Reassessment of Exosome Composition.

The heterogeneity of small extracellular vesicles and presence of non-vesicular extracellular matter have led to debate about contents and functional properties of exosomes. Here, we employ high-resolution density gradient fractionation and direct immunoaffinity capture to precisely characterize the RNA, DNA, and protein constituents of exosomes and other non-vesicle material. Extracellular RNA, RNA-binding proteins, and other cellular proteins are differentially expressed in exosomes and non-vesicle compartments. Argonaute 1-4, glycolytic enzymes, and cytoskeletal proteins were not detected in exosomes. We identify annexin A1 as a specific marker for microvesicles that are shed directly from the plasma membrane. We further show that small extracellular vesicles are not vehicles of active DNA release. Instead, we propose a new model for active secretion of extracellular DNA through an autophagy- and multivesicular-endosome-dependent but exosome-independent mechanism. This study demonstrates the need for a reassessment of exosome composition and offers a framework for a clearer understanding of extracellular vesicle heterogeneity.

Expanding the genetic and phenotypic landscape of replication factor C complex-related disorders: RFC4 deficiency is linked to a multisystemic disorder.

The precise regulation of DNA replication is vital for cellular division and genomic integrity. Central to this process is the replication factor C (RFC) complex, encompassing five subunits, which loads proliferating cell nuclear antigen onto DNA to facilitate the recruitment of replication and repair proteins and enhance DNA polymerase processivity. While RFC1's role in cerebellar ataxia, neuropathy, and vestibular areflexia syndrome (CANVAS) is known, the contributions of RFC2-5 subunits on human Mendelian disorders is largely unexplored. Our research links bi-allelic variants in RFC4, encoding a core RFC complex subunit, to an undiagnosed disorder characterized by incoordination and muscle weakness, hearing impairment, and decreased body weight. We discovered across nine affected individuals rare, conserved, predicted pathogenic variants in RFC4, all likely to disrupt the C-terminal domain indispensable for RFC complex formation. Analysis of a previously determined cryo-EM structure of RFC bound to proliferating cell nuclear antigen suggested that the variants disrupt interactions within RFC4 and/or destabilize the RFC complex. Cellular studies using RFC4-deficient HeLa cells and primary fibroblasts demonstrated decreased RFC4 protein, compromised stability of the other RFC complex subunits, and perturbed RFC complex formation. Additionally, functional studies of the RFC4 variants affirmed diminished RFC complex formation, and cell cycle studies suggested perturbation of DNA replication and cell cycle progression. Our integrated approach of combining in silico, structural, cellular, and functional analyses establishes compelling evidence that bi-allelic loss-of-function RFC4 variants contribute to the pathogenesis of this multisystemic disorder. These insights broaden our understanding of the RFC complex and its role in human health and disease.

A large-scale microRNA transcriptome-wide association study identifies two susceptibility microRNAs, miR-1307-5p and miR-192-3p, for colorectal cancer risk.

Transcriptome-wide association studies (TWAS) have identified many putative susceptibility genes for colorectal cancer (CRC) risk. However, susceptibility miRNAs, critical dysregulators of gene expression, remain unexplored. We genotyped DNA samples from 313 CRC East Asian patients and performed small RNA sequencing in their normal colon tissues distant from tumors to build genetic models for predicting miRNA expression. We applied these models and data from genome-wide association studies (GWAS) including 23 942 cases and 217 267 controls of East Asian ancestry to investigate associations of predicted miRNA expression with CRC risk. Perturbation experiments separately by promoting and inhibiting miRNAs expressions and further in vitro assays in both SW480 and HCT116 cells were conducted. At a Bonferroni-corrected threshold of P < 4.5 × 10-4, we identified two putative susceptibility miRNAs, miR-1307-5p and miR-192-3p, located in regions more than 500 kb away from any GWAS-identified risk variants in CRC. We observed that a high predicted expression of miR-1307-5p was associated with increased CRC risk, while a low predicted expression of miR-192-3p was associated with increased CRC risk. Our experimental results further provide strong evidence of their susceptible roles by showing that miR-1307-5p and miR-192-3p play a regulatory role, respectively, in promoting and inhibiting CRC cell proliferation, migration, and invasion, which was consistently observed in both SW480 and HCT116 cells. Our study provides additional insights into the biological mechanisms underlying CRC development.

