Cancer Genome Atlas Project Research Articles

Forkhead box D subfamily genes in colorectal cancer: potential biomarkers and therapeutic targets.

The forkhead box (FOX) family members regulate gene transcription and expression. FOX family members regulate various biological processes, such as cell proliferation and tumorigenesis. FOXD, a FOX protein subfamily, is associated with poor prognosis for various cancers. However, the potential clinical value of FOXD subfamily members in colorectal cancer (CRC) has not yet been elucidated. Therefore, in this study, we aimed to determine the role of the FOXD subfamily members in CRC development. Using HTSeq-count data, clinical data, and single-nucleotide polymorphisms (obtained from The Cancer Genome Atlas Project), and bioinformatics analyses (using DESEQ2 software), we identified differentially expressed genes (DEGs) in CRC. Next, each DEG expression was validated in vitro using reverse transcription-quantitative polymerase chain reaction, western blotting, and immunohistochemistry (IHC). Among the FOXD subfamily members, the area under the receiver operating characteristic curve of FOXD3 was 0.949, indicating that FOXD3 has a high overall diagnostic accuracy for CRC. Gene Set Enrichment Analysis revealed that FOXD-DEGs were mainly related to pathways such as cytokine, cytokine, and extracellular matrix receptor interactions. Kaplan-Meier curves and nomograms showed that FOXD1, FOXD3, and FOXD4 were prognostically significant. In conclusion, FOXD subfamily members (especially FOXD3) could serve as diagnostic and prognostic biomarkers for CRC and an immunotherapy target in patients with CRC.

Exploiting common patterns in diverse cancer types via multi-task learning

Cancer prognosis requires precision to identify high-risk patients and improve survival outcomes. Conventional methods struggle with the complexity of genetic biomarkers and diverse medical data. Our study uses deep learning to distil high-dimensional medical data into low-dimensional feature vectors exploring shared patterns across cancer types. We developed a multi-task bimodal neural network integrating RNA Sequencing and clinical data from three The Cancer Genome Atlas project datasets: Breast Invasive Carcinoma, Lung Adenocarcinoma, and Colon Adenocarcinoma. Our approach significantly improved prognosis prediction, especially for Colon Adenocarcinoma, with up to 26% increase in concordance index and 41% in the area under the precision-recall curve. External validation with Small Cell Lung Cancer achieved comparable metrics, indicating that supplementing small datasets with data from other cancers can improve performance. This work represents initial strides in using multi-task learning for prognosis prediction across cancer types, potentially revealing shared mechanisms among cancers and contributing to future applications in precision medicine.

Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning.

Although the criteria for follicular-pattern thyroid tumors are well-established, diagnosing these lesions remains challenging in some cases. In the recent World Health Organization Classification of Endocrine and Neuroendocrine Tumors (5th edition), the invasive encapsulated follicular variant of papillary thyroid carcinoma was reclassified as its own entity. It is crucial to differentiate this variant of papillary thyroid carcinoma from low-risk follicular pattern tumors due to their shared morphological characteristics. Proteomics holds significant promise for detecting and quantifying protein biomarkers. We investigated the potential value of a protein biomarker panel defined by machine learning for identifying the invasive encapsulated follicular variant of papillary thyroid carcinoma, initially using formalin- fixed paraffin-embedded samples. We developed a supervised machine-learning model and tested its performance using proteomics data from 46 thyroid tissue samples. We applied a random forest classifier utilizing five protein biomarkers (ZEB1, NUP98, C2C2L, NPAP1, and KCNJ3). This classifier achieved areas under the curve (AUCs) of 1.00 and accuracy rates of 1.00 in training samples for distinguishing the invasive encapsulated follicular variant of papillary thyroid carcinoma from non-malignant samples. Additionally, we analyzed the performance of single-protein/gene receiver operating characteristic in differentiating the invasive encapsulated follicular variant of papillary thyroid carcinoma from others within The Cancer Genome Atlas projects, which yielded an AUC > 0.5. We demonstrated that integration of high-throughput proteomics with machine learning can effectively differentiate the invasive encapsulated follicular variant of papillary thyroid carcinoma from other follicular pattern thyroid tumors.

