Candidate Cancer Genes Research Articles

Identification of candidate genes for endometrial cancer in multi-omics: a Mendelian randomization analysis

Endometrial cancer is the most common malignant tumor of the uterus, but the underlying genetic mechanisms of EC remain unclear. To identify candidate genes and investigate genetic mechanisms for endometrial cancer, we utilized the summary-data-based Mendelian randomization (SMR) method to investigate causal associations between genetic variants, gene expression, DNA methylation, and endometrial cancer. Three main analyses were conducted utilizing cis-expression and methylation quantitative trait loci (eQTLs and mQTLs) as instrumental variables to examine causal relationships with endometrial cancer, and assessing the causal relationship between DNA methylation and gene expression. Data sources included genetic association data from O'Mara et al. eQTL data from the GTEx database, and mQTL data from McRae et al. Analysis involved the HEIDI test to distinguish pleiotropy, SMR analysis with multiple testing correction, and colocalization analysis to assess associations driven by linkage disequilibrium. Functional enrichment analysis was performed by the Metascape tool. Our study showed that three genes, SNX11, LINC00243, and EVI2A, were identified as causally related to endometrial cancer. SNX11 exhibited a positive causal relationship, while LINC00243 and EVI2A showed negative ones. Furthermore, 24 CpG sites were identified as causally related to endometrial cancer, with cg14424631 (CYP19A1) being the most significant. The study revealed common genes implicated in endometrial cancer, gene expression, and methylation sites, with LINC00243 playing a key role. Colocalization analysis confirmed significant causal relationships between LINC00243, SNX11, and endometrial cancer. Enrichment analysis uncovered pathways like interferon gamma signaling enriched in both endometrial cancer GWAS and e/mQTL. These findings shed light on the molecular mechanisms underlying endometrial cancer development. The study identified candidate genes and DNA methylation loci causally associated with endometrial cancer, which are expected to serve as potential targets for treatment.

Whole-exome sequencing reveals novel cancer genes and actionable targets in biliary tract cancers in primary sclerosing cholangitis.

People with primary sclerosing cholangitis (PSC) have a 20% lifetime risk of biliary tract cancer (BTC). Using whole-exome sequencing, we characterized genomic alterations in tissue samples from BTC with underlying PSC. We extracted DNA from formalin-fixed, paraffin-embedded tumor and paired nontumor tissue from 52 resection or biopsy specimens from patients with PSC and BTC and performed whole-exome sequencing. Following copy number analysis, variant calling, and filtering, putative PSC-BTC-associated genes were assessed by pathway analyses and annotated to targeted cancer therapies. We identified 53 candidate cancer genes with a total of 123 nonsynonymous alterations passing filtering thresholds in 2 or more samples. Of the identified genes, 19% had not previously been implicated in BTC, including CNGA3, KRT28, and EFCAB5. Another subset comprised genes previously implicated in hepato-pancreato-biliary cancer, such as ARID2, ELF3, and PTPRD. Finally, we identified a subset of genes implicated in a wide range of cancers such as the tumor suppressor genes TP53, CDKN2A, SMAD4, and RNF43 and the oncogenes KRAS, ERBB2, and BRAF. Focal copy number variations were found in 51.9% of the samples. Alterations in potential actionable genes, including ERBB2, MDM2, and FGFR3 were identified and alterations in the RTK/RAS (p = 0.036), TP53 (p = 0.04), and PI3K (p = 0.043) pathways were significantly associated with reduced overall survival. In this exome-wide characterization of PSC-associated BTC, we delineated both PSC-specific and universal cancer genes. Our findings provide opportunities for a better understanding of the development of BTC in PSC and could be used as a platform to develop personalized treatment approaches.

Prioritizing cervical cancer candidate genes using chaos game and fractal-based time series approach.