Novel breast cancer susceptibility loci under linkage peaks identified in African ancestry consortia.

BACKGROUND: Expansion of genome-wide association studies across population groups is needed to improve our understanding of shared and unique genetic contributions to breast cancer. We performed association and replication studies guided by a priori linkage findings from African ancestry (AA) relative pairs. METHODS: We performed fixed-effect inverse-variance weighted meta-analysis under three significant AA breast cancer linkage peaks (3q26-27, 12q22-23, and 16q21-22) in 9241 AA cases and 10 193 AA controls. We examined associations with overall breast cancer as well as estrogen receptor (ER)-positive and negative subtypes (193,132 SNPs). We replicated associations in the African-ancestry Breast Cancer Genetic Consortium (AABCG). RESULTS: In AA women, we identified two associations on chr12q for overall breast cancer (rs1420647, OR = 1.15, p = 2.50×10-6; rs12322371, OR = 1.14, p = 3.15×10-6), and one for ER-negative breast cancer (rs77006600, OR = 1.67, p = 3.51×10-6). On chr3, we identified two associations with ER-negative disease (rs184090918, OR = 3.70, p = 1.23×10-5; rs76959804, OR = 3.57, p = 1.77×10-5) and on chr16q we identified an association with ER-negative disease (rs34147411, OR = 1.62, p = 8.82×10-6). In the replication study, the chr3 associations were significant and effect sizes were larger (rs184090918, OR: 6.66, 95% CI: 1.43, 31.01; rs76959804, OR: 5.24, 95% CI: 1.70, 16.16). CONCLUSION: The two chr3 SNPs are upstream to open chromatin ENSR00000710716, a regulatory feature that is actively regulated in mammary tissues, providing evidence that variants in this chr3 region may have a regulatory role in our target organ. Our study provides support for breast cancer variant discovery using prioritization based on linkage evidence.

Genome- and transcriptome-wide association studies of 386,000 Asian and European-ancestry women provide new insights into breast cancer genetics.

By combining data from 160,500 individuals with breast cancer and 226,196 controls of Asian and European ancestry, we conducted genome- and transcriptome-wide association studies of breast cancer. We identified 222 genetic risk loci and 137 genes that were associated with breast cancer risk at a p < 5.0 × 10-8 and a Bonferroni-corrected p < 4.6 × 10-6, respectively. Of them, 32 loci and 15 genes showed a significantly different association between ER-positive and ER-negative breast cancer after Bonferroni correction. Significant ancestral differences in risk variant allele frequencies and their association strengths with breast cancer risk were identified. Of the significant associations identified in this study, 17 loci and 14 genes are located 1Mb away from any of the previously reported breast cancer risk variants. Pathways analyses including 221 putative risk genes identified multiple signaling pathways that may play a significant role in the development of breast cancer. Our study provides a comprehensive understanding of and new biological insights into the genetics of this common malignancy.

A proteome-wide association study identifies putative causal proteins for breast cancer risk.

