Novel common target genes for breast cancer and colorectal cancer: A mendelian randomization and spatial transcriptomics study

Novel common target genes for breast cancer and colorectal cancer: A mendelian randomization and spatial transcriptomics study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Novel common target genes for breast cancer and colorectal cancer: A mendelian randomization and spatial transcriptomics study Rui Tang, Hongquan Cui, Pengyu Miao, Zhengrui Li, Keliang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4992169/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Breast and colorectal cancer are a major global public health problem. Breast cancer is one of the most common cancers worldwide. Colorectal cancer is the third most common cancer and the second most common cause of tumor death worldwide. Central memory T (TCM) cells are closely related to the development of tumors and important targets for immunotherapy. Therefore, identifying the common signaling molecules of these two diseases in TCM cells can improve our understanding of these diseases and lead to the development of therapies that can be effective for treating both. Methods: Single-cell RNA (scRNA) data of breast cancer (GSE161529) and colorectal cancer (GSE222300) patients was downloaded from the GEO database. The data were normalized and dimension reduced, then different T cell subsets were identified and differential gene expression analysis of central memory CD 8+ T cells was conducted. Mendelian randomization analysis, reverse causality detection, and co-localization analysis was performed to explore the relationship between differentially-expressed genes and the disease. Quasi-temporal analysis and metabolic analysis was done using scRNA sequencing technology and further analysis of gene expression and metabolism in spatial transcriptomes. Finally, the degree of association between drug target genes was analyzed by protein-protein interaction (PPI) analysis. Results: Our analysis identified four genes ( ZFP36L2 , CKS1B , PTTG1 , and ITGAE ) that were associated with risk of both breast and colorectal cancer. In the pseudotime analysis, we found that the expression levels of CKS1B and PTTG1 decreased over time (P <0.05) while ZFP36L2 and ITGAE increased over time (P <0.05). In the metabolic analysis, these four genes were closely associated with the cysteine and methionine metabolism pathways, which was corroborated in the spatial transcription analysis. Finally, the PPI analysis among the drug target genes identified an interaction between PTTG1 and CKS1B genes. Conclusion: This study reports that the ZFP36L2 , CKS1B , PTTG1 , and ITGAE genes could potentially influence breast cancer and colorectal cancer development via TCM CD8+ T cells. These four genes are putative common markers for diagnosis, treatment, and monitoring tumor response to therapies. Mendelian randomization Breast cancer Colorectal cancer Single-Cell RNA Sequencing Spatial transcriptomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Breast cancer is one of the most common cancers[ 1 ], with 2.3 million new cases diagnosed in 2020, accounting for 12.5% of all cancer diagnoses, and causing 685,000 deaths. Projections indicate that this will grow to 3 million new annual cases and more than 1 million yearly deaths by 2040[ 2 ]. Genetic predisposition is a key risk factor for breast cancer and families have been identified with high risk of developing the disease[ 3 ]. Fluctuations in the levels of hormones that affect sexual development and reproduction such as estrogen, ages at first menstruation and menopause, and aspects of pregnancy and breastfeeding have also been identified to contribute to risk levels[ 4 – 8 ]. Lifestyle and environmental factors, including physical activity, obesity, diet, alcohol intake, and smoking, further influence breast cancer incidence and progression[ 9 – 13 ]. Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer death globally[ 1 ]. In 2020, it accounted for nearly 1.93 million new cases and 940,000 deaths[ 1 ]. The incidence of CRC varies by region, with the highest numbers reported in China and the United States. While rates have stabilized or declined in developed countries, they are expected to rise in lower-income regions[ 14 , 15 ]. Lifestyle factors such as diet, alcohol, meat consumption, smoking, obesity, and low intake of dietary fiber, vitamin D, and calcium are linked to CRC incidence and mortality[ 16 ]. Aging and genetics also play significant roles in CRC risk. Advances in CRC treatment, including endoscopic therapy, surgery, radiotherapy, ablation, chemotherapy, and immunotherapy, have improved patient survival. Colonoscopy has been instrumental in early detection and treatment, reducing mortality rates[ 17 ]. However, the growing incidence of CRC presents economic and public health challenges, making early prevention and diagnosis essential. Breast cancer and CRC are major public health issues, with a shared risk profile, including family history[ 18 ]. Estrogen, while protective against CRC, can increase breast cancer risk[ 19 ]. The BRCA2 gene is overexpressed in CRC cells that are resistant to olaparib, which suggests that targeting it could improve treatment efficacy[ 20 ]. The AIB1 gene is associated with breast and other cancers, and has been found to promote CRC progression via the Notch signaling pathway[ 21 ]. These shared risks underscore the need for early diagnostic markers and new therapeutic targets for both cancers. T cells, particularly central memory T (TCM) cells, are pivotal in tumor suppression and immune therapy, with CD8 + T cells playing a crucial role in the anti-tumor response[ 22 ]. Enhancing early memory T cells can improve the efficacy of adoptive cancer immunotherapy, with CD8 + T cells exhibiting potent anti-tumor immunity[ 23 ]. We sought to identify shared druggable targets for breast cancer and CRC. We analyzed single-cell RNA (scRNA) sequencing data from cancerous and normal breast and CRC tissues from the GEO database. After isolating T cells and performing dimensionality reduction and clustering, we integrated the data to identify differentially expressed genes between CD8 + T cells and other cell types. These genes were linked to expression quantitative trait loci (eQTLs) and genome-wide association study (GWAS) data helped identify potential therapeutic targets. We validated these targets through reverse causation tests and Bayesian colocalization analysis, followed by single-cell time-series and metabolic analyses, and spatial transcriptomics to confirm metabolic profiles and gene distribution. Finally, protein-protein interaction (PPI) analyses and drug evaluations were performed to identify shared therapeutic targets for both cancers. 2. Materials and Methods 2.1 single-cell RNA sequencing Single-cell RNA sequencing (scRNA) data from breast (GSE161529) and colorectal (GSE222300) cancer patients were sourced from the GEO database. Using the Seurat V4.0 R package, cells with gene counts between 200 and 5,000 and mitochondrial gene counts less than 2,000 were selected. The data were normalized and merged to minimize batch effects. Cellular clusters were identified with "FindNeighbors" and "FindClusters," and visualized using UMAP. The celldex package was used for cell annotation. Integrated scRNA data from cancer and normal tissues were analyzed for cell-cell communication using "cell-cell chat," projecting ligand-receptor pairs onto a PPI network. Differential expression analysis between CD8 + T cells and other cells was conducted, with results considered significant at P < 0.05. 2.2 Data source The data are divided into exposure and outcome groups. For the exposure group, we used the eQTL derived from the differentially expressed genes between CD8 + T and other cells in the integrated single-cell data. Only eQTLs meeting the following criteria were included: (1) demonstrate genome-wide significant associations(P < 5× 10 − 8 ); (2) linkage disequilibrium (LD) clumping r 2 10. The outcome data for breast cancer are derived from large-scale case-control GWAS statistics involving 46,785 breast cancer cases and 42,892 control cases of European descent. The outcome data for colorectal cancer come from the Finnish database, which includes 3,022 cases of colorectal cancer and 174,006 control cases of European ancestry. These data are publicly available at https://gwas.mrcieu.ac.uk/ and https://www.finngen.fi/en/access_results . 2.3 Mendelian randomization If only one eQTL was available for a given gene, then the Wald ratio was used. When two or more genetic instruments were available, then inverse variance weighted Mendelian Randomization (MR-IVW) was applied. Heterogeneity was assessed using the Cochran Q test to identify genes associated with the risk of breast cancer and colorectal cancer. A bidirectional Mendelian randomization analysis was conducted by treating the outcome group as the exposure group and vice versa . The effect estimation was performed using MR-IVW, MR-Egger, Weighted Median, Simple Patterns, and Weighted Patterns. Results were considered statistically significant at P < 0.05. 2.4 Bayesian colocalization analysis Bayesian colocalization analysis is used to assess the probability that two traits share the same causal variant, employing the "coloc" package with default parameters to evaluate whether GWAS loci and eQTLs share the same causal variation. Bayesian colocalization provides the posterior probability for the five hypotheses of whether a single variant is shared between the two traits. In this study, we tested the posterior probability of hypothesis 3 (PPH3) where both protein and MS are associated to this region via different variants and hypothesis 4 (PPH 4) where both protein and MS are associated to this region via shared variants. Due to the limited effectiveness of colocalization analysis, the analysis is constrained to cases where the sum of PPH3 (the posterior probability that the GWAS significant signal is related to the eQTL expression, but not at the same locus) and PPH4 exceeds 0.8[ 24 , 25 ], thus identifying shared therapeutic targets for breast and colorectal cancer. 2.5 Downstream analysis of single-cell RNA sequencing technology Using single-cell RNA sequencing, we analyzed therapeutic target genes for breast and colorectal cancer identified through Mendelian randomization, filtering out genes expressed in fewer than five cells. Log-normalized single-cell data with pseudotime information were visualized. For positively and negatively expressed CD8_CM target genes, cell-cell communication analysis was conducted using Cell-Cell Chat. Ligand-receptor pairs were identified using subsetData parameters and projected onto the PPI network. 2.6 Spatial transcriptomics processing and annotation Breast cancer data was obtained from a publically available database ( https://www.10xgenomics.com/datasets/human-breast-cancer-targeted-immunology-panel-1-standard-1-2–0 ) and colorectal cancer data was sourced from a published database ( https://aacrjournals.org/cancerdiscovery/article/12/1/134/675646/Spatiotemporal-Immune-Landscape-of-Colorectal ). The Load10X_Spatial function was used to read and analyze the data for both breast cancer and CRC. Principal Component Analysis (PCA) was utilized to reduce the dimensionality of the integrated data, facilitating further downstream analysis in R (version 4.2.2), with the “RunUMAP” function applied to perform UMAP on these components. Expression of therapeutic target genes in breast cancer and CRC across cell clusters was visualized using DotPlot, and cellular metabolism was analyzed using scMetabolism. Furthermore, parametric analysis was conducted in R to explore the correlations between metabolic scoring data at each spatial location and gene features and expression patterns in T cells[ 26 ]. The results from the deconvolution of cell types were further submitted to SPOTlight for visualization[ 27 ]. 2.7 Protein-protein interaction analysis The PPI Core Network (PPICN) was used to link compounds to disease-related protein molecules. Additionally, DrugBank Online ( https://go.drugbank.com/ ) was used to evaluate the drugs that could target these genes. 