Cervical cancer is one of the most severe threats to women worldwide and holds fourth rank in lethality. It is estimated that 604, 127 cervical cancer cases have been reported in 2020 globally. With advancements in high throughput technologies and bioinformatics, several cervical candidate genes have been proposed for better therapeutic strategies. In this paper, we intend to prioritize the candidate genes that are involved in cervical cancer progression through a fractal time series-based cross-correlations approach. we apply the chaos game representation theory combining a two-dimensional multifractal detrended cross-correlations approach among the known and candidate genes involved in cervical cancer progression to prioritize the candidate genes. We obtained 16 candidate genes that showed cross-correlation with known cancer genes. Functional enrichment analysis of the candidate genes shows that they involve GO terms: biological processes, cell-cell junction assembly, cell-cell junction organization, regulation of cell shape, cortical actin cytoskeleton organization, and actomyosin structure organization. KEGG pathway analysis revealed genes' role in Rap1 signaling pathway, ErbB signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, mTOR signaling pathway, Acute myeloid leukemia, chronic myeloid leukemia, Breast cancer, Thyroid cancer, Bladder cancer, and Gastric cancer. Further, we performed survival analysis and prioritized six genes CDH2, PAIP1, BRAF, EPB41L3, OSMR, and RUNX1 as potential candidate genes for cervical cancer that has a crucial role in tumor progression. We found that our study through this integrative approach an efficient tool and paved a new way to prioritize the candidate genes and these genes could be evaluated experimentally for potential validation. We suggest this may be useful in analyzing the nucleotide sequences and protein sequences for clustering, classification, class affiliation, etc.

Intergenomic signatures of coevolution between Tasmanian devils and an infectious cancer

Coevolution is common and frequently governs host-pathogen interaction outcomes. Phenotypes underlying these interactions often manifest as the combined products of the genomes of interacting species, yet traditional quantitative trait mapping approaches ignore these intergenomic interactions. Devil facial tumor disease (DFTD), an infectious cancer afflicting Tasmanian devils (Sarcophilus harrisii), has decimated devil populations due to universal host susceptibility and a fatality rate approaching 100%. Here, we used a recently developed joint genome-wide association study (i.e., co-GWAS) approach, 15 y of mark-recapture data, and 960 genomes to identify intergenomic signatures of coevolution between devils and DFTD. Using a traditional GWA approach, we found that both devil and DFTD genomes explained a substantial proportion of variance in how quickly susceptible devils became infected, although genomic architectures differed across devils and DFTD; the devil genome had fewer loci of large effect whereas the DFTD genome had a more polygenic architecture. Using a co-GWA approach, devil-DFTD intergenomic interactions explained ~3× more variation in how quickly susceptible devils became infected than either genome alone, and the top genotype-by-genotype interactions were significantly enriched for cancer genes and signatures of selection. A devil regulatory mutation was associated with differential expression of a candidate cancer gene and showed putative allele matching effects with two DFTD coding sequence variants. Our results highlight the need to account for intergenomic interactions when investigating host-pathogen (co)evolution and emphasize the importance of such interactions when considering devil management strategies.

Germline mutations in cancer predisposition genes among pediatric patients with cancer and congenital anomalies.

Childhood cancer has a poorly known etiology, and investigating the underlying genetic background may provide novel insights. A recognized association exists between non-chromosomal birth defects and childhood cancer susceptibility. We performed whole-exome sequencing and chromosomal microarray analysis in a cohort of childhood cancer (22 individuals, 50% with congenital anomalies) to unravel deleterious germline variants. A diagnostic yield of 14% was found, encompassing heterozygous variants in bona fide dominant Cancer Predisposition Genes (CPGs). Considering candidate and recessive CPGs harboring monoallelic variants, which were also deemed to play a role in the phenotype, the yield escalated to 45%. Most of the deleterious variants were mapped in genes not conventionally linked to the patient's tumor type. Relevant findings were detected in 55% of the syndromic individuals, mostly variants potentially underlying both phenotypes. We uncovered a remarkable prevalence of germline deleterious CPG variants, highlighting the significance of a comprehensive genetic analysis in pediatric cancer, especially when coupled with additional clinical signs. Moreover, our findings emphasized the potential for oligogenic inheritance, wherein multiple genes synergistically increase cancer risk. Lastly, our investigation unveiled potentially novel genotype-phenotype associations, such as SETD5 in neuroblastoma, KAT6A in gliomas, JAG1 in hepatoblastomas, and TNFRSF13B in Langerhans cell histiocytosis. Novel gene-phenotype associations and candidate genes for pediatric cancer were unraveled, such as KAT6A in gliomas, SETD5 in neuroblastoma, JAG1 in hepatoblastomas, and TNFRSF13B in Langerhans cell histiocytosis. Our analysis revealed a high frequency of deleterious germline variants, particularly in cases accompanied by additional clinical signs, highlighting the importance of a comprehensive genetic evaluation in childhood cancer. Our findings also underscored the potential for oligogenic inheritance in pediatric cancer risk. Understanding the cancer etiology is crucial for genetic counseling, often influencing therapeutic decisions and offering valuable insights into molecular targets for the development of oncological therapies.

