Germline Genetic Variations and Breast Cancer Prognosis: A Systematic Review and Meta-Analysis

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Understanding the genetic factors that influence its prognosis is crucial for improving patient outcomes. This systematic review aims to synthesize the evidence on the relationship between germline DNA variations and breast cancer prognosis. Methods A comprehensive search was conducted across PubMed, Scopus, MEDLINE, Web of Science, and QInsight to identify studies from January 2000 to June 2024 that examined associations between DNA germline variations and breast cancer prognosis, including survival and recurrence. Two reviewers independently extracted data and assessed bias. Genes were mapped and clustered using STRING, based on functional and physical protein associations. The protocol was registered on PROSPERO (CRD42022308746) and followed the PRISMA guidelines. Results 54 studies were analyzed: 37 cohort, 14 retrospective, and three case-control studies. Due to heterogeneity in study design, populations, and geography, a global meta-analysis was not feasible. Instead, we performed separate meta-analyses for Disease Free Survival (DFS) and Overall Survival (OS). The combined effect sizes for DFS were 2.72 (95% CI 1.81-3.62) and 2.53 (95% CI 1.80-3.26) for OS. Seven gene pathways, particularly those involving EGFR resistance, drug metabolism, and immune system, showed relevance to survival outcomes. Despite methodological variability, consistent evidence highlighted the prognostic value of specific germline variants. A significant lack of population diversity was observed. Conclusions This systematic review underscores the prognostic relevance of germline DNA variations in breast cancer. However, further large-scale, longitudinal studies in diverse populations must validate and integrate these associations into clinical practice. DNA germline mutations polymorphisms survival recurrence breast cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Breast cancer (BC) is the most common cancer in females worldwide, and the incidence rates have kept increasing by 0.6% per year [ 1 ]. The prognosis for these women is widely heterogeneous due to the high diversity between tumors and individuals [ 2 ]. Prognosis relates to the risk of developing an outcome, such as recurrence or death, based on clinical and non-clinical characteristics [ 3 ]. Cancer recurrence is the reappearance of cancer after a period of absence of disease [ 4 ]. It can be classified into three main types based on the location of the recurrence: local, regional, and distant recurrence [ 5 ]. The latter occurs when cancer has spread to other organs or tissues far from the site of the primary tumor; this is called metastatic recurrence, and it has been reported that the majority of deaths from cancer of solid tumors are caused by metastasis [ 6 ]. Due to this, predicting the risk of breast cancer recurrence is of great importance in determining breast cancer prognosis. Understanding the risk of recurrence allows the physician to choose the best treatment and the optimal clinical management and surveillance for the patient. Disease-free survival (DFS) is a clinical endpoint commonly used in oncology to measure the length of time after primary treatment for a cancer during which the patient survives without any sign or symptoms of the disease [ 7 ], [ 8 ], [ 9 ], [ 10 ]. DFS is often used as a surrogate of overall survival (OS) in clinical trials [ 11 ]. OS is the period from diagnosis to death from any cause [ 12 ]. The prediction factors of recurrence for breast cancer used in clinical practice are age, clinical stage at diagnosis, histological grade, and molecular subtype [ 13 ]. In addition to these factors, genomic tools, known as genomic signatures, have been developed to calculate a patient's recurrence risk score based on the expression of a certain number of somatic genes, such as Oncotype DX® and MammaPrint® [ 14 ], [ 15 ]. Another type of genomic prediction tool is risk scores created from germline variant information; this new approach has been under study in recent years. Germline variants have been used to determine an individual's predisposition to develop cancer; however, it has been reported that there are also germline variants with a high predictive value for recurrence [ 16 ], [ 17 ], [ 18 ]. Determining germline pathogenic/likely pathogenic (P/LP) mutations in BC already has significant implications in treating these patients. Identifying mutations in high-penetrance genes such as BRCA1, BRCA2, PALB2, TP53, PTEN , and CDH1 can influence decisions on surgical and systemic treatments [ 19 ]. For example, the presence of germline BRCA (gBRCA) mutations can dictate the use of poly-ADP-ribose polymerase inhibitors (PARPi), such as Olaparib and Talazoparib, which have shown efficacy in improving outcomes in both early and metastatic settings [ 20 ], [ 21 ]. However, the relationship between these germline mutations and the patient's prognosis is poorly defined. Some studies have reported a worse clinical outcome, whereas others report no difference in BC prognosis [ 22 ], [ 23 ], [ 24 ]. To date, no published systematic review has examined the association between DNA germline variants and BC prognosis. Therefore, this study aimed to analyze the available literature to assess these associations. Methods We defined the study's primary outcomes as DFS and OS, crucial for determining cancer prognosis. The study was registered on PROSPERO and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (PROSPERO 2022 CRD42022308746) [ 25 ]. The first search was conducted in January 2022, using PubMed, Scopus, MEDLINE, Web of Science, and QInsight as sources. An updated search was performed in June 2024 across all sources except QInsight. The complete search included studies published between January 2000 and June 2024. We utilized 10 search terms with variations on "germline variants" to "germline polymorphisms," along with the primary outcomes (see Tables S1 and S2, Additional File 1). We conducted 20 searches to ensure comprehensive coverage of relevant studies. We searched for observational studies (cohorts and case controls), retrospective studies, and randomized/non-randomized clinical trials. The inclusion criteria comprised studies of women with breast cancer diagnosis, information about germline variants, and survival/recurrence outcomes. On the other hand, the exclusion criteria involved unrelated studies with no information about germline variations and no survival/recurrence data, duplicated or unavailable full texts, or abstract-only articles. After identifying corresponding publications, two independent reviewers selected the studies based on the eligibility criteria. A third reviewer resolved any disagreements. The initial database search identified 576 studies. After removing duplicates, the number of studies was 282. During the eligibility assessment, 117 articles were excluded by title, and 97 articles were excluded by abstract, leaving 68 articles to be assessed by full text. The article "Association of BRCA1 K1183R polymorphism with survival in BRCA1/2 -negative Chinese familial breast cancer" was excluded due to the unavailability of the full text [ 26 ]. Fifty-six studies were included for thorough review and analysis. However, during data extraction, we found that no data about gene variants and measure of association was reported in the articles "Germline variants associated with leukocyte genes predict tumor recurrence in breast cancer patients" and "Effect of Adjuvant Paclitaxel and Carboplatin on Survival in Women With Triple-Negative Breast Cancer: A Phase 3 Randomized Clinical Trial" [ 27 ], [ 28 ]; therefore, we decided to exclude them. Finally, 54 studies met the inclusion criteria and were included in the systematic review (see Fig. 1 ). Data extraction was performed on the 54 articles included, and the information was stored in an Excel database sheet (see Data Extraction Form in Additional File 2). Two reviewers performed the extraction process, verifying the data to identify human errors. Variants that showed statistical significance in multivariate analysis were meta-analyzed by subgroups. The variants were first divided into two groups according to the outcome studied (OS or DFS). For a second meta-analysis, the variants were grouped in 6 clusters according to the genes' functional and physical protein associations, using STRING [ 29 ]. We used the predetermined settings on STRING, and the network type selected was "full STRING network", with all the interaction sources active and a minimum required interaction score of 4.0 (medium confidence). These subgroup meta-analyses were performed using the package Metafor in R to create forest plots and determine heterogeneity through Cochrane's Q, I 2, and Tau 2 tests. Results Figure 2 shows the summarized characteristics of all included studies. All studies exhibited variability in their design, population, and geographic location. Most of the studies were conducted in Europe (n = 28), Asia (n = 16), and North America (n = 8), and the study design most common was the cohort (n = 37) followed by retrospective studies (n = 14) and case control (n = 3). Nearly all studies (n = 52) identified at least one genetic variant that showed a statistically significant association with BC prognosis in univariate analysis, while 30 studies reported statistically significant associations in multivariate analysis. The most common outcome was disease-free survival (DFS) (n = 34), and the most frequently used measure of association was the hazard ratio (HR) (n = 41). Most of the studies' weakness was the lack of multivariate statistical analysis and no statistical data, such as p-values and non-significant effect sizes. HR is the most commonly used measure in oncological research to analyze time-to-event. It quantifies the relationship between an independent variable and the outcome (survival) by calculating the difference in logarithms of hazard functions (22,23). However, not all the studies included in this systematic review incorporated this risk estimate, and only a few adjusted for cofounders. Table 1 lists genes and variants associated with breast cancer prognosis with an adjusted HR and statistical significance. Some studies grouped the germline mutations instead of analyzing each one separately. Interestingly, all of them studied pathogenic mutations in BRCA genes (reported in Table 2 ). Among the excluded studies, there is one that should be taken into consideration. The study was conducted by Milanese et al., and it shows the results of the eTumorMetastasis algorithm applied to breast cancer patients. The eTumorMetastasis algorithm converts tumor functional mutations into network-based profiles to identify network operational gene (NOG) signatures [ 30 ]. These NOG signatures capture the transition point at which a tumor cell becomes likely to recur. The eTumorMetastasis algorithm was applied to exome sequencing data from 755 patients with estrogen receptor positive (ER+) breast cancer. This algorithm identified gene signatures from germline variants that effectively distinguished between patients with a recurrence or not in two independent cohorts (n = 200 and 295, P = 0.0014). These predictions outperformed the widely used Oncotype DX test for both high- and low-risk groups [ 27 ]. In addition to Milanese et al., other authors have developed an algorithm to calculate a score that predicts breast cancer prognosis, such as Maria-Escala Garcia et al., Ke-Da Yu et al., and Tuomas Heikkinen et al. (19–21). Table 3 describes these studies as they showed statistical significance in multivariate analysis. Table 1 Descriptive table of variants that showed statistical significance in adjusted effect estimate by covariates [ 24 ], [ 31 ], [ 32 ], [ 33 ], [ 34 ], [ 35 ], [ 36 ], [ 37 ], [ 38 ], [ 39 ], [ 40 ], [ 41 ], [ 42 ], [ 43 ], [ 44 ], [ 45 ], [ 46 ], [ 47 ], [ 48 ], [ 49 ], [ 50 ], [ 51 ], [ 52 ], [ 53 ], [ 54 ]. *ER positive, **PR