Pan-cancer analysis of TP53 mutations and their association with liver cancer and aristolochic acids

Liver cancer is a major global health concern, as its incidence continues to rise in numerous countries, making it a leading cause of cancer-related deaths. The TP53 gene, which encodes the p53 tumor suppressor protein, plays a crucial role in regulating cell growth and division. Mutations in TP53 have been implicated in increased risk for liver cancer development. In this study, we aim to conduct a pan-cancer analysis to further investigate the potential role of TP53 mutations in various cancer types and their relationship with liver cancer. Additionally, we will explore the interplay between TP53 gene mutations, liver cancer, and exposure to Aristolochic acids (AAs). To achieve these goals, we will utilize data from The Cancer Genome Atlas (TCGA) project and the Gene Expression Omnibus (GEO) databases, enabling a comprehensive analysis of TP53 in the context of multiple cancer types.

Mutational signatures in 175 Chinese gastric cancer patients.

Gastric cancer (GC), a molecularly heterogeneous disease, is the third leading cause of cancer death worldwide. The majority of GC cases worldwide occur in East Asia, predominantly China. Mutational Signature Framework offers an elegant approach to identify mutational processes present in tumors. To identify mutational signature patterns, we conducted whole exome sequencing (WES) analysis in Chinese patients with GC. Mutect2 and MutsigCV were used to identify significantly mutated genes in 175 Chinese GC cases using paired tumor-normal tissues. We investigated mutational signatures using Catalogue of Somatic Mutations in Cancer (COSMIC) Version 2 (V2) and Version 3 (V3). We identified 104 mutated genes with P < 0.01. Seven genes (OR6B1, B2M, ELF3, RHOA, RPL22, TP53, ARIDIA) had q < 0.0001, including six previously associated with GC. Mutational signatures (COSMIC-V3) observed include 14 single base substitutions (SBS), one doublet base substitution (DBS) Signature A, and one InDel (ID2). The most frequent SBS signatures (SBS05, SBS01, SBS15, SBS20, SBS40) were also observed in 254 White GC cases from The Cancer Genome Atlas (TCGA) Project. However, SBS01 and SBS20 showed significant differences between Whites vs. All Asians (19.3% vs. 11.3% for SBS 1 (P = 0.012) and 11.4% vs. 5.9% for SBS20 (P = 0.025), respectively). Using COSMIC V2, signatures 6, 15, and 1 were the most frequent in Chinese GC cases. Further, most Chinese GC cases carried multiple signatures. This effort represents the most detailed mutational signatures analysis of GC cases from China to date. Results hold promise for new insights in understanding risk and prognosis factors in GC.

Unlocking the Potential: Epstein-Barr Virus (EBV) in Gastric Cancer and Future Treatment Prospects, a Literature Review.

Gastric cancer (GC) is a complex disease with various etiologies. While Helicobacter pylori infection is still one of the leading risk factors for GC, increasing evidence suggests a link between GC and other infective agents such as Epstein Bar Virus (EBV). EBV-associated gastric cancer (EBVaGC) is now recognized as a distinct subgroup of GC, and the complex interactions between the virus and gastric mucosa may influence its development. A recent integrative analysis of the genome and proteome of GC tissues by The Cancer Genome Atlas project has identified EBVaGC as a specific subtype characterized by PIK3CA and ARID1A mutations, extensive DNA hyper-methylation, and activation of immune signaling pathways. These molecular characteristics are markers of the unique molecular profile of this subset of GC and are potential targets for therapy. This review aims to provide an overview of the current knowledge on EBVaGC. It will focus on the epidemiology, clinic-pathological features, and genetic characteristics of EBVaGC. Additionally, it will discuss recent data indicating the potential use of EBV infection as a predictive biomarker of response to chemotherapy and immune checkpoint inhibitors. The review also delves into potential therapeutic approaches for EBVaGC, including targeted therapies and adoptive immunotherapy, highlighting the promising potential of EBV as a therapeutic target.