Sleeping Beauty transposon mutagenesis in mouse intestinal organoids identifies genes involved in tumor progression and metastasis.

To identify genes important for colorectal cancer (CRC) development and metastasis, we established a new metastatic mouse organoid model using Sleeping Beauty (SB) transposon mutagenesis. Intestinal organoids derived from mice carrying actively mobilizing SB transposons, an activating KrasG12D, and an inactivating ApcΔ716 allele, were transplanted to immunodeficient mice. While 66.7% of mice developed primary tumors, 7.6% also developed metastatic tumors. Analysis of SB insertion sites in tumors identified numerous candidate cancer genes (CCGs) identified previously in intestinal SB screens performed in vivo, in addition to new CCGs, such as Slit2 and Atxn1. Metastatic tumors from the same mouse were clonally related to each other and to primary tumors, as evidenced by the transposon insertion site. To provide functional validation, we knocked out Slit2, Atxn1, and Cdkn2a in mouse tumor organoids and transplanted to mice. Tumor development was promoted when these gene were knocked out, demonstrating that these are potent tumor suppressors. Cdkn2a knockout cells also metastasized to the liver in 100% of the mice, demonstrating that Cdkn2a loss confers metastatic ability. Our organoid model thus provides a new approach that can be used to understand the evolutionary forces driving CRC metastasis and a rich resource to uncover CCGs promoting CRC.

Parallel DNA/RNA NGS Using an Identical Target Enrichment Panel in the Analysis of Hereditary Cancer Predisposition.

Germline DNA testing using the next-gene-ration sequencing (NGS) technology has become the analytical standard for the diagnostics of hereditary diseases, including cancer. Its increasing use places high demands on correct sample identification, independent confirmation of prioritized variants, and their functional and clinical interpretation. To streamline these processes, we introduced parallel DNA and RNA capture-based NGS using identical capture panel CZECANCA, which is routinely used for DNA analysis of hereditary cancer predisposition. Here, we present the analytical workflow for RNA sample processing and its analytical and diagnostic performance. Parallel DNA/RNA analysis allowed credible sample identification by calculating the kinship coefficient. The RNA capture-based approach enriched transcriptional targets for the majority of clinically relevant cancer predisposition genes to a degree that allowed analysis of the effect of identified DNA variants on mRNA processing. By comparing the panel and whole-exome RNA enrichment, we demonstrated that the tissue-specific gene expression pattern is independent of the capture panel. Moreover, technical replicates confirmed high reproducibility of the tested RNA analysis. We concluded that parallel DNA/RNA NGS using the identical gene panel is a robust and cost-effective diagnostic strategy. In our setting, it allows routine analysis of 48 DNA/RNA pairs using NextSeq 500/550 Mid Output Kit v2.5 (150 cycles) in a single run with sufficient coverage to analyse 226 cancer predisposition and candidate ge-nes. This approach can replace laborious Sanger confirmatory sequencing, increase testing turnaround, reduce analysis costs, and improve interpretation of the impact of variants by analysing their effect on mRNA processing.

Abstract C050: Allele-specific expression analysis identifies novel dysregulated genes in high-risk prostate tumors from African-American men

Abstract African-American (AA) patients are twice as likely to die from prostate cancer (PCa) compared to European American (EA) patients. Racism, socioeconomic status, access to healthcare, and tumor biology have been attributed to this mortality gap. Tumor biology has not been fully characterized in AA patients, particularly the landscape of gene-regulatory alterations and how they may contribute to tumor aggressiveness. We use allele specific expression (ASE) analysis, which detects genes showing imbalanced expression of alleles often due to altered gene regulation on one allele, to comprehensively identify dysregulated genes in high-risk PCa tumors from AA patients. We develop a framework for identifying candidate cancer genes by integrating ASE data, clinical data, and epigenetics. Our approach identifies genes that recurrently undergo ASE across AA patient tumors. Further, we describe genes that more frequently undergo ASE in AA patient tumors compared to EA patient tumors, some of which are implicated in hormone therapy resistance, aggressive disease, and worse disease-free survival. Our work demonstrates the potential for ASE to uncover dysregulated genes that may not have been identified through RNAseq alone. Ultimately, this work increases our understanding of the effects of regulatory alterations in tumors and how they may contribute to disparities in PCa. Citation Format: Margaret Tsui, Kevin Hu, Hanbing Song, Franklin W. Huang. Allele-specific expression analysis identifies novel dysregulated genes in high-risk prostate tumors from African-American men [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C050.