3. Results 3.1 Single-cell RNA sequencing results The distribution of cells after normalization from colorectal cancer, breast cancer, and normal tissues is shown in Fig. 1 A, where the cells are relatively dispersed. After filtering and dimensionality reduction, the distribution of cells became more concentrated, as shown in Fig. 1 B. Cells from breast cancer, colorectal cancer, and normal tissues were then merged and aggregated, and visualized using the "UMAP" method to cluster each cell with similar expression together, resulting in 22 cell clusters (Fig. 1 C). Each cluster was then annotated using reference datasets from the celldex package, which includes human primary cell atlas data, identifying cell identities such as T cells, epithelial cells, endothelial cells, macrophages, fibroblasts, B cells, monocytes, tissue stem cells, and neutrophils (Fig. 1 D). As T cells are a major component of the Tumor Microenvironment (TME) and play a crucial role in tumor suppression, further clustering analysis of T cells was performed, resulting in 14 distinct clusters (Figs. 2 A and B). Based on the characteristic gene expression of the cells, manual annotations were made (Fig. 2 C), categorizing T cells into CD8_EM, CD4_REG, CD8_CM, CD8_exhau, CD4_Naiv, TH17, and CD4_EM (Fig. 2 D). The analysis then focused on cell-cell communication studies involving CD8_CM. 3.2 Interactions of CD8_CM with other cell types Cell communication analysis found that the interaction pathways between CD8_CM and other T cell clusters (CD8_EM, CD4_REG, CD4_EM, CD4_Naive, TH17, and CD8_exhau) as well as epithelial cells, endothelial cells, macrophages, fibroblasts, B cells, monocytes, tissue stem cells, and neutrophils were similar between breast and colorectal cancers. However, in colorectal cancer, CD8_CM have a greater number of interaction pathways with these cell types compared to breast cancer. In breast cancer, CD8_CM primarily interacts with these cells through pathways such as MIF-(CD74 + CXCR4), CXCL13-CXCR3, and MIF-(CD74 + CD44) as shown in Figures S1 C and D. In colorectal cancer, CD8_CM mainly engages through pathways including CCL5-CCR1, ANXA1-FRR1, MIF-(CD74 + CD44), and MIF-(CD74 + CXCR4), impacting the same classes of cells, as illustrated in Figures S1 A and B. 3.3 Screening for risk genes in breast and colorectal cancers We identified genes that were differentially expressed (P < 0.05) between CD8_CM and the above-mentioned cell types (Table S1 ). These genes were then converted into eQTLs, and significant eQTLs (P < 5×10 − 8 ) were selected (Table S2). A Mendelian randomization (MR) analysis was conducted for breast and colorectal cancers with a significance threshold of P < 0.05. The analysis identified 64 genes ( NUCKS1 , FOSL2 , ITGAE , BIRC5 , CENPM , CDKN3 , UBE2S , CLNK , SMC4 , CDC20 , FASLG , STMN1 , CENPF , TMPO ZWINT , CENPK , DLGAP5 , MAP2K2 , DUT , NASP , CCNB1 , PTPN22 , AHI1 , SMC2 , ANP32B , NUSAP1 , KIF20B , RASGEF1B , CENPE , VPS37B , SAE1 , NUF2 , ASXL2 , CCNA2 , MKI67 , PDE3B , ZFP36L2 , NR4A2 , SGO2 , HMGB2 , PTTG1 , PCLAF , TMIGD2 , TK1 , LDLRAD4 , ZEB2 , ARL6IP1 , CKS1B , UBE2C , AURKB , KPNA2 , HMGB1 , HMGN2 , CD3E , CENPW , LAYN , MXD3 , DNAJC9 , TRGC2 , TUBA1B , PCNA , ANP32E , SRSF7 , and DEK ) associated with the risk of breast cancer and seven genes ( CKS1B , PTTG1 , LCP1 , HSP90AA1 , ITGAE , PRF1 , and ZFP36L2 ) associated with the risk of CRC (Fig. 3 A and B). The genes associated with breast cancer risk are shown in Table 1 and Fig. 4 A. The genes associated with the risk of colorectal cancer are shown in Table 2 and Fig. 4 B. The genes analyzed in this study did not exhibit heterogeneity (Tables S3 and S4). Table 1 Genes Associated with Breast Cancer Risk Gene OR 95% CI P-value Risk Association NUCKS1 0.0892 0.0234–0.3400 3.99462×10^-4 Reduced Risk ITGAE 0.0447 0.0403–0.0495 Approaches 0 Reduced Risk BIRC5 0.0252 0.0233–0.0272 Approaches 0 Reduced Risk CENPM 0.0112 0.0100–0.0124 Approaches 0 Reduced Risk UBE2S 0.0099 0.0019–0.0517 4.307373×10^-8 Reduced Risk CDC20 0.0001 0.0001–0.0001 Approaches 0 Reduced Risk FASLG 0.0653 0.0591–0.0721 Approaches 0 Reduced Risk STMN1 0.0367 0.0334–0.0402 Approaches 0 Reduced Risk ZWINT 0.2073 0.1611–0.2668 2.172205×10^-34 Reduced Risk CENPK 0.3936 0.3741–0.4141 2.08499×10^-283 Reduced Risk DEK 0.0984 0.0629–0.1540 3.541778×10^-24 Reduced Risk PCNA 0.0325 0.0288–0.0367 Approaches 0 Reduced Risk CCNB1 0.0065 0.0058–0.0071 Approaches 0 Reduced Risk AHI1 0.2741 0.2595–0.2896 2.228502×10^-5 Reduced Risk ANP32B 0.0058 0.0052–0.0066 Approaches 0 Reduced Risk RASGEF1B 0.0043 0.0030–0.0062 7.349116×10^189 Reduced Risk VPS37B 0.4877 0.4502–0.5282 1.814115×10^-69 Reduced Risk SAE1 0.0225 0.0169–0.0298 1.474272×10^-151 Reduced Risk NUF2 0.0058 0.0047–0.0072 Approaches 0 Reduced Risk CCNA2 0.001 0.0008–0.0011 Approaches 0 Reduced Risk MKI67 0 0.00–0.00 Approaches 0 Reduced Risk NR4A2 1464.3163 1250.5872-1714.5724 Approaches 0 Increased Risk LDLRAD4 13.61 12.5485–14.7611 Approaches 0 Increased Risk ZEB2 39610.9575 165.9823–9452984.1047 1.506391×10^-4 Increased Risk ARL6IP1 0.0753 0.0716–0.0792 Approaches 0 Reduced Risk AURKB 0.0159 0.0146–0.0174 Approaches 0 Reduced Risk KPNA2 0.0006 0.0004–0.0007 Approaches 0 Reduced Risk HMGB1 0.0106 0.0078–0.0145 4.381093×10^-183 Reduced Risk HMGN2 0.0778 0.0682–0.0887 Approaches 0 Reduced Risk CENPW 0.0002 0.0002–0.0003 Approaches 0 Reduced Risk DNAJC9 0.0532 0.0161–0.1765 1.603169×10^-6 Reduced Risk TRGC2 0.3183 0.2965–0.3413 1.452474×10^-223 Reduced Risk FOSL2 5519.5871 4269.0160–7136.5022 Approaches 0 Increased Risk CDKN3 272.5049 241.0733–308.0345 Approaches 0 Increased Risk CLNK 23541.1161 18746.8445–29561.4629 Approaches 0 Increased Risk SMC4 56.6641 52.3585–61.3237 Approaches 0 Increased Risk SRSF7 979126.666 739095.3724–1297111.3929 Approaches 0 Increased Risk CENPF 2.2175 1.7736–2.7724 2.775729×10^-12 Increased Risk TMPO 797.1278 683.0948–930.1971 Approaches 0 Increased Risk TUBA1B 3473.9359 2954.3501–4084.9019 Approaches 0 Increased Risk DLGAP5 24349.4693 19882.9579–29819.3387 Approaches 0 Increased Risk MAP2K2 23.9293 22.1831–25.8130 Approaches 0 Increased Risk DUT 5.0387 4.2692–5.9470 1.576729×10^-81 Increased Risk PTPN22 28.8905 27.0316–30.8772 Approaches 0 Increased Risk SMC2 27.6446 25.9311–29.4713 Approaches 0 Increased Risk ANP32B 0.0058 0.0052–0.0066 Approaches 0 Reduced Risk NUSAP1 8.1568 4.3107–15.4344 1.115594×10^-10 Increased Risk KIF20B 18.3976 17.2445–19.6278 Approaches 0 Increased Risk CENPE 5609.514 4561.0927–6898.9273 Approaches 0 Increased Risk ANP32E 14.6967 13.4874–16.0143 Approaches 0 Increased Risk ASXL2 103.2294 87.7300–121.4671 Approaches 0 Increased Risk ZFP36L2 10992.8283 9201.7136–13132.5837 Approaches 0 Increased Risk SGO2 850.0735 722.9885–999.4972 Approaches 0 Increased Risk HMGB2 422.228 325.3194–548.0046 Approaches 0 Increased Risk PTTG1 83.366 69.6113–99.8385 Approaches 0 Increased Risk PCLAF 6.208 5.4655–7.0514 1.187580×10^-173 Increased Risk TMIGD2 10.0123 8.5656–11.7034 4.39975×10^-184 Increased Risk MXD3 293.2167 11.8219–7272.5849 5.249580×10^-4 Increased Risk CKS1B 17308.2749 13304.8564–22516.3182 Approaches 0 Increased Risk UBE2C 6136.5452 5158.1841–7300.4738 Approaches 0 Increased Risk LAYN 9.5281 7.6405–11.8821 4.356807×10^-89 Increased Risk CD3E 90.1386 82.4799–98.5086 Approaches 0 Increased Risk TK1 336.804 242.1629–468.4322 5.838693×10^-262 Increased Risk NR4A2 1464.3163 1250.5872-1714.5724 Approaches 0 Increased Risk Table 2 Genes Associated with Colorectal Cancer Risk Gene OR 95% CI P-value Risk Association CKS1B 0.3363 0.1537–0.7360 0.006386 Reduced Risk PTTG1 0.6654 0.4515–0.9806 0.039511 Reduced Risk LCP1 0.8625 0.6975–0.9937 0.042347 Reduced Risk HSP90AA1 1.2731 1.0782–1.5033 0.004406 Increased Risk ITGAE 1.305 1.0275–1.6575 0.029074 Increased Risk PRF1 1.368 1.0177–1.8389 0.037864 Increased Risk ZFP36L2 2.2596 1.3801–3.6997 0.001192 Increased Risk 3.4 Sensitivity analysis of pathogenic genes Reverse Mendelian randomization analysis revealed that SRSF7 , TUBA1B , DEK , PCNA , and ANP32E have inverse causal relationships with breast cancer, while the remaining genes show no reverse causal relationship with breast cancer (Figure S2). We did not find any of the genes that we analyzed to exhibit a reverse causal relationship with CRC (Figure S3). Bayesian colocalization analysis indicated that 59 genes ( NUCKS1 , FOSL2 , ITGAE , BIRC5 , CENPM , CDKN3 , UBE2S , CLNK , SMC4 , CDC20 , FASLG , STMN1 , CENPF , TMPO , ZWINT , CENPK, DLGAP5 , MAP2K2 , DUT , NASP , CCNB1 , PTPN22 , AHI1 , SMC2 , ANP32B , NUSAP1 , KIF20B , RASGEF1B , CENPE , VPS37B , SAE1 , NUF2 , ASXL2 , CCNA2 , MKI67 , PDE3B , ZFP36L2 , NR4A2 , SGO2 , HMGB2 , PTTG1 , PCLAF , TMIGD2 , TK1 , LDLRAD4 , ZEB2 , ARL6IP1 , CKS1B , UBE2C , AURKB , KPNA2 , HMGB1 , HMGN2 , CD3E , CENPW , LAYN , MXD3 , DNAJC9 , and TRGC2 ) had PPH3 + PPH4 > 0.8, suggesting a strong causal relationship with breast cancer (P 0.8, indicating a strong causal relationship with the disease (P < 0.05) (Table S5), making this gene a priority candidate for a common therapeutic target for both diseases. Although the colocalization results for ITGAE , PTTG1 , and CKS1B were not significant, MR analysis showed that these three genes still have a causal relationship with both breast cancer and CRC (P < 0.05), suggesting that they may also serve as common therapeutic targets (Figure S9). 3.5 Downstream analysis We identified the ZFP36L2 gene as a common therapeutic target for both breast cancer and colorectal cancer that should be prioritized. The ITGAE , PTTG1 and CKS1B genes are also potential common therapeutic targets for these conditions. We therefore analyzed these four therapeutic target genes to validate them as targets at the single-cell level. Additionally, we performed simulated time-series analyses of these as drug targets associated with the two diseases. As depicted in Figs. 5 A and 6 A, the genes marked in purple represent risk genes. Genes positioned above the horizontal line in the diagrams indicate an increase in gene expression over time, whereas those below the line exhibit a decrease in gene expression as time progresses. ZFP36L2 and ITGAE are above the horizontal line, indicating an increase in their expression levels over time. Conversely, CKS1B and PTTG1 are below the line, indicating that their expression levels are decreasing over time (P < 0.05). Specifically, the expression of CKS1B (Figs. 5 B and 6 B ), PTTG1 (Figs. 5 E and 6 E ) decreases over time in both diseases(P < 0.05). Conversely, the expression levels of ZFP36L2 (Figs. 5 C and 6 C) and ITGAE (Fig. 5 D and 6 D) increased over time (P < 0.05). We next explored the interactions between these target genes within CD8_CM and other clustered cells. In breast cancer, we found that CKS1B + CD8_CM, ZFP36L2 + CD8_CM, ITGAE + CD8_CM, PTTG1 + CD8_CM, CKS1B-CD8_CM, ZFP36L2-CD8_CM, ITGAE- CD8_CM PTTG1- CD8_CM mainly interact with other cell types through the CXCL13-CXCR3 pathways (Figure S10 A-H).. In CRC, we found that CKS1B + CD8_CM, ZFP36L2 + CD8_CM, ITGAE + CD8_CM, PTTG1 + CD8_CM, CKS1B-CD8_CM, ITGAE-CD8_CM, PTTG1-CD8_CM mainly interact with other cell types through the MIF- (CD74 + CXCR4), MIF- (CD74 + CD44) pathways (Figure S11 A-H). Additionally, the metabolic roles of cells were explored, revealing associations between these four drug-target genes ( ZFP36L2 , ITGAE , PTTG1 , and CKS1B ) and pathways involving cysteine and methionine metabolism (Figure S10 I–L and Figure S11 I–L). 3.6 Spatial transcriptomics We identified 12 distinct cell clusters in breast cancer samples (Fig. 7 A) and 13 distinct cell clusters in CRC (Fig. 7 B) after unsupervised clustering and UMAP visualization of spatial barcode patches. DotPlot analysis was employed to assess expression levels of common drug target (Fig. 7 C, D). Subsequent analysis identified ZFP36L2 as having the highest expression level in cell cluster 0 of breast cancer and in cell cluster 2 of CRC. Additionally, CKS1B , ITGAE , and PPTG1 exhibited the highest expression levels in breast cancer cell cluster 12 and CRC cell cluster 4. Metabolic assessment using scMetabolism showed that in clusters with expression of CKS1B , ITGAE , and PPTG1 , the average scores for cysteine and methionine metabolism pathways were 0.8 higher compared to other cell clusters, which is consistent with single-cell sequencing results (Figure S13). Positive correlations in expression and spatial distribution of ZFP36L2 , CKS1B , ITGAE , and PPTG1 were observed (Figs. 8 and 9 ), indicating that regulation of these drug target genes is altered in both breast cancer and CRC. 3.7 Pharmacological evaluation of protein-protein interactions and potential drug targets PPI analysis among these four genes only predicted interactions between PTTG1 and CKS1B (Figure S13). Among these four genes, only CKS1B has been targeted for drug development, and fluoxetine has been investigated as a treatment to inhibit its expression to arrest the growth of breast cancer[ 28 ] and enhance the sensitivity of bladder cancer to cisplatin[ 29 ]. 