SUGT1 regulates the progression of ovarian cancer through the AKT/PI3K/mTOR signaling pathway

This study investigates the expression and functional roles of SUGT1 in ovarian cancer, utilizing data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) projects. Our analyses reveal that SUGT1 is significantly upregulated in ovarian cancer tissues compared to normal controls. We further explore the prognostic value of SUGT1, where elevated expression correlates with poorer patient outcomes, particularly in ovarian cancer. The functional implications of SUGT1 in cancer biology were assessed through in vitro and in vivo experiments. Gene Set Enrichment Analysis (GSEA) indicates a significant association between high SUGT1 expression and the activation of glycolytic pathways, suggesting a potential role in metabolic reprogramming. Inhibition of SUGT1 via siRNA in ovarian cancer cell lines results in decreased proliferation and increased apoptosis, along with reduced migration and invasion capabilities. Additionally, our study identifies the transcription factor ELF1 as a significant regulator of SUGT1 expression. Through promoter analysis and chromatin immunoprecipitation, we demonstrate that ELF1 directly binds to the SUGT1 promoter, enhancing its transcription. This regulatory mechanism underscores the importance of transcriptional control in cancer metabolism, providing insights into potential therapeutic targets. Our findings establish SUGT1 as a crucial player in the oncogenic processes of ovarian cancer, influencing both metabolic pathways and transcriptional regulation. This highlights its potential as a biomarker and therapeutic target in managing ovarian cancer.

Comprehensive analysis of microbial content in whole-genome sequencing samples from The Cancer Genome Atlas project.

Analysis of microbial content in 5,734 whole-genome sequencing samples from TCGA yields a comprehensive new resource for investigating the role of microbes in cancer.

Discovery of an 8-oxoguanine regulator PCBP1 inhibitor by virtual screening and its synergistic effects with ROS-modulating agents in pancreatic cancer.

Introduction: Drugs that target reactive oxygen species (ROS) metabolism have progressed the treatment of pancreatic cancer treatment, yet their efficacy remains poor because of the adaptation of cancer cells to high concentration of ROS. Cells cope with ROS by recognizing 8-oxoguanine residues and processing severely oxidized RNA, which make it feasible to improve the efficacy of ROS-modulating drugs in pancreatic cancer by targeting 8-oxoguanine regulators. Methods: Poly(rC)-binding protein 1 (PCBP1) was identified as a potential oncogene in pancreatic cancer through datasets of The Cancer Genome Atlas (TCGA) project and Gene Expression Omnibus (GEO). High-throughput virtual screening was used to screen out potential inhibitors for PCBP1. Computational molecular dynamics simulations was used to verify the stable interaction between the two compounds and PCBP1 and their structure-activity relationships. In vitro experiments were performed for functional validation of silychristin. Results: In this study, we identified PCBP1 as a potential oncogene in pancreatic cancer. By applying high-throughput virtual screening, we identified Compound 102 and Compound 934 (silychristin) as potential PCBP1 inhibitors. Computational molecular dynamics simulations and virtual alanine mutagenesis verified the structure-activity correlation between PCBP1 and the two identified compounds. These two compounds interfere with the PCBP1-RNA interaction and impair the ability of PCBP1 to process RNA, leading to intracellular R loop accumulation. Compound 934 synergized with ROS agent hydrogen peroxide to strongly improve induced cell death in pancreatic cancer cells. Discussion: Our results provide valuable insights into the development of drugs that target PCBP1 and identified promising synergistic agents for ROS-modulating drugs in pancreatic cancer.

A Deep Learning Approach for the Identification of the Molecular Subtypes of Pancreatic Ductal Adenocarcinoma Based on Whole Slide Pathology Images

Delayed diagnosis and treatment resistance make pancreatic ductal adenocarcinoma (PDAC) mortality rates high. Identifying molecular subtypes can improve treatment, but current methods are costly and time-consuming. In this study, deep learning models were utilized to identify histological features that classify PDAC molecular subtypes based on routine hematoxylin-eosin (H&E)-stained histopathological slides. 97 histopathology slides associated with resectable PDAC from the Cancer Genome Atlas (TCGA) project were utilized to train a deep learning model and tested the performance on 44 needle biopsy material (110 slides) from a local annotated patient cohort. The model achieved balanced accuracy of 96.19% and 83.03% in identifying the classical and basal subtypes of PDAC in the TCGA and the local cohort, respectively. This study provides a promising method to cost-effectively and rapidly classifying PDAC molecular subtypes based on routine H&E slides, potentially leading to more effective clinical management of this disease.

Comprehensive analysis of distinct circadian clock subtypes of adult diffuse glioma and their associations with clinicopathological, genetic, and epigenetic profiles.