positive, ***Lymph node metastasis. Author, Year Population/Country Sample size Follow-up (mean years) Confounders Gene ID Allele/ Genotype Outcome HR/RRR 95% CI p-value Pathway Zhu Qianqian, 2022 Trans-ethnic (European 70.50%, East Asian 11.32%, Hispanic 9.87%, and African 8.31%) 3973 5 Age at diagnosis, body mass index, tumor grade, stage of disease, ER, PR, and HER2 status, hormonal therapy, chemotherapy, radiation therapy, surgery type, and all population stratification principal components. ARRDC3 rs421379 T OS 5.93 - 0.0145 Cancer suppression UACA rs720251 T OS 2.79 - 4.19E-09 Programmed Cell Death-Apoptosis rs62019060 G OS 3.01 - 1.27E-09 Viktor Hlaváč, 2021 European 805 10 Tumor size and grade, lymph node metastasis, and estrogen receptor expression. CYP4X1 rs17102977 G DFS 1.69 1.01–2.85 0.048 Metabolism-Xenobiotics CYP26B1 rs62150087 G DFS 0.54 0.33–0.89 0.016 Metabolism-Xenobiotics Anna Morra, 2021 European 91686 8.1 Age at diagnosis, ER status, PR status, HER2 status, tumor grade, and the use and type of systemic treatment. TBL1X rs5934618 G OS 1.31 1.18–1.44 3.00E-07 Metabolism LINC01487, STRIT1 rs4679741 G OS 1.2 1.13–1.28 1.10E-08 Metabolism - Estrogen biosynthesis GRIP2 rs1106333 A OS 1.67 1.39-2 4.10E-08 Neuronal System ARAP2 rs78754389 A OS 1.7 1.41–2.05 4.40E-08 Signal Transduction-RHO GTPase Cycle Jolanta Pamuła-Piłat, 2020 Caucasian 305 5 Not listed. NR1/2 rs3732359 AA OS 1.82 1.24–2.8 0.003 Xenobiotics Metabolism SLC22A16 rs7756222 CC OS 1.58 1.05–2.36 0.027 Transport of small molecules SLC22A16 rs7756222 CC PFS 1.57 1.07–2.32 0.021 SLC22A16 rs9487402 G OS 1.72 1.14–2.59 0.009 PGR rs1824125 GG PFS 1.76 1.06–2.95 0.029 Metabolism of proteins PGR rs12224560 CC PFS 1.76 1.06–2.92 0.029 DPYD rs291593 CC DFS 5.89 1.29–26.88 0.022 Metabolism - Nucelotide catabolism AKR1C3 rs3209896 AG DFS 5.49 1.2-25.05 0.028 Metabolism Taru A. Muranen, 2020 European 3008 15 Tumor grade, size, PR expression status, and lymph node involvement. DCAF1 rs57025206 CC OS 6.19 3.73–10.3 - Cell cycle Damien Coté, 2018 UK 157 10 Age, ER, and PR status. ERBB3 rs2229046 C DFS 2.79 1.91–4.95 1.51E-03 Gene expression ERBB3 rs773123 A DFS 2.67 1.02–7.03 0.05 BARD1 rs2070096 T DFS 3.27 1.16–9.17 0.05 Disease ERBB2 rs1136201 G DFS 2.67 1.05–6.78 0.05 Gene expression Sinead Toomey, 2016 Irish 194 11.67 Age, tumor grade, ER state, and LN state. EGFR N158N C OS 3.47 1.11–10.9 0.03 Signal Transduction EGFR I655V G DFS 2.36 1.02–5.5 0.04 EGFR D994D C OS 3.46 1.1–10.9 0.02 EGFR rs1140475 T DFS 6.51 1.98–21.36 0.01 Rasa Ugenskienė, 2016 Lithuanian 100 5.83 Age at diagnosis, ER status, PR status, HER2 status, tumor size, tumor grade, and lymph node status. SIPA1 rs3741378 T PFS 5.169 1.81–14.73 0.002 Immune System - Rap1 Signaling SIPA1 rs3741378 T MFS 6.526 2.15–19.81 0.001 Yu-Mian Jia, 2015 Chinese 1091 3.4 ER status, PR status, HER2 status, tumor size, clinical stage, lymph node metastasis, chemotherapy, and endocrine therapy status. CDH1 rs7186053* A DFS 0.29 0.12–0.67 0.0039 Signal Transduction - ESR-mediated signaling CDH1 rs7186053** A DFS 0.42 0.18-1 0.051 CDH1 rs7186053*** A DFS 0.35 0.13–0.95 0.0397 CDH1 rs7200690 T DFS 10.3 1.42–74.73 0.0211 CDH1 rs7198799 T DFS 10.91 1.13-105.34 0.0389 CTNNB1 rs4533622 A DFS 9.04 0.93–87.96 0.058 Signal Transduction Dan-Na Chen, 2015 Chinese 715 6.12 Lymph node status, tumor size, chemotherapy, endocrine therapy, and ER, PR, HER2 statuses. TLR3 rs3775291 AA DFS 3.53 1.98–6.31 < 0.01 Immune System - TLR3 cascade Petra Seibold, 2015 German 1408 6 Tumor size, nodal status, baseline metastases status, tumor grade, estrogen/progesterone receptor status, mode of detection, smoking status, menopausal hormone therapy, as well as radiotherapy and chemotherapy. PARP2 rs878156 G BCSM 0.75 0.53–1.07 0.002 DNA repair PARP2 rs878156 G BCSM 1.42 1.08–1.85 0.002 Erika Korobeinikova, 2015 Lithuanian 100 5.83 Age group, tumor size, lymph node status, histological grade, and intrinsic subtype. TNFα rs1800629 A PFS 4.631 1.59–13.51 0.005 Signal Transduction - TNF Signaling TNFα rs1800629 A MFS 4.708 1.45–15.35 0.01 TNFα rs1800629 A OS 4.829 1.1-21.24 0.037 C. Vulsteke, 2014 Belgium 991 5.2 Subtype, stage, White Blood Cell Count, and hemoglobin. CYP2C9 rs1057910 C BCSS 30.4 6.1-151.5 > 0.001 Metabolism ABCB1 rs2032582 T BCSS 0.5 0.3–0.9 0.021 Drug ADME CYP2C9 rs1057910 C DFS 10.9 2.5–47.9 0.002 Metabolism CYBA rs4673 T DFS 1.8 1.2–2.7 0.006 Immune System UGT2B7 rs3924194 G DFS 3.4 1.2–9.7 0.023 Drug ADME Mala Pande, 2014 Trans-ethnic (72% were non-Hispanic white, 13% were African American, 11% were Hispanic, and 3% were of unknown race/ethnicity). 1019 Not listed. Race/ethnicity, age at diagnosis, tumor stage, and treatment. ADIPOQ rs1063539 C DFS 0.4 0.19–0.86 0.02 Metabolism LEPR rs11585329 T DFS 0.72 0.53–0.98 0.04 Metabolism of proteins TSC1 rs2519757 C DFS 0.29 0.09–0.91 0.03 Autophagy IGF1 rs1520220 C DFS 1.56 1.21-2 0.001 Growth and Development PIK3CA rs2677760 C DFS 1.43 1.07–1.92 0.03 Signal Transduction Axel Muendlein, 2013 Austrian 161 4.2 TNM stage and age. IGF1 rs2946834 A DFS 1.79 1.02–3.15 0.043 Growth and Development Gudrun Absenger, 2013 Austrian 539 5.07 Menopausal status, stage, grading, receptor status, HER-2 neu status, and adjuvant therapy. VEGF-A rs3025039 C > T T DFS 1.88 1.02–3.47 0.043 Angiogenesis James L. Murray, 2013 Caucasian 997 18 Stage, age, and treatment. NFKB1 rs230532 TT DFS 1.41 1.02–1.95 0.001 Immune System Caucasian IL-13 rs1800925 GG DFS 1.47 1.04–2.07 0.034 African-American and Hispanic NFKB1 rs3774932 GG DFS 2.52 0.91–2.89 0.02 African-American and Hispanic patients IL4R rs3024543 AA + AG DFS 1.57 0.93–2.64 0.03 Peter A Fasching, 2012 Germany 20,073 6.4 Age at diagnosis and categorical variables for tumor size, lymph nodes status, and grade. TOX3 rs3803662 T BCSM 1.21 1.09–1.35 0.0002 DNA bending and unwinding TOX3 rs3803662 C BCSM 1.29 1.12–1.47 0.0003 LSP1 rs3817198 C BCSM 0.74 0.54-1 0.05 Immune System Else Maae, 2012 Danish 116 5.1 Genotype, adjuvant chemotherapy, tumor size, axillary lymph node status, and dichotomized histopathological tumor grade. VEGF-A rs2010963 C DFS 2.57 1.05–6.3 0.04 Angiogenesis Ke-Da Yu, 2012 Chinese 806 4.33 Lymph node status, tumor size, ER, HER2, and chemotherapy. NQO2 rs2071002 C DFS 0.3 0.14–0.66 0.003 Metabolism - Functionalization NQO2 rs9501910 C DFS 1.59 1.07–2.37 0.023 GSTM1 Null/present NA DFS 0.25 0.1–0.56 0.001 Drug ADME Yee Soo Chae, 2011 Korean 240 4.45 Age, stage, histological grade, and ER, P53, HER2 statuses. VARS2 rs2074511 G DFS 0.289 0.86–0.97 0.044 Metabolism of proteins - Aminoacylation POLE rs5744857 G OS 5.445 1.65-18 0.005 DNA repair Verena Varadi, 2011 Swedish 783 4.7 ER and PR status, tumor size, lymph node metastasis, and histological grade. REV3L rs11153292 C BCSS 3.67 1.56–8.62 0.003 DNA repair Size, grade, and regional lymph node metastasis. REV3L rs462779 C BCSS 3.26 1.11–9.55 0.03 Elizabeth M Azzato, 2010 European 3761 3 Stage, histopathologic grade, and ER status. OCA2 rs4778137 G OS 0.76 0.67–0.86 1.90E-05 Metabolism - Melanin biosynthesis OCA2 rs6626269 G OS 1.35 1.19–1.52 2.20E-06 OCA2 rs4778137 G OS 0.93 0.87–0.98 0.011 OCA2 rs6626269 G OS 1.11 1.04–1.19 0.0025 Gudrun Knechtel, 2010 Caucasian 216 10 Age at diagnosis, tumor size, lymph node status, clinical stage, histological grade, ER status, PR, and treatment modalities. FAS rs2234767 A DFS 0.5 0.31–0.81 0.05 Programmed Cell Death IL-10 rs1800872 A OS 1.841 1.14–2.97 0.013 Immune System Armin Gerger, 2010 Austrian 432 6.54 Age at diagnosis, tumor size, lymph node status, clinical stage, histological grade, ER status, PR status, and treatment modalities. IL-10 rs1800872 A DFS 1.48 1.07–2.04 0.019 Immune System Table 2 Descriptive table of grouped mutations that showed statistical significance in adjusted effect estimate by covariates [ 18 ], [ 23 ]. Author, Year Population / Country Sample size Follow-up (mean years) Confounders Gene Type of variant Outcome HR 95% CI p- value Pathway Matteo Lambertini, 2021 Europe, North America, Latin America, Israel 1,236 7.9 Nodal status, grade, HER2, type of breast surgery, chemotherapy use, age, year of diagnosis, and country. BRCA1 Pathogenic DFS 0.76 0.6–0.96 - DNA repair Yong Alison Wang, 2018 Chinese 480 5 Tumor size > 2 cm, lymph node positivity, triple negative tumor type, young age of onset, mastectomy, adjuvant chemotherapy, adjuvant radiotherapy, and hormonal therapy. BRCA Pathogenic DFS 3.04 1.4–6.06 0.05 BRCA Pathogenic DFS 2.7 1.2–6.06 0.016 BRCA Pathogenic DFS 2.86 1.11–7.35 0.029 BRCA Pathogenic OS 8.01 1.44–44.7 0.018 Table 3 Descriptive table of scores developed to predict prognosis and showed statistical significance in adjusted effect estimate by covariates [ 49 ], [ 55 ], [ 56 ] Author, Year Population / Country Sample size Follow-up (mean years) Confounders Genes Group Outcome HR 95% CI p-value Pathway Maria Escala-Garcia, 2020 European 84,457 - Not listed. ADRBK2, CCL16, CNR2, CXCR5, DNAJB4, F2R, GNA15, GNAT1, GRM4, GUCA1A, GUCA1B, GUCA2B, GUCY2D, HRH4, LTB4R, OPRK1, OPRM1, RGS9 , and RGS9BP GRPM a1 OS 1.13 1.07–1.21 Mentioned as significant, but no p-value reported. G-alpha signaling events ADCY10, GNA11, PTGIR , and RGS3 GRPM a2 1.15 1.08–1.22 PER1, PER3, TIMELESS , and TIPIN GRPM b 1.28 1.19–1.37 Circadian clocks CHCHD4, PDE9A, SLC36A1 , and PHYHIPL GRPM c 1.2 1.12–1.28 Regulators of cell growth and angiogenesis ARHGAP10, CCNT2, CDR2, HEXIM1, NEUROD2, PKN1 , and ZFAND6 GRPM d 1.21 1.16–1.28 Rho GTPases and apoptosis Ke-Da Yu, 2012 Korean 240 4.45 Age, stage, histological grade, and ER, P53, and HER2 status. NQO2 and GSTM1 Combination of 3 SNPs: rs2071002, rs9501910 and null/present of GSTM1 DFS 0.25 0.1–0.63 0.005 Metabolism - Functionalization and Drug ADME Tuomas Heikkinen, 2011 Finish 2,204 6.92 Tumor size, nodal status, primary metastasis, estrogen receptor, progesterone receptor, HER2, p53, Ki67, grade. PTEN Promoter polymorphisms − 903GA, -975GC, and − 1026CA BCSS 2.01 1.17–3.46 0.0119 Signal Transduction - Activation of AKT signaling 3.92 MFS 1.79 1.03–3.11 0.0381 The forest plots obtained from the meta-analysis of the gene variants associated with DFS and OS are presented in Figs. 3 and 4 , respectively. The combined effect sizes for DFS were 2.72 (95% CI 1.81–3.62) and 2.53 (95% CI 1.80–3.26) for OS. Heterogeneity tests of both forest plots showed a high level of heterogeneity, with an I 2 = 99.4% for DFS and I 2 = 99.8% for OS. STRING analysis generated six clusters according to the functional associations of the proteins (see Table 4 and Fig. 5 ). To visualize the effect sizes of associations of the polymorphisms of the genes grouped in each cluster, we performed a forest plot for each of the six clusters, and an additional forest plot for BRCA mutations. These forest plots are presented in Figure S1 , Additional File 1, with the overall mean estimates obtained for each outcome in every cluster. Table 4. Descriptive table of STRING MCL clusters and their proteins. Discussion This is the first systematic review and meta-analysis to date investigating the prognostic significance of DNA germline variations in BC. In multiple studies, the findings consistently highlight specific germline genetic variants associated with survival and recurrence outcomes. Overall, we found 74 germline variants, 5 grouped pathogenic mutations, and seven groups of SNPs that reported statistical significance in multivariate analysis associated with BC prognosis, indicating that DNA germline variations influence BC prognosis. The combined effect sizes obtained for DFS (2.72 [95% CI 1.81–3.62]) and OS (2.53 [95% CI 1.80–3.26]) suggest an association of germline variations with breast cancer DFS and OS. However, the high level of heterogeneity observed (I² = 99.4% for DFS and I 2 = 99.8% for OS) points out that the included studies exhibit significant variability [ 57 ]. Despite attempts to account for dependencies within studies, significant dispersion persists, which could indicate that the studies differ in their outcomes or settings. This limits the generalizability of the average effect and highlights