Analysis of the TP53 Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach

A p53 transcriptional signature in primary and metastatic cancers derived using machine learning

The tumor suppressor gene, TP53, has the highest rate of mutation among all genes in human cancer. This transcription factor plays an essential role in the regulation of many cellular processes. Mutations in TP53 result in loss of wild-type p53 function in a dominant negative manner. Although TP53 is a well-studied gene, the transcriptome modifications caused by the mutations in this gene have not yet been explored in a pan-cancer study using both primary and metastatic samples. In this work, we used a random forest model to stratify tumor samples based on TP53 mutational status and detected a p53 transcriptional signature. We hypothesize that the existence of this transcriptional signature is due to the loss of wild-type p53 function and is universal across primary and metastatic tumors as well as different tumor types. Additionally, we showed that the algorithm successfully detected this signature in samples with apparent silent mutations that affect correct mRNA splicing. Furthermore, we observed that most of the highly ranked genes contributing to the classification extracted from the random forest have known associations with p53 within the literature. We suggest that other genes found in this list including GPSM2, OR4N2, CTSL2, SPERT, and RPE65 protein coding genes have yet undiscovered linkages to p53 function. Our analysis of time on different therapies also revealed that this signature is more effective than the recorded TP53 status in detecting patients who can benefit from platinum therapies and taxanes. Our findings delineate a p53 transcriptional signature, expand the knowledge of p53 biology and further identify genes important in p53 related pathways.

Oncological drug discovery: AI meets structure-based computational research

The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of neoplastic diseases portrayed by the different hallmarks of cancer. Here, we review six use case scenarios for integrated computational methods, namely driver prediction, computational mutagenesis, (off)-target prediction, binding site prediction, virtual screening, and allosteric modulation analysis. We address the heterogeneity of integration approaches and individual methods, while acknowledging their current limitations and highlighting their potential to bring drugs for personalized oncological therapies to the market faster.

Impact of deleterious missense PRKCI variants on structural and functional dynamics of protein

Protein kinase C iota (PKC_ɩ) is a novel protein containing 596 amino acids and is also a member of atypical kinase family. The role of PKC_ɩ has been explored in neurodegenerative diseases, neuroblastoma, ovarian and pancreatic cancers. Single nucleotide polymorphisms (SNPs) have not been studied in PKC_ɩ till date. The purpose of the current study is to scrutinize the deleterious missense variants in PKC_ɩ and determine the effect of these variants on stability and dynamics of the protein. The structure of protein PKC_ɩ was predicted for the first time and post translational modifications were determined. Genetic variants of PKC_ɩ were retrieved from ENSEMBL and only missense variants were further analyzed because of its linkage with diseases. The pathogenicity of missense variants, effect on structure and function of protein, association with cancer and conservancy of the protein residues were determined through computational approaches. It is observed that C1 and the pseudo substrate region has the highest number of pathogenic SNPs. Variations in the kinase domain of the protein are predicted to alter overall phosphorylation of the protein. Molecular dynamic simulations predicted noteworthy change in structural and functional dynamics of the protein because of these variants. The study revealed that nine deleterious variants can possibly contribute to malfunctioning of the protein and can be associated with diseases. This can be useful in diagnostics and developing therapeutics for diseases related to these polymorphisms.

Computational and Mass Spectrometry-Based Approach Identify Deleterious Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs) in JMJD6

The jumonji domain-containing protein 6 (JMJD6) gene catalyzes the arginine demethylation and lysine hydroxylation of histone and a growing list of its known substrate molecules, including p53 and U2AF65, suggesting a possible role in mRNA splicing and transcription in cancer progression. Mass spectrometry-based technology offers the opportunity to detect SNP variants accurately and effectively. In our study, we conducted a combined computational and filtration workflow to predict the nonsynonymous single nucleotide polymorphisms (nsSNPs) present in JMJD6, followed by a liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis and validation. The computational approaches SIFT, PolyPhen-2, SNAP, I-Mutant 2.0, PhD-SNP, PANTHER, and SNPS&GO were integrated to screen out the predicted damaging/deleterious nsSNPs. Through the three-dimensional structure of JMJD6, H187R (rs1159480887) was selected as a candidate for validation. The validation experiments showed that the mutation of this nsSNP in JMJD6 obviously affected mRNA splicing or the transcription of downstream genes through the reduced lysyl-hydroxylase activity of its substrates, U2AF65 and p53, further indicating the accuracy of this prediction method. This research provides an effective computational workflow for researchers with an opportunity to select prominent deleterious nsSNPs and, thus, remains promising for examining the dysfunction of proteins.

