Genome-Wide Identification of a Novel Autophagy-Related Signature for Colorectal Cancer

Autophagy-related genes affect the survival of multiple myeloma patients depending on chromosomal abnormality

Background

Targeting autophagy at gene level may be promising in multiple myeloma (MM) treatment depending on chromosomal abnormality (ABN) status.

Objectives

We aimed to investigate the role of ABN on survival of MM patients and to identify prognosis related autophagy-related genes (ARGs) for patients with or without ABN.

Methods

Gene intensity values of 222 ARG for 548 MM patients were obtained from the Affymetrix Human Genome U133 Plus 2.0 Array (GPL570) platform containing 54,675 probes (GSE24080). A dataset containing data from 1576 MM patients with 1q21 amplification (GSE4204, GSE4452, GSE4581, and GSE2658) was used for validation. Survival analysis of the patients was analyzed using univariate and multivariate Cox regression method with the help of R3.53 programming language and Kaplan-Meier graphics were created. The Gene Ontology enRIchmentanaLysis and visuaLizAtion (GOrilla) tool was used to define the related biological processes and pathways.

Results

The overall survival (OS) and event-free survival (EFS) in all MM patients were strongly influenced by ABN. In the group of patients with ABN, 41 ARGs were found to be important in prognosis, whereas in the group of patients without ABN, 13 ARGs were found to be important in prognosis. CDKN1A, FKBP1B, FOXO3, and NCKAP1 ARGs were commonly significant in both groups and found to be survival triggering.

Conclusions

The classification of MM patients according to the absence or presence of ABN is important in the determination of survival status. Detection of survival related ARGs in patients with chromosomal anomalies may be a new therapeutic target in treatment.

Identification of an Autophagy-Related Gene Signature for the Prediction of Prognosis in Early-Stage Colorectal Cancer

Purpose: A certain number of early-stage colorectal cancer (CRC) patients suffer tumor recurrence after initial curative resection. In this context, an effective prognostic biomarker model is constantly in need. Autophagy exhibits a dual role in tumorigenesis. Our study aims to develop an autophagy-related gene (ATG) signature-based on high-throughput data analysis for disease-free survival (DFS) prognosis of patients with stage I/II CRC.

Methods: Gene expression profiles and clinical information of CRC patients extracted from four public datasets were distributed to discovery and training cohort (GSE39582), validation cohort (TCGA CRC, n = 624), and meta-validation cohort (GSE37892 and GSE14333, n = 420). Autophagy genes significantly associated with prognosis were identified.

Results: Among 655 autophagy-related genes, a 10-gene ATG signature, which was significantly associated with DFS in the training cohort (HR, 2.76[1.56–4.82]; p = 2.06 × 10–4), was constructed. The ATG signature, stratifying patients into high and low autophagy risk groups, was validated in the validation (HR, 2.29[1.15–4.55]; p = 1.5 × 10–2) and meta-validation cohorts (HR, 2.5[1.03–6.06]; p = 3.63 × 10–2) and proved to be prognostic in a multivariate analysis. Functional analysis revealed enrichment of several immune/inflammatory pathways in the high autophagy risk group, where increased infiltration of T regulatory cells (Tregs) and decreased infiltration of M1 macrophages were observed.

Conclusion: Our study established a prognostic ATG signature that effectively predicted DFS for early-stage CRC patients. Meanwhile, the study also revealed the possible relationship among autophagy process, immune/inflammatory response, and tumorigenesis.

Activation of oxidative phosphorylation in TP53-inactive endometrial carcinomas with a poor prognosis

OBJECTIVE

We aimed to identify pathways for potential therapeutic targets by conducting molecular profiling of endometrial carcinomas in patients with poor prognosis.

