The malignant transformation of a cell produces the accumulation of several cellular adaptions. These changes determine variations in biological processes that are necessary for a cancerous cell to survive during stressful conditions. Autophagy is the main nutrient recycling and metabolic adaptor mechanism in eukaryotic cells, represents a continuous source of energy and biomolecules, and is fundamental to preserve the correct cellular homeostasis during unfavorable conditions. In recent decades, several findings demonstrate a close relationship between autophagy, malignant transformation, and cancer progression. The evidence suggests that autophagy in the cancer context has a bipolar role (it may act as a tumor suppressor and as a mechanism of cell survival for established tumors) and demonstrates that the targeting of autophagy may represent novel therapeutic opportunities. Accordingly, the modulation of autophagy has important clinical benefits in patients affected by diverse cancer types. Currently, about 30 clinical trials are actively investigating the efficacy of autophagy modulators to enhance the efficacy of cytotoxic chemotherapy treatments. A deeper understanding of the molecular pathways regulating autophagy in the cancer context will provide new ways to target autophagy for improving the therapeutic benefits. Herein, we describe how autophagy participates during malignant transformation and cancer progression, and we report the ultimate efforts to translate this knowledge into specific therapeutic approaches to treat and cure human cancers.

Understanding the Role of Autophagy in Cancer Formation and Progression Is a Real Opportunity to Treat and Cure Human Cancers

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

Colorectal cancer (CRC) is one of the most common human malignancies worldwide. Along with apoptosis and inflammation, autophagy is one of three important mechanisms in CRC. The presence of autophagy/mitophagy in most normal mature intestinal epithelial cells has been confirmed, where it has mainly protective functions against reactive oxygen species (ROS)-induced DNA and protein damage. Autophagy regulates cell proliferation, metabolism, differentiation, secretion of mucins and/or anti-microbial peptides. Abnormal autophagy in intestinal epithelial cells leads to dysbiosis, a decline in local immunity and a decrease in cell secretory function. The insulin-like growth factor (IGF) signaling pathway plays an important role in colorectal carcinogenesis. This is evidenced by the biological activities of IGFs (IGF-1 and IGF-2), IGF-1 receptor type 1 (IGF-1R) and IGF-binding proteins (IGF BPs), which have been reported to regulate cell survival, proliferation, differentiation and apoptosis. Defects in autophagy are found in patients with metabolic syndrome (MetS), inflammatory bowel diseases (IBD) and CRC. In neoplastic cells, the IGF system modulates the autophagy process bidirectionally. In the current era of improving CRC therapies, it seems important to investigate the exact mechanisms not only of apoptosis, but also of autophagy in different populations of tumor microenvironment (TME) cells. The role of the IGF system in autophagy in normal as well as transformed colorectal cells still seems poorly understood. Hence, the aim of the review was to summarize the latest knowledge on the role of the IGF system in the molecular mechanisms of autophagy in the normal colon mucosa and in CRC, taking into account the cellular heterogeneity of the colonic and rectal epithelium.

/pdf/autophagy-and-the-insulin-like-growth-factor-igf-system-in-1zj345wl.pdf

Autophagy and the Insulin-like Growth Factor (IGF) System in Colonic Cells: Implications for Colorectal Neoplasia

Background Because of poor compliance or low sensitivity, existing diagnostic approaches are unable to provide an efficient diagnosis of patients with gastric and colorectal cancer. Here, we developed the ColoCaller test, which simultaneously detects the methylation status of the SDC2, TFPI2, WIF1, and NDRG4 genes in stool DNA, to optimize the screening of gastric and colorectal cancer in high-risk populations. Methods A total of 217 stool samples from patients with gastrointestinal cancer and from patients with negative endoscopy were prospectively collected, complete with preoperative and postoperative clinical data from patients. The methylation of these samples was detected using ColoCaller, which was designed by selecting CpGs with a two-step screening strategy, and was interpreted using a prediction model built using libSVM to evaluate its clinical value for gastric and colorectal cancer screening. Results Compared to pathological diagnosis, the sensitivity and specificity of the ColoCaller test in 217 stool DNA samples were 95.56% and 91.86%, respectively, for colorectal cancer, and 67.5% and 97.81%, respectively, for gastric cancer. The detection limit was as low as 1% in 8 ng of DNA. Conclusion In this study, we developed and established a new test, ColoCaller, which can be used as a screening tool or as an auxiliary diagnostic approach in high-risk populations with gastric and colorectal cancer to promote timely diagnosis and treatment.