BACKGROUND: Genome-wide association studies (GWAS) have identified more than 200 breast cancer risk-associated genetic loci, yet the causal genes and biological mechanisms for most loci remain elusive. Proteins, as final gene products, are pivotal in cellular function. In this study, we conducted a proteome-wide association study (PWAS) to identify proteins in breast tissue related to breast cancer risk. METHODS: We profiled the proteome in fresh frozen breast tissue samples from 120 cancer-free European-ancestry women from the Susan G. Komen Tissue Bank (KTB). Protein expression levels were log2-transformed then normalized via quantile and inverse-rank transformations. GWAS data were also generated for these 120 samples. These data were used to build statistical models to predict protein expression levels via cis-genetic variants using the elastic net method. The prediction models were then applied to the GWAS summary statistics data of 133,384 breast cancer cases and 113,789 controls to assess the associations of genetically predicted protein expression levels with breast cancer risk overall and its subtypes using the S-PrediXcan method. RESULTS: A total of 6388 proteins were detected in the normal breast tissue samples from 120 women with a high detection false discovery rate (FDR) p value < 0.01. Among the 5820 proteins detected in more than 80% of participants, prediction models were successfully built for 2060 proteins with R > 0.1 and P < 0.05. Among these 2060 proteins, five proteins were significantly associated with overall breast cancer risk at an FDR p value < 0.1. Among these five proteins, the corresponding genes for proteins COPG1, DCTN3, and DDX6 were located at least 1 Megabase away from the GWAS-identified breast cancer risk variants. COPG1 was associated with an increased risk of breast cancer with a p value of 8.54 × 10-4. Both DCTN3 and DDX6 were associated with a decreased risk of breast cancer with p values of 1.01 × 10-3 and 3.25 × 10-4, respectively. The corresponding genes for the remaining two proteins, LSP1 and DNAJA3, were located in previously GWAS-identified breast cancer risk loci. After adjusting for GWAS-identified risk variants, the association for DNAJA3 was still significant (p value of 9.15 × 10-5 and adjusted p value of 1.94 × 10-4). However, the significance for LSP1 became weaker with a p value of 0.62. Stratification analyses by breast cancer subtypes identified three proteins, SMARCC1, LSP1, and NCKAP1L, associated with luminal A, luminal B, and ER-positive breast cancer. NCKAP1L was located at least 1Mb away from the GWAS-identified breast cancer risk variants. After adjusting for GWAS-identified breast cancer risk variants, the association for protein LSP1 was still significant (adjusted p value of 6.43 × 10-3 for luminal B subtype). CONCLUSION: We conducted the first breast-tissue-based PWAS and identified seven proteins associated with breast cancer, including five proteins not previously implicated. These findings help improve our understanding of the underlying genetic mechanism of breast cancer development.

Drug-target Mendelian randomization revealed a significant association of genetically proxied metformin effects with increased prostate cancer risk.

The association between metformin use and risk of prostate cancer remains controversial, while data from randomized trials is lacking. We aim to evaluate the association of genetically proxied metformin effects with prostate cancer risk using a drug-target Mendelian randomization (MR) approach. Summary statistics for prostate cancer were obtained from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome Consortium (79,148 cases and 61,106 controls). Cis-expression quantitative trait loci (cis-eQTL) variants in the gene targets of metformin were identified in the GTEx project and eQTLGen consortium. We also obtained male-specific genome-wide association study data for type 2 diabetes, body mass index (BMI), total testosterone, bioavailable testosterone, estradiol, and sex hormone binding globulin for mediation analysis. Inverse-variance weighted (IVW) regression, weighted median, MR-Egger regression, and MR-PRESSO were performed in the main MR analysis. Multivariable MR was used to identify potential mediators and genetic colocalization analysis was performed to assess any shared genetic basis between two traits of interest. We found that genetically proxied metformin effects (1-SD HbA1c reduction, equivalent to 6.75 mmol/mol) were associated with higher risk of prostate cancer (odds ratioIVW [ORIVW]: 1.55, 95% confidence interval, CI: 1.23-1.96, p = 3.0 × 10-3). Two metformin targets, mitochondrial complex I (ORIVW: 1.48, 95% CI: 1.07-2.03, p = 0.016) and gamma-secretase complex (ORIVW: 2.58, 95%CI :1.47-4.55, p = 0.001), showed robust associations with prostate cancer risk, and their effects were partly mediated through BMI (16.4%) and total testosterone levels (34.3%), respectively. These results were further supported by colocalization analysis that expressions of NDUFA13 and BMI, APH1A, and total testosterone may be influenced by shared genetic factors, respectively. In summary, our study indicated that genetically proxied metformin effects may be associated with an increased risk of prostate cancer. Repurposing metformin for prostate cancer prevention in general populations is not supported by our findings.