the need to consider differences when interpreting the results carefully. In Cluster 2 "Xenobiotic Metabolism Enzymes and Transporters (XMETs)", it is important to mention that the overall estimate HR for DFS was 4.02 (95% CI 1.09–16.26) and this cluster includes some of the most frequently studied genes associated with BC prognosis, the cytochrome enzyme family (4 variants associated with BC prognosis). This family is known to be primarily responsible for metabolizing most anticancer therapies. Studying the potential prognosis of these genes could contribute to developing new therapies, such as gene-directed enzyme prodrug therapy (GDEPT). Through GDEPT, CYP enzymes can be genetically modified to enhance the conversion of anticancer prodrugs into their active metabolites, thereby reducing prodrug dosage and minimizing chemotherapy side effects [ 58 ]. An example of this GDEPT is the use of oxazaphosphorines, such as cyclophosphamide (CPA) and ifosfamide (IFA), which are prodrugs activated by hydroxylation to produce cytotoxic metabolites like phosphoramide mustard. However, this metabolite is limited by its inability to cross cell membranes effectively when activated in the liver [ 59 ]. The P450 GDEPT strategy addresses this by delivering P450-expressing genes directly to tumor cells, enabling local activation and improved therapeutic effects, as demonstrated in a Phase 1 clinical trial using the MetXia-P450 vector with oral CPA. This approach has shown safety, consistent gene expression in cancer cells, and promising results, prompting further clinical trials [ 60 ]. As mentioned before, gBRCA mutations can influence surgical decisions and systemic therapies, such as the use of PARPi [ 20 ], [ 21 ]. In this work, the meta-analysis of the "BRCA mutations" cluster showed that these gBRCA mutations could be responsible for worse DFS in BC patients, with an overall estimate HR of 2.34 (95% CI 1.08–5.11) and one study that reported an OS HR of 8.01 (95% CI 1.44–44.70). In metastatic HER2-negative BC, gBRCA testing is recommended to prioritize platinum-based treatments, with trials demonstrating that PARPi improve progression-free survival and quality of life compared to chemotherapy [ 61 ], [ 62 ], [ 63 ], [ 64 ]. An interesting finding in this work is the new approach of creating scores based on germline genetic variants to predict BC prognosis and how these scores can also highlight some signaling pathways involved in BC recurrence, as Milanese et. al reported. The study found that the recurred patients had a higher frequency of germline variants, particularly in genes related to T-cell function, antigen presentation, and cytokine interactions, which likely impair immune responses and promote a pro-tumorigenic environment [ 27 ]. This involvement of genes in immune system pathways was also observed in other articles in cluster 3 "Immune system", such as IL-10, TNFα, ILR-4, and IL-13 [ 41 ], [ 46 ], [ 53 ], [ 54 ]. The overall estimate of cluster 3 for OS was 3.34 (95% CI 1.12–12.11) along with estimates for PFS and MFS higher than HR 4.5 [PFS HR 4.63 (95% CI 1.59–13.61); MFS HR 4.71 (95% CI 1.45–15.35)]. These findings open a new direction in the research of germline genetic variants and their association with BC prognosis. The strengths of this study include a comprehensive search strategy across five different databases and the intentional search to include studies from diverse populations; however, this was not achieved because there are not enough studies from non-white or Caucasian populations. The diversity of study populations must be carefully considered, as Hispanic and Black individuals remain significantly underrepresented. As previously noted, most studies were conducted in Europe and Asia. In particular, the representation of Hispanic participants accounted for only 0.21% (543 out of 253,768 total participants), while Black participants represented just 0.25% (624 participants) in European and American studies. In stark contrast, White/Caucasian individuals comprised 89.57% (226,884 participants) of the total sample of included studies. These disparities highlight the urgent need for more inclusive clinical research that adequately represents these underserved populations. Our findings have potential implications for clinical practice as they suggest that identifying genetic variations linked to breast cancer prognosis could help to stratify the risk of recurrence and death to personalize treatment strategies. With the increase in genomic studies, germline testing for predictive variants will be incorporated into the management of breast cancer patients, and it must be accompanied by genetic counseling before and after testing. The genetic counseling enhances patient engagement, reduces patient distress, improves the accuracy of risk perception, and facilitates shared decision-making, which can improve clinical outcomes and quality of life [ 65 ], [ 66 ], [ 67 ]. Effective management would require a collaborative team comprising oncologists and geneticists in the clinical setting to address the new challenges associated with accurately interpreting genomic analyses. Our study has limitations, such as significant design heterogeneity and variability in outcome definitions and measurement methods across the studies, which could bias the quantitative synthesis. Additionally, some studies lacked detailed data on confounding variables and follow-up duration. New research should focus on validating identified germline genetic variants in diverse populations in large-scale studies and conducting longitudinal studies to better understand these associations' temporal dynamics. Complete reports of the analyses, including adjustments by covariates and data accessibility, are necessary for reproducibility studies. Integrative analyses that combine genetic, epigenetic, and environmental factors are necessary to create a comprehensive prognostic model. Performing refined subgroup analyses, improving study design in underrepresented populations, leveraging advanced computational models, and standardizing variant interpretation could help researchers to better delineate the full clinical potential of germline variants in breast cancer. Ultimately, integrating germline genetic testing into routine Oncology care may enable more precise stratification of recurrence risk and improved outcomes for patients worldwide. Conclusions This systematic review and meta-analysis emphasizes the importance of DNA germline variations in breast cancer prognosis. While specific germline genetic variants have been identified as significant prognostic markers, further reproducible research is needed in diverse populations to fully understand their clinical relevance and integration into standard care practices. Understanding these genetic factors has the potential to lead to more personalized and effective strategies for managing breast cancer. Abbreviations CI Confidence interval BC Breast Cancer PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses DFS Disease-free survival OS Overall survival PFS Progression Free Survival MFS Metastasis Free Survival BCSS Breast Cancer Specific Survival BCSM Breast Cancer Specific Metastasis HR Hazard ratio NOG Network operational gene ER Estrogen receptor PR Progesterone receptor RRR Relative Risk Ratio LN Lymph node GDEPT Gene-directed enzyme prodrug therapy CPA Cyclophosphamide IFA Iphosphamide gBRCA Germline BRCA PARPi PARP inhibitors Declarations *Clinical trial number: Not applicable. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All data generated or analyzed during this study are included in this published article and can be consulted in Additional file 2. Competing interests CVG Cynthia Villarreal-Garza has received honoraria from Novartis, Pfizer, Lilly, and MSD Oncology. She has been a consultant or advisor for Novartis, Lilly, MSD Oncology, and Amplity Health. Research funding has been provided to her institution by Pfizer. Additionally, she has received travel support, accommodations, or expense reimbursements from MSD Oncology and Pfizer. The author declares no employment, leadership roles, stock ownership, speaker bureau affiliations, patents, royalties, or other financial interests relevant to this work. The rest of the authors declare that they have no competing interests. Funding This work was supported by funding from Secretaría de Ciencia, Humanidades, Tecnología e Inovación (SECIHTI), previously known as CONAHCYT (FOSSIS-272823), and Tecnologico de Monterrey. Authors' contributions LGFR conceptualized the study design, acted as the third reviewer to solve any disagreements in the inclusion process, and substantially reviewed and edited the manuscript. 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Med. , vol. 26, no. 2, p. 101032, 2024, 10.1016/j.gim.2023.101032 Additional Declarations Competing interest reported. CVG (Cynthia Villarreal-Garza) has received honoraria from Novartis, Pfizer, Lilly, and MSD Oncology. She has been a consultant or advisor for Novartis, Lilly, MSD Oncology, and Amplity Health. Research funding has been provided to her institution by Pfizer. Additionally, she has received travel support, accommodations, or expense reimbursements from MSD Oncology and Pfizer. The author declares no employment, leadership roles, stock ownership, speaker bureau affiliations, patents, royalties, or other financial interests relevant to this work. The rest of the authors declare that they have no competing interests. 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2","display":"","copyAsset":false,"role":"figure","size":19201,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph representing the main methodology characteristics and results of the 54 articles included.\u003c/strong\u003e Created with R Studio.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6823944/v1/c890ed10122da5e4a8f49edd.png"},{"id":86673655,"identity":"4329f523-30bd-4728-9f6e-7b31578310f7","added_by":"auto","created_at":"2025-07-14 11:49:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":124710,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of gene variants associated with DFS.\u003c/strong\u003e The width of each estimate's square reflects the corresponding Standard Error (SE). DFS: Disease-Free Survival; CI: Confidence Interval.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6823944/v1/fdeeab424096ff06dd967a19.png"},{"id":86673658,"identity":"1f783d53-8299-4da2-8b34-50719dc9f860","added_by":"auto","created_at":"2025-07-14 11:49:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":199563,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of gene variants associated with OS.\u003c/strong\u003e The width of each estimate's square reflects the corresponding Standard Error (SE). OS: Overall Survival; CI: Confidence Interval.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6823944/v1/fde6ea8b2b1d4c1c13b91b74.png"},{"id":86673674,"identity":"48db69b0-7926-430e-8cc6-19c0290771c5","added_by":"auto","created_at":"2025-07-14 11:49:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1776061,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSTRING mapping of the studied genes and STRING MCL clusters.\u003c/strong\u003e Obtained by MCL clustering (finds natural clusters based on the stochastic flow) with an inflation parameter of 3 and dotted line as edges between clusters.