/pdf/a-novel-stool-methylation-test-for-the-non-invasive-q463kyfm.pdf

A Novel Stool Methylation Test for the Non-Invasive Screening of Gastric and Colorectal Cancer

LncRNA HOTAIR as a ceRNA is related to breast cancer risk and prognosis

Aim To develop an autophagy-gene-based signature that could help to anticipate the therapeutic effects of Colorectal Cancer (CRC). Methods We downloaded the gene expression profiles of CRC samples from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets. Genes with significant prognostic value in CRC were screened through univariate Cox regression analysis, while the LASSO Cox regression method was applied to screen optimal genes to construct the autophagy-related prognostic signature. Results 11 autophagy genes were identified and selected for the establishment of prognosis prediction model for CRC patients. The CRC patients were classified into the low- and high-risk groups according to the optimal cutoff value. The time-dependent ROC curves indicated the good performance of this model in prognosis prediction, with AUC values of 0.66, 0.66, and 0.67 at 1, 3 and 5 years for TCGA samples, as well as AUC values of 0.63, 0.65 and 0.64 for GEO samples, respectively. The multivariate Cox regression analysis results confirmed risk score as the independent marker for prognosis prediction in CRC. Besides, the constructed nomogram also had high predictive value. The results analysis on the tumor infiltrating immune cells (TIICs) relative ratios and mRNA levels of key immune checkpoint receptors indicated the signature was closely related to immune microenvironment of CRC in the context of TIICs and immune checkpoint receptors' mRNA level. The proportion of MSI-L + MSI-H in the high-risk group was higher than that in the low-risk group. Moreover, the tumor purity was evaluated by estimate function package suggested that lower tumor purity in CRC might lead to a poorer prognosis. Conclusion The autophagy-related features obtained in this study were able to divide the CRC patients into low- and high-risk groups, which should be contribute to the decision-making of CRC treatment.

/pdf/a-signature-based-on-11-autophagy-genes-for-prognosis-hnqbyy9qcb.pdf

A signature based on 11 autophagy genes for prognosis prediction of colorectal cancer.

Background: Colorectal cancer (CRC) is a common human malignancy worldwide. The prognosis of patients is largely frustrated by delayed diagnosis or misdiagnosis. DNA methylation alterations have been previously proved to be involved in CRC carcinogenesis. Methods: In this study, we proposed to identify CRC-related diagnostic biomarkers by analyzing DNA methylation and gene expression profiles. TCGA-COAD datasets downloaded from the Cancer Genome Atlas (TCGA) were used as the training set to screen differential expression genes (DEGs) and methylation CpG sites (dmCpGs) in CRC samples. A logistic regression model was constructed based on hyper-methylated CpG sites which were located in downregulated genes for CRC diagnosis. Another two independent datasets from the Gene Expression Omnibus (GEO) were used as a testing set to evaluate the performance of the model in CRC diagnosis. Results: We found that CpG island methylator phenotype (CIMP) was a potential signature of poor prognosis by dividing CRC samples into CIMP and noCIMP groups based on a set of CpG sites with methylation standard deviation (sd) > 0.2 among CRC samples and low methylation levels (mean β < 0.05) in adjacent samples. Hyper-methylated CpGs tended to be more closed to CpG island (CGI) and transcription start site (TSS) relative to hypo-methylated CpGs (p-value < 0.05, Fisher exact test). A logistic regression model was finally constructed based on two hyper-methylated CpGs, which had an area under receiver operating characteristic curve of 0.98 in the training set, and 0.85 and 0.95 in the two independent testing sets. Conclusions: In conclusion, our study identified promising DNA methylation biomarkers for CRC diagnosis.

/pdf/integrative-analysis-of-dna-methylation-and-gene-expression-585obmbxtz.pdf

Integrative Analysis of DNA Methylation and Gene Expression Profiles Identifies Colorectal Cancer-Related Diagnostic Biomarkers

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A signature based on 11 autophagy genes for prognosis prediction of colorectal cancer.

Integrative Analysis of DNA Methylation and Gene Expression Profiles Identifies Colorectal Cancer-Related Diagnostic Biomarkers