An in-silico study investigating the differential gene expression of Rho family GTPases and their putative association with HNSCC

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6402 ◽  
Author(s):  
Yashoda Ghanekar ◽  
Subhashini Sadasivam

Background Sequencing studies across multiple cancers continue to reveal mutations and genes involved in the pathobiology of these cancers. Exome sequencing of oral cancers, a subset of Head and Neck Squamous cell Carcinomas (HNSCs) common among tobacco-chewing populations, revealed that ∼34% of the affected patients harbor mutations in the CASP8 gene. Uterine Corpus Endometrial Carcinoma (UCEC) is another cancer where ∼10% cases harbor CASP8 mutations. Caspase-8, the protease encoded by CASP8 gene, plays a dual role in programmed cell death, which in turn has an important role in tumor cell death and drug resistance. CASP8 is a protease required for the extrinsic pathway of apoptosis and is also a negative regulator of necroptosis. Using multiple tools such as differential gene expression, gene set enrichment, gene ontology, in silico immune cell estimates, and survival analyses to mine data in The Cancer Genome Atlas, we compared the molecular features and survival of these carcinomas with and without CASP8 mutations. Results Differential gene expression followed by gene set enrichment analysis showed that HNSCs with CASP8 mutations displayed a prominent signature of genes involved in immune response and inflammation. Analysis of abundance estimates of immune cells in these tumors further revealed that mutant-CASP8 HNSCs were rich in immune cell infiltrates. However, in contrast to Human Papilloma Virus-positive HNSCs that also exhibit high immune cell infiltration, which in turn is correlated with better overall survival, HNSC patients with mutant-CASP8 tumors did not display any survival advantage. Similar analyses of UCECs revealed that while UCECs with CASP8 mutations also displayed an immune signature, they had better overall survival, in contrast to the HNSC scenario. There was also a significant up-regulation of neutrophils (p-value = 0.0001638) as well as high levels of IL33 mRNA (p-value = 7.63747E−08) in mutant-CASP8 HNSCs, which were not observed in mutant-CASP8 UCECs. Conclusions These results suggested that carcinomas with mutant CASP8 have broadly similar immune signatures albeit with different effects on survival. We hypothesize that subtle tissue-dependent differences could influence survival by modifying the micro-environment of mutant-CASP8 carcinomas. High neutrophil numbers, a well-known negative prognosticator in HNSCs, and/or high IL33 levels may be some of the factors affecting survival of mutant-CASP8 cases.


Author(s):  
Raffaele Altara ◽  
Fouad A. Zouein ◽  
Rita Dias Brandão ◽  
Saeed N. Bajestani ◽  
Alessandro Cataliotti ◽  
...  

2021 ◽  
Vol 13 (2) ◽  
pp. 585-592
Author(s):  
D. M. Agase ◽  
K. K. Gupta ◽  
A. Wasnik ◽  
M. S. Markam ◽  
S. B. Zade ◽  
...  

A biomarker can be measured, used to diagnose or classify disease, and measure progress as well as the therapeutic response of the disease. Early diagnosis and selection of appropriate treatment can be critical for the successful treatment of diseases. Identification and characterization of potent diagnostic biomarkers, and therapeutic targets rely heavily on traditional in vitro screens which require extensive resources and time. Integration of in silico screens prior to experimental validation can improve the efficiency and potency of biomarkers as well as reduce the cost and time of biomarker discovery. Considering the need, present work was undertaken to identify biomarkers for different classes of leukemia. Differential Gene Expression (DGE) analysis and co-regulated expression analysis were used for in silico identification and characterise a potent biomarker for leukemia. On the basis of in silico screening, the present study proposed seven protein-coding (CD38, TSC22D3, TNFRSF25, AGL, LARGE1, ARHGAP32, and PARM1) genes for the diagnosis of leukemia. The study also proposed a novel three-step lineage-specific model for the diagnosis of leukemia. In the three-step diagnosis model, the first group of biomarkers with an association of clinical and hematological parameters diagnose leukemia. The second group of biomarkers diagnoses acute and chronic form of leukemia. The third group of biomarkers identifies whether it belongs to myeloid lineage or lymphoid lineage.


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