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6823944/v1/c54cab847ffca3420c434174.png"},{"id":92091356,"identity":"74179ef9-01c9-47d2-bc0c-b5d2551924da","added_by":"auto","created_at":"2025-09-24 13:39:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3322217,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6823944/v1/9a9c9faa-d889-4a89-91cf-130ec80dcd68.pdf"},{"id":86673659,"identity":"eff2b0ae-8f90-4633-a265-59d2cdf4e135","added_by":"auto","created_at":"2025-07-14 11:49:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1362304,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional File 1\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"AdditionalFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6823944/v1/fe88517f7e027e67e8fcb20f.docx"},{"id":86675328,"identity":"12d946e7-d0dd-4458-a64d-22c218d16677","added_by":"auto","created_at":"2025-07-14 11:57:06","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":84943,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional File 2\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"AdditionalFile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6823944/v1/35126d02286f4c6267302c67.xlsx"}],"financialInterests":"Competing interest reported. CVG (Cynthia Villarreal-Garza) has received honoraria from Novartis, Pfizer, Lilly, and MSD Oncology. She has been a consultant or advisor for Novartis, Lilly, MSD Oncology, and Amplity Health. Research funding has been provided to her institution by Pfizer. Additionally, she has received travel support, accommodations, or expense reimbursements from MSD Oncology and Pfizer. The author declares no employment, leadership roles, stock ownership, speaker bureau affiliations, patents, royalties, or other financial interests relevant to this work. The rest of the authors declare that they have no competing interests.","formattedTitle":"Germline Genetic Variations and Breast Cancer Prognosis: A Systematic Review and Meta-Analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eBreast cancer (BC) is the most common cancer in females worldwide, and the incidence rates have kept increasing by 0.6% per year [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The prognosis for these women is widely heterogeneous due to the high diversity between tumors and individuals [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Prognosis relates to the risk of developing an outcome, such as recurrence or death, based on clinical and non-clinical characteristics [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Cancer recurrence is the reappearance of cancer after a period of absence of disease [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It can be classified into three main types based on the location of the recurrence: local, regional, and distant recurrence [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The latter occurs when cancer has spread to other organs or tissues far from the site of the primary tumor; this is called metastatic recurrence, and it has been reported that the majority of deaths from cancer of solid tumors are caused by metastasis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Due to this, predicting the risk of breast cancer recurrence is of great importance in determining breast cancer prognosis. Understanding the risk of recurrence allows the physician to choose the best treatment and the optimal clinical management and surveillance for the patient.\u003c/p\u003e\u003cp\u003eDisease-free survival (DFS) is a clinical endpoint commonly used in oncology to measure the length of time after primary treatment for a cancer during which the patient survives without any sign or symptoms of the disease [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. DFS is often used as a surrogate of overall survival (OS) in clinical trials [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. OS is the period from diagnosis to death from any cause [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe prediction factors of recurrence for breast cancer used in clinical practice are age, clinical stage at diagnosis, histological grade, and molecular subtype [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition to these factors, genomic tools, known as genomic signatures, have been developed to calculate a patient's recurrence risk score based on the expression of a certain number of somatic genes, such as Oncotype DX\u0026reg; and MammaPrint\u0026reg; [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAnother type of genomic prediction tool is risk scores created from germline variant information; this new approach has been under study in recent years. Germline variants have been used to determine an individual's predisposition to develop cancer; however, it has been reported that there are also germline variants with a high predictive value for recurrence [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDetermining germline pathogenic/likely pathogenic (P/LP) mutations in BC already has significant implications in treating these patients. Identifying mutations in high-penetrance genes such as \u003cem\u003eBRCA1, BRCA2, PALB2, TP53, PTEN\u003c/em\u003e, and \u003cem\u003eCDH1\u003c/em\u003e can influence decisions on surgical and systemic treatments [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For example, the presence of germline BRCA (gBRCA) mutations can dictate the use of poly-ADP-ribose polymerase inhibitors (PARPi), such as Olaparib and Talazoparib, which have shown efficacy in improving outcomes in both early and metastatic settings [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, the relationship between these germline mutations and the patient's prognosis is poorly defined. Some studies have reported a worse clinical outcome, whereas others report no difference in BC prognosis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo date, no published systematic review has examined the association between DNA germline variants and BC prognosis. Therefore, this study aimed to analyze the available literature to assess these associations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe defined the study's primary outcomes as DFS and OS, crucial for determining cancer prognosis. The study was registered on PROSPERO and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (PROSPERO 2022 CRD42022308746) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe first search was conducted in January 2022, using PubMed, Scopus, MEDLINE, Web of Science, and QInsight as sources. An updated search was performed in June 2024 across all sources except QInsight. The complete search included studies published between January 2000 and June 2024. We utilized 10 search terms with variations on \"germline variants\" to \"germline polymorphisms,\" along with the primary outcomes (see Tables S1 and S2, Additional File 1). We conducted 20 searches to ensure comprehensive coverage of relevant studies.\u003c/p\u003e\u003cp\u003eWe searched for observational studies (cohorts and case controls), retrospective studies, and randomized/non-randomized clinical trials. The inclusion criteria comprised studies of women with breast cancer diagnosis, information about germline variants, and survival/recurrence outcomes. On the other hand, the exclusion criteria involved unrelated studies with no information about germline variations and no survival/recurrence data, duplicated or unavailable full texts, or abstract-only articles.\u003c/p\u003e\u003cp\u003eAfter identifying corresponding publications, two independent reviewers selected the studies based on the eligibility criteria. A third reviewer resolved any disagreements. The initial database search identified 576 studies. After removing duplicates, the number of studies was 282. During the eligibility assessment, 117 articles were excluded by title, and 97 articles were excluded by abstract, leaving 68 articles to be assessed by full text. The article \"Association of \u003cem\u003eBRCA1\u003c/em\u003e K1183R polymorphism with survival in \u003cem\u003eBRCA1/2\u003c/em\u003e-negative Chinese familial breast cancer\" was excluded due to the unavailability of the full text [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFifty-six studies were included for thorough review and analysis. However, during data extraction, we found that no data about gene variants and measure of association was reported in the articles \"Germline variants associated with leukocyte genes predict tumor recurrence in breast cancer patients\" and \"Effect of Adjuvant Paclitaxel and Carboplatin on Survival in Women With Triple-Negative Breast Cancer: A Phase 3 Randomized Clinical Trial\" [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]; therefore, we decided to exclude them. Finally, 54 studies met the inclusion criteria and were included in the systematic review (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eData extraction was performed on the 54 articles included, and the information was stored in an Excel database sheet (see Data Extraction Form in Additional File 2). Two reviewers performed the extraction process, verifying the data to identify human errors.\u003c/p\u003e\u003cp\u003eVariants that showed statistical significance in multivariate analysis were meta-analyzed by subgroups. The variants were first divided into two groups according to the outcome studied (OS or DFS). For a second meta-analysis, the variants were grouped in 6 clusters according to the genes' functional and physical protein associations, using STRING [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. We used the predetermined settings on STRING, and the network type selected was \"full STRING network\", with all the interaction sources active and a minimum required interaction score of 4.0 (medium confidence). These subgroup meta-analyses were performed using the package Metafor in R to create forest plots and determine heterogeneity through Cochrane's Q, I\u003csup\u003e2,\u003c/sup\u003e and Tau\u003csup\u003e2\u003c/sup\u003e tests.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the summarized characteristics of all included studies. All studies exhibited variability in their design, population, and geographic location. Most of the studies were conducted in Europe (n\u0026thinsp;=\u0026thinsp;28), Asia (n\u0026thinsp;=\u0026thinsp;16), and North America (n\u0026thinsp;=\u0026thinsp;8), and the study design most common was the cohort (n\u0026thinsp;=\u0026thinsp;37) followed by retrospective studies (n\u0026thinsp;=\u0026thinsp;14) and case control (n\u0026thinsp;=\u0026thinsp;3). Nearly all studies (n\u0026thinsp;=\u0026thinsp;52) identified at least one genetic variant that showed a statistically significant association with BC prognosis in univariate analysis, while 30 studies reported statistically significant associations in multivariate analysis.\u003c/p\u003e\n\u003cp\u003eThe most common outcome was disease-free survival (DFS) (n\u0026thinsp;=\u0026thinsp;34), and the most frequently used measure of association was the hazard ratio (HR) (n\u0026thinsp;=\u0026thinsp;41). Most of the studies\u0026apos; weakness was the lack of multivariate statistical analysis and no statistical data, such as p-values and non-significant effect sizes.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eHR is the most commonly used measure in oncological research to analyze time-to-event. It quantifies the relationship between an independent variable and the outcome (survival) by calculating the difference in logarithms of hazard functions (22,23). However, not all the studies included in this systematic review incorporated this risk estimate, and only a few adjusted for cofounders. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e lists genes and variants associated with breast cancer prognosis with an adjusted HR and statistical significance. Some studies grouped the germline mutations instead of analyzing each one separately. Interestingly, all of them studied pathogenic mutations in BRCA genes (reported in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAmong the excluded studies, there is one that should be taken into consideration. The study was conducted by Milanese et al., and it shows the results of the eTumorMetastasis algorithm applied to breast cancer patients. The eTumorMetastasis algorithm converts tumor functional mutations into network-based profiles to identify network operational gene (NOG) signatures [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. These NOG signatures capture the transition point at which a tumor cell becomes likely to recur. The eTumorMetastasis algorithm was applied to exome sequencing data from 755 patients with estrogen receptor positive (ER+) breast cancer. This algorithm identified gene signatures from germline variants that effectively distinguished between patients with a recurrence or not in two independent cohorts (n\u0026thinsp;=\u0026thinsp;200 and 295, P\u0026thinsp;=\u0026thinsp;0.0014). These predictions outperformed the widely used Oncotype DX test for both high- and low-risk groups [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIn addition to Milanese et al., other authors have developed an algorithm to calculate a score that predicts breast cancer prognosis, such as Maria-Escala Garcia et al., Ke-Da Yu et al., and Tuomas Heikkinen et al. (19\u0026ndash;21). Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e describes these studies as they showed statistical significance in multivariate analysis.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u003cbr\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescriptive table of variants that showed statistical significance in adjusted effect estimate by covariates\u003c/strong\u003e [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]. *ER positive, **PR positive, ***Lymph node metastasis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAuthor, Year\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePopulation/Country\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFollow-up (mean years)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConfounders\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAllele/ Genotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR/RRR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePathway\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eZhu Qianqian, 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eTrans-ethnic (European 70.50%, East Asian 11.32%, Hispanic 9.87%, and African 8.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e3973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAge at diagnosis, body mass index, tumor grade, stage of disease, ER, PR, and HER2 status, hormonal therapy, chemotherapy, radiation therapy, surgery type, and all population stratification principal components.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eARRDC3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers421379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer suppression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eUACA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers720251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.19E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eProgrammed Cell Death-Apoptosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers62019060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eViktor Hlav\u0026aacute;č, 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTumor size and grade, lymph node metastasis, and estrogen receptor expression.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCYP4X1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers17102977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u0026ndash;2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolism-Xenobiotics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCYP26B1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers62150087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u0026ndash;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolism-Xenobiotics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eAnna Morra, 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e91686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eAge at diagnosis, ER status, PR status, HER2 status, tumor grade, and the use and type of systemic treatment.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTBL1X\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers5934618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u0026ndash;1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLINC01487, STRIT1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4679741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u0026ndash;1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolism - Estrogen biosynthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGRIP2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1106333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.10E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeuronal System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eARAP2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers78754389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41\u0026ndash;2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.40E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignal Transduction-RHO GTPase Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003eJolanta Pamuła-Piłat, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"8\"\u003e\n \u003cp\u003e305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003eNot listed.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNR1/2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers3732359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u0026ndash;2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXenobiotics Metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSLC22A16\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7756222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u0026ndash;2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eTransport of small molecules\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSLC22A16\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7756222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u0026ndash;2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSLC22A16\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers9487402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u0026ndash;2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePGR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1824125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u0026ndash;2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMetabolism of proteins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePGR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12224560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u0026ndash;2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDPYD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers291593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29\u0026ndash;26.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolism - Nucelotide catabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAKR1C3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers3209896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2-25.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaru A. Muranen, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor grade, size, PR expression status, and lymph node involvement.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDCAF1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers57025206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.73\u0026ndash;10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCell cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eDamien Cot\u0026eacute;, 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eAge, ER, and PR status.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eERBB3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2229046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91\u0026ndash;4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGene expression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eERBB3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers773123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u0026ndash;7.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBARD1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2070096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u0026ndash;9.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eERBB2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1136201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u0026ndash;6.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGene expression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eSinead Toomey, 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eIrish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e11.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eAge, tumor grade, ER state, and LN state.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEGFR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN158N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u0026ndash;10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eSignal Transduction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEGFR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI655V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u0026ndash;5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEGFR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD994D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1\u0026ndash;10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEGFR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1140475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.98\u0026ndash;21.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRasa Ugenskienė, 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLithuanian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e5.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAge at diagnosis, ER status, PR status, HER2 status, tumor size, tumor grade, and lymph node status.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSIPA1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers3741378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81\u0026ndash;14.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eImmune System - Rap1 Signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSIPA1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers3741378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.15\u0026ndash;19.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eYu-Mian Jia, 2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eChinese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"6\"\u003e\n \u003cp\u003e1091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eER status, PR status, HER2 status, tumor size, clinical stage, lymph node metastasis, chemotherapy, and endocrine therapy status.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCDH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7186053*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u0026ndash;0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eSignal Transduction - ESR-mediated signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCDH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7186053**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCDH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7186053***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u0026ndash;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0397\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCDH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7200690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42\u0026ndash;74.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCDH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7198799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13-105.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCTNNB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4533622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u0026ndash;87.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignal Transduction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDan-Na Chen, 2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChinese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymph node status, tumor size, chemotherapy, endocrine therapy, and ER, PR, HER2 statuses.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTLR3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers3775291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.98\u0026ndash;6.