Papillary thyroid cancer (PTC) is the most common form of thyroid cancer, comprising 80% of all thyroid malignancies. The phosphoinositide-3-kinase-protein kinase B/Akt (PI3K-PKB/Akt) pathway is a main pathway in control of cell growth. Activated mTOR and Akt are involved in the development and progression of the PTC. This study aimed to evaluate the effects of MTOR (rs2536 and rs2295080) and AKT1 (rs2494732, rs1130214, and rs1130233) polymorphisms on PTC susceptibility. This study was conducted on 131 PTC patients and 144 healthy subjects. Genotype analysis was done using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) methods. Our results showed no statistically significant association between MTOR rs2536, AKT1 rs2494732, and rs1130214 polymorphisms and PTC development. However, MTOR rs2295080 polymorphism was found to be associated with a decreased risk of PTC in dominant and allelic models. The TT genotype of AKT1 rs1130233 was significantly higher in the PTC group in comparison to the controls, with a 3.5-fold increased risk for developing PTC. Furthermore, the allelic distribution also showed the T allele of rs1130233 as a risk factor for PTC occurrence. In conclusion, our results suggest the MTOR rs2295080 and AkT1 rs1130233 as the protective and risk factors for PTC development, respectively.

Effects of the MTOR and AKT1 genes polymorphisms on papillary thyroid cancer development.

Genome-wide association studies (GWAS) provide clear insight into understanding genetic variations and environmental influences responsible for various human diseases. Cancer identification through genetic interactions (epistasis) is one of the significant ongoing researches in GWAS. The growth of the cancer cell emerges from multi-locus as well as complex genetic interaction. It is impractical for the physician to detect cancer via manual examination of SNPs interaction. Due to its importance, several computational approaches have been modeled to infer epistasis effects. This article includes a comprehensive and multifaceted review of all relevant genetic studies published between 2001 and 2020. In this contemporary review, various computational methods are as follows: multifactor dimensionality reduction-based approaches, statistical strategies, machine learning, and optimization-based techniques are carefully reviewed and presented with their evaluation results. Moreover, these computational approaches' strengths and limitations are described. The issues behind the computational methods for identifying the cancer disease through genetic interactions and the various evaluation parameters used by researchers have been analyzed. This review is highly beneficial for researchers and medical professionals to learn techniques adapted to discover the epistasis and aids to design novel automatic epistasis detection systems with strong robustness and maximum efficiency to address the different research problems in finding practical solutions effectively.

Genetic interactions effects for cancer disease identification using computational models: a review

The mammalian target of rapamycin complex 1 (mTORC1) signaling pathway plays a vital role in cancer development and progression. This study aimed to investigate the relationship between genotype variants in mTORC1 pathway and the risk of brain metastasis (BM) in patients with non-small cell lung cancer (NSCLC). We extracted genomic DNA from blood samples of 501 NSCLC patients and genotyped eight single-nucleotide polymorphisms (SNPs) in three core genes [mammalian target of rapamycin (mTOR), mammalian lethal with sec-13 protein 8 (mLST8) and regulatory-associated protein of mTOR (RPTOR)] of the mTORC1 pathway. The associations between these SNPs and the risk of BM development were assessed. The AG/GG genotype of mLST8:rs26865 and TC/CC genotype of mLST8:rs3160 were associated with an increased risk of BM [hazard ratios (HR) 2.938, 95% confidence interval (CI) 1.664–5.189, p < 0.001 and HR = 2.490, 95% CI = 1.543–4.016, p < 0.001, respectively]. These risk polymorphisms had a cumulative effect on BM risk, with two risk genotypes exhibiting the highest increased risk (p < 0.001). Furthermore, these risk SNPs were associated with the lymph node metastasis (N2/3), body mass index (BMI) (≥ 25 kg/m2), high level of squamous cell carcinoma (SCC) antigen and Ki-67 proliferation index. Moreover, patients with AG/GG genotype of mLST8:rs26865 had significantly lower median overall survival than those with AA genotype (12.1 months versus 21.6 months, p = 0.04). Our results indicate that polymorphisms in mTORC1 pathway were significantly associated with increased risk of BM and may be valuable biomarkers to identify NSCLC patients with a high risk of BM.