Mutational Landscape and In-Silico Analysis of TP53, PIK3CA, and PTEN in Patients with Breast Cancer from Khyber Pakhtunkhwa.

Herein, we report the mutational spectrum of three breast cancer candidate genes (TP53, PIK3CA, and PTEN) using WES for identifying potential biomarkers. The WES data were thoroughly analyzed using SAMtools for variant calling and identification of the mutations. Various bioinformatic tools (SIFT, PolyPhen-2, Mutation Taster, ISPRED-SEQ, SAAFEQ-SEQ, ConSurf, PROCHECK etc.) were used to determine the pathogenicity and nature of the SNVs. Selected interaction site (IS) mutations were visualized in PyMOL after building 3D structures in Swiss-Model. Ramachandran plots were generated by using the PROCHECK server. The selected IS mutations were subjected to molecular dynamic simulation (MDS) studies using Gromacs 4.5. STRING and GeneMANIA were used for the prediction of gene-gene interactions and pathways. Our results revealed that the luminal A molecular subtype of the breast cancer was most common, whereas a high percentage of was Her2 negatives. Moreover, the somatic mutations were more common as compared to the germline mutations in TP53, PIK3CA, and PTEN. 20% of the identified mutations are reported for the first time from Khyber Pakhtunkhwa. In the enrolled cohort, 23 mutations were nonsynonymous SNVs. The frequency of mutations was the highest in PIK3CA, followed by TP53 and PTEN. A total of 13 mutations were found to be highly pathogenic. Four novel mutations were identified on PIK3CA and one each on PTEN and TP53. SAAFEQ-SEQ predicted the destabilizing effect for all mutations. ISPRED-SEQ predicted 9 IS mutations (6 on TP53 and 3 on PIK3CA), whereas no IS mutation was predicted on PTEN. The TP53 IS mutations were TP53R43H, TP53Y73X, TP53K93Q, TP53K93R, TP53D149E, and TP53Q199X; whereas for PIK3CA, the IS mutations were PIK3CAL156V, PIK3CAM610K, and PIK3CAH1047R. Analysis from the ConSurf Web server revealed five SNVs with a highly conserved status (conservation score 9) across TP53 and PTEN. TP53P33R was found predominant in the grade 3 tumors, whereas PTENp.C65S was distributed on ER+, ER-, PR+, PR-, Her2+, and Her2- patients. TP53p.P33R mutation was found to be recurring in the 14/19 (73.6%) patients and, therefore, can be considered as a potential biomarker. Finally, these mutations were studied in the context of their potential association with different hormonal and social factors.

Identification of Novel Candidate Genes for Familial Thyroid Cancer by Whole Exome Sequencing.

Thyroid carcinoma (TC) can be classified as medullary (MTC) and non-medullary (NMTC). While most TCs are sporadic, familial forms of MTC and NMTC also exist (less than 1% and 3-9% of all TC cases, respectively). Germline mutations in RET are found in more than 95% of familial MTC, whereas familial NMTC shows a high degree of genetic heterogeneity. Herein, we aimed to identify susceptibility genes for familial NMTC and non-RET MTC by whole exome sequencing in 58 individuals belonging to 18 Spanish families with these carcinomas. After data analysis, 53 rare candidate segregating variants were identified in 12 of the families, 7 of them located in previously TC-associated genes. Although no common mutated genes were detected, biological processes regulating functions such as cell proliferation, differentiation, survival and adhesion were enriched. The reported functions of the identified genes together with pathogenicity and structural predictions, reinforced the candidacy of 36 of them, suggesting new loci related to TC and novel genotype-phenotype correlations. Therefore, our strategy provides clues to possible molecular mechanisms underlying familial forms of MTC and NMTC. These new molecular findings and clinical data of patients may be helpful for the early detection, development of tailored therapies and optimizing patient management.

A novel heterophilic graph diffusion convolutional network for identifying cancer driver genes.