3DSNP 2.0: update and expansion of the noncoding genomic variant annotation database.

The rapid development of single-molecule long-read sequencing (LRS) and single-cell assay for transposase accessible chromatin sequencing (scATAC-seq) technologies presents both challenges and opportunities for the annotation of noncoding variants. Here, we updated 3DSNP, a comprehensive database for human noncoding variant annotation, to expand its applications to structural variation (SV) and to implement variant annotation down to single-cell resolution. The updates of 3DSNP include (i) annotation of 108 317 SVs from a full spectrum of functions, especially their potential effects on three-dimensional chromatin structures, (ii) evaluation of the accessible chromatin peaks flanking the variants across 126 cell types/subtypes in 15 human fetal tissues and 54 cell types/subtypes in 25 human adult tissues by integrating scATAC-seq data and (iii) expansion of Hi-C data to 49 human cell types. In summary, this version is a significant and comprehensive improvement over the previous version. The 3DSNP v2.0 database is freely available at https://omic.tech/3dsnpv2/.

A High-Nuclearity Copper Sulfide Nanocluster [S-Cu50] Featuring a Double-Shell Structure Configuration with Cu(II)/Cu(I) Valences.

This work represents an important step in the quest for creating atomically precise binary semiconductor nanoclusters (BS-NCs). Compared with coinage metal NCs, the preparation of BS-NCs requires strict control of the reaction kinetics to guarantee the formation of an atomically precise single phase under mild conditions, which otherwise could lead to the generation of multiple phases. Herein, we developed an acid-assisted thiolate dissociation approach that employs suitable acid to induce cleavage of the S-C bonds in the Cu-S-R (R = alkyl) precursor, spontaneously fostering the formation of the [Cu-S-Cu] skeleton upon the addition of extra Cu sources. Through this method, a high-nuclearity copper sulfide nanocluster, Cu50S12(SC(CH3)3)20(CF3COO)12 (abbreviated as [S-Cu50] hereafter), has been successfully prepared in high yield, and its atomic structure was accurately modeled through single-crystal X-ray diffraction. It was revealed that [S-Cu50] exhibits a unique double-shell structural configuration of [Cu14S12]@[Cu36S20], and the innermost [Cu14] moiety displays a rhombic dodecahedron geometry, which has never been observed in previously synthesized Cu metal, hydride, or chalcogenide NCs. Importantly, [S-Cu50] represents the first example incorporating mixed Cu(II)/Cu(I) valences in reported atomically precise copper sulfide NCs, which was unambiguously confirmed by XPS, EPR, and XANES. In addition, the electronic structure of [S-Cu50] was established by a variety of optical investigations, including absorption, photoluminescence, and ultrafast transient absorption spectroscopies, as well as theoretical calculations. Moreover, [S-Cu50] is air-stable and demonstrates electrocatalytic activity in ORR with a four-electron pathway.

Identification of target proteins for breast cancer genetic risk loci and blood risk biomarkers in a large study by integrating genomic and proteomic data.