METHODS

The classification of endometrial carcinomas has undergone a paradigm shift with the advent of next generation sequencing based molecular profiling. Although this emerging classification reflects poor prognosis in patients with endometrial carcinoma, knowledge of affected biological pathways is still lacking. In this study, 85 patients with endometrial carcinomas at the Shizuoka Cancer Center were evaluated from January 2014 to March 2019 and classified based on The Cancer Genome Atlas subgroups. The accumulation of germline and somatic mutations was determined using next generation sequencing. Gene expression profiling was used to determine the effect of TP53 inactivation on the recurrence of endometrial carcinoma. Additionally, the biological pathways associated with TP53 inactivation were estimated by pathway analysis based on gene expression.

RESULTS

Based on The Cancer Genome Atlas classification, the ratio of polymerase-epsilon to copy number-high subgroups and the frequency of PTEN and TP53 mutations differed in patients, and mutations of ARHGAP35 observed in normal endometrium were accumulated in the polymerase-epsilon and microsatellite instability subgroups. We revealed that copy number-high reflects TP53 inactivation in endometrial carcinomas, and that TP53-inactive tumors with or without TP53 mutations have poor prognosis. Furthermore, overexpression of aurora kinase A and activation of oxidative phosphorylation were found in TP53-inactivated endometrial carcinomas, suggesting that the PI3K/mTOR and autophagy pathways are potential drug targets.

CONCLUSION

Our analysis revealed a relationship between pathways involved in oxidative phosphorylation and poor prognosis and provides insight into potential drug targets.

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.

Prognostic Significance of Autophagy-Relevant Gene Markers in Colorectal Cancer

Background

Colorectal cancer (CRC) is a common malignant solid tumor with an extremely low survival rate after relapse. Previous investigations have shown that autophagy possesses a crucial function in tumors. However, there is no consensus on the value of autophagy-associated genes in predicting the prognosis of CRC patients. This work screens autophagy-related markers and signaling pathways that may participate in the development of CRC, and establishes a prognostic model of CRC based on autophagy-associated genes.

Methods

Gene transcripts from the TCGA database and autophagy-associated gene data from the GeneCards database were used to obtain expression levels of autophagy-associated genes, followed by Wilcox tests to screen for autophagy-related differentially expressed genes. Then, 11 key autophagy-associated genes were identified through univariate and multivariate Cox proportional hazard regression analysis and used to establish prognostic models. Additionally, immunohistochemical and CRC cell line data were used to evaluate the results of our three autophagy-associated genes EPHB2, NOL3, and SNAI1 in TCGA. Based on the multivariate Cox analysis, risk scores were calculated and used to classify samples into high-risk and low-risk groups. Kaplan-Meier survival analysis, risk profiling, and independent prognosis analysis were carried out. Receiver operating characteristic analysis was performed to estimate the specificity and sensitivity of the prognostic model. Finally, GSEA, GO, and KEGG analysis were performed to identify the relevant signaling pathways.

Results

A total of 301 autophagy-related genes were differentially expressed in CRC. The areas under the 1-year, 3-year, and 5-year receiver operating characteristic curves of the autophagy-based prognostic model for CRC were 0.764, 0.751, and 0.729, respectively. GSEA analysis of the model showed significant enrichment in several tumor-relevant pathways and cellular protective biological processes. The expression of EPHB2, IL-13, MAP2, RPN2, and TRAF5 was correlated with microsatellite instability (MSI), while the expression of IL-13, RPN2, and TRAF5 was related to tumor mutation burden (TMB). GO analysis showed that the 11 target autophagy genes were chiefly enriched in mRNA processing, RNA splicing, and regulation of the mRNA metabolic process. KEGG analysis showed enrichment mainly in spliceosomes. We constructed a prognostic risk assessment model based on 11 autophagy-related genes in CRC.

Conclusion

A prognostic risk assessment model based on 11 autophagy-associated genes was constructed in CRC. The new model suggests directions and ideas for evaluating prognosis and provides guidance to choose better treatment strategies for CRC.

A novel prognostic risk score model based on immune-related genes in patients with stage IV colorectal cancer

PURPOSE

The aims of the present study were to explore immune-related genes (IRGs) in stage IV colorectal cancer (CRC) and construct a prognostic risk score model to predict patient overall survival (OS), providing a reference for individualized clinical treatment.