Identifying cancer driver genes plays a curial role in the development of precision oncology and cancer therapeutics. Although a plethora of methods have been developed to tackle this problem, the complex cancer mechanisms and intricate interactions between genes still make the identification of cancer driver genes challenging. In this work, we propose a novel machine learning method of heterophilic graph diffusion convolutional networks (called HGDCs) to boost cancer-driver gene identification. Specifically, HGDC first introduces graph diffusion to generate an auxiliary network for capturing the structurally similar nodes in a biomolecular network. Then, HGDC designs an improved message aggregation and propagation scheme to adapt to the heterophilic setting of biomolecular networks, alleviating the problem of driver gene features being smoothed by its neighboring dissimilar genes. Finally, HGDC uses a layer-wise attention classifier to predict the probability of one gene being a cancer driver gene. In the comparison experiments with other existing state-of-the-art methods, our HGDC achieves outstanding performance in identifying cancer driver genes. The experimental results demonstrate that HGDC not only effectively identifies well-known driver genes on different networks but also novel candidate cancer genes. Moreover, HGDC can effectively prioritize cancer driver genes for individual patients. Particularly, HGDC can identify patient-specific additional driver genes, which work together with the well-known driver genes to cooperatively promote tumorigenesis.

Abstract 1182: Association of prostate cancer candidate genes with overall and aggressive prostate cancer in men of African ancestry

Abstract Background: There is a growing body of evidence supporting the contributions of germline rare variants to the susceptibility of prostate cancer (PCa), especially aggressive PCa. Our previous exome sequencing analysis highlighted 36 aggressive PCa candidate genes in populations of European ancestry. Here we investigated whether rare germline pathogenic, likely pathogenic, or deleterious (P/LD/D) variants in these genes were associated with overall and aggressive PCa risk in men of African ancestry. Methods: This exome sequencing analysis consists of 7,176 prostate cancer cases and 4,873 controls from the Research on Prostate Cancer in Men of African Ancestry (RESPOND) study. Among the PCa cases, 3,283 are aggressive cases (tumor stage T3/T4, regional lymph node involvement, metastatic disease, Gleason score >= 8.0, prostate-specific antigen [PSA] level >= 20 ng/mL or PCa as the underlying cause of death) including 1,074 metastatic cases, and 1,752 are non-aggressive cases (Gleason score ⇐ 7.0, PSA < 20 ng/mL, and tumor stage T1/T2). P/LP/D variants analyzed were rare (minor allele frequency < 1% in controls) and had either a Variant Effect Predictor impact score of “high” or a pathogenic or likely pathogenic ClinVar classification. The association between P/LP/D carrier status with risk of overall PCa, aggressive PCa, and metastatic PCa was evaluated in logistic regression models, adjusting for age and the top ten principal components. All statistical tests are two-sided. Results: Of the 36 PCa candidate genes, BRCA2 was the most frequently affected gene, with 1.7% of cases and 1.1% of controls harboring a germline P/LP/D variant, followed by MUTYH (1.5%/1.3%) ATM (0.93%/0.49%), MSH5 (0.70%/0.51%) and HOXB13 (0.70%/0.35%). Nominally significant associations with overall PCa were observed for ATM (OR=1.83, 95% CI=1.14-2.92, P=0.012), BRCA2 (OR=1.52, 95% CI=1.10-2.10, P=0.011), HOXB13 (OR=2.10, 95% CI=1.12-3.66, P=0.008), and PALB2 (OR=3.46, 95% CI=1.18-10.1, P=0.02). In case-case analyses (aggressive vs. non-aggressive cases), the association with aggressive PCa was nominally significant for ATM (OR=5.10, 95% CI=1.96-13.3, P=8.7 × 10−4) and BRCA2 (OR=2.00, 95% CI=1.19-3.38, P=0.009) and was suggestive for PALB2 (OR=2.99, 95% CI=0.83-10.7, P=0.09). Similar associations with metastatic PCa were also observed for these three genes. Conclusion: The associations of BRCA2, ATM, and PALB2 with overall PCa and aggressive PCa observed in men of African ancestry are consistent with findings from our previous study in men of European ancestry. These findings further support the importance of these genes in the consideration of screening and active surveillance for high-risk and advanced disease. Citation Format: Fei Chen, Burcu F. Darst, Xin Sheng, Anqi Wang, Yili Xu, Raymond Hughley, Ben Adusei, Mohamed Jalloh, Serigne Magueye Gueye, Andrew A. Adjei, James Mensah, Pedro W. Fernandez, Akindele O. Adebiyi, Oseremen Aisuodionoe-Shadrach, Lindsay Petersen, Maureen Joffe, Jo McBride, Jeannette T. Bensen, James L. Mohler, Jack A. Taylor, Eboneé N. Butler, Sue A. Ingles, Benjamin A. Rybicki, Janet L. Stanford, Wei Zheng, Sonja I. Berndt, Chad D. Huff, Joseph Lachance, Luc Multigner, Caroline Andrews, Timothy R. Rebbeck, Laurent Brureau, Stephen J. Chanock, David V. Conti, Christopher A. Haiman. Association of prostate cancer candidate genes with overall and aggressive prostate cancer in men of African ancestry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1182.

