Functional Impact of Autophagy-Related Genes on the Homeostasis and Dynamics of Pancreatic Cancer Cell Lines

Galectin-3 Expression in Pancreatic Cell Lines Under Distinct Autophagy-Inducing Stimulus

Hypoxia and nutrient deprivation are responsible for inducing malignant behavior in neoplastic cells. In these conditions, metabolic stress leads the cells to enhance their autophagic flux and to activate key molecules for homeostasis maintenance. Galectin-3 (Gal-3) is upregulated in pancreatic cancer and it is activated under the hypoxic atmosphere. We aimed to analyze the most effective autophagic-inducing conditions in pancreatic ductal adenocarcinoma cells and the effect exerted under these conditions in association with hypoxia on the Gal-3 expression. Gal-3 and the microtubule-associated protein light chain 3 beta (LC3) were accessed through western blot and immunofluorescence. Degradative vacuole quantification was analyzed by transmission electronic microscopy, and inhibition of Gal-3 was performed using siRNA. According to the analyses, the most effective conditions in the inducement of autophagy for PANC-1 and MIA PaCa-2 cells were nutritional deprivation and complete amino acid/glucose deprivation, respectively. PANC-1 cells presented higher Gal-3 when they were submitted to 24 h of nutritional deprivation alone and simultaneously nutritional and oxygen deprivation. Inhibition of Gal-3 causes a decrease of LC3 levels in all experimental conditions. These results confirm that Gal-3 is modulated by microenvironment factors and the possibility of Gal-3 participating in an adaptive response from PDAC cells to extreme conditions.

Tracing the footsteps of autophagy in computational biology

Autophagy plays a crucial role in maintaining cellular homeostasis through the degradation of unwanted materials like damaged mitochondria and misfolded proteins. However, the contribution of autophagy toward a healthy cell environment is not only limited to the cleaning process. It also assists in protein synthesis when the system lacks the amino acids’ inflow from the extracellular environment due to diet consumptions. Reduction in the autophagy process is associated with diseases like cancer, diabetes, non-alcoholic steatohepatitis, etc., while uncontrolled autophagy may facilitate cell death. We need a better understanding of the autophagy processes and their regulatory mechanisms at various levels (molecules, cells, tissues). This demands a thorough understanding of the system with the help of mathematical and computational tools. The present review illuminates how systems biology approaches are being used for the study of the autophagy process. A comprehensive insight is provided on the application of computational methods involving mathematical modeling and network analysis in the autophagy process. Various mathematical models based on the system of differential equations for studying autophagy are covered here. We have also highlighted the significance of network analysis and machine learning in capturing the core regulatory machinery governing the autophagy process. We explored the available autophagic databases and related resources along with their attributes that are useful in investigating autophagy through computational methods. We conclude the article addressing the potential future perspective in this area, which might provide a more in-depth insight into the dynamics of autophagy.

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.

Circadian Genes as Therapeutic Targets in Pancreatic Cancer

Pancreatic cancer is one of the most lethal cancers worldwide due to its symptoms, early metastasis, and chemoresistance. Thus, the mechanisms contributing to pancreatic cancer progression require further exploration. Circadian rhythms are the daily oscillations of multiple biological processes regulated by an endogenous clock. Several evidences suggest that the circadian clock may play an important role in the cell cycle, cell proliferation and apoptosis. In addition, timing of chemotherapy or radiation treatment can influence the efficacy and toxicity treatment. Here, we revisit the studies on circadian clock as an emerging target for therapy in pancreatic cancer. We highlight those potential circadian genes regulators that are commonly affected in pancreatic cancer according to most recent reports.

An NMF-L2,1-Norm Constraint Method for Characteristic Gene Selection

Recent research has demonstrated that characteristic gene selection based on gene expression data remains faced with considerable challenges. This is primarily because gene expression data are typically high dimensional, negative, non-sparse and noisy. However, existing methods for data analysis are able to cope with only some of these challenges. In this paper, we address all of these challenges with a unified method: nonnegative matrix factorization via the L_2,1-norm (NMF-L_2,1). While L_2,1-norm minimization is applied to both the error function and the regularization term, our method is robust to outliers and noise in the data and generates sparse results. The application of our method to plant and tumor gene expression data demonstrates that NMF-L_2,1 can extract more characteristic genes than other existing state-of-the-art methods.