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Effects of single-nucleotide polymorphisms in the mTORC1 pathway on the risk of brain metastasis in patients with non-small cell lung cancer.

The mammalian/mechanistic target of rapamycin (mTOR) protein is an important growth regulator and has been linked with multiple diseases including cancer and diabetes. Non-synonymous mutations of this gene have already been found in patients with renal clear cell carcinoma, melanoma, and acute lymphoid leukemia among many others. Such mutations can potentially affect a protein’s structure and hence its functions. In this study, therefore, the most deleterious SNPs of mTOR protein have been determined to identify potential biomarkers for various disease treatments. The aim is to generate a structured dataset of the mTOR gene’s SNPs that may prove to be an asset for the identification and treatment of multiple diseases associated with the target gene. Both sequence and structure-based approaches were adopted and a wide variety of bioinformatics tools were applied to analyze the SNPs of mTOR protein. In total 11 nsSNPs have been filtered out of 2178 nsSNPs along with two non-coding variations. All of the nsSNPs were found to destabilize the protein structure and disrupt its function. While R619C, A1513D, and T1977R mutations were shown to alter C alpha distances and bond angles of the mTOR protein, L509Q, R619C and N2043S were predicted to disrupt the mTOR protein’s interaction with NBS1 protein and FKBP1A/rapamycin complex. In addition, one of the non-coding SNPs was shown to alter miRNA binding sites. Characterizing nsSNPs and non-coding SNPs and their harmful effects on a protein’s structure and functions will enable researchers to understand the critical impact of mutations on the molecular mechanisms of various diseases. This will ultimately lead to the identification of potential targets for disease diagnosis and therapeutic interventions.

In silico comprehensive analysis of coding and non-coding SNPs in human mTOR protein

mTOR pathway candidate genes and obesity interaction on breast cancer risk in black women from the Women’s Circle of Health Study

Objective: Mammalian target of rapamycin complex 1 (mTORC1) plays an important role in maintaining proper cellular functions in gastric cancer (GC). Previous studies demonstrated genetic variants within mTORC1 genes were associated with GC risk. However, no studies reported the associations between genetic variants within mTORC1 genes and GC prognosis. Herein, we firstly assessed the associations of genetic variants of mTORC1 genes with overall survival (OS) of GC in Chinese populations. Methods: We genotyped eight single nucleotide polymorphisms (SNPs) in mTORC1 genes (i.e., rs2536 T>C and rs1883965 G>A for mTOR, rs3160 T>C and rs26865 A>G for MLST8, rs3751934 C>A, rs1062935 T>C, rs3751932 T>C and rs12602885 G>A for RPTOR) by the TaqMan method in 197 Chinese GC patients who had surgical resection in Xinhua Hospital. We conducted Kaplan-Meier survival plots and Cox hazards regression analysis to explore the associations of these SNPs with OS. Results: The single-locus analysis indicated that RPTOR rs1062935 T>C was associated with an increased risk of poor GC prognosis (CC vs. TT/TC: adjusted Hazard ratio (HR) = 1.71, 95% confidence interval (CI) = 1.04-2.82). The combined analysis of all eight SNPs showed that patients with more than three risk genotypes significantly increased risk of death (adjusted HR = 2.44, 95% CI = 1.30-4.58), when compared to those with three or less risk genotypes. Conclusions: Our findings indicated that genetic variants within mTORC1 genes may predict GC prognosis in Chinese populations. The results need to be validated in future studies with larger sample sizes.

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Xue-Feng Wang

Papers

Genetic Variants Within MTORC1 Genes Predict Gastric Cancer Prognosis in Chinese Populations.