Identification of Autophagy-Associated Biomarkers and Corresponding Regulatory Factors in the Progression of Colorectal Cancer

Measurement of Autophagy Activity Reveals Time-Dependent, Bacteria-Specific Turnover during Mycobacterium tuberculosis Infection

The intracellular pathogen, Mycobacterium tuberculosis (M. tb) uses various mechanisms to evade its killing. One of such is phagosomal damage and cytosolic translocation which is then targeted by the host's bactericidal autophagy pathway. It is suggested that cytosolic translocation of M. tb is time-dependent, occurring at later time points of 48 to 72 h post-infection. It is, however, not known whether increased autophagic targeting correlates with these time points of infection. We investigated the time-dependent profile of autophagy activity through the course of M. tb infection in mammalian macrophages. Autophagy activity was inferred by the turnover measurement of autophagy markers and M. tb bacilli in THP-1 and RAW 264.7 macrophages. Over a period of 4 to 72 h, we observed highest autophagy turnover at 48 h of infection in M. tb-containing cells. This was evident by the highest turnover levels of p62 and intracellular M. tb. This supports observations of phagosomal damage mostly occurring at this time point and reveal the correlation of increased autophagy activity. The findings support the preservation of autophagy activity despite M. tb infection while also highlighting time-dependent differences in M. tb-infected macrophages. Future studies may explore time-dependent exogenous autophagy targeting towards host-directed anti-tuberculosis therapy.

DDX17 promotes the growth and metastasis of lung adenocarcinoma

DEAD box RNA helicase 17 (DDX17) has been shown to be an RNA binding protein involved in RNA metabolism and associated with cancer progression. However, the biological role of DDX17 in the pathogenesis of lung adenocarcinoma (LUAD) has not been well characterized. Here, we demonstrated that DDX17 promoted the proliferation, migration and invasion of H1299 and A549 lung adenocarcinoma cells. Analyses of public datasets showed that DDX17 is upregulated in LUAD specimens. Our tumor xenograft models confirmed the in vivo promoting role of DDX17 in the growth and metastasis of LUAD. Mechanistic analyses further revealed that DDX17 protein interacts with the mRNA of MYL9 and MAGEA6 and upregulates their levels. MYL9 could mediate the function of DDX17 to regulate the actin cytoskeleton rearrangement and cell adhesion, particularly by modulating the stress fiber and focal adhesion formation, whereas DDX17 might inhibit the autophagy process through MAGEA6/AMPKα1 axis in LUAD cells. Collectively, our study revealed the oncogenic role and pathways of DDX17 in LUAD.

Identification and validation of P4HB as a novel autophagy-related biomarker in diabetic nephropathy

Diabetic nephropathy (DN), a frequent microvascular complication of diabetes, has been recognized as a primary cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD). Previous studies found that autophagy of renal tubular epithelial cells plays an important role in DN pathogenesis. Our research aimed to investigate the differentially expressed autophagy-related genes (DEARGs) between DN and healthy renal tubule samples and identify a novel autophagy-related biomarker associated with tubulointerstitial injury in DN. In this study, gene expression profiles of renal tubules from 10 DN patients and 24 healthy controls in the GSE30122 dataset were analyzed, and 43 DEARGs were identified by bioinformatics analysis. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and correlation analysis were performed on DEARGs, and the hub gene prolyl 4-hydroxylase subunit beta (P4HB) was screened by protein–protein interaction and verified by utilizing other datasets and stimulating HK-2 cells under high glucose concentration. We found that the expression of P4HB in renal tubules was correlated with renal function. In summary, our research provided novel insights for comprehension of DN molecular mechanisms and identified P4HB as a novel autophagy-related biomarker of DN.

Prediction of Prognostic Biomarkers and Construction of an Autophagy Prognostic Model for Colorectal Cancer Using Bioinformatics

Objective:

The incidence of colorectal cancer is increasing every year, and autophagy may be related closely to the pathogenesis of colorectal cancer. Autophagy is a natural catabolic mechanism that allows the degradation of cellular components in eukaryotic cells. However, autophagy plays a dual role in tumorigenesis. It not only promotes normal cell survival and tumor growth but also induces cell death and suppresses tumors survival. In addition, the pathogenesis of various conditions, including inflammation, neurodegenerative diseases, or tumors, is associated with abnormal autophagy. The present work aimed to examine the significance of autophagy-related genes (ARGs) in prognosis prediction, to construct an autophagy prognostic model, and to identify independent prognostic factors for colorectal cancer (CRC).

Methods:

This study discovered a total of 36 ARGs in CRC cases using The Cancer Genome Atlas (TCGA) and Human Autophagy-dedicated (HADd) databases along with functional enrichment analysis. Then, an autophagy prognostic model was constructed using univariate Cox regression analysis, and the key prognostic genes were screened. Finally, independent prognostic markers were determined through independent prognostic analysis and clinical correlation analysis of key genes.

Results:

Of the 36 differentially expressed ARGs, 13 were related to prognosis, as determined by univariate Cox regression analysis. A total of 6 key genes were obtained by a multivariate Cox regression analysis. Independent prognostic values were shown by 3 genes, namely, microtubule-associated protein 1 light chain 3 (MAP1LC3C), small GTPase superfamily and Rab family (RAB7A), and WD-repeat domain phosphoinositide-interacting protein 2 (WIPI2) by independent prognostic analysis and clinical correlation.

Conclusions:

In this study, molecular bioinformatics technology was employed to determine and construct a prognostic model of autophagy for colon cancer patients, which revealed 3 autophagy-related features, namely, MAP1LC3C, WIPI2, and RAB7A.

Establishment and Validation of a Prognostic Risk Model for Autophagy-Related Genes in Clear Cell Renal Cell Carcinoma

Background

Autophagy plays an essential role in tumorigenesis. At present, due to the unclear role of autophagy in renal clear cell carcinoma, we studied the potential value of autophagy-related genes (ARGs) in renal clear cell carcinoma (ccRCC).

Methods

We obtained all ccRCC data from The Cancer Genome Atlas (TCGA). We extracted the expression data of ARGs for difference analysis and carried out biological function analysis on the different results. The autophagy risk model was constructed. The 5-year survival rate was assessed using the model, and the predictive power of the model was evaluated from multiple perspectives. Cox regression analysis was use to assess whether the model could be an independent prognostic factor. Finally, the correlation between the model and clinical indicators is analyzed.

Results

The patients were divided into the high-risk group and low-risk group according to the median of autophagy risk score, and the results showed that the prognosis of the low-risk group was better than that of a high-risk group. The validation results of external data sets show that our model has good predictive value for ccRCC patients. The model can be an independent prognostic factor. Finally, the results show that our model has a stable predictive ability.

Conclusion

The autophagy gene model we constructed can be used as an excellent prognostic indicator for ccRCC. Our study provides the possibility of individualized and precise treatment for ccRCC patients.