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImmune System - TLR3 cascade\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePetra Seibold, 2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGerman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e1408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTumor size, nodal status, baseline metastases status, tumor grade, estrogen/progesterone receptor status, mode of detection, smoking status, menopausal hormone therapy, as well as radiotherapy and chemotherapy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePARP2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers878156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u0026ndash;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDNA repair\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePARP2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers878156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u0026ndash;1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eErika Korobeinikova, 2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eLithuanian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e5.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAge group, tumor size, lymph node status, histological grade, and intrinsic subtype.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTNF\u0026alpha;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1800629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59\u0026ndash;13.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSignal Transduction - TNF Signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTNF\u0026alpha;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1800629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u0026ndash;15.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTNF\u0026alpha;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1800629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1-21.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eC. Vulsteke, 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eBelgium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eSubtype, stage, White Blood Cell Count, and hemoglobin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCYP2C9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1057910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.1-151.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eABCB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2032582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u0026ndash;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrug ADME\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCYP2C9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1057910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u0026ndash;47.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCYBA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u0026ndash;2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImmune System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eUGT2B7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers3924194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u0026ndash;9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrug ADME\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eMala Pande, 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eTrans-ethnic (72% were non-Hispanic white, 13% were African American, 11% were Hispanic, and 3% were of unknown race/ethnicity).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e1019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eNot listed.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eRace/ethnicity, age at diagnosis, tumor stage, and treatment.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eADIPOQ\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1063539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u0026ndash;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLEPR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers11585329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u0026ndash;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolism of proteins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTSC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2519757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u0026ndash;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutophagy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIGF1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1520220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrowth and Development\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePIK3CA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2677760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u0026ndash;1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignal Transduction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAxel Muendlein, 2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustrian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNM stage and age.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIGF1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2946834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u0026ndash;3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrowth and Development\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGudrun Absenger, 2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustrian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMenopausal status, stage, grading, receptor status, HER-2 neu status, and adjuvant therapy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eVEGF-A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers3025039 C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u0026ndash;3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAngiogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eJames L. Murray, 2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eStage, age, and treatment.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNFKB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers230532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u0026ndash;1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eImmune System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIL-13\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1800925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u0026ndash;2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfrican-American and Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNFKB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers3774932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u0026ndash;2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfrican-American and Hispanic patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIL4R\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers3024543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAA\u0026thinsp;+\u0026thinsp;AG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u0026ndash;2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ePeter A Fasching, 2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e20,073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAge at diagnosis and categorical variables for tumor size, lymph nodes status, and grade.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTOX3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers3803662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u0026ndash;1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDNA bending and unwinding\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTOX3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers3803662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u0026ndash;1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLSP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers3817198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImmune System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElse Maae, 2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDanish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenotype, adjuvant chemotherapy, tumor size, axillary lymph node status, and dichotomized histopathological tumor grade.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eVEGF-A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2010963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u0026ndash;6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAngiogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eKe-Da Yu, 2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eChinese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eLymph node status, tumor size, ER, HER2, and chemotherapy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNQO2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2071002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u0026ndash;0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMetabolism - Functionalization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNQO2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers9501910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u0026ndash;2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGSTM1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNull/present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u0026ndash;0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrug ADME\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eYee Soo Chae, 2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eKorean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e4.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAge, stage, histological grade, and ER, P53, HER2 statuses.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eVARS2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2074511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u0026ndash;0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolism of proteins - Aminoacylation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePOLE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers5744857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDNA repair\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVerena Varadi, 2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSwedish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eER and PR status, tumor size, lymph node metastasis, and histological grade.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eREV3L\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers11153292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56\u0026ndash;8.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDNA repair\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSize, grade, and regional lymph node metastasis.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eREV3L\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers462779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u0026ndash;9.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eElizabeth M Azzato, 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e3761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eStage, histopathologic grade, and ER status.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOCA2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4778137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u0026ndash;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eMetabolism - Melanin biosynthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOCA2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers6626269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u0026ndash;1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.20E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOCA2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4778137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u0026ndash;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOCA2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers6626269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u0026ndash;1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGudrun Knechtel, 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAge at diagnosis, tumor size, lymph node status, clinical stage, histological grade, ER status, PR, and treatment modalities.