Genome-wide association studies (GWAS) have identified around 200 loci associated with breast cancer risk. However, protein targets for these loci remain largely unknown. Identifying protein targets and biomarkers can improve the understanding of cancer biology and etiology and identify high-risk individuals for cancer prevention. In this study, we investigated genetically predicted levels of 1142 circulating proteins with breast cancer risk in 133 384 cases and 113 789 controls of European ancestry included in the Breast Cancer Association Consortium (BCAC). We identified 22 blood protein biomarkers associated with the risk of overall breast cancer at a false discovery rate (FDR) <0.05, including nine proteins encoded by genes located at least 500 kb away from previously reported risk variants for breast cancer. Analyses focusing on 124 encoding genes located at GWAS-identified breast cancer risk loci found 20 proteins associated with overall breast cancer risk and one protein associated with triple-negative breast cancer risk at FDR <0.05. Adjustment for the GWAS-identified risk variants significantly attenuated the association for 13 of these proteins, suggesting that these proteins may be the targets of these GWAS-identified risk loci. The identified proteins are involved in various biological processes, including glutathione conjugation, STAT5 signaling and NF-κB signaling pathways. Our study identified novel protein targets and risk biomarkers for breast cancer risk.

Integrating genomics and proteomics data to identify candidate plasma biomarkers for lung cancer risk among European descendants.

BACKGROUND: Plasma proteins are potential biomarkers for complex diseases. We aimed to identify plasma protein biomarkers for lung cancer. METHODS: We investigated genetically predicted plasma levels of 1130 proteins in association with lung cancer risk among 29,266 cases and 56,450 controls of European descent. For proteins significantly associated with lung cancer risk, we evaluated associations of genetically predicted expression of their coding genes with the risk of lung cancer. RESULTS: Nine proteins were identified with genetically predicted plasma levels significantly associated with overall lung cancer risk at a false discovery rate (FDR) of <0.05. Proteins C2, MICA, AIF1, and CTSH were associated with increased lung cancer risk, while proteins SFTPB, HLA-DQA2, MICB, NRP1, and GMFG were associated with decreased lung cancer risk. Stratified analyses by histological types revealed the cross-subtype consistency of these nine associations and identified an additional protein, ICAM5, significantly associated with lung adenocarcinoma risk (FDR < 0.05). Coding genes of NRP1 and ICAM5 proteins are located at two loci that have never been reported by previous GWAS. Genetically predicted blood levels of genes C2, AIF1, and CTSH were associated with lung cancer risk, in directions consistent with those shown in protein-level analyses. CONCLUSION: Identification of novel plasma protein biomarkers provided new insights into the biology of lung cancer.

Development of a Facile Lysosome-Targeting Nitric Oxide Fluorescent Probe Based on a Novel Reaction Mechanism.

As a ubiquitous signal molecule in biosystems, nitric oxide (NO) plays an important role in many physiological and pathological processes. Therefore, it is of great significance to detect NO in organisms for the study of related diseases. Currently, a variety of NO fluorescent probes have been developed based on several types of reaction mechanisms. However, due to the inherent disadvantages of these reactions, like potential interference by biologically related species, there is a great need to develop NO probes based on the new reactions. Herein, we report our discovery of the unprecedented reaction between a widely used fluorophore of 4-(dicyanomethylene)-2-methyl-6-(p-(dimethylamino)styryl)-4H-pyran (DCM) and NO under mild conditions with fluorescence changes. By the analysis of the structure of the product, we proved that DCM undergoes a particular nitration process and proposed a mechanism for fluorescence changes due to the interruption of the intramolecular charge transfer (ICT) process of DCM by the nitrated product of DCM-NO2. Based on the understanding of this specific reaction, we then easily constructed our lysosomal-localized NO fluorescent probe LysoNO-DCM by linking DCM and a morpholine group, a lysosomal-targeting functional group. LysoNO-DCM exhibits excellent selectivity, sensitivity, pH stability, and outstanding lysosome localization ability with Pearson's colocalization coefficient of up to 0.92 and is successfully applied to the imaging of exogenous and endogenous NO in cells and zebrafish. Our studies expand design methods for NO fluorescence probes based on the novel reaction mechanism and will benefit the studies of this signaling molecule.

Associations between high-altitude adaptation and risk of cardiovascular diseases: a bidirectional Mendelian randomization study.