METHODS

High-throughput RNA-sequencing, phenotype, and survival data from patients with stage IV CRC were downloaded from TCGA. Candidate genes were identified by screening for differentially expressed IRGs (DE-IRGs). Univariate Cox regression, LASSO, and multivariate Cox regression analyses were used to determine the final variables for construction of the prognostic risk score model. GSE17536 from the GEO database was used as an external validation dataset to evaluate the predictive power of the model.

RESULTS

A total of 770 candidate DE-IRGs were obtained, and a prognostic risk score model was constructed by variable screening using the following 12 genes: FGFR4, LGR6, TRBV12-3, NUDT6, MET, PDIA2, ORM1, IGKV3D-20, THRB, WNT5A, FGF18, and CCR8. In the external validation set, the survival prediction C-index was 0.685, and the AUC values were 0.583, 0.731, and 0.837 for 1-, 2- and 3-year OS, respectively. Univariate and multivariate Cox regression analyses demonstrated that the risk score model was an independent prognostic factor for patients with stage IV CRC. High- and low-risk patient groups had significant differences in the expression of checkpoint coding genes (ICGs).

CONCLUSION

The prognostic risk score model for stage IV CRC developed in the present study based on immune-related genes has acceptable predictive power, and is closely related to the expression of ICGs.

Prognostic implications of an autophagy-based signature in colorectal cancer

BACKGROUND

The heterogeneity of colorectal cancer (CRC) poses a significant challenge to the precise treatment of patients. CRC has been divided into 4 consensus molecular subtypes (CMSs) with distinct biological and clinical characteristics, of which CMS4 has the mesenchymal identity and the highest relapse rate. Autophagy plays a vital role in CRC development and therapeutic response.

METHODS

The gene expression profiles collected from 6 datasets were applied to this study. Network analysis was applied to integrate the subtype-specific molecular modalities and autophagy signature to establish an autophagy-based prognostic signature for CRC (APSCRC).

RESULTS

Network analysis revealed that 6 prognostic autophagy genes (VAMP7, DLC1, FKBP1B, PEA15, PEX14, and DNAJB1) predominantly regulated the mesenchymal modalities of CRC. The APSCRC was constructed by these 6 core genes and applied for risk calculation. Patients were divided into high- and low-risk groups based on APSCRC score in all cohorts. Patients within the high-risk group showed an unfavorable prognosis. In multivariate analysis, the APSCRC remained an independent predictor of prognosis. Moreover, the APSCRC achieved higher prognostic power than commercialized multigene signatures.

CONCLUSIONS

We proposed and validated an autophagy-based signature, which is a promising prognostic biomarker of CRC patients. Further prospective studies are warranted to test and validate its efficiency for clinical application.

Identification of Three Autophagy-Related Long Non-Coding RNAs as a Novel Head and Neck Squamous Cell Carcinoma Prognostic Signature

Head and neck squamous cell carcinoma (HNSCC) has a poor prognosis. Considerable evidence indicates that autophagy and non-coding RNA play essential roles in the biological processes involved in cancers, but associations between autophagy-related long non-coding RNAs (lncRNAs) and HNSCC remain unclear. In the present study, HNSCC RNA sequences and autophagy-related gene data were extracted from The Cancer Genome Atlas database and the Human Autophagy Database. A total of 1,153 autophagy-related lncRNAs were selected via calculating Pearson’s correlation coefficient. Three prognosis-related autophagy lncRNAs were identified via univariate Cox regression, least absolute shrinkage and selection operator analysis, and multivariate Cox regression analysis. We also constructed a prognostic model based on these autophagy-related lncRNAs and evaluated its ability to accurately and independently predict the prognosis of HNSCC patients. The area under the curve (AUC) was 0.864 (3-year) and 0.836 (5-year), and our model can independently predict the prognosis of HNSCC. The prognostic value of the three autophagy lncRNAs was confirmed via analysis of samples from five databases. To further identify the functions of the three lncRNAs, a co-expression network was constructed and pathway analysis was performed. In that analysis the lncRNAs were correlated with 189 related genes and 20 autophagy-related genes, and these lncRNAs mainly involved homologous recombination, the Fanconi anemia pathway, the autophagy-related pathway, and immune-related pathways. In addition, we validated the expression levels of three lncRNAs and autophagy markers (ATG12, BECN1, and MAP1LC3B) based on TIMER, Oncomine, and HPA database analysis. Our results indicated that TTTY15 was increased in HPV positive and HPV negative HNSCC patients, and three autophagy markers were up-regulated in all HNSCCC patients. Lastly, association between three lncRNAs and autophagy markers was performed, and our results showed that TTTY15 and MIF-AS1 were associated with autophagy markers. Collectively, these results suggested that three autophagy-related lncRNAs have prognostic value in HNSCC patients.