4. Discussion This study employed scRNA sequencing to identify distinct subpopulations of T cells and analyze differentially expressed genes between CD8_CM and other cell types using R programming tools. We further performed cellular communication analysis to reveal genetic interactions and associations among cells. The screened genes were transformed into eqtLs to identify common drug targets for breast and colorectal cancer. We employed a combination of MR and colocalization analyses to evaluate potential therapeutic targets for breast cancer and CRC as a clinical translation of GWAS findings[ 30 ]. "Causal relationships" identified by MR could represent reverse causation, horizontal pleiotropy, or confounding due to linkage disequilibrium (LD)[ 31 ]. By conducting primary MR analyses on identified differentially-expressed genes, we were able to exclude risk genes associated with reverse causation from further analysis. To mitigate bias due to horizontal pleiotropy, eQTLs were used as exposures, given their direct role in the transcription and/or translation of genes associated with eQTLs[ 32 ]. Moreover, Bayesian colocalization set a critical threshold for posterior probabilities at 0.8, identifying NUCKS1 , FOSL2 , ITGAE, BIRC5 , CENPM , CDKN3 , UBE2S , CLNK , SMC4 , CDC20 , FASLG , STMN1 , CENPF , TMPO , ZWINT , CENPK, DLGAP5 , MAP2K2 , DUT , NASP , CCNB1 , PTPN22 , AHI1 , SMC2 , ANP32B , NUSAP1 , KIF20B , RASGEF1B , CENPE , VPS37B , SAE1 , NUF2 , ASXL2 , CCNA2 , MKI67 , PDE3B , ZFP36L2 , NR4A2 , SGO2 , HMGB2 , PTTG1 , PCLAF , TMIGD2 , TK1 , LDLRAD4 , ZEB2 , ARL6IP1 , CKS1B , UBE2C, AURKB, KPNA2 , HMGB1 , HMGN2 , CD3E , CENPW , LAYN , MXD3 , DNAJC9 , and TRGC2 as likely harboring the same variants. In the colocalization analysis for CRC, ZFP36L2 was identified by MR as being suggested to share the same variants. Because this gene was identified in analyses of both breast cancer and CRC, it could potentially be a common therapeutic target for both diseases. Although the colocalization between CKS1B , PTTG1 , and ITGAE is not strong in CRC, Mendelian analysis indicates a causal relationship between these genes and both diseases, suggesting that they might represent common targets for colorectal cancer and breast cancer (P < 0.05). ZFP36L2 dysregulation is one of the key drivers of colorectal cancer. Mutations in this gene reduce levels of the encoded RNA-binding protein and CRISPR/Cas9-mediated knockout of this gene results in an enhancement of the migration and invasion capabilities of cells to promote carcinogenesis[ 33 ]. This study is the first to find an association between ZFP36L2 and the risk of breast cancer, with increased expression levels of ZFP36L2 raising the risk of developing the disease (P < 0.05).CKS1B is typically upregulated in CRC tissues and knockout of the gene can inhibit the proliferation and migration of CRC cells, providing a new potential strategy for treating colorectal cancer[ 34 ]. Specifically in breast cancer, elevated expression of CKS1B is associated with increased turnover of the Kip1 gene and cell renewal, leading to increased cell proliferation and poor prognosis[ 34 ]. Overall, CKS1B may become a potential therapeutic target and prognostic marker for various cancers including CRC, breast cancer, pancreatic cancer, and retinoblastoma. In breast cancer patients, PTTG1 mRNA levels increase with elevated estrogen levels, which known risk factor for breast cancer, suggesting that estrogen may influence the progression of breast cancer by regulating PTTG1[ 35 ]. This study is the first to find an association between PTTG1 and the risk of CRC with increased expression levels of ZFP36L2 raising the risk of developing the disease (P < 0.05). ITGAE, also known as CD103 or CD4T[ 36 ]. Mature differentiated CD103-specific cytotoxic T lymphocytes can self-regulate by producing activated TGFβ1, increasing T cell receptor antigen sensitivity and enhancing the rapid recognition and clearance of cancer cells[ 37 ]. In CRC patients, the degree of ITGAE + lymphocyte invasion correlates with patient survival, which is potentially linked to interferon-responsive chemokine and epithelial-mesenchymal transition signaling pathways, suggesting that ITGAE is a possible biomarker for CRC[ 38 ]. The number of CD103 + T cells is higher in breast cancer tissues than in normal tissues because of increased lymphocyte migration and retention, which impacts anti-tumor immune functions[ 39 ]. Through single-cell analysis and simulated time-series, we found that the expression level of ZFP36L2, ITGAE gradually increases over time, whereas the expression of CKS1B, PTTG1 decreases. We examined the relationships between drug target genes that are differentially expressed between different cells and identified ligands which could facilitate communication between different cell types. These include ANXA1-FPR1, ANXA1-FPR2, MIF-(CD74 + CD44), LGALS9-CD45, MIF-(CD74 + CXCR4), CCL5-CCR5 for + CD8_CM, and CCL5-CCR1, MIF-(CD74 + CD44), CCL5-CCR5, MIF-(CD74 + CXCR4) for -CD8_CM. Notably, the LGALS9-CD45 signaling pathway, in addition to impacting breast and colorectal cancers, also affects gastric cancer by influencing monocytes within the tumor microenvironment, thereby significantly impacting T cells and endothelial cells[ 40 ]. We performed metabolomic studies and found that the drug target genes were linked to the metabolism of cysteine and methionine. Previous reports suggest that cysteine metabolism is associated with a variety of diseases, such as cardiovascular diseases (CVD), ischemic stroke, neurological disorders, diabetes, lung and colorectal cancer, renal failure-related diseases, and vitiligo[ 41 ], whereas the relationship of methionine metabolism with diseases has not been extensively studied yet. In the spatial transcriptomics metabolic analysis, we found that the highest scoring pathways related to drug targets were cysteine and methionine metabolism. Analysis of the distribution and expression of drug target genes revealed that most are in a mid-differentiation state. PPI analysis showed very limited interactions among potential drug target genes, with a significant interaction only observed between PTTG1 and CKS1B . Fluoxetine is a drug that has been developed to reduce expression of CKS1B which has been shown to inhibit the growth of breast cancer[ 26 ] and enhance the sensitivity of bladder cancer to cisplatin[ 27 ]. The database used in this study is limited to a European population, and so whether the conclusions that we have made are specific to this population or globally applicable requires further investigation. Additionally, the statistical analyses employed and the strict significance thresholds may have filtered out some marker genes which could potentially be common drug targets for breast and colorectal cancer. Therefore, further extensive database analyses and human case studies are necessary to assess drug target genes associated with the development and progression of breast cancer and CRC. While every cancer has context-specific biology, previous studies have reported that some risk genes, such as CDC20 , play a common role in both breast and colorectal cancers[ 42 ]. We did not find that CDC20 is a common risk for both breast cancer and CRC, but this may be due to sample size and population-specific factors. We identified several drug targets and their interactions with common signaling pathways in the PPI analysis. However, PPI analysis is only suggestive, not conclusive for clinical research, and has its limitations. Although we have reported the roles of pathways as well as the expression and distribution of genes in single-cell and spatial transcriptomics, further experimental validation is required. Additionally, studies need to include more diverse, non-European populations to evaluate the applicability of these findings for clinical use. 5. Conclusion In conclusion, the genes ZFP36L2 , CKS1B , PTTG1 , and ITGAE may serve as common drug targets for breast and colorectal cancers. They might influence the pathogenesis and pathophysiology of these cancers through functioning in CD8_CM in addition to potentially affecting the cancer cells through altering cysteine and methionine metabolic pathways. The four genes could be potential therapeutic targets as well as biomarkers for screening for the presence of cancer and monitoring the effectiveness of therapies. Abbreviations CRC: Colorectal cancer; BRCA2: Breast Cancer Gene 2; TME: Tumor Microenvironment; T_CM: central memory T cells; CD8_CM: central memory CD8+ T cells; PPI: protein-protein interaction; scRNA: single-cell RNA; GEO: Gene Expression Omnibus; eQTL: expression quantitative trait loci; MR-IVW: inverse variance weighted Mendelian Randomization; PCA: Principal Component Analysis; PPICN: Protein-Protein Interaction Core Network; MR: Mendelian randomization; ECM: extracellular matrix; LD: linkage disequilibrium; CVD: cardiovascular diseases. Declarations Ethics approval The data used is from public dataset, and the original data has obtained ethical approval. Consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The data used and/or analysed during the current study available from the corresponding author on reasonable request. Competing interests All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Funding Financial support came from Shanghai Jiao Tong University School of Medicine. Authors' contributions Rui Tang: Conceptualization, Formal analysis, Methodology, Writing-original draft; Hongquan Cui: Validation, Visualization, Writing-original draft; Pengyu Miao: Resources, Writing-review & editing; Zhengrui Li: Investigation, Validation, Resources, Writing-review & editing; Keliang Wang: Supervision, Resources, Writing-review & editing. Acknowledgements We would also like to thank all laboratory members for helpful discussions. References Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians 2021 , 71 , 209-249, doi:10.3322/caac.21660. Arnold, M.; Morgan, E.; Rumgay, H.; Mafra, A.; Singh, D.; Laversanne, M.; Vignat, J.; Gralow, J.R.; Cardoso, F.; Siesling, S.; et al. Current and future burden of breast cancer: Global statistics for 2020 and 2040. 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Rehman, T.; Shabbir, M.A.; Inam-Ur-Raheem, M.; Manzoor, M.F.; Ahmad, N.; Liu, Z.W.; Ahmad, M.H.; Siddeeg, A.; Abid, M.; Aadil, R.M. Cysteine and homocysteine as biomarker of various diseases. Food science & nutrition 2020 , 8 , 4696-4707, doi:10.1002/fsn3.1818. Wang, L.; Zhang, J.; Wan, L.; Zhou, X.; Wang, Z.; Wei, W. Targeting Cdc20 as a novel cancer therapeutic strategy. Pharmacology & therapeutics 2015 , 151 , 141-151, doi:10.1016/j.pharmthera.2015.04.002. Additional Declarations No competing interests reported. Supplementary Files originaldata.rar Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4992169","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":347264490,"identity":"a9e993a2-9c75-45c2-9218-ba97e3875428","order_by":0,"name":"Rui Tang","email":"","orcid":"","institution":"Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Tang","suffix":""},{"id":347264492,"identity":"b7c4c526-7c4a-444e-97a4-2b189f02b530","order_by":1,"name":"Hongquan Cui","email":"","orcid":"","institution":"Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongquan","middleName":"","lastName":"Cui","suffix":""},{"id":347264493,"identity":"a3ad514b-b68b-45da-a389-7543a435764d","order_by":2,"name":"Pengyu Miao","email":"","orcid":"","institution":"Suzhou Jiulong Hospital, School of Medicine, Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Pengyu","middleName":"","lastName":"Miao","suffix":""},{"id":347264496,"identity":"541f8eaa-bfca-4592-861d-c2a35bfbb0ad","order_by":3,"name":"Zhengrui Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBACPhCRYCAhxw+kQGzGBkJa2EDEhwIbY8kGhsQGorUwzviQlrjhAEQ1EVr4zz5g5jE4bGx8/sDzxzwMNrIbDjA/e4DfluMGIC1yZjcSEpt5GNKMNxxgMzfAq4WxjQFsi9kNBpCWw0AX8rBJ4NXCzAbWkri5/wBIy38itLCxAb1vAPQ+A9hhB4jQwgMMsw8GNsYSQL/MnGOQbDzzMJsZXi38/MeAcfgHGJX9ZxI+vKmwk+073vwMrxYgYP8BoXkSGBhAQcVMQD2y1gPEqx0Fo2AUjIIRBQAYTELV7NPWCAAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Zhengrui","middleName":"","lastName":"Li","suffix":""},{"id":347264499,"identity":"0720b353-50d2-4632-8c80-ca9e6df066b2","order_by":4,"name":"Keliang Wang","email":"","orcid":"","institution":"Ningbo No. 2 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Keliang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-08-28 15:19:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4992169/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4992169/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66894556,"identity":"949bb15b-ecf2-431e-9ba8-6d45130d5be9","added_by":"auto","created_at":"2024-10-17 15:04:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":757703,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell RNA sequencing analysis. A: Distribution of unfiltered cells, showing scattered cells in breast cancer (BC), colorectal cancer (CC) and normal corresponding tissues (CT). B: After the distribution of the cells after filtration and dimension reduction, the cells are more concentrated. C: Classification of cell clusters under the UAMP algorithm showing 22 cell clusters. D: Cell annotation performed under the UAMP algorithm.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4992169/v1/55ad848883545c72c4c9758c.png"},{"id":66893877,"identity":"1ec86a16-4354-4af6-bcdd-fcfc4d0bd143","added_by":"auto","created_at":"2024-10-17 14:56:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":535202,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell RNA sequencing analysis. A: Classification of T cell clusters under the UAMP algorithm, showing 14 cell clusters. B: The proportion of cell clusters of T cells in breast cancer (BC), colorectal cancer (CC) and normal corresponding tissues (CT). C: Expression of the cell cluster signature genes. D: Cell annotation performed under the UAMP algorithm.