Bioinformatic Analysis and Integration of Transcriptome and Proteome Results Identify Key Coding and Noncoding Genes Predicting Malignancy in Intraductal Papillary Mucinous Neoplasms of the Pancreas.

BACKGROUND: Intraductal papillary mucinous neoplasms (IPMNs) are precursor lesions of pancreatic ductal adenocarcinoma (PDAC). IPMNs are generally associated with high risk of developing malignancy and therefore need to be diagnosed and assessed accurately, once detected. Existing diagnostic methods are inadequate, and identification of efficient biomarker capable of detecting high-risk IPMNs is necessitated. Moreover, the mechanism of development of malignancy in IPMNs is also elusive. METHODS: Gene expression meta-analysis conducted using 12 low-risk IPMN and 23 high-risk IPMN tissue samples. We have also listed all the altered miRNAs and long noncoding RNAs (lncRNAs), identified their target genes, and performed pathway analysis. We further enlisted cyst fluid proteins detected to be altered in high-risk or malignant IPMNs and compared them with fraction of differentially expressed genes secreted into cyst fluid. RESULTS: Our meta-analysis identified 270 upregulated and 161 downregulated genes characteristically altered in high-risk IPMNs. We further identified 61 miRNAs and 14 lncRNAs and their target genes and key pathways contributing towards understanding of the gene regulation during the progression of the disease. Most importantly, we have detected 12 genes altered significantly both in cystic lesions and cyst fluid. CONCLUSION: Our study reports, for the first time, a meta-analysis identifying key changes in gene expression between low-risk and high-risk IPMNs and also explains the regulatory aspect through construction of a miRNA-lncRNA-mRNA interaction network. The 12-gene-signature could function as potential biomarker in cyst fluid for detection of IPMN with a high risk of developing malignancy.

Noncoding RNAs as key modulators of autophagy in pancreatic cancer (Review).

Inability of early detection as well as lack of proper therapeutic intervention, both add to the complexity of pancreatic cancer. Understanding of the basic cellular processes is of the utmost importance and autophagy is one of these processes. Considering the importance of this process in normal cellular functions as well as in pathological states, elaboration of the updated information on the mechanism of autophagy was initially carried out. Autophagy is a process for degradation of damaged cellular organelles, abnormal proteins and even nutrients which happen via formation of autophagosomes. Incidentally, autophagy has been shown to play both oncogenic and tumour­suppressive functions in cancer and has also been shown to modulate stemness of cancer cells, recurrence and resistance to chemotherapeutic agents. The contribution of autophagy genes and pathways in pancreatic tumorigenesis was also evaluated. Regulation is the key step in any such cellular phenomenon and noncoding RNA­mediated regulation is an emerging field. While miRNAs participate mainly in post­transcriptional regulation, long noncoding RNAs and circular RNAs have more diverse regulatory functions. Noncoding RNAs are also shown to modulate both the tumour­promoting and tumour­suppressing functions of autophagy in pancreatic cancer. The implication of noncoding RNA­mediated regulation with respect to radio­resistance and chemo­resistance of pancreatic cancer cells was also assessed. To the best of our knowledge, this is the first ever attempt trying to decipher the cross­talk between autophagy­noncoding RNAs and genes involved in the development and progression of pancreatic cancer.

Transcriptome analysis identifies putative multi-gene signature distinguishing benign and malignant pancreatic head mass.

BACKGROUND: Most often, the patients with pancreatic diseases are presented with a mass in pancreatic head region and existing methods of diagnosis fail to confirm whether the head mass is malignant or benign. As subsequent management of the disease hugely depends on the correct diagnosis, we wanted to explore possible biomarkers which could distinguish benign and malignant pancreatic head masses. METHODS: In order to address that gap, we performed a case-control study to identify genome-wide differentially expressed coding and noncoding genes between pancreatic tissues collected from benign and malignant head masses. These genes were next shortlisted using stringent criteria followed by selection of top malignancy specific genes. They subsequently got validated by quantitative RT-PCR and also in other patient cohorts. Survival analysis and ROC analysis were also performed. RESULTS: We identified 55 coding and 13 noncoding genes specific for malignant pancreatic head masses. Further shortlisting and validation, however, resulted in 5 coding genes as part of malignancy specific multi-gene signature, which was validated in three independent patient cohorts of 145 normal and 153 PDAC patients. We also found that overexpression of these genes resulted in survival disadvantage in the patients and ROC analysis identified that combination of 5 coding genes had the AUROC of 0.94, making them potential biomarker. CONCLUSIONS: Our study identified a multi-gene signature comprising of 5 coding genes (CDCA7, DLGAP5, FOXM1, TPX2 and OSBPL3) to distinguish malignant head masses from benign ones.

Novel coronavirus SARS-CoV-2 (Covid-19) dynamics inside the human body.

A knowledge-based cybernetic framework model representing the dynamics of SARS-CoV-2 inside the human body has been studied analytically and in silico to explore the pathophysiologic regulations. The following modeling methodology was developed as a platform to introduce a predictive tool supporting a therapeutic approach to Covid-19 disease. A time-dependent nonlinear system of ordinary differential equations model was constructed involving type-I cells, type-II cells, SARS-CoV-2 virus, inflammatory mediators, interleukins along with host pulmonary gas exchange rate, thermostat control, and mean pressure difference. This formalism introduced about 17 unknown parameters. Estimating these unknown parameters requires a mathematical association with the in vivo sparse data and the dynamic sensitivities of the model. The cybernetic model can simulate a dynamic response to the reduced pulmonary alveolar gas exchange rate, thermostat control, and mean pressure difference under a very critical condition based on equilibrium (steady state) values of the inflammatory mediators and system parameters. In silico analysis of the current cybernetical approach with system dynamical modeling can provide an intellectual framework to help experimentalists identify more active therapeutic approaches.