cells (r=-0.24, p=0.0092), and plasma cells (r=-0.2, p<0.026) were statistically correlated with the ARGP risk score. The top 3 enriched GO-BPs were signal release, regulation of transsynaptic signaling, and modulation of chemical synaptic transmission, and the top 3 enriched KEGG pathways were the insulin secretion, dopaminergic synapse, and NF-kappa B signaling pathways. Several potential small-molecule compounds targeting ARGs were also identified.

CONCLUSION

Our results demonstrate that the ARGP-based model may be a promising prognostic indicator for identifying drug targets in patients with PAAD.