High-altitude adaptation (HAA) was reported to be significantly associated with reduced risks for multiple cardiovascular diseases (CVDs). However, the causality and direction of the associations are largely uncharacterized. We aimed to examine the potential causal relationships between HAA and six types of CVD, including coronary artery disease (CAD), cerebral aneurysm, ischemic stroke, peripheral artery disease, arrhythmia and atrial fibrillation. We obtained the summary data from largest available genome-wide association study of HAA and six types of CVD. Two-sample bidirectional Mendelian randomization (MR) analyses were performed to infer the causality between them. In the sensitivity analyses, MR-Egger regression analyses and MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO) global analyses were used to assess the pleiotropic effects; Cochran's Q tests were used to test the heterogeneity by inverse variance-weighted (IVW) and MR-Egger methods; and the leave-one-out analyses were used to examine whether some single nucleotide polymorphisms (SNPs) could influence the results independently. The MR main analyses showed that the genetically instrumented HAA was significantly causally associated with the reduced risks of CAD (odds ratio [OR] = 0.029; 95% confidence interval [CI] = 0.004-0.234; P = 8.64 × 10-4). In contrast, there was no statistically significant relationship between CVDs and HAA. Our findings provide evidence for the causal effects of HAA on the reduced risks of CAD. However, there is no causality of CVDs on HAA. These findings might be helpful in developing the prevention and intervention strategies for CAD.

Deep neural network based tissue deconvolution of circulating tumor cell RNA.

Prior research has shown that the deconvolution of cell-free RNA can uncover the tissue origin. The conventional deconvolution approaches rely on constructing a reference tissue-specific gene panel, which cannot capture the inherent variation present in actual data. To address this, we have developed a novel method that utilizes a neural network framework to leverage the entire training dataset. Our approach involved training a model that incorporated 15 distinct tissue types. Through one semi-independent and two complete independent validations, including deconvolution using a semi in silico dataset, deconvolution with a custom normal tissue mixture RNA-seq data, and deconvolution of longitudinal circulating tumor cell RNA-seq (ctcRNA) data from a cancer patient with metastatic tumors, we demonstrate the efficacy and advantages of the deep-learning approach which were exerted by effectively capturing the inherent variability present in the dataset, thus leading to enhanced accuracy. Sensitivity analyses reveal that neural network models are less susceptible to the presence of missing data, making them more suitable for real-world applications. Moreover, by leveraging the concept of organotropism, we applied our approach to trace the migration of circulating tumor cell-derived RNA (ctcRNA) in a cancer patient with metastatic tumors, thereby highlighting the potential clinical significance of early detection of cancer metastasis.

The putative oncogenic role of WDTC1 in colorectal cancer.

Microsatellite instability (MSI) is detected in approximately 15% of colorectal cancers (CRCs). WD40 and tetratricopeptide repeats 1 (WDTC1) is frequently mutated in MSI CRC, indicating that it may contribute to CRC development. However, the functional evidence of the role of WDTC1 in CRC development remains unknown. Herein, we conducted in vitro assays to examine the function of WDTC1 using knockdown experiments in three CRC cell lines, SW480, CACO2, and LoVo. We provided strong evidence that silencing WDTC1 significantly suppressed cell proliferation, migration, and invasion consistently in all three CRC cell lines. To evaluate the potential role of WDTC1 in regulating CRC-related genes, we conducted RNA sequencing after 24 and 48 h in SW480 cells after treating WDTC1-siRNA and its vehicle control cells. Differential gene expression analysis identified 44 (42 downregulated and 2 upregulated) and 16 (all downregulated) genes, at time points of 24 and 48 h, respectively, whereas 15 downregulated genes were commonly detected at both time points. The ingenuity pathways analysis suggested that the most significant enrichments associated with cancer function and upstream regulator ATM/ATR were observed for these commonly observed genes. We further verified differential gene expression of eight cancer-related genes, ARHGEF12, GSTP1, FNDC3A, TMTC3, RTN4, RRM2, UHMK1, and PTPRF, using RT-PCR in all three cell lines. Our findings provided additional insight into the oncogenic role of WDTC1 in CRC development.