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4992169/v1/5bfc930c8865e64bb76a8d70.png"},{"id":66893872,"identity":"9ffe8878-6e03-47b7-892f-6dd48b52bfb5","added_by":"auto","created_at":"2024-10-17 14:56:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":177002,"visible":true,"origin":"","legend":"\u003cp\u003eRisk plots for cancer and genes. A: Risk map of breast cancer and genes showing the genetic prediction level of 64 genes and breast cancer risk. B: Risk map of colorectal cancer and genes showing that the genetic prediction level of seven genes is related to colorectal cancer risk.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4992169/v1/c45a10ebe1f5d90c67f27663.png"},{"id":66893880,"identity":"86f7a943-f900-4774-8167-88becefb40de","added_by":"auto","created_at":"2024-10-17 14:56:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2475686,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization analysis of cancer and genes. A: Mendelian randomization analysis indicates a causal relationship for these genes and breast cancer (P \u0026lt;0.05). B: Mendelian randomization analysis indicates a causal relationship for these genes and colorectal cancer (P \u0026lt;0.05).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4992169/v1/bd05405ae10071986544e607.png"},{"id":66894557,"identity":"67e278b0-7920-403a-9c9d-b55eeff7f68d","added_by":"auto","created_at":"2024-10-17 15:04:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":527341,"visible":true,"origin":"","legend":"\u003cp\u003eQuasi-timing analysis. A: Time timing analysis of central memory T cell target genes in breast cancer. The purple label in the figure represents risk genes. The genes above the horizontal line show increased expression over time, while the genes below the horizontal line show decreased expression over time. B: Trend of \u003cem\u003eCKS1B\u003c/em\u003eexpression in breast cancer, showing decreased \u003cem\u003eCKS1B\u003c/em\u003e expression over time (P\u0026lt;0.05). C: Trend of \u003cem\u003eZFP36L2\u003c/em\u003eexpression in breast cancer, showing an increase in \u003cem\u003eZFP36L2\u003c/em\u003e expression levels over time (P\u0026lt;0.05). D: Trend of \u003cem\u003eITGAE\u003c/em\u003e expression in breast cancer, showing a progressive increase of \u003cem\u003eITGAE\u003c/em\u003eexpression over time (P\u0026lt;0.05). E: Trend of \u003cem\u003ePTTG1\u003c/em\u003e expression in breast cancer, showing a progressive decrease in \u003cem\u003ePTTG1\u003c/em\u003e expression over time (P\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4992169/v1/1ae971c1b9b3318076cbf415.png"},{"id":66893873,"identity":"71de84a0-6777-45ad-b94d-dac62b88224e","added_by":"auto","created_at":"2024-10-17 14:56:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":461680,"visible":true,"origin":"","legend":"\u003cp\u003eQuasi-timing analysis. A: Time timing analysis of central memory T cell target genes in breast cancer. The purple label in the figure represents risk genes. The genes above the horizontal line show increased expression over time, while the genes below the horizontal line show decreased expression over time. B: Trend of \u003cem\u003eCKS1B\u003c/em\u003eexpression in colorectal cancer, showing decreased \u003cem\u003eCKS1B\u003c/em\u003e expression over time (P\u0026lt;0.05). C: Trend of \u003cem\u003eZFP36L2\u003c/em\u003eexpression in colorectal cancer, showing an increase in \u003cem\u003eZFP36L2\u003c/em\u003eexpression levels over time (P\u0026lt;0.05). D: Trend of \u003cem\u003eITGAE\u003c/em\u003e expression observed in colorectal cancer, showing a progressive increase in \u003cem\u003eITGAE\u003c/em\u003e expression over time (P\u0026lt;0.05). E: Trend of \u003cem\u003ePTTG1\u003c/em\u003eexpression in colorectal cancer, showing a progressive decrease in \u003cem\u003ePTTG1\u003c/em\u003eexpression over time (P\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4992169/v1/edd8c86e363227cee39f509a.png"},{"id":66893879,"identity":"6f06f890-00c5-4d99-808c-c0d1f1d92400","added_by":"auto","created_at":"2024-10-17 14:56:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1353255,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003eluster analysis. A: Categorical cluster of breast cancer, showing 12 clusters. B: Classification clusters of colorectal cancer, showing 14 clusters. C: Expression of memory T cell target genes in breast cancer, showing the expression of drug target genes in the cell cluster. D: Expression of memory T cell target genes in colorectal cancer. \u003cem\u003eZFP36L2\u003c/em\u003ehad the highest expression level in breast cancer cluster 0 and colorectal cancer cluster 2. \u003cem\u003eCKS1B\u003c/em\u003e, \u003cem\u003eITGAE\u003c/em\u003e, and \u003cem\u003ePPTG1\u003c/em\u003e showed the highest expression levels in breast cancer cluster 12 and colorectal cancer cluster 4.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4992169/v1/e1a20aa6907092762bbe2aa0.png"},{"id":66893875,"identity":"2fa91c10-7158-4211-8aa7-433951087e92","added_by":"auto","created_at":"2024-10-17 14:56:35","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3472323,"visible":true,"origin":"","legend":"\u003cp\u003eExpression and distribution of drug target genes in breast cancer. A: \u003cem\u003eZFP36L2\u003c/em\u003e. B: \u003cem\u003eCKS1B\u003c/em\u003e. C: \u003cem\u003ePTTG1\u003c/em\u003e. D: \u003cem\u003eITGAE\u003c/em\u003e. \u003cem\u003eZFP36L2\u003c/em\u003e, \u003cem\u003eCKS1B\u003c/em\u003e, and \u003cem\u003ePTTG1\u003c/em\u003e are in a mid-stage differentiation and \u003cem\u003eITGAE \u003c/em\u003eis in an intermediate-stage differentiation.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4992169/v1/c49aa2357d9822c58c8917d2.png"},{"id":66893878,"identity":"efee394e-a76a-4ecb-8dc7-008881fbe1f1","added_by":"auto","created_at":"2024-10-17 14:56:35","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2780158,"visible":true,"origin":"","legend":"\u003cp\u003eExpression and distribution of drug target genes in colorectal cancer. A: \u003cem\u003eZFP36L2\u003c/em\u003e. B: \u003cem\u003eCKS1B\u003c/em\u003e. C: \u003cem\u003ePTTG1\u003c/em\u003e. D: \u003cem\u003eITGAE\u003c/em\u003e. \u003cem\u003eZFP36L2\u003c/em\u003e, \u003cem\u003eCKS1B\u003c/em\u003e, and \u003cem\u003ePTTG1\u003c/em\u003e are in a mid-stage differentiation and \u003cem\u003eITGAE \u003c/em\u003eis in an intermediate-stage differentiation.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4992169/v1/bd38ad5f5d16380204997904.png"},{"id":70666898,"identity":"d2256697-4e88-4a5c-b9b7-3da674cd97f3","added_by":"auto","created_at":"2024-12-05 12:02:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14115627,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4992169/v1/1b5ef062-d5bd-43ee-99bf-11d57551928c.pdf"},{"id":66893882,"identity":"f9f134d5-292f-4c80-a256-da21b0377adf","added_by":"auto","created_at":"2024-10-17 14:56:46","extension":"rar","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":586959511,"visible":true,"origin":"","legend":"","description":"","filename":"originaldata.rar","url":"https://assets-eu.researchsquare.com/files/rs-4992169/v1/881ab7e9f611fb5c1931261c.rar"}],"financialInterests":"No competing interests reported.","formattedTitle":"Novel common target genes for breast cancer and colorectal cancer: A mendelian randomization and spatial transcriptomics study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBreast cancer is one of the most common cancers[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], with 2.3\u0026nbsp;million new cases diagnosed in 2020, accounting for 12.5% of all cancer diagnoses, and causing 685,000 deaths. Projections indicate that this will grow to 3\u0026nbsp;million new annual cases and more than 1\u0026nbsp;million yearly deaths by 2040[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Genetic predisposition is a key risk factor for breast cancer and families have been identified with high risk of developing the disease[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Fluctuations in the levels of hormones that affect sexual development and reproduction such as estrogen, ages at first menstruation and menopause, and aspects of pregnancy and breastfeeding have also been identified to contribute to risk levels[\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Lifestyle and environmental factors, including physical activity, obesity, diet, alcohol intake, and smoking, further influence breast cancer incidence and progression[\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eColorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer death globally[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In 2020, it accounted for nearly 1.93\u0026nbsp;million new cases and 940,000 deaths[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The incidence of CRC varies by region, with the highest numbers reported in China and the United States. While rates have stabilized or declined in developed countries, they are expected to rise in lower-income regions[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Lifestyle factors such as diet, alcohol, meat consumption, smoking, obesity, and low intake of dietary fiber, vitamin D, and calcium are linked to CRC incidence and mortality[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Aging and genetics also play significant roles in CRC risk. Advances in CRC treatment, including endoscopic therapy, surgery, radiotherapy, ablation, chemotherapy, and immunotherapy, have improved patient survival. Colonoscopy has been instrumental in early detection and treatment, reducing mortality rates[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, the growing incidence of CRC presents economic and public health challenges, making early prevention and diagnosis essential.\u003c/p\u003e \u003cp\u003eBreast cancer and CRC are major public health issues, with a shared risk profile, including family history[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Estrogen, while protective against CRC, can increase breast cancer risk[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The \u003cem\u003eBRCA2\u003c/em\u003e gene is overexpressed in CRC cells that are resistant to olaparib, which suggests that targeting it could improve treatment efficacy[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The \u003cem\u003eAIB1\u003c/em\u003e gene is associated with breast and other cancers, and has been found to promote CRC progression via the Notch signaling pathway[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These shared risks underscore the need for early diagnostic markers and new therapeutic targets for both cancers. T cells, particularly central memory T (TCM) cells, are pivotal in tumor suppression and immune therapy, with CD8\u0026thinsp;+\u0026thinsp;T cells playing a crucial role in the anti-tumor response[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Enhancing early memory T cells can improve the efficacy of adoptive cancer immunotherapy, with CD8\u0026thinsp;+\u0026thinsp;T cells exhibiting potent anti-tumor immunity[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe sought to identify shared druggable targets for breast cancer and CRC. We analyzed single-cell RNA (scRNA) sequencing data from cancerous and normal breast and CRC tissues from the GEO database. After isolating T cells and performing dimensionality reduction and clustering, we integrated the data to identify differentially expressed genes between CD8\u0026thinsp;+\u0026thinsp;T cells and other cell types. These genes were linked to expression quantitative trait loci (eQTLs) and genome-wide association study (GWAS) data helped identify potential therapeutic targets. We validated these targets through reverse causation tests and Bayesian colocalization analysis, followed by single-cell time-series and metabolic analyses, and spatial transcriptomics to confirm metabolic profiles and gene distribution. Finally, protein-protein interaction (PPI) analyses and drug evaluations were performed to identify shared therapeutic targets for both cancers.