The circadian clock (CC) has biological and clinical implications in gliomas. Most studies focused on CC effects on the tumor microenvironment and the application of chronotherapy. The present study focused on CC gene expression patterns and intracellular oncogenic activities. Glioma gene expression data were collected from The Human Cancer Genome Atlas (TCGA) project. After applying inclusion and exclusion criteria, we selected 666 patients from TCGA-GBM and TCGA-LGG projects and included important clinicopathological variables. The entire cohort was subjected to clustering analysis and divided into CC1 and CC2 subtypes based on statistical, biological, and clinical criteria. CC2 gliomas showed higher expression of BMAL1 and CRY1 and lower expression of CRY2 and PER2 (adjusted P < .001). CC2 gliomas had q higher activity of cell proliferation, metabolic reprogramming, angiogenesis, hypoxia, and many oncogenic signals (P < .001). The CC2 subtype contained a higher proportion of glioblastomas (P < .001) and had a worse prognosis (P < .001). Stratified Kaplan-Meier and multivariable Cox analyses illustrated that the CC subtype is an independent prognostic factor to clinicopathological characteristics (P < .001), genetic aberrations (P = .006), and biological processes (P < .001). Thus, this study shows statistical evidence of CC subtypes and their biological, and clinicopathological significance in adult gliomas.

Adjuvant Therapy for Endometrial Cancer in the Era of Molecular Classification

ABSTRACT Endometrial cancer primarily undergoes surgical intervention, with adjuvant treatments such as external beam pelvic radiotherapy, vaginal brachytherapy, chemotherapy, and combined therapy investigated in randomized trials. Treatment decisions hinge on clinicopathological risk factors. Low-risk cases usually require surgery alone, whereas high-intermediate risk often benefit from adjuvant vaginal brachytherapy for enhanced local control with minimal side effects. Recent trials advocate pelvic radiotherapy for high-risk cases, particularly in Stage I–II tumors with risk factors. Chemoradiation proves advantageous for serous cancers and Stage III disease, improving recurrence-free, and overall survival. Molecular studies, notably the Cancer Genome Atlas project, identified four distinct molecular classes, transcending stages, and histological types. These molecular subtypes exhibit a stronger prognostic impact than histopathological characteristics, heralding a shift toward molecular-integrated diagnostics and treatments. Incorporating molecular factors into adjuvant strategies, including targeted therapies, marks a new paradigm in endometrial cancer management, underpinning ongoing research, and clinical trials. This review outlines current adjuvant approaches, underscores the emergence of molecular-integrated risk profiling, and touches on developments in targeted therapy.

Comparison of the classifiers based on mRNA, microRNA and lncRNA expression and DNA methylation profiles for the tumor origin detection.

Tumor tissue origin detection is of great importance in determining the appropriate course of treatment for cancer patients. Classifiers based on gene expression and DNA methylation profiles have been confirmed to be feasible and reliable to predict the tumor primary. However, few works have been performed to compare the performance of these classifiers based on different profiles. Using gene expression and DNA methylation profiles from The Cancer Genome Atlas (TCGA) project, eight machine learning methods were employed for the tumor tissue origin detection. We then evaluated the predictive performance using DNA methylation, mRNA, microRNA (miRNA) and long non-coding RNA (lncRNA) expression profiles in a comparative manner. A statistical method was introduced to select the most informative CpG sites. We found that LASSO is the most predictive models based on various profiles. Further analyses indicated that the results derived from DNA methylation (overall accuracy: 97.77%) are better than those derived from mRNA expression (overall accuracy: 88.01%), microRNA expression (overall accuracy: 91.03%) and lncRNA expression (overall accuracy: 95.7%). It has been suggested that we can achieve an overall accuracy >90% using only 1,000 methylated CpG sites for prediction. In this work, we comprehensively evaluated the performance of classifiers based on different profiles for the tumor origin detection. Our findings demonstrated the effectiveness of DNA methylation as biomarker for tracing tumor tissue origin using LASSO and neural network.