Molecular profiling of EBV associated diffuse large B-cell lymphoma

Epstein-Barr virus (EBV) associated diffuse large B-cell lymphoma (DLBCL) represents a rare aggressive B-cell lymphoma subtype characterized by an adverse clinical outcome. EBV infection of lymphoma cells has been associated with different lymphoma subtypes while the precise role of EBV in lymphomagenesis and specific molecular characteristics of these lymphomas remain elusive. To further unravel the biology of EBV associated DLBCL, we present a comprehensive molecular analysis of overall 60 primary EBV positive (EBV+) DLBCLs using targeted sequencing of cancer candidate genes (CCGs) and genome-wide determination of recurrent somatic copy number alterations (SCNAs) in 46 cases, respectively. Applying the LymphGen classifier 2.0, we found that less than 20% of primary EBV + DLBCLs correspond to one of the established molecular DLBCL subtypes underscoring the unique biology of this entity. We have identified recurrent mutations activating the oncogenic JAK-STAT and NOTCH pathways as well as frequent amplifications of 9p24.1 contributing to immune escape by PD-L1 overexpression. Our findings enable further functional preclinical and clinical studies exploring the therapeutic potential of targeting these aberrations in patients with EBV + DLBCL to improve outcome.

Abstract P003: Computational identification of ovarian cancer candidate genes with mutated protein structures caused by missense variants

Abstract Ovarian cancer is one of the leading killers in the US for women. The American Cancer Society (ACR) ranks ovarian cancer as the fifth leading cause of cancer deaths among women. This cancer is hereditary, with higher risks if the patient has more relatives who have had ovarian cancer before. The Cancer Genome Atlas (TCGA) is a joint-effort project by NCI and NHGRI that generates genomic data for the cancer patients and matches normal data samples with these patients. Missense mutations are single nucleotide polymorphisms (SNPs) in which the amino acids are changed. A missense variant could disrupt protein structures; thus the function of the disrupted protein is altered. In this study, we investigated genes with high expression levels in the TCGA RNA-seq dataset by characterizing protein structure change patterns of candidate genes due to missense mutations in association with ovarian cancer. We also analyzed the association of these genes with ovarian cancer prognosis. We aim to select genes that have pathogenic consequences. We selected 44 genes with the highest expression values based on TCGA ovarian cancer RNA-seq data. We then retrieved protein sequences from the Ensembl BioMart database and missense mutation annotation information from cBioPortal for 44 input genes. Specifically, the missense mutation dataset, which is projected by the Pan-Cancer Analysis of Whole Genomes, was retrieved from cBioPortal. We then used the Phyre2 database to predict the 3-D structures of the wild and mutated type genes to identify structural changes. However, some genes were not suitable for Phyre2 analysis. For example, RPL41 was not long enough for analysis with Phyre2 as it only had 25 amino acids whereas Phyre2 required 30. TMSL3 (TMSB4XP8) is a pseudogene not found in both NCBI and AlphaFold. We then searched DrugBank for 42 genes with the predicted protein structure changes by Phyre2 to identify drug targets. We also performed Kaplan-Meier survival analysis to further evaluate the clinical consequences of the candidate genes. Our preliminary results show 26 genes that have drug targets and several of them have been identified to either decrease or increase the survival rate when the expression is high in ovarian cancer patients. This indicates that the highly expressed genes with protein structure changes due to missense mutations could be potential biomarkers for treating ovarian cancer. The functional annotation study could further validate ovarian cancer association for our prioritized gene list. In the future, we also plan to compare prediction results between genders and race groups. Our results could guide medical researchers in prioritizing drug targets and developing better treatment strategies for ovarian cancer. Our approach in prioritizing candidate genes is applicable to other cancers as well, and we plan to develop bioinformatic pipelines for automatically predicting candidate genes with significant effect on patient survival for any cancer type. Citation Format: Ian W. Hou, Yongsheng Bai. Computational identification of ovarian cancer candidate genes with mutated protein structures caused by missense variants. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P003.

PiRNAs may regulate expression of candidate genes of esophageal adenocarcinoma.