High-expression of LncRNA MAFG-AS1 is associated with the prognostic of patients with colorectal cancer

OBJECTIVE

Long noncoding RNAs (lncRNAs) have been proven to exhibit distinct functions on the convoluted processes of tumor developments. Some studies on the biological functions of lncRNA MAFG-AS1 (MAFG-AS1) in cancers revealed that they may serve as an oncogene in some kinds of tumors, including colorectal cancer (CRC). However, little is known about the role of MAFG-AS1 in the prognostic of CRC.

METHODS

A public dataset was mined for the screening of dysregulated lncRNAs in CRC. Quantitative real-time reverse transcription-polymerase chain reaction(qPCR) was used to compare the levels of MAFG-AS1 between paired MAFG-AS1 specimens and normal adjacent tissues. The correlations between MAFG-AS1 and clinic pathological features in CRC were analyzed using the chi-square test. The log-rank test and Kaplan-Meier test were carried out to compare the survival time of patients with high and low expressions of MAFG-AS1. Cox regression was applied for univariate and multivariate assays to validate whether MAFG-AS1 could be an independent factor in the prognosis of CRC.

RESULTS

We found that the distinct upregulation of MAFG-AS1 in various tumors was a common event. MAFG-AS1 was distinctly up-regulated in CRC specimens compared to matched non-tumor specimens (p < 0.01). High MAFG-AS1 expressions were closely associated with depth of invasion (p = 0.011) and TNM stage (p = 0.022). Survival assays revealed that patients with high expression of MAFG-AS1 have a shorter overall survival (p = 0.0030) and disease-free survival (p = 0.0002).

CONCLUSIONS

MAFG-AS1 can serve as a novel potential biomarker to predict CRC patients' survival time.

Transcriptomic Profiling for the Autophagy Pathway in Colorectal Cancer

The role of autophagy in colorectal cancer (CRC) pathogenesis appears to be crucial. Autophagy acts both as a tumor suppressor, by removing redundant cellular material, and a tumor-promoting factor, by providing access to components necessary for growth, metabolism, and proliferation. To date, little is known about the expression of genes that play a basal role in the autophagy in CRC. In this study, we aimed to compare the expression levels of 46 genes involved in the autophagy pathway between tumor-adjacent and tumor tissue, employing large RNA sequencing (RNA-seq) and microarray datasets. Additionally, we verified our results using data on 38 CRC cell lines. Gene set enrichment analysis revealed a significant deregulation of autophagy-related gene sets in CRC. The unsupervised clustering of tumors using the mRNA levels of autophagy-related genes revealed the existence of two major clusters: microsatellite instability (MSI)-enriched and -depleted. In cluster 1 (MSI-depleted), ATG9B and LAMP1 genes were the most prominently expressed, whereas cluster 2 (MSI-enriched) was characterized by DRAM1 upregulation. CRC cell lines were also clustered according to MSI-enriched/-depleted subgroups. The moderate deregulation of autophagy-related genes in cancer tissue, as compared to adjacent tissue, suggests a prominent field cancerization or early disruption of autophagy. Genes differentiating these clusters are promising candidates for CRC targeting therapy worthy of further investigation.