Exploring CK2 transcriptional regulation and signaling pathways in cancer.

e15199 Background: CK2 genes have been implicated in oncogenic processes in different cancer types, and their expression is known to be increased in many cancers relative to their normal tissue types. However the regulation of the three CK2 alpha transcripts and beta transcript is not well understood. Studies investigating the alpha gene ( CSNK2A1) and beta gene ( CSNK2B) found NFκB, ETS1, and SP1 as likely transcription factor regulators in lymphoid and fibroblast cell lines. Our study seeks to explore correlations between CK2 gene expression and the expression of these transcription factors in cancer. Since CK2 also regulates intracellular signaling through pathways such as PI3K-Akt, Jak-Stat, NFkB, Wnt, IL-6, TGF-β, Ras and Notch, this study also seeks to explore the correlation between CK2 expression and gene expression of known downstream signaling pathways across a variety of cancers. Methods: 32 non-overlapping cancer datasets were obtained from the The Cancer Genome Atlas (TCGA) and Broad GDAC Firehose projects which were obtained via cBioPortal.org and analyzed using a combination of cBioportal and RStudio statistical analysis packages. For each cancer type, mRNA expression of each of the four CK2 genes in cancer was compared to expression of transcription factors both previously identified in the literature and predicted generated by TFBIND analysis (via the transcription factor database TRANSFAC R3.4) of the CK2 minimal promoter regions. CK2 gene expression was also compared to expression of genes in known CK2 signaling pathways. Results: Our analysis found CSNK2A1 and CSNK2A3 expression to have a significant, strong positive correlation in nearly all cancer types studied, except pancreatic adenocarcinoma. CSNK2A1 demonstrated significant positive correlation with expression of the transcription factors NFKB1 and c-Rel in lymphoma, thymoma, and other cancers. CSNK2B demonstrated a significant, strong positive correlation with the transcription factors NELFE in every cancer examined. CK2 transcripts also showed significant correlations with other transcription factors and genes involved in IL-6, TGF-β, MAPK, and several other CK2 signaling pathways in many different cancer types. Conclusions: Overall this study demonstrates significant correlations between CK2 gene expression and previously proposed transcriptional regulation mechanisms as well as known CK2 signaling pathways in cancer.

Bioinformatics and Experimental Study Revealed LINC00982/ miR-183-5p/ABCA8 Axis Suppresses LUAD Progression.

Lung adenocarcinoma (LUAD) is a major health challenge worldwide with an undesirable prognosis. LINC00982 has been implicated as a tumor suppressor in diverse human cancers; however, its role in LUAD has not been fully characterized. Expression level and prognostic value of LINC00982 were investigated in pan-cancer and lung cancer from The Cancer Genome Atlas (TCGA) project. Differential expression analysis based on the LINC00982 expression level was performed in LUAD followed by gene set enrichment analysis (GSEA) and functional enrichment analyses. The association between LINC00982 expression and tumor immune microenvironment characteristics was evaluated. A potential ceRNA regulatory axis was identified and experimentally validated. We found that LINC00982 expression was downregulated and correlated with poor prognosis in LUAD. Enrichment analyses revealed that LINC00982 could inhibit DNA damage repair and cell proliferation, but enhance tumor metabolic reprogramming. We identified a competing endogenous RNA network involving LINC00982, miR-183-5p, and ATP-binding cassette subfamily A member 8 (ABCA8). Luciferase assays confirmed that miR-183-5p can interact with LINC00982 and ABCA8. Forced miR-183-5p expression reduced LINC00982 transcript levels and suppressed ABCA8 expression. Our findings revealed the LINC00982/miR-183-5p/ABCA8 axis as a potential therapeutic target in LUAD.

CK2 expression in cancer and associated clinical characteristics.