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFAS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2234767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u0026ndash;0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProgrammed Cell Death\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIL-10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1800872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u0026ndash;2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImmune System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArmin Gerger, 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustrian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge at diagnosis, tumor size, lymph node status, clinical stage, histological grade, ER status, PR status, and treatment modalities.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIL-10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1800872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u0026ndash;2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImmune System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u003cbr\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescriptive table of grouped mutations that showed statistical significance in adjusted effect estimate by covariates\u003c/strong\u003e [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAuthor, Year\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePopulation / Country\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFollow-up (mean years)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConfounders\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType of variant\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep- value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePathway\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMatteo Lambertini, 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEurope, North America, Latin America, Israel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNodal status, grade, HER2, type of breast surgery, chemotherapy use, age, year of\u003c/p\u003e\n \u003cp\u003ediagnosis, and country.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBRCA1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u0026ndash;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eDNA repair\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eYong Alison Wang, 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eChinese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eTumor size\u0026thinsp;\u0026gt;\u0026thinsp;2 cm, lymph node positivity, triple negative tumor type, young age of onset, mastectomy, adjuvant chemotherapy, adjuvant radiotherapy, and hormonal therapy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBRCA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4\u0026ndash;6.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBRCA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2\u0026ndash;6.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBRCA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u0026ndash;7.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBRCA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.44\u0026ndash;44.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescriptive table of scores developed to predict prognosis and showed statistical significance in adjusted effect estimate by covariates\u003c/strong\u003e [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAuthor, Year\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePopulation / Country\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFollow-up (mean years)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConfounders\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePathway\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eMaria Escala-Garcia, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e84,457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eNot listed.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eADRBK2, CCL16, CNR2, CXCR5, DNAJB4, F2R, GNA15, GNAT1, GRM4, GUCA1A, GUCA1B, GUCA2B, GUCY2D, HRH4, LTB4R, OPRK1, OPRM1, RGS9\u003c/em\u003e, and \u003cem\u003eRGS9BP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRPM a1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07\u0026ndash;1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eMentioned as significant, but no p-value reported.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eG-alpha signaling events\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eADCY10, GNA11, PTGIR\u003c/em\u003e, and \u003cem\u003eRGS3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRPM a2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.08\u0026ndash;1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePER1, PER3, TIMELESS\u003c/em\u003e, and \u003cem\u003eTIPIN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRPM b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.19\u0026ndash;1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCircadian clocks\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCHCHD4, PDE9A, SLC36A1\u003c/em\u003e, and \u003cem\u003ePHYHIPL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRPM c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.12\u0026ndash;1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegulators of cell growth and angiogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eARHGAP10, CCNT2, CDR2, HEXIM1, NEUROD2, PKN1\u003c/em\u003e, and \u003cem\u003eZFAND6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRPM d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.16\u0026ndash;1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRho GTPases and apoptosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKe-Da Yu, 2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKorean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, stage, histological grade, and ER, P53, and HER2 status.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNQO2\u003c/em\u003e and \u003cem\u003eGSTM1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCombination of 3 SNPs: rs2071002, rs9501910 and null/present of GSTM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u0026ndash;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolism - Functionalization and Drug ADME\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTuomas Heikkinen, 2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFinish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e2,204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTumor size, nodal status, primary metastasis, estrogen receptor, progesterone receptor, HER2, p53, Ki67, grade.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ePTEN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePromoter polymorphisms \u0026minus;\u0026thinsp;903GA, -975GC, and \u0026minus;\u0026thinsp;1026CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.17\u0026ndash;3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSignal Transduction - Activation of AKT signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u0026ndash;3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0381\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe forest plots obtained from the meta-analysis of the gene variants associated with DFS and OS are presented in Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, respectively. The combined effect sizes for DFS were 2.72 (95% CI 1.81\u0026ndash;3.62) and 2.53 (95% CI 1.80\u0026ndash;3.26) for OS. Heterogeneity tests of both forest plots showed a high level of heterogeneity, with an I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;99.4% for DFS and I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;99.8% for OS.\u003c/p\u003e\n\u003cp\u003eSTRING analysis generated six clusters according to the functional associations of the proteins (see Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). To visualize the effect sizes of associations of the polymorphisms of the genes grouped in each cluster, we performed a forest plot for each of the six clusters, and an additional forest plot for BRCA mutations. These forest plots are presented in Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e, Additional File 1, with the overall mean estimates obtained for each outcome in every cluster.\u003c/p\u003e\n\u003cp style='margin:0in;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:justify;'\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e\u003cstrong\u003eTable 4. Descriptive table of STRING MCL clusters and their proteins.\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp style='margin:0in;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:justify;'\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e\u003cstrong\u003e\u003cimg 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\"\u003e\u003c/strong\u003e\u003c/span\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first systematic review and meta-analysis to date investigating the prognostic significance of DNA germline variations in BC. In multiple studies, the findings consistently highlight specific germline genetic variants associated with survival and recurrence outcomes. Overall, we found 74 germline variants, 5 grouped pathogenic mutations, and seven groups of SNPs that reported statistical significance in multivariate analysis associated with BC prognosis, indicating that DNA germline variations influence BC prognosis.\u003c/p\u003e\u003cp\u003eThe combined effect sizes obtained for DFS (2.72 [95% CI 1.81\u0026ndash;3.62]) and OS (2.53 [95% CI 1.80\u0026ndash;3.26]) suggest an association of germline variations with breast cancer DFS and OS. However, the high level of heterogeneity observed (I\u0026sup2; = 99.4% for DFS and I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;99.8% for OS) points out that the included studies exhibit significant variability [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Despite attempts to account for dependencies within studies, significant dispersion persists, which could indicate that the studies differ in their outcomes or settings. This limits the generalizability of the average effect and highlights the need to consider differences when interpreting the results carefully.