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 single-cell RNA sequencing\u003c/h2\u003e \u003cp\u003eSingle-cell RNA sequencing (scRNA) data from breast (GSE161529) and colorectal (GSE222300) cancer patients were sourced from the GEO database. Using the Seurat V4.0 R package, cells with gene counts between 200 and 5,000 and mitochondrial gene counts less than 2,000 were selected. The data were normalized and merged to minimize batch effects. Cellular clusters were identified with \"FindNeighbors\" and \"FindClusters,\" and visualized using UMAP. The celldex package was used for cell annotation. Integrated scRNA data from cancer and normal tissues were analyzed for cell-cell communication using \"cell-cell chat,\" projecting ligand-receptor pairs onto a PPI network. Differential expression analysis between CD8\u0026thinsp;+\u0026thinsp;T cells and other cells was conducted, with results considered significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data source\u003c/h2\u003e \u003cp\u003eThe data are divided into exposure and outcome groups. For the exposure group, we used the eQTL derived from the differentially expressed genes between CD8\u0026thinsp;+\u0026thinsp;T and other cells in the integrated single-cell data. Only eQTLs meeting the following criteria were included: (1) demonstrate genome-wide significant associations(P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e); (2) linkage disequilibrium (LD) clumping r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; (3) F-statistics\u0026thinsp;\u0026gt;\u0026thinsp;10.\u003c/p\u003e \u003cp\u003eThe outcome data for breast cancer are derived from large-scale case-control GWAS statistics involving 46,785 breast cancer cases and 42,892 control cases of European descent. The outcome data for colorectal cancer come from the Finnish database, which includes 3,022 cases of colorectal cancer and 174,006 control cases of European ancestry. These data are publicly available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/en/access_results\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/en/access_results\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Mendelian randomization\u003c/h2\u003e \u003cp\u003eIf only one eQTL was available for a given gene, then the Wald ratio was used. When two or more genetic instruments were available, then inverse variance weighted Mendelian Randomization (MR-IVW) was applied. Heterogeneity was assessed using the Cochran Q test to identify genes associated with the risk of breast cancer and colorectal cancer.\u003c/p\u003e \u003cp\u003eA bidirectional Mendelian randomization analysis was conducted by treating the outcome group as the exposure group and \u003cem\u003evice versa\u003c/em\u003e. The effect estimation was performed using MR-IVW, MR-Egger, Weighted Median, Simple Patterns, and Weighted Patterns. Results were considered statistically significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Bayesian colocalization analysis\u003c/h2\u003e \u003cp\u003eBayesian colocalization analysis is used to assess the probability that two traits share the same causal variant, employing the \"coloc\" package with default parameters to evaluate whether GWAS loci and eQTLs share the same causal variation. Bayesian colocalization provides the posterior probability for the five hypotheses of whether a single variant is shared between the two traits. In this study, we tested the posterior probability of hypothesis 3 (PPH3) where both protein and MS are associated to this region via different variants and hypothesis 4 (PPH 4) where both protein and MS are associated to this region via shared variants. Due to the limited effectiveness of colocalization analysis, the analysis is constrained to cases where the sum of PPH3 (the posterior probability that the GWAS significant signal is related to the eQTL expression, but not at the same locus) and PPH4 exceeds 0.8[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], thus identifying shared therapeutic targets for breast and colorectal cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Downstream analysis of single-cell RNA sequencing technology\u003c/h2\u003e \u003cp\u003eUsing single-cell RNA sequencing, we analyzed therapeutic target genes for breast and colorectal cancer identified through Mendelian randomization, filtering out genes expressed in fewer than five cells. Log-normalized single-cell data with pseudotime information were visualized. For positively and negatively expressed CD8_CM target genes, cell-cell communication analysis was conducted using Cell-Cell Chat. Ligand-receptor pairs were identified using subsetData parameters and projected onto the PPI network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Spatial transcriptomics processing and annotation\u003c/h2\u003e \u003cp\u003eBreast cancer data was obtained from a publically available database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.10xgenomics.com/datasets/human-breast-cancer-targeted-immunology-panel-1-standard-1-2\u0026ndash;0\u003c/span\u003e\u003cspan address=\"https://www.10xgenomics.com/datasets/human-breast-cancer-targeted-immunology-panel-1-standard-1-2\u0026ndash;0\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and colorectal cancer data was sourced from a published database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://aacrjournals.org/cancerdiscovery/article/12/1/134/675646/Spatiotemporal-Immune-Landscape-of-Colorectal\u003c/span\u003e\u003cspan address=\"https://aacrjournals.org/cancerdiscovery/article/12/1/134/675646/Spatiotemporal-Immune-Landscape-of-Colorectal\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Load10X_Spatial function was used to read and analyze the data for both breast cancer and CRC. Principal Component Analysis (PCA) was utilized to reduce the dimensionality of the integrated data, facilitating further downstream analysis in R (version 4.2.2), with the \u0026ldquo;RunUMAP\u0026rdquo; function applied to perform UMAP on these components.\u003c/p\u003e \u003cp\u003eExpression of therapeutic target genes in breast cancer and CRC across cell clusters was visualized using DotPlot, and cellular metabolism was analyzed using scMetabolism. Furthermore, parametric analysis was conducted in R to explore the correlations between metabolic scoring data at each spatial location and gene features and expression patterns in T cells[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The results from the deconvolution of cell types were further submitted to SPOTlight for visualization[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Protein-protein interaction analysis\u003c/h2\u003e \u003cp\u003eThe PPI Core Network (PPICN) was used to link compounds to disease-related protein molecules. Additionally, DrugBank Online (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://go.drugbank.com/\u003c/span\u003e\u003cspan address=\"https://go.drugbank.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to evaluate the drugs that could target these genes.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Single-cell RNA sequencing results\u003c/h2\u003e \u003cp\u003eThe distribution of cells after normalization from colorectal cancer, breast cancer, and normal tissues is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, where the cells are relatively dispersed. After filtering and dimensionality reduction, the distribution of cells became more concentrated, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB. Cells from breast cancer, colorectal cancer, and normal tissues were then merged and aggregated, and visualized using the \"UMAP\" method to cluster each cell with similar expression together, resulting in 22 cell clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEach cluster was then annotated using reference datasets from the celldex package, which includes human primary cell atlas data, identifying cell identities such as T cells, epithelial cells, endothelial cells, macrophages, fibroblasts, B cells, monocytes, tissue stem cells, and neutrophils (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). As T cells are a major component of the Tumor Microenvironment (TME) and play a crucial role in tumor suppression, further clustering analysis of T cells was performed, resulting in 14 distinct clusters (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and B). Based on the characteristic gene expression of the cells, manual annotations were made (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), categorizing T cells into CD8_EM, CD4_REG, CD8_CM, CD8_exhau, CD4_Naiv, TH17, and CD4_EM (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The analysis then focused on cell-cell communication studies involving CD8_CM.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Interactions of CD8_CM with other cell types\u003c/h2\u003e \u003cp\u003eCell communication analysis found that the interaction pathways between CD8_CM and other T cell clusters (CD8_EM, CD4_REG, CD4_EM, CD4_Naive, TH17, and CD8_exhau) as well as epithelial cells, endothelial cells, macrophages, fibroblasts, B cells, monocytes, tissue stem cells, and neutrophils were similar between breast and colorectal cancers. However, in colorectal cancer, CD8_CM have a greater number of interaction pathways with these cell types compared to breast cancer. In breast cancer, CD8_CM primarily interacts with these cells through pathways such as MIF-(CD74\u0026thinsp;+\u0026thinsp;CXCR4), CXCL13-CXCR3, and MIF-(CD74\u0026thinsp;+\u0026thinsp;CD44) as shown in Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC and D. In colorectal cancer, CD8_CM mainly engages through pathways including CCL5-CCR1, ANXA1-FRR1, MIF-(CD74\u0026thinsp;+\u0026thinsp;CD44), and MIF-(CD74\u0026thinsp;+\u0026thinsp;CXCR4), impacting the same classes of cells, as illustrated in Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA and B.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Screening for risk genes in breast and colorectal cancers\u003c/h2\u003e \u003cp\u003eWe identified genes that were differentially expressed (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between CD8_CM and the above-mentioned cell types (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These genes were then converted into eQTLs, and significant eQTLs (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) were selected (Table S2). A Mendelian randomization (MR) analysis was conducted for breast and colorectal cancers with a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The analysis identified 64 genes (\u003cem\u003eNUCKS1\u003c/em\u003e, \u003cem\u003eFOSL2\u003c/em\u003e, \u003cem\u003eITGAE\u003c/em\u003e, \u003cem\u003eBIRC5\u003c/em\u003e, \u003cem\u003eCENPM\u003c/em\u003e, \u003cem\u003eCDKN3\u003c/em\u003e, \u003cem\u003eUBE2S\u003c/em\u003e, \u003cem\u003eCLNK\u003c/em\u003e, \u003cem\u003eSMC4\u003c/em\u003e, \u003cem\u003eCDC20\u003c/em\u003e, \u003cem\u003eFASLG\u003c/em\u003e, \u003cem\u003eSTMN1\u003c/em\u003e, \u003cem\u003eCENPF\u003c/em\u003e, \u003cem\u003eTMPO ZWINT\u003c/em\u003e, \u003cem\u003eCENPK\u003c/em\u003e, \u003cem\u003eDLGAP5\u003c/em\u003e, \u003cem\u003eMAP2K2\u003c/em\u003e, \u003cem\u003eDUT\u003c/em\u003e, \u003cem\u003eNASP\u003c/em\u003e, \u003cem\u003eCCNB1\u003c/em\u003e, \u003cem\u003ePTPN22\u003c/em\u003e, \u003cem\u003eAHI1\u003c/em\u003e, \u003cem\u003eSMC2\u003c/em\u003e, \u003cem\u003eANP32B\u003c/em\u003e, \u003cem\u003eNUSAP1\u003c/em\u003e, \u003cem\u003eKIF20B\u003c/em\u003e, \u003cem\u003eRASGEF1B\u003c/em\u003e, \u003cem\u003eCENPE\u003c/em\u003e, \u003cem\u003eVPS37B\u003c/em\u003e, \u003cem\u003eSAE1\u003c/em\u003e, \u003cem\u003eNUF2\u003c/em\u003e, \u003cem\u003eASXL2\u003c/em\u003e, \u003cem\u003eCCNA2\u003c/em\u003e, \u003cem\u003eMKI67\u003c/em\u003e, \u003cem\u003ePDE3B\u003c/em\u003e, \u003cem\u003eZFP36L2\u003c/em\u003e, \u003cem\u003eNR4A2\u003c/em\u003e, \u003cem\u003eSGO2\u003c/em\u003e, \u003cem\u003eHMGB2\u003c/em\u003e, \u003cem\u003ePTTG1\u003c/em\u003e, \u003cem\u003ePCLAF\u003c/em\u003e, \u003cem\u003eTMIGD2\u003c/em\u003e, \u003cem\u003eTK1\u003c/em\u003e, \u003cem\u003eLDLRAD4\u003c/em\u003e, \u003cem\u003eZEB2\u003c/em\u003e, \u003cem\u003eARL6IP1\u003c/em\u003e, \u003cem\u003eCKS1B\u003c/em\u003e, \u003cem\u003eUBE2C\u003c/em\u003e, \u003cem\u003eAURKB\u003c/em\u003e, \u003cem\u003eKPNA2\u003c/em\u003e, \u003cem\u003eHMGB1\u003c/em\u003e, \u003cem\u003eHMGN2\u003c/em\u003e, \u003cem\u003eCD3E\u003c/em\u003e, \u003cem\u003eCENPW\u003c/em\u003e, \u003cem\u003eLAYN\u003c/em\u003e, \u003cem\u003eMXD3\u003c/em\u003e, \u003cem\u003eDNAJC9\u003c/em\u003e, \u003cem\u003eTRGC2\u003c/em\u003e, \u003cem\u003eTUBA1B\u003c/em\u003e, \u003cem\u003ePCNA\u003c/em\u003e, \u003cem\u003eANP32E\u003c/em\u003e, \u003cem\u003eSRSF7\u003c/em\u003e, \u003cem\u003eand DEK\u003c/em\u003e) associated with the risk of breast cancer and seven genes (\u003cem\u003eCKS1B\u003c/em\u003e, \u003cem\u003ePTTG1\u003c/em\u003e, \u003cem\u003eLCP1\u003c/em\u003e, \u003cem\u003eHSP90AA1\u003c/em\u003e, \u003cem\u003eITGAE\u003c/em\u003e, \u003cem\u003ePRF1\u003c/em\u003e, \u003cem\u003eand ZFP36L2\u003c/em\u003e) associated with the risk of CRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and B). The genes associated with breast cancer risk are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. The genes associated with the risk of colorectal cancer are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB. The genes analyzed in this study did not exhibit heterogeneity (Tables S3 and S4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenes Associated with Breast Cancer Risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRisk Association\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNUCKS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0234\u0026ndash;0.3400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.99462\u0026times;10^-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITGAE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0403\u0026ndash;0.0495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIRC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0233\u0026ndash;0.0272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCENPM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0100\u0026ndash;0.0124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUBE2S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0019\u0026ndash;0.0517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.307373\u0026times;10^-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDC20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u0026ndash;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFASLG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0591\u0026ndash;0.0721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTMN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0334\u0026ndash;0.0402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZWINT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1611\u0026ndash;0.2668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.172205\u0026times;10^-34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCENPK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3741\u0026ndash;0.4141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.08499\u0026times;10^-283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0629\u0026ndash;0.1540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.541778\u0026times;10^-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0288\u0026ndash;0.0367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCNB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0058\u0026ndash;0.0071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAHI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2595\u0026ndash;0.2896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.228502\u0026times;10^-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANP32B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0052\u0026ndash;0.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRASGEF1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0030\u0026ndash;0.0062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.349116\u0026times;10^189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVPS37B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4502\u0026ndash;0.5282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.814115\u0026times;10^-69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0169\u0026ndash;0.0298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.474272\u0026times;10^-151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNUF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0047\u0026ndash;0.0072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCNA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0008\u0026ndash;0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMKI67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u0026ndash;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR4A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1464.3163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1250.5872-1714.5724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDLRAD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.5485\u0026ndash;14.7611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZEB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39610.9575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e165.9823\u0026ndash;9452984.1047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.506391\u0026times;10^-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARL6IP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0716\u0026ndash;0.0792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAURKB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0146\u0026ndash;0.0174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKPNA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0004\u0026ndash;0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHMGB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0078\u0026ndash;0.0145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.381093\u0026times;10^-183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHMGN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0682\u0026ndash;0.0887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCENPW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0002\u0026ndash;0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNAJC9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0161\u0026ndash;0.1765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.603169\u0026times;10^-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRGC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2965\u0026ndash;0.3413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.452474\u0026times;10^-223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOSL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5519.5871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4269.0160\u0026ndash;7136.5022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDKN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e272.5049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e241.0733\u0026ndash;308.0345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCLNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23541.1161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18746.8445\u0026ndash;29561.4629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.6641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.3585\u0026ndash;61.3237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRSF7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e979126.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e739095.3724\u0026ndash;1297111.3929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCENPF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7736\u0026ndash;2.7724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.775729\u0026times;10^-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMPO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e797.1278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e683.0948\u0026ndash;930.1971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTUBA1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3473.9359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2954.3501\u0026ndash;4084.9019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLGAP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24349.4693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19882.9579\u0026ndash;29819.3387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP2K2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.9293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.1831\u0026ndash;25.8130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDUT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.2692\u0026ndash;5.9470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.576729\u0026times;10^-81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTPN22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.8905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.0316\u0026ndash;30.8772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.6446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.9311\u0026ndash;29.4713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANP32B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0052\u0026ndash;0.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNUSAP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.1568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.3107\u0026ndash;15.4344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.115594\u0026times;10^-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKIF20B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.3976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.2445\u0026ndash;19.6278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCENPE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5609.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4561.0927\u0026ndash;6898.9273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANP32E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.6967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.4874\u0026ndash;16.0143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASXL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103.2294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.7300\u0026ndash;121.4671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZFP36L2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10992.8283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9201.7136\u0026ndash;13132.5837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSGO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e850.0735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e722.9885\u0026ndash;999.4972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHMGB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e422.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e325.3194\u0026ndash;548.0046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTTG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.6113\u0026ndash;99.8385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCLAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.4655\u0026ndash;7.0514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.187580\u0026times;10^-173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMIGD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.5656\u0026ndash;11.7034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.39975\u0026times;10^-184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMXD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e293.2167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.8219\u0026ndash;7272.5849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.249580\u0026times;10^-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKS1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17308.2749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13304.8564\u0026ndash;22516.3182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUBE2C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6136.5452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5158.1841\u0026ndash;7300.4738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAYN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.5281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.6405\u0026ndash;11.8821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.356807\u0026times;10^-89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD3E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.1386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.4799\u0026ndash;98.5086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e336.