e15156 Background: The protein casein kinase 2 (CK2) is a ubiquitously expressed, highly pleiotropic kinase. CK2 may have incredibly varied substrate specificity and catalytic activity, as it functions as both a tetrameric and monomeric enzyme. CK2 participates in many cellular processes such as cellular proliferation, growth, and survival, and has significant potential to assist in oncogenic signaling. We and others have demonstrated CK2 gene and protein overexpression, and sometimes underexpression, in a variety of cancers. Here we investigate how the expression of CK2 subunits in cancer is associated with clinical characteristics in several distinct cancer types. Methods: Data analysis was performed on 32 non-overlapping cancer datasets obtained from The Cancer Genome Atlas (TCGA) and Broad GDAC Firehose Legacy projects, one dataset from Cancer Cell Line Encyclopedia (CCLE), and normal tissue datasets from Gene Expression Omnibus. Statistical analysis was performed using RStudio to explore the relationship between the expression levels of the three CK2 alpha transcripts (CSNK2A1, CSNK2A2, and CSNK2A3) and beta transcripts (CSNK2B) with sample clinical characteristics provided by TCGA/Broad. Relationships between CK2 expression and cancer-type specific characteristics were also explored, such as KRAS mutations in colorectal adenocarcinoma and serum AFP at sample procurement in hepatocellular carcinoma. CCLE data was used to explore correlations between cancer-type dependent CK2 gene expression and half maximal inhibitory concentration (IC50) of over 250 anticancer drugs. Tumor Immune Estimation Resource (TIMER) 2.0 was used to assess the relationship between CK2 subunit expression and tumor immune infiltration. Results: As seen in our previous analysis, CK2 transcripts were upregulated in the majority of cancer types analyzed. We also found that CK2 transcript upregulation correlated with worse survival outcomes in several previously unstudied cancers. CK2 expression differed significantly between cancer subtypes such as in ductal carcinoma and lobular carcinoma in breast cancer. CK2 expression significantly correlated with tumor grade, TNM staging, clinical staging in certain cancer types. CK2 expression significantly correlated with altered response to various anticancer drugs in certain cancers. CK2 expression was associated with both increased and decreased immune infiltration in a variety of cancers. Conclusions: Overall, this study demonstrates that CK2 transcript expression correlates with a variety of cancer-type dependent characteristics and clinical outcomes. As a result, CK2 may be useful as a prognostic biomarker and therapeutic target.

CARTAR: a comprehensive web tool for identifying potential targets in chimeric antigen receptor therapies using TCGA and GTEx data.

Chimeric antigen receptor (CAR) therapy has emerged as a ground-breaking advancement in cancer treatment, harnessing the power of engineered human immune cells to target and eliminate cancer cells. The escalating interest and investment in CAR therapy in recent years emphasize its profound significance in clinical research, positioning it as a rapidly expanding frontier in the field of personalized cancer therapies. A crucial step in CAR therapy design is choosing the right target as it determines the therapy's effectiveness, safety and specificity against cancer cells, while sparing healthy tissues. Herein, we propose a suite of tools for the identification and analysis of potential CAR targets leveraging expression data from The Cancer Genome Atlas and Genotype-Tissue Expression Project, which are implemented in CARTAR website. These tools focus on pinpointing tumor-associated antigens, ensuring target selectivity and assessing specificity to avoid off-tumor toxicities and can be used to rationally designing dual CARs. In addition, candidate target expression can be explored in cancer cell lines using the expression data for the Cancer Cell Line Encyclopedia. To our best knowledge, CARTAR is the first website dedicated to the systematic search of suitable candidate targets for CAR therapy. CARTAR is publicly accessible at https://gmxenomica.github.io/CARTAR/.

The landscape and clinical relevance of intronic polyadenylation in human cancers

Intronic polyadenylation (IPA) is an RNA 3′ end processing event which has been reported to play important roles in cancer development. However, the comprehensive landscape of IPA events across various cancer types is lacking. Here, we apply IPAFinder to identify and quantify IPA events in 10,383 samples covering all 33 cancer types from The Cancer Genome Atlas (TCGA) project. We identify a total of 21,835 IPA events, almost half of which are ubiquitously expressed. We identify 2761 unique dynamically changed IPA events across cancer types. Furthermore, we observe 8855 non-redundant clinically relevant IPA events, which could potentially be used as prognostic indicators. Our analysis also reveals that dynamic IPA usage within cancer signaling pathways may affect drug response. Finally, we develop a user-friendly data portal, IPACancer Atlas (http://www.tingni-lab.com/Pancan_IPA/), to search and explore IPAs in cancer.