TBX1 functions as a putative oncogene of breast cancer through promoting cell cycle progression.

We have previously identified a genetic variant, rs34331122 in the 22q11.21 locus, as being associated with breast cancer risk in a genome-wide association study. This novel variant is located in the intronic region of the T-box transcription factor 1 (TBX1) gene. Cis-expression quantitative trait loci analysis showed that expression of TBX1 was regulated by the rs34331122 variant. In the current study, we investigated biological functions and potential molecular mechanisms of TBX1 in breast cancer. We found that TBX1 expression was significantly higher in breast cancer tumor tissues than adjacent normal breast tissues and increased with tumor stage (P < 0.05). We further knocked-down TBX1 gene expression in three breast cancer cell lines, MDA-MB-231, MCF-7 and T47D, using small interfering RNAs and examined consequential changes on cell oncogenicity and gene expression. TBX1 knock-down significantly inhibited breast cancer cell proliferation, colony formation, migration and invasion. RNA sequencing and flow cytometry analysis revealed that TBX1 knock-down in breast cancer cells induced cell cycle arrest in the G1 phase through disrupting expression of genes involved in the cell cycle pathway. Furthermore, survival analysis using the online Kaplan-Meier Plotter suggested that higher TBX1 expression was associated with worse outcomes in breast cancer patients, especially for estrogen receptor-positive breast cancer, with HRs (95% CIs) for overall survival (OS) and distant metastasis free survival (DMFS) of 1.5 (1.05-2.15) and 1.55 (1.10-2.18), respectively. In conclusion, our results suggest that the TBX1 gene may act as a putative oncogene of breast cancer through regulating expressions of cell cycle-related genes.

Developing and validating polygenic risk scores for colorectal cancer risk prediction in East Asians.

Several polygenic risk scores (PRSs) have been developed to predict the risk of colorectal cancer (CRC) in European descendants. We used genome-wide association study (GWAS) data from 22 702 cases and 212 486 controls of Asian ancestry to develop PRSs and validated them in two case-control studies (1454 Korean and 1736 Chinese). Eleven PRSs were derived using three approaches: GWAS-identified CRC risk SNPs, CRC risk variants identified through fine-mapping of known risk loci and genome-wide risk prediction algorithms. Logistic regression was used to estimate odds ratios (ORs) and area under the curve (AUC). PRS115-EAS , a PRS with 115 GWAS-reported risk variants derived from East-Asian data, validated significantly better than PRS115-EUR derived from European descendants. In the Korea validation set, OR per SD increase of PRS115-EAS was 1.63 (95% CI = 1.46-1.82; AUC = 0.63), compared with OR of 1.44 (95% CI = 1.29-1.60, AUC = 0.60) for PRS115-EUR . PRS115-EAS/EUR derived using meta-analysis results of both populations slightly improved the AUC to 0.64. Similar but weaker associations were found in the China validation set. Individuals among the highest 5% of PRS115-EAS/EUR have a 2.52-fold elevated CRC risk compared with the medium (41-60th) risk group and have a 12% to 20% risk of developing CRC by age 85. PRSs constructed using results from fine-mapping and genome-wide algorithms did not perform as well as PRS115-EAS and PRS115-EAS/EUR in risk prediction, possibly due to a small sample size. Our results indicate that CRC PRSs are promising in predicting CRC risk in East Asians and highlights the importance of using population-specific data to build CRC risk prediction models.

MutEx: a multifaceted gateway for exploring integrative pan-cancer genomic data.