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e242.1629\u0026ndash;468.4322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.838693\u0026times;10^-262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR4A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1464.3163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1250.5872-1714.5724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproaches 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenes Associated with Colorectal Cancer Risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRisk Association\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKS1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1537\u0026ndash;0.7360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTTG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4515\u0026ndash;0.9806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6975\u0026ndash;0.9937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHSP90AA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.2731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0782\u0026ndash;1.5033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITGAE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0275\u0026ndash;1.6575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0177\u0026ndash;1.8389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZFP36L2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.2596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3801\u0026ndash;3.6997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased Risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Sensitivity analysis of pathogenic genes\u003c/h2\u003e \u003cp\u003eReverse Mendelian randomization analysis revealed that \u003cem\u003eSRSF7\u003c/em\u003e, \u003cem\u003eTUBA1B\u003c/em\u003e, \u003cem\u003eDEK\u003c/em\u003e, \u003cem\u003ePCNA\u003c/em\u003e, and \u003cem\u003eANP32E\u003c/em\u003e have inverse causal relationships with breast cancer, while the remaining genes show no reverse causal relationship with breast cancer (Figure S2). We did not find any of the genes that we analyzed to exhibit a reverse causal relationship with CRC (Figure S3). Bayesian colocalization analysis indicated that 59 genes (\u003cem\u003eNUCKS1\u003c/em\u003e, \u003cem\u003eFOSL2\u003c/em\u003e, \u003cem\u003eITGAE\u003c/em\u003e, \u003cem\u003eBIRC5\u003c/em\u003e, \u003cem\u003eCENPM\u003c/em\u003e, \u003cem\u003eCDKN3\u003c/em\u003e, \u003cem\u003eUBE2S\u003c/em\u003e, \u003cem\u003eCLNK\u003c/em\u003e, \u003cem\u003eSMC4\u003c/em\u003e, \u003cem\u003eCDC20\u003c/em\u003e, \u003cem\u003eFASLG\u003c/em\u003e, \u003cem\u003eSTMN1\u003c/em\u003e, \u003cem\u003eCENPF\u003c/em\u003e, \u003cem\u003eTMPO\u003c/em\u003e, \u003cem\u003eZWINT\u003c/em\u003e, CENPK, \u003cem\u003eDLGAP5\u003c/em\u003e, \u003cem\u003eMAP2K2\u003c/em\u003e, \u003cem\u003eDUT\u003c/em\u003e, \u003cem\u003eNASP\u003c/em\u003e, \u003cem\u003eCCNB1\u003c/em\u003e, \u003cem\u003ePTPN22\u003c/em\u003e, \u003cem\u003eAHI1\u003c/em\u003e, \u003cem\u003eSMC2\u003c/em\u003e, \u003cem\u003eANP32B\u003c/em\u003e, \u003cem\u003eNUSAP1\u003c/em\u003e, \u003cem\u003eKIF20B\u003c/em\u003e, \u003cem\u003eRASGEF1B\u003c/em\u003e, \u003cem\u003eCENPE\u003c/em\u003e, \u003cem\u003eVPS37B\u003c/em\u003e, \u003cem\u003eSAE1\u003c/em\u003e, \u003cem\u003eNUF2\u003c/em\u003e, \u003cem\u003eASXL2\u003c/em\u003e, \u003cem\u003eCCNA2\u003c/em\u003e, \u003cem\u003eMKI67\u003c/em\u003e, \u003cem\u003ePDE3B\u003c/em\u003e, \u003cem\u003eZFP36L2\u003c/em\u003e, \u003cem\u003eNR4A2\u003c/em\u003e, \u003cem\u003eSGO2\u003c/em\u003e, \u003cem\u003eHMGB2\u003c/em\u003e, \u003cem\u003ePTTG1\u003c/em\u003e, \u003cem\u003ePCLAF\u003c/em\u003e, \u003cem\u003eTMIGD2\u003c/em\u003e, \u003cem\u003eTK1\u003c/em\u003e, \u003cem\u003eLDLRAD4\u003c/em\u003e, \u003cem\u003eZEB2\u003c/em\u003e, \u003cem\u003eARL6IP1\u003c/em\u003e, \u003cem\u003eCKS1B\u003c/em\u003e, \u003cem\u003eUBE2C\u003c/em\u003e, \u003cem\u003eAURKB\u003c/em\u003e, \u003cem\u003eKPNA2\u003c/em\u003e, \u003cem\u003eHMGB1\u003c/em\u003e, \u003cem\u003eHMGN2\u003c/em\u003e, \u003cem\u003eCD3E\u003c/em\u003e, \u003cem\u003eCENPW\u003c/em\u003e, \u003cem\u003eLAYN\u003c/em\u003e, \u003cem\u003eMXD3\u003c/em\u003e, \u003cem\u003eDNAJC9\u003c/em\u003e, and \u003cem\u003eTRGC2\u003c/em\u003e) had PPH3\u0026thinsp;+\u0026thinsp;PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.8, suggesting a strong causal relationship with breast cancer (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Figures S4\u0026ndash;8). However, in CRC, only the \u003cem\u003eZFP36L2\u003c/em\u003e gene showed PPH3\u0026thinsp;+\u0026thinsp;PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.8, indicating a strong causal relationship with the disease (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table S5), making this gene a priority candidate for a common therapeutic target for both diseases. Although the colocalization results for \u003cem\u003eITGAE\u003c/em\u003e, \u003cem\u003ePTTG1\u003c/em\u003e, and \u003cem\u003eCKS1B\u003c/em\u003e were not significant, MR analysis showed that these three genes still have a causal relationship with both breast cancer and CRC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that they may also serve as common therapeutic targets (Figure S9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Downstream analysis\u003c/h2\u003e \u003cp\u003eWe identified the \u003cem\u003eZFP36L2\u003c/em\u003e gene as a common therapeutic target for both breast cancer and colorectal cancer that should be prioritized. The \u003cem\u003eITGAE\u003c/em\u003e, \u003cem\u003ePTTG1\u003c/em\u003e and \u003cem\u003eCKS1B\u003c/em\u003e genes are also potential common therapeutic targets for these conditions. We therefore analyzed these four therapeutic target genes to validate them as targets at the single-cell level. Additionally, we performed simulated time-series analyses of these as drug targets associated with the two diseases. As depicted in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, the genes marked in purple represent risk genes. Genes positioned above the horizontal line in the diagrams indicate an increase in gene expression over time, whereas those below the line exhibit a decrease in gene expression as time progresses. \u003cem\u003eZFP36L2\u003c/em\u003e and \u003cem\u003eITGAE\u003c/em\u003e are above the horizontal line, indicating an increase in their expression levels over time. Conversely, \u003cem\u003eCKS1B\u003c/em\u003e and \u003cem\u003ePTTG1\u003c/em\u003e are below the line, indicating that their expression levels are decreasing over time (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, the expression of \u003cem\u003eCKS1B\u003c/em\u003e (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB ), \u003cem\u003ePTTG1\u003c/em\u003e (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE ) decreases over time in both diseases(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, the expression levels of \u003cem\u003eZFP36L2\u003c/em\u003e (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC) and \u003cem\u003eITGAE\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD) increased over time (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next explored the interactions between these target genes within CD8_CM and other clustered cells. In breast cancer, we found that CKS1B\u0026thinsp;+\u0026thinsp;CD8_CM, ZFP36L2\u0026thinsp;+\u0026thinsp;CD8_CM, ITGAE\u0026thinsp;+\u0026thinsp;CD8_CM, PTTG1\u0026thinsp;+\u0026thinsp;CD8_CM, CKS1B-CD8_CM, ZFP36L2-CD8_CM, ITGAE- CD8_CM PTTG1- CD8_CM mainly interact with other cell types through the CXCL13-CXCR3 pathways (Figure S10 A-H).. In CRC, we found that CKS1B\u0026thinsp;+\u0026thinsp;CD8_CM, ZFP36L2\u0026thinsp;+\u0026thinsp;CD8_CM, ITGAE\u0026thinsp;+\u0026thinsp;CD8_CM, PTTG1\u0026thinsp;+\u0026thinsp;CD8_CM, CKS1B-CD8_CM, ITGAE-CD8_CM, PTTG1-CD8_CM mainly interact with other cell types through the MIF- (CD74\u0026thinsp;+\u0026thinsp;CXCR4), MIF- (CD74\u0026thinsp;+\u0026thinsp;CD44) pathways (Figure S11 A-H). Additionally, the metabolic roles of cells were explored, revealing associations between these four drug-target genes (\u003cem\u003eZFP36L2\u003c/em\u003e, \u003cem\u003eITGAE\u003c/em\u003e, \u003cem\u003ePTTG1\u003c/em\u003e, and \u003cem\u003eCKS1B\u003c/em\u003e) and pathways involving cysteine and methionine metabolism (Figure S10 I\u0026ndash;L and Figure S11 I\u0026ndash;L).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Spatial transcriptomics\u003c/h2\u003e \u003cp\u003eWe identified 12 distinct cell clusters in breast cancer samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA) and 13 distinct cell clusters in CRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB) after unsupervised clustering and UMAP visualization of spatial barcode patches. DotPlot analysis was employed to assess expression levels of common drug target (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequent analysis identified \u003cem\u003eZFP36L2\u003c/em\u003e as having the highest expression level in cell cluster 0 of breast cancer and in cell cluster 2 of CRC. Additionally, \u003cem\u003eCKS1B\u003c/em\u003e, \u003cem\u003eITGAE\u003c/em\u003e, and \u003cem\u003ePPTG1\u003c/em\u003e exhibited the highest expression levels in breast cancer cell cluster 12 and CRC cell cluster 4. Metabolic assessment using scMetabolism showed that in clusters with expression of \u003cem\u003eCKS1B\u003c/em\u003e, \u003cem\u003eITGAE\u003c/em\u003e, and \u003cem\u003ePPTG1\u003c/em\u003e, the average scores for cysteine and methionine metabolism pathways were 0.8 higher compared to other cell clusters, which is consistent with single-cell sequencing results (Figure S13).\u003c/p\u003e \u003cp\u003ePositive correlations in expression and spatial distribution of \u003cem\u003eZFP36L2\u003c/em\u003e, \u003cem\u003eCKS1B\u003c/em\u003e, \u003cem\u003eITGAE\u003c/em\u003e, and \u003cem\u003ePPTG1\u003c/em\u003e were observed (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), indicating that regulation of these drug target genes is altered in both breast cancer and CRC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Pharmacological evaluation of protein-protein interactions and potential drug targets\u003c/h2\u003e \u003cp\u003ePPI analysis among these four genes only predicted interactions between \u003cem\u003ePTTG1\u003c/em\u003e and \u003cem\u003eCKS1B\u003c/em\u003e (Figure S13). Among these four genes, only \u003cem\u003eCKS1B\u003c/em\u003e has been targeted for drug development, and fluoxetine has been investigated as a treatment to inhibit its expression to arrest the growth of breast cancer[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and enhance the sensitivity of bladder cancer to cisplatin[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study employed scRNA sequencing to identify distinct subpopulations of T cells and analyze differentially expressed genes between CD8_CM and other cell types using R programming tools. We further performed cellular communication analysis to reveal genetic interactions and associations among cells. The screened genes were transformed into eqtLs to identify common drug targets for breast and colorectal cancer. We employed a combination of MR and colocalization analyses to evaluate potential therapeutic targets for breast cancer and CRC as a clinical translation of GWAS findings[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. \"Causal relationships\" identified by MR could represent reverse causation, horizontal pleiotropy, or confounding due to linkage disequilibrium (LD)[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. By conducting primary MR analyses on identified differentially-expressed genes, we were able to exclude risk genes associated with reverse causation from further analysis. To mitigate bias due to horizontal pleiotropy, eQTLs were used as exposures, given their direct role in the transcription and/or translation of genes associated with eQTLs[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Moreover, Bayesian colocalization set a critical threshold for posterior probabilities at 0.8, identifying \u003cem\u003eNUCKS1\u003c/em\u003e, \u003cem\u003eFOSL2\u003c/em\u003e, \u003cem\u003eITGAE, BIRC5\u003c/em\u003e, \u003cem\u003eCENPM\u003c/em\u003e, \u003cem\u003eCDKN3\u003c/em\u003e, \u003cem\u003eUBE2S\u003c/em\u003e, \u003cem\u003eCLNK\u003c/em\u003e, \u003cem\u003eSMC4\u003c/em\u003e, \u003cem\u003eCDC20\u003c/em\u003e, \u003cem\u003eFASLG\u003c/em\u003e, \u003cem\u003eSTMN1\u003c/em\u003e, \u003cem\u003eCENPF\u003c/em\u003e, \u003cem\u003eTMPO\u003c/em\u003e, \u003cem\u003eZWINT\u003c/em\u003e, \u003cem\u003eCENPK, DLGAP5\u003c/em\u003e, \u003cem\u003eMAP2K2\u003c/em\u003e, \u003cem\u003eDUT\u003c/em\u003e, \u003cem\u003eNASP\u003c/em\u003e, \u003cem\u003eCCNB1\u003c/em\u003e, \u003cem\u003ePTPN22\u003c/em\u003e, \u003cem\u003eAHI1\u003c/em\u003e, \u003cem\u003eSMC2\u003c/em\u003e, \u003cem\u003eANP32B\u003c/em\u003e, \u003cem\u003eNUSAP1\u003c/em\u003e, \u003cem\u003eKIF20B\u003c/em\u003e, \u003cem\u003eRASGEF1B\u003c/em\u003e, \u003cem\u003eCENPE\u003c/em\u003e, \u003cem\u003eVPS37B\u003c/em\u003e, \u003cem\u003eSAE1\u003c/em\u003e, \u003cem\u003eNUF2\u003c/em\u003e, \u003cem\u003eASXL2\u003c/em\u003e, \u003cem\u003eCCNA2\u003c/em\u003e, \u003cem\u003eMKI67\u003c/em\u003e, \u003cem\u003ePDE3B\u003c/em\u003e, \u003cem\u003eZFP36L2\u003c/em\u003e, \u003cem\u003eNR4A2\u003c/em\u003e, \u003cem\u003eSGO2\u003c/em\u003e, \u003cem\u003eHMGB2\u003c/em\u003e, \u003cem\u003ePTTG1\u003c/em\u003e, \u003cem\u003ePCLAF\u003c/em\u003e, \u003cem\u003eTMIGD2\u003c/em\u003e, \u003cem\u003eTK1\u003c/em\u003e, \u003cem\u003eLDLRAD4\u003c/em\u003e, \u003cem\u003eZEB2\u003c/em\u003e, \u003cem\u003eARL6IP1\u003c/em\u003e, \u003cem\u003eCKS1B\u003c/em\u003e, \u003cem\u003eUBE2C, AURKB, KPNA2\u003c/em\u003e, \u003cem\u003eHMGB1\u003c/em\u003e, \u003cem\u003eHMGN2\u003c/em\u003e, \u003cem\u003eCD3E\u003c/em\u003e, \u003cem\u003eCENPW\u003c/em\u003e, \u003cem\u003eLAYN\u003c/em\u003e, \u003cem\u003eMXD3\u003c/em\u003e, \u003cem\u003eDNAJC9\u003c/em\u003e, and \u003cem\u003eTRGC2\u003c/em\u003e as likely harboring the same variants. In the colocalization analysis for CRC, \u003cem\u003eZFP36L2\u003c/em\u003e was identified by MR as being suggested to share the same variants. Because this gene was identified in analyses of both breast cancer and CRC, it could potentially be a common therapeutic target for both diseases. Although the colocalization between \u003cem\u003eCKS1B\u003c/em\u003e, \u003cem\u003ePTTG1\u003c/em\u003e, and \u003cem\u003eITGAE\u003c/em\u003e is not strong in CRC, Mendelian analysis indicates a causal relationship between these genes and both diseases, suggesting that they might represent common targets for colorectal cancer and breast cancer (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eZFP36L2 dysregulation is one of the key drivers of colorectal cancer. Mutations in this gene reduce levels of the encoded RNA-binding protein and CRISPR/Cas9-mediated knockout of this gene results in an enhancement of the migration and invasion capabilities of cells to promote carcinogenesis[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This study is the first to find an association between ZFP36L2 and the risk of breast cancer, with increased expression levels of ZFP36L2 raising the risk of developing the disease (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).CKS1B is typically upregulated in CRC tissues and knockout of the gene can inhibit the proliferation and migration of CRC cells, providing a new potential strategy for treating colorectal cancer[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Specifically in breast cancer, elevated expression of CKS1B is associated with increased turnover of the Kip1 gene and cell renewal, leading to increased cell proliferation and poor prognosis[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Overall, CKS1B may become a potential therapeutic target and prognostic marker for various cancers including CRC, breast cancer, pancreatic cancer, and retinoblastoma. In breast cancer patients, PTTG1 mRNA levels increase with elevated estrogen levels, which known risk factor for breast cancer, suggesting that estrogen may influence the progression of breast cancer by regulating PTTG1[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This study is the first to find an association between PTTG1 and the risk of CRC with increased expression levels of ZFP36L2 raising the risk of developing the disease (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). ITGAE, also known as CD103 or CD4T[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Mature differentiated CD103-specific cytotoxic T lymphocytes can self-regulate by producing activated TGFβ1, increasing T cell receptor antigen sensitivity and enhancing the rapid recognition and clearance of cancer cells[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In CRC patients, the degree of ITGAE\u0026thinsp;+\u0026thinsp;lymphocyte invasion correlates with patient survival, which is potentially linked to interferon-responsive chemokine and epithelial-mesenchymal transition signaling pathways, suggesting that ITGAE is a possible biomarker for CRC[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The number of CD103\u0026thinsp;+\u0026thinsp;T cells is higher in breast cancer tissues than in normal tissues because of increased lymphocyte migration and retention, which impacts anti-tumor immune functions[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThrough single-cell analysis and simulated time-series, we found that the expression level of \u003cem\u003eZFP36L2, ITGAE\u003c/em\u003e gradually increases over time, whereas the expression of \u003cem\u003eCKS1B, PTTG1\u003c/em\u003e decreases. We examined the relationships between drug target genes that are differentially expressed between different cells and identified ligands which could facilitate communication between different cell types. These include ANXA1-FPR1, ANXA1-FPR2, MIF-(CD74\u0026thinsp;+\u0026thinsp;CD44), LGALS9-CD45, MIF-(CD74\u0026thinsp;+\u0026thinsp;CXCR4), CCL5-CCR5 for +\u0026thinsp;CD8_CM, and CCL5-CCR1, MIF-(CD74\u0026thinsp;+\u0026thinsp;CD44), CCL5-CCR5, MIF-(CD74\u0026thinsp;+\u0026thinsp;CXCR4) for -CD8_CM. Notably, the LGALS9-CD45 signaling pathway, in addition to impacting breast and colorectal cancers, also affects gastric cancer by influencing monocytes within the tumor microenvironment, thereby significantly impacting T cells and endothelial cells[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. We performed metabolomic studies and found that the drug target genes were linked to the metabolism of cysteine and methionine. Previous reports suggest that cysteine metabolism is associated with a variety of diseases, such as cardiovascular diseases (CVD), ischemic stroke, neurological disorders, diabetes, lung and colorectal cancer, renal failure-related diseases, and vitiligo[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], whereas the relationship of methionine metabolism with diseases has not been extensively studied yet. In the spatial transcriptomics metabolic analysis, we found that the highest scoring pathways related to drug targets were cysteine and methionine metabolism. Analysis of the distribution and expression of drug target genes revealed that most are in a mid-differentiation state. PPI analysis showed very limited interactions among potential drug target genes, with a significant interaction only observed between \u003cem\u003ePTTG1\u003c/em\u003e and \u003cem\u003eCKS1B\u003c/em\u003e. Fluoxetine is a drug that has been developed to reduce expression of \u003cem\u003eCKS1B\u003c/em\u003e which has been shown to inhibit the growth of breast cancer[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and enhance the sensitivity of bladder cancer to cisplatin[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe database used in this study is limited to a European population, and so whether the conclusions that we have made are specific to this population or globally applicable requires further investigation. Additionally, the statistical analyses employed and the strict significance thresholds may have filtered out some marker genes which could potentially be common drug targets for breast and colorectal cancer. Therefore, further extensive database analyses and human case studies are necessary to assess drug target genes associated with the development and progression of breast cancer and CRC. While every cancer has context-specific biology, previous studies have reported that some risk genes, such as \u003cem\u003eCDC20\u003c/em\u003e, play a common role in both breast and colorectal cancers[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. We did not find that \u003cem\u003eCDC20\u003c/em\u003e is a common risk for both breast cancer and CRC, but this may be due to sample size and population-specific factors. We identified several drug targets and their interactions with common signaling pathways in the PPI analysis. However, PPI analysis is only suggestive, not conclusive for clinical research, and has its limitations. Although we have reported the roles of pathways as well as the expression and distribution of genes in single-cell and spatial transcriptomics, further experimental validation is required. Additionally, studies need to include more diverse, non-European populations to evaluate the applicability of these findings for clinical use.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, the genes \u003cem\u003eZFP36L2\u003c/em\u003e, \u003cem\u003eCKS1B\u003c/em\u003e, \u003cem\u003ePTTG1\u003c/em\u003e, and \u003cem\u003eITGAE\u003c/em\u003e may serve as common drug targets for breast and colorectal cancers. They might influence the pathogenesis and pathophysiology of these cancers through functioning in CD8_CM in addition to potentially affecting the cancer cells through altering cysteine and methionine metabolic pathways. The four genes could be potential therapeutic targets as well as biomarkers for screening for the presence of cancer and monitoring the effectiveness of therapies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCRC: Colorectal cancer; BRCA2: Breast Cancer Gene 2; TME: Tumor Microenvironment; T_CM: central memory T cells; CD8_CM: central memory CD8+ T cells; PPI: protein-protein interaction; scRNA: single-cell RNA; GEO: Gene Expression Omnibus; eQTL: expression quantitative trait loci; MR-IVW: inverse variance weighted Mendelian Randomization; PCA: Principal Component Analysis; PPICN: Protein-Protein Interaction Core Network; MR: Mendelian randomization; ECM: extracellular matrix; LD: linkage disequilibrium; CVD: cardiovascular diseases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used is from public dataset, and the original data has obtained ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinancial support came from Shanghai Jiao Tong University School of Medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRui Tang: Conceptualization, Formal analysis, Methodology, Writing-original draft; Hongquan Cui: Validation, Visualization, Writing-original draft; Pengyu Miao: Resources, Writing-review \u0026amp; editing; Zhengrui Li: Investigation, Validation, Resources, Writing-review \u0026amp; editing; Keliang Wang: Supervision, Resources, Writing-review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe would also like to thank all laboratory members for helpful discussions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. 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Targeting Cdc20 as a novel cancer therapeutic strategy. \u003cem\u003ePharmacology \u0026amp; therapeutics \u003c/em\u003e\u003cstrong\u003e2015\u003c/strong\u003e, \u003cem\u003e151\u003c/em\u003e, 141-151, doi:10.1016/j.pharmthera.2015.04.002.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mendelian randomization, Breast cancer, Colorectal cancer, Single-Cell RNA Sequencing, Spatial transcriptomics","lastPublishedDoi":"10.21203/rs.3.rs-4992169/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4992169/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction: \u003c/strong\u003eBreast and colorectal cancer are a major global public health problem. Breast cancer is one of the most common cancers worldwide. Colorectal cancer is the third most common cancer and the second most common cause of tumor death worldwide.\u003cstrong\u003e \u003c/strong\u003eCentral memory T (TCM) cells are closely related to the development of tumors and important targets for immunotherapy. Therefore, identifying the common signaling molecules of these two diseases in TCM cells can improve our understanding of these diseases and lead to the development of therapies that can be effective for treating both.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eSingle-cell RNA (scRNA) data of breast cancer (GSE161529) and colorectal cancer (GSE222300) patients was downloaded from the GEO database. The data were normalized and dimension reduced, then different T cell subsets were identified and differential gene expression analysis of central memory CD 8+ T cells was conducted. Mendelian randomization analysis, reverse causality detection, and co-localization analysis was performed to explore the relationship between differentially-expressed genes and the disease. Quasi-temporal analysis and metabolic analysis was done using scRNA sequencing technology and further analysis of gene expression and metabolism in spatial transcriptomes. Finally, the degree of association between drug target genes was analyzed by protein-protein interaction (PPI) analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOur analysis identified four genes (\u003cem\u003eZFP36L2\u003c/em\u003e,\u003cem\u003eCKS1B\u003c/em\u003e,\u003cem\u003e PTTG1\u003c/em\u003e, and\u003cem\u003e ITGAE\u003c/em\u003e) that were associated with risk of both breast and colorectal cancer. In the pseudotime analysis, we found that the expression levels of \u003cem\u003eCKS1B\u003c/em\u003e and \u003cem\u003ePTTG1\u003c/em\u003e decreased over time (P \u0026lt;0.05) while \u003cem\u003eZFP36L2\u003c/em\u003e and \u003cem\u003eITGAE\u003c/em\u003e increased over time (P \u0026lt;0.05). In the metabolic analysis, these four genes were closely associated with the cysteine and methionine metabolism pathways, which was corroborated in the spatial transcription analysis. Finally, the PPI analysis among the drug target genes identified an interaction between \u003cem\u003ePTTG1\u003c/em\u003e and \u003cem\u003eCKS1B\u003c/em\u003egenes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eThis study reports that the\u003cem\u003e ZFP36L2\u003c/em\u003e,\u003cem\u003e CKS1B\u003c/em\u003e,\u003cem\u003e PTTG1\u003c/em\u003e,\u003cem\u003e \u003c/em\u003eand \u003cem\u003eITGAE\u003c/em\u003e genes could potentially influence breast cancer and colorectal cancer development via TCM CD8+ T cells. These four genes are putative common markers for diagnosis, treatment, and monitoring tumor response to therapies.\u003c/p\u003e","manuscriptTitle":"Novel common target genes for breast cancer and colorectal cancer: A mendelian randomization and spatial transcriptomics study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-17 14:56:30","doi":"10.21203/rs.3.rs-4992169/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e0e0acdd-ad56-4b0b-a6d5-df0110f8c289","owner":[],"postedDate":"October 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-05T11:54:05+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-17 14:56:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4992169","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4992169","identity":"rs-4992169","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}