The specific W-boxes of GAPC5 promoter bound by TaWRKY are involved in drought stress response in wheat

Drought is one of the most common abiotic stresses, and can limit wheat yield, crops and productivity. GAPCs play vital roles under drought stress conditions in multiple species. The aim of this experiment was to determine the regulatory mechanism of TaGAPC5 under drought stress. In this study, the genes and promoters of TaGAPC5 in diverse drought-tolerant cultivars were cloned. The amino acid sequences were conserved, while the promoter fragments were not identical. Under abiotic stress, the expression level of TaGAPC5 was substantially different among the diverse drought-tolerant cultivars and the promoter activities were significantly improved. The yeast one-hybrid system and Electrophoretic mobility shift assay (EMSA) demonstrated that TaWRKYs bound to specific W-boxes: TaWRKY28, TaWRKY33, TaWRKY40 and TaWRKY47 bind to G/ATGACG/C/A, C/G/ATGACG, C/ATGACC and C/ATGACC/G, respectively. By analyzing different 5' deletion mutants of these promoters, it was determined that these W-boxes in CW-TaGAPC5 promoter (-1262, -1202, -904, -880 and -207) and ZY-TaGAPC5 promoter (-697 and -220) bound by these four TaWRKYs and were functional under drought stress. The deletion or addition of specific W-boxes in the promoter fragments significantly restrained or advanced the promoter activity under drought stress, and these results further confirmed that these W-boxes play vital roles in improving transcription levels under drought stress. The W-boxes in CW-TaGAPC5P (-1262, -1202, -904, -880 and -207) and ZY-TaGAPC5P (-697 and -220) were identified as the key cis-elements for responding to drought stress and were bound by the transcription factor TaWRKY.

Roles of HOTAIR in lung cancer susceptibility and prognosis

Background

Long noncoding (lncRNA) single‐nucleotide polymorphisms (SNPs) are associated with the susceptibility to the development of various malignant tumors. The aim of this study was to investigate the roles of HOX transcript antisense intergenic RNA (HOTAIR) and its SNPs in lung cancer.

Methods

Initially, the expression of HOTAIR in different tumors was investigated using the online Gene Expression Profiling Interactive Analysis (GEPIA) resource. Three SNPs (rs920778, rs1899663, and rs4759314) of HOTAIR were identified using the MassArray system. Following this, the relationship between these SNPs and susceptibility to lung cancer was investigated.

Results

Expression of HOTAIR was found to increase in a variety of cancers, including nonsmall cell lung cancer (NSCLC). We found that the genotypes of these SNPs (rs920778, rs1899663, and rs4759314) were not significantly associated with lung cancer type, family history, lymph node metastasis, or lung cancer stage. In gender stratification, the results of rs920778 genotypes showed that, compared to genotype AA, the AG (OR = 0.344, 95% CI: 0.133–0.893, p = .028) and AG + GG (OR = 0.378, 95% CI: 0.153–0.932, p = .035) genotypes of rs920778 are protective factors against NSCLC in females. In smoking stratification, compared with AA of rs920778, the genotype AG + GG (OR = 0.507, 95% CI: 0.263–0.975, p = .042) was a protective factor against NSCLC in nonsmoking people. No statistical differences were observed in the classifications of rs1899663 and rs4759314 genotypes. Linkage disequilibrium analysis revealed a high linkage disequilibrium between the rs920778 and rs1899663 (D′ = 0.99, r² = .74), rs920778 and rs4759314 (D′ = 0.85, r² = .13), and rs1899663 and rs4759314 (D′ = 0.79, r² = .00).

Conclusion

Our study demonstrated that HOTAIR expression increased in NSCLC, and that the genotypes of rs920778 could be useful in the diagnosis and prognosis of lung cancer.