Elucidation of ways to regulate the expression of candidate cancer genes will contribute to the development of methods for cancer diagnosis and therapy. The aim of the present study was to show the role of piRNAs as efficient regulators of mRNA translation of esophageal adenocarcinoma (EAC) candidate genes. We used bioinformatic methods to study the interaction characteristics of up to 200 thousand piRNAs with mRNAs of 38 candidate EAC genes. The piRNAs capable of binding to mRNA of AR, BTG3, CD55, ERBB3, FKBP5, FOXP1, LEP, SEPP1, SMAD4, and TP53 genes with high free energy by the formation of hydrogen bonds between canonical (G-C, A-U) and noncanonical (G-U, A-C) piRNA and mRNA nucleotide pairs were revealed. The organization of piRNA binding sites (BSs) in the mRNA of candidate genes was found to overlap nucleotide sequences to form clusters. Clusters of piRNA BSs were detected in the 5'-untranslated region, coding domain sequence, and 3'-untranslated region of mRNA. Due to the formation of piRNA binding site clusters, compaction of BSs occurs and competition between piRNAs for binding to mRNA of candidate EAC genes occurs. Associations of piRNA and candidate genes were selected for use as markers for the diagnosis of EAC.

Identification of Candidate Genes in Breast Cancer Induced by Estrogen Plus Progestogens Using Bioinformatic Analysis.

Menopausal hormone therapy (MHT) was widely used to treat menopause-related symptoms in menopausal women. However, MHT therapies were controversial with the increased risk of breast cancer because of different estrogen and progestogen combinations, and the molecular basis behind this phenomenon is currently not understood. To address this issue, we identified differentially expressed genes (DEGs) between the estrogen plus progestogens treatment (EPT) and estrogen treatment (ET) using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data. As a result, a total of 96 upregulated DEGs were first identified. Seven DEGs related to the cell cycle (CCNE2, CDCA5, RAD51, TCF19, KNTC1, MCM10, and NEIL3) were validated by RT-qPCR. Specifically, these seven DEGs were increased in EPT compared to ET (p < 0.05) and had higher expression levels in breast cancer than adjacent normal tissues (p < 0.05). Next, we found that estrogen receptor (ER)-positive breast cancer patients with a higher CNNE2 expression have a shorter overall survival time (p < 0.05), while this effect was not observed in the other six DEGs (p > 0.05). Interestingly, the molecular docking results showed that CCNE2 might bind to 17β-estradiol (−6.791 kcal/mol), progesterone (−6.847 kcal/mol), and medroxyprogesterone acetate (−6.314 kcal/mol) with a relatively strong binding affinity, respectively. Importantly, CNNE2 protein level could be upregulated with EPT and attenuated by estrogen receptor antagonist, acolbifene and had interactions with cancer driver genes (AKT1 and KRAS) and high mutation frequency gene (TP53 and PTEN) in breast cancer patients. In conclusion, the current study showed that CCNE2, CDCA5, RAD51, TCF19, KNTC1, MCM10, and NEIL3 might contribute to EPT-related tumorigenesis in breast cancer, with CCNE2 might be a sensitive risk indicator of breast cancer risk in women using MHT.

Neutrophil Transcriptional Deregulation by the Periodontal Pathogen Fusobacterium nucleatum in Gastric Cancer: A Bioinformatic Study.

Background Infection with the periodontal pathogen Fusobacterium nucleatum (F. nucleatum) has been associated with gastric cancer. The present study is aimed at uncovering the putative biological mechanisms underlying effects of F. nucleatum–mediated neutrophil transcriptional deregulation in gastric cancer. Materials and Methods A gene expression dataset pertaining to F. nucleatum-infected human neutrophils was utilized to identify differentially expressed genes (DEGs) using the GEO2R tool. Candidate genes associated with gastric cancer were sourced from the “Candidate Cancer Gene Database” (CCGD). Overlapping genes among these were identified as link genes. Functional profiling of the link genes was performed using “g:Profiler” tool to identify enriched Gene Ontology (GO) terms, pathways, miRNAs, transcription factors, and human phenotype ontology terms. Protein-protein interaction (PPI) network was constructed for the link genes using the “STRING” tool, hub nodes were identified as key candidate genes, and functionally enriched terms were determined. Results The gene expression dataset GEO20151 was downloaded, and 589 DEGs were identified through differential analysis. 886 candidate gastric cancer genes were identified in the CGGD database. Among these, 36 overlapping genes were identified as the link genes. Enriched GO terms included molecular function “enzyme building,” biological process “protein folding,'” cellular components related to membrane-bound organelles, transcription factors ER71 and Sp1, miRNAs miR580 and miR155, and several human phenotype ontology terms including squamous epithelium of esophagus. The PPI network contained 36 nodes and 53 edges, where the top nodes included PH4 and CANX, and functional terms related to intracellular membrane trafficking were enriched. Conclusion F nucleatum-induced neutrophil transcriptional activation may be implicated in gastric cancer via several candidate genes including DNAJB1, EHD1, IER2, CANX, and PH4B. Functional analysis revealed membrane-bound organelle dysfunction, intracellular trafficking, transcription factors ER71 and Sp1, and miRNAs miR580 and miR155 as other candidate mechanisms, which should be investigated in experimental studies.