Development of Prognostic Indicator Based on Autophagy-Related lncRNA Analysis in Colon Adenocarcinoma

There were no systematic researches about autophagy-related long noncoding RNA (lncRNA) signatures to predict the survival of patients with colon adenocarcinoma. It was necessary to set up corresponding autophagy-related lncRNA signatures. The expression profiles of lncRNAs which contained 480 colon adenocarcinoma samples were obtained from The Cancer Genome Atlas (TCGA) database. The coexpression network of lncRNAs and autophagy-related genes was utilized to select autophagy-related lncRNAs. The lncRNAs were further screened using univariate Cox regression. In addition, Lasso regression and multivariate Cox regression were used to develop an autophagy-related lncRNA signature. A risk score based on the signature was established, and Cox regression was used to test whether it was an independent prognostic factor. The functional enrichment of autophagy-related lncRNAs was visualized using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Ten prognostic autophagy-related lncRNAs (AC027307.2, AC068580.3, AL138756.1, CD27-AS1, EIF3J-DT, LINC01011, LINC01063, LINC02381, AC073896.3, and SNHG16) were identified to be significantly different, which made up an autophagy-related lncRNA signature. The signature divided patients with colon adenocarcinoma into the low-risk group and the high-risk group. A risk score based on the signature was a significantly independent factor for the patients with colon adenocarcinoma (HR = 1.088, 95%CI = 1.057 - 1.120; P < 0.001). Additionally, the ten lncRNAs were significantly enriched in autophagy process, metabolism, and tumor classical pathways. In conclusion, the ten autophagy-related lncRNAs and their signature might be molecular biomarkers and therapeutic targets for the patients with colon adenocarcinoma.

Construction prognosis model based on autophagy-related gene signatures in hepatocellular carcinoma

Aim: To develop robust and accurate prognostic biomarkers to help clinicians optimize therapeutic strategies. Materials & methods: Differentially prognosis-related autophagy genes were identified by bioinformatics analysis method. Results: Seven prognosis-related autophagy genes were more significantly related to the prognosis of hepatocellular carcinoma (HCC). Functional enrichment analysis demonstrated that these genes were mainly enriched in the autophagy pathway. BIRC5, HSPB8 and TMEM74 exhibited significant prognostic value for HCC. Besides, the risk score and BIRC5 have significant significance with clinicopathological significance of HCC. Conclusion: The research has identified a number of prognosis-related autophagy genes that associated with the survival and clinical stage of HCC. In addition, the prognostic model can be used to calculate the patient's risk score and these prognosis-related autophagy genes might serve as therapeutic targets.

The Construction and Comprehensive Prognostic Analysis of the LncRNA-Associated Competitive Endogenous RNAs Network in Colorectal Cancer

Competing endogenous RNAs (ceRNAs) are a newly proposed RNA interaction mechanism that has been associated with the tumorigenesis, metastasis, diagnosis, and predicting survival of various cancers. In this study, we constructed a ceRNA network in colorectal cancer (CRC). Then, we sought to develop and validate a composite clinicopathologic–genomic nomogram using The Cancer Genome Atlas (TCGA) database. To construct the ceRNA network in CRC, we analyzed the mRNAseq, miRNAseq data, and clinical information from TCGA database. LncRNA, miRNA, and mRNA signatures were identified to construct risk score as independent indicators of the prognostic value in CRC patients. A composite clinicopathologic–genomic nomogram was developed to predict the overall survival (OS). One hundred sixty-one CRC-specific lncRNAs, 97 miRNAs, and 161 mRNAs were identified to construct the ceRNA network. Multivariate Cox proportional hazards regression analysis indicated that nine-lncRNA signatures, eight-miRNA signatures, and five-mRNA signatures showed a significant prognostic value for CRC. Furthermore, a clinicopathologic–genomic nomogram was constructed in the primary cohort, which performed well in both the primary and validation sets. This study presents a nomogram that incorporates the CRC-specific ceRNA expression profile, clinical features, and pathological factors, which demonstrate its excellent differentiation and risk stratification in predicting OS in CRC patients.