Abstract PO2-01-08: Differential HOXB13 gene expression and promoter methylation analysis in breast cancer

Abstract Background: The Breast Cancer Index (BCI) is a gene expression-based signature comprising two functional biomarker panels: the Molecular Grade Index (MGI) and BCI (H/I), which is a ratio of the HOXB13 and IL17BR gene expression. Several studies have shown that BCI (H/I) is a predictive biomarker for extended adjuvant endocrine therapy benefit in hormone receptor-positive (HR+) early-stage breast cancer. Molecular mechanisms underlying endocrine responsiveness remain to be fully explored. The objective of this analysis is to evaluate the correlation between gene expression and methylation in breast cancer patients displaying different HOXB13 mRNA expression levels from The Cancer Genome Atlas (TCGA) project. Methods: Data from methylation microarrays and mRNA sequencing were examined in combination with clinical metadata from 1,095 TCGA breast cancer patients. After removing outliers, HR+ samples were ordered based on their HOXB13 expression levels and the top 10% of samples (HOXB13-high; n=95) and bottom 10% (HOXB13-low; n=95) were selected for further analysis. Differentially expressed genes (DEGs) were determined by comparing all protein-coding genes between the HOXB13-high and HOXB13-low sets, based on an absolute log-fold change above 2 and a Benjamini-Hochberg adjusted p-value threshold of 0.01. Among the subset of samples with matching methylation data (n=60 for the HOXB13-high group and n=48 for the HOXB13-low group), hypo-methylated and hyper-methylated differentially methylated probes (DMPs) were identified based on at least 10% change of β (i.e., average promoter methylation) and a Benjamini-Hochberg adjusted p-value threshold of 0.01. Motif and transcription factor (TF) enrichment analyses were identified at a minimum incidence of 10 and a lower boundary of 1.5 for the 95% confidence interval of the odds ratio. Results: Our analysis identified a total of 613 DEGs between the HOXB13-high and HOXB13-low groups. Gene ontology analysis of DEGs revealed a statistically significant enrichment of genes associated with PTK6 regulated cell cycle, oncogene-induced cellular senescence, and regulation of T cell activation. Analysis of protein-protein interactions of differentially expressed genes revealed differences in key biological processes including changes in cell cycle regulation, p53 pathway, and IL-17 signaling. A total of 5,295 hyper-methylated and 580 hypo-methylated DMPs were discovered when comparing HOXB13-high and HOXB13-low samples. Changes in global methylation patterns enabled classification of breast cancer samples with high or low HOXB13 expression with an accuracy of 0.90. Reconstruction of gene regulatory networks from DNA methylation and transcriptome profiles elucidated significant pairs of DMPs and differentially expressed genes. Specifically, a total of 197 differentially expressed genes were attributed to hyper-methylated DMPs, including genes involved in response to estrogen, activation of HOX genes during differentiation, and drug-mediated inhibition of CDK4/6 activity. Additionally, 8 differentially expressed genes were attributed to hypo-methylated DMPs and were associated with proteasomal protein catabolic processes. Lastly, motif analysis of transcriptional factors for the hyper-methylated DMPs revealed enrichment of binding motifs attributed to FOXA1, ESR1, XBR1, and FOXP1. Conclusion: A comprehensive and comparative analysis of mRNA expression levels and methylation patterns between TCGA breast cancer samples with high and low expression of HOXB13 revealed key signaling pathways and biological processes that may provide insights on the molecular mechanism of HOXB13 in the regulation of response to endocrine therapy in HR+ breast cancer. Citation Format: Natalia Siuliukina, Yi Zhang, Kai Treuner, Mark Pegram. Differential HOXB13 gene expression and promoter methylation analysis in breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-01-08.

Challenges in genetic screening for inherited endocrinopathy affecting the thyroid, parathyroid and adrenal glands in Singapore.

Significant progress has been made in the understand-ing of many human diseases, especially cancers, which has contributed to improved and increased survival. The Human Genome Project and The Cancer Genome Atlas project brought about a new era, with an understanding of inherited diseases at a molecular level, which subsequently facilitated the option of precision medicine. Precision medicine has helped tailor treatment decisions at an individual level, for instance in terms of surgical treatments or targeted therapies in advanced diseases. Despite the increasing advances in genetic-lead precision medicine, this has not translated into increasing uptake among patients. Reasons for this may be potential knowledge gaps among clinicians; on reasons for poor uptake of genetic testing such as for cultural, religious or personal beliefs; and on financial implications such as lack of support from insurance companies. In this review, we look at the current scenario of genetic screening for common inherited endocrine conditions affecting the thyroid, parathyroid and adrenal glands in Singapore, and the implications associated with it.