Somatic mutation and gene expression dysregulation are considered two major tumorigenesis factors. While independent investigations of either factor pervade, studies of associations between somatic mutations and gene expression changes have been sporadic and nonsystematic. Utilizing genomic data collected from 11 315 subjects of 33 distinct cancer types, we constructed MutEx, a pan-cancer integrative genomic database. This database records the relationships among gene expression, somatic mutation and survival data for cancer patients. MutEx can be used to swiftly explore the relationship between these genomic/clinic features within and across cancer types and, more importantly, search for corroborating evidence for hypothesis inception. Our database also incorporated Gene Ontology and several pathway databases to enhance functional annotation, and elastic net and a gene expression composite score to aid in survival analysis. To demonstrate the usability of MutEx, we provide several application examples, including top somatic mutations associated with the most extensive expression dysregulation in breast cancer, differential mutational burden downstream of DNA mismatch repair gene mutations and composite gene expression score-based survival difference in breast cancer. MutEx can be accessed at http://www.innovebioinfo.com/Databases/Mutationdb_About.php.

Evaluating breast cancer predisposition genes in women of African ancestry.

PURPOSE: Studies conducted primarily among European ancestry women reported 12 breast cancer predisposition genes. However, etiologic roles of these genes in breast cancer among African ancestry women have been less well-investigated. METHODS: We conducted a case-control study in African American women, which included 1117 breast cancer cases and 2169 cancer-free controls, and a pooled analysis, which included 7096 cases and 8040 controls of African descent. Odds ratios of associations with breast cancer risk were estimated. RESULTS: Using sequence data, we identified 61 pathogenic variants in 12 breast cancer predisposition genes, including 11 pathogenic variants not yet reported in previous studies. Pooled analysis showed statistically significant associations of breast cancer risk with pathogenic variants in BRCA1, BRCA2, PALB2, ATM, CHEK2, TP53, NF1, RAD51C, and RAD51D (all P < .05). The associations with BRCA1, PALB2, and RAD51D were stronger for estrogen receptor (ER)-negative than for ER-positive breast cancer (P heterogeneity < .05), whereas the association with CHEK2 was stronger for ER-positive than for ER-negative breast cancer. CONCLUSION: Our study confirmed previously identified associations of breast cancer risk with BRCA1, BRCA2, PALB2, ATM, TP53, NF1, and CHEK2 and provided new evidence to extend the associations of breast cancer risk with RAD51C and RAD51D, which was identified previously in European ancestry populations, to African ancestry women.

MetaGSCA: A tool for meta-analysis of gene set differential coexpression.

Analyses of gene set differential coexpression may shed light on molecular mechanisms underlying phenotypes and diseases. However, differential coexpression analyses of conceptually similar individual studies are often inconsistent and underpowered to provide definitive results. Researchers can greatly benefit from an open-source application facilitating the aggregation of evidence of differential coexpression across studies and the estimation of more robust common effects. We developed Meta Gene Set Coexpression Analysis (MetaGSCA), an analytical tool to systematically assess differential coexpression of an a priori defined gene set by aggregating evidence across studies to provide a definitive result. In the kernel, a nonparametric approach that accounts for the gene-gene correlation structure is used to test whether the gene set is differentially coexpressed between two comparative conditions, from which a permutation test p-statistic is computed for each individual study. A meta-analysis is then performed to combine individual study results with one of two options: a random-intercept logistic regression model or the inverse variance method. We demonstrated MetaGSCA in case studies investigating two human diseases and identified pathways highly relevant to each disease across studies. We further applied MetaGSCA in a pan-cancer analysis with hundreds of major cellular pathways in 11 cancer types. The results indicated that a majority of the pathways identified were dysregulated in the pan-cancer scenario, many of which have been previously reported in the cancer literature. Our analysis with randomly generated gene sets showed excellent specificity, indicating that the significant pathways/gene sets identified by MetaGSCA are unlikely false positives. MetaGSCA is a user-friendly tool implemented in both forms of a Web-based application and an R package "MetaGSCA". It enables comprehensive meta-analyses of gene set differential coexpression data, with an optional module of post hoc pathway crosstalk network analysis to identify and visualize pathways having similar coexpression profiles.