Analysis of microarray data by genetic algorithm

Microarray gene expression data is used to understand the actions of thousands of genes. Just a few genes out of thousands have a significant impact in any cancer process. Finding these defective genes using experimental data is impractical. To locate the relevant genes, computational techniques are required. A method to identifying cancer candidate genes from microarray data is created. Clustering of similar genes is necessary to find co-expressed genes in different biological conditions. It is important to develop methods to find the few candidate genes for cancer. An optimization process is used for such purpose. A genetic algorithm employs the principles of evolution: selection, recombination, and mutation to solve an optimization problem. Mutual information is used to find the dependency between genes. The two genes are similar if their expression levels are comparable. The similarity as well as positive and negative correlations between genes is considered while clustering them. Interdependence measure tells how the genes are correlated. The genes responsible for a sick state have higher interdependence measures. These genes are defective genes having cancer diagnostic information. Here microarray gene expression datasets from gastric cancer and colon cancer from the public domain are considered.

Comparative modeling, comparative molecular docking analyses, and revealing of potential binding pockets of MDM-2: A candidate cancer gene

MDM-2 is also known as E3 ubiquitin-protein ligase encoded by Mdm-2. MDM-2 is an important negative regulator of p53 tumor suppressor and performs key function as an inhibitor of p53 transcriptional activation and E3 ubiquitin ligase. MDM-2 also plays significant role in human cancers and therapeutic target. Hundred different structures were predicted through comparative modeling, threading and ab initio approaches followed by the evaluation of predicted structures through various evaluation tools including ERRAT, ProSa-web, Rampage, molprobidity, verify3D and Anolea. The selected 3D structure of MDM-2 showed 13 α- helix chains, 2 β-pleated sheets along with 97.4468% overall quality factor of the predicted structure. Interestingly, it was observed that only 4.5% residues were present in outlier region and the observed errors were fixed. Moreover, 91.1% residues of the selected structure were present in favored region and 8.9% in allowed region having -6.0 Z-score. High throughput virtual screening and comparative molecular docking studies was performed. Four novel compounds have been reported that showed minimum binding energy (-8.1 Kcal/mol) and maximum binding affinity against MDM-2. Molecular docking analyses revealed that Ser154, Arg155, Pro156, Ser157, Lys185, Ser186, Ser188, Ser190, Ile189, Val247, Glu257, Asp173, Glu174, Glu178, Arg161, Ard181, Lys182, Arg183 and His184 residues are significant residues for therapeutic drug targets. The reported compounds showed effective energy scores. In addition, the site-directed mutagenesis may be helpful for further analyses. The reported compounds may act like potent drug compounds against MDM-2.

Identification of lung cancer drivers by comparison of the observed and the expected numbers of missense and nonsense mutations in individual human genes.

Largely, cancer development is driven by acquisition and positive selection of somatic mutations that increase proliferation and survival of tumor cells. As a result, genes related to cancer development tend to have an excess of somatic mutations in them. An excess of missense and/or nonsense mutations in a gene is an indicator of its cancer relevance. To identify genes with an excess of potentially functional missense or nonsense mutations one needs to compare the observed and expected numbers of mutations in the gene. We estimated the expected numbers of missense and nonsense mutations in individual human genes using (i) the number of potential sites for missense and nonsense mutations in individual transcripts and (ii) histology-specific nucleotide context-dependent mutation rates. To estimate mutation rates defined as the number of mutations per site per tumor we used silent mutations reported in the Catalog Of Somatic Mutations In Cancer (COSMIC). The estimates were nucleotide context dependent. We have identified 26 genes with an excess of missense and/or nonsense mutations for lung adenocarcinoma, 18 genes for small cell lung cancer, and 26 genes for squamous cell carcinoma of the lung. These genes include known genes and novel lung cancer gene candidates.