\u003c/p\u003e\u003cp\u003eIn Cluster 2 \"Xenobiotic Metabolism Enzymes and Transporters (XMETs)\", it is important to mention that the overall estimate HR for DFS was 4.02 (95% CI 1.09\u0026ndash;16.26) and this cluster includes some of the most frequently studied genes associated with BC prognosis, the cytochrome enzyme family (4 variants associated with BC prognosis). This family is known to be primarily responsible for metabolizing most anticancer therapies. Studying the potential prognosis of these genes could contribute to developing new therapies, such as gene-directed enzyme prodrug therapy (GDEPT). Through GDEPT, \u003cem\u003eCYP\u003c/em\u003e enzymes can be genetically modified to enhance the conversion of anticancer prodrugs into their active metabolites, thereby reducing prodrug dosage and minimizing chemotherapy side effects [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. An example of this GDEPT is the use of oxazaphosphorines, such as cyclophosphamide (CPA) and ifosfamide (IFA), which are prodrugs activated by hydroxylation to produce cytotoxic metabolites like phosphoramide mustard. However, this metabolite is limited by its inability to cross cell membranes effectively when activated in the liver [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The P450 GDEPT strategy addresses this by delivering P450-expressing genes directly to tumor cells, enabling local activation and improved therapeutic effects, as demonstrated in a Phase 1 clinical trial using the MetXia-P450 vector with oral CPA. This approach has shown safety, consistent gene expression in cancer cells, and promising results, prompting further clinical trials [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAs mentioned before, gBRCA mutations can influence surgical decisions and systemic therapies, such as the use of PARPi [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this work, the meta-analysis of the \"BRCA mutations\" cluster showed that these gBRCA mutations could be responsible for worse DFS in BC patients, with an overall estimate HR of 2.34 (95% CI 1.08\u0026ndash;5.11) and one study that reported an OS HR of 8.01 (95% CI 1.44\u0026ndash;44.70). In metastatic HER2-negative BC, gBRCA testing is recommended to prioritize platinum-based treatments, with trials demonstrating that PARPi improve progression-free survival and quality of life compared to chemotherapy [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAn interesting finding in this work is the new approach of creating scores based on germline genetic variants to predict BC prognosis and how these scores can also highlight some signaling pathways involved in BC recurrence, as Milanese et. al reported. The study found that the recurred patients had a higher frequency of germline variants, particularly in genes related to T-cell function, antigen presentation, and cytokine interactions, which likely impair immune responses and promote a pro-tumorigenic environment [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This involvement of genes in immune system pathways was also observed in other articles in cluster 3 \"Immune system\", such as IL-10, TNFα, ILR-4, and IL-13 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The overall estimate of cluster 3 for OS was 3.34 (95% CI 1.12\u0026ndash;12.11) along with estimates for PFS and MFS higher than HR 4.5 [PFS HR 4.63 (95% CI 1.59\u0026ndash;13.61); MFS HR 4.71 (95% CI 1.45\u0026ndash;15.35)]. These findings open a new direction in the research of germline genetic variants and their association with BC prognosis.\u003c/p\u003e\u003cp\u003eThe strengths of this study include a comprehensive search strategy across five different databases and the intentional search to include studies from diverse populations; however, this was not achieved because there are not enough studies from non-white or Caucasian populations.\u003c/p\u003e\u003cp\u003eThe diversity of study populations must be carefully considered, as Hispanic and Black individuals remain significantly underrepresented. As previously noted, most studies were conducted in Europe and Asia. In particular, the representation of Hispanic participants accounted for only 0.21% (543 out of 253,768 total participants), while Black participants represented just 0.25% (624 participants) in European and American studies. In stark contrast, White/Caucasian individuals comprised 89.57% (226,884 participants) of the total sample of included studies. These disparities highlight the urgent need for more inclusive clinical research that adequately represents these underserved populations.\u003c/p\u003e\u003cp\u003eOur findings have potential implications for clinical practice as they suggest that identifying genetic variations linked to breast cancer prognosis could help to stratify the risk of recurrence and death to personalize treatment strategies. With the increase in genomic studies, germline testing for predictive variants will be incorporated into the management of breast cancer patients, and it must be accompanied by genetic counseling before and after testing. The genetic counseling enhances patient engagement, reduces patient distress, improves the accuracy of risk perception, and facilitates shared decision-making, which can improve clinical outcomes and quality of life [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Effective management would require a collaborative team comprising oncologists and geneticists in the clinical setting to address the new challenges associated with accurately interpreting genomic analyses.\u003c/p\u003e\u003cp\u003eOur study has limitations, such as significant design heterogeneity and variability in outcome definitions and measurement methods across the studies, which could bias the quantitative synthesis. Additionally, some studies lacked detailed data on confounding variables and follow-up duration.\u003c/p\u003e\u003cp\u003eNew research should focus on validating identified germline genetic variants in diverse populations in large-scale studies and conducting longitudinal studies to better understand these associations' temporal dynamics. Complete reports of the analyses, including adjustments by covariates and data accessibility, are necessary for reproducibility studies. Integrative analyses that combine genetic, epigenetic, and environmental factors are necessary to create a comprehensive prognostic model.\u003c/p\u003e\u003cp\u003ePerforming refined subgroup analyses, improving study design in underrepresented populations, leveraging advanced computational models, and standardizing variant interpretation could help researchers to better delineate the full clinical potential of germline variants in breast cancer. Ultimately, integrating germline genetic testing into routine Oncology care may enable more precise stratification of recurrence risk and improved outcomes for patients worldwide.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis systematic review and meta-analysis emphasizes the importance of DNA germline variations in breast cancer prognosis. While specific germline genetic variants have been identified as significant prognostic markers, further reproducible research is needed in diverse populations to fully understand their clinical relevance and integration into standard care practices. Understanding these genetic factors has the potential to lead to more personalized and effective strategies for managing breast cancer.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBreast Cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePRISMA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePreferred Reporting Items for Systematic Reviews and Meta-Analyses\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eDFS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDisease-free survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eOS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOverall survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePFS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProgression Free Survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eMFS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMetastasis Free Survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBCSS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBreast Cancer Specific Survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBCSM\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBreast Cancer Specific Metastasis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHazard ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNOG\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNetwork operational gene\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eER\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEstrogen receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProgesterone receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eRRR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRelative Risk Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eLN\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLymph node\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eGDEPT\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene-directed enzyme prodrug therapy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCPA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCyclophosphamide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eIFA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIphosphamide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003egBRCA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGermline BRCA\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePARPi\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePARP inhibitors\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e*Clinical trial number: Not applicable.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent 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\u003eAll data generated or analyzed during this study are included in this published article and can be consulted in Additional file 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCVG Cynthia Villarreal-Garza has received honoraria from Novartis, Pfizer, Lilly, and MSD Oncology. She has been a consultant or advisor for Novartis, Lilly, MSD Oncology, and Amplity Health. Research funding has been provided to her institution by Pfizer. Additionally, she has received travel support, accommodations, or expense reimbursements from MSD Oncology and Pfizer. The author declares no employment, leadership roles, stock ownership, speaker bureau affiliations, patents, royalties, or other financial interests relevant to this work. The rest of the authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by funding from Secretaría de Ciencia, Humanidades, Tecnología e Inovación (SECIHTI), previously known as CONAHCYT (FOSSIS-272823), and Tecnologico de Monterrey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLGFR conceptualized the study design, acted as the third reviewer to solve any disagreements in the inclusion process, and substantially reviewed and edited the manuscript. CVG, AMB, LG, MTM, and NRN provided guidance on the data analysis and contributed to reviewing and editing the manuscript. EJMC acted as the second reviewer, conducting the second inclusion process, data extraction, and analysis. ACGG conducted the searches, the inclusion process, data extraction, data analysis, and manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024, \u003cem\u003eCA. Cancer J. Clin.\u003c/em\u003e, vol. 74, no. 1, pp. 12\u0026ndash;49, Jan. 2024, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21820\u003c/span\u003e\u003cspan address=\"10.3322/caac.21820\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePolyak K. Heterogeneity in breast cancer. J Clin Invest. 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Feb., Impact of genetic counseling strategy on diagnostic yield and workload for genome-sequencing-based tumor diagnostics, \u003cem\u003eGenet. Med.\u003c/em\u003e, vol. 26, no. 2, p. 101032, 2024, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.gim.2023.101032\u003c/span\u003e\u003cspan address=\"10.1016/j.gim.2023.101032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"DNA germline mutations, polymorphisms, survival, recurrence, breast cancer","lastPublishedDoi":"10.21203/rs.3.rs-6823944/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6823944/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBreast cancer is a complex disease with a significant global health burden. Understanding the genetic factors that influence its prognosis is crucial for improving patient outcomes. This systematic review aims to synthesize the evidence on the relationship between germline DNA variations and breast cancer prognosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comprehensive search was conducted across PubMed, Scopus, MEDLINE, Web of Science, and QInsight to identify studies from January 2000 to June 2024 that examined associations between DNA germline variations and breast cancer prognosis, including survival and recurrence. Two reviewers independently extracted data and assessed bias. Genes were mapped and clustered using STRING, based on functional and physical protein associations. The protocol was registered on PROSPERO (CRD42022308746) and followed the PRISMA guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e54 studies were analyzed: 37 cohort, 14 retrospective, and three case-control studies. Due to heterogeneity in study design, populations, and geography, a global meta-analysis was not feasible. Instead, we performed separate meta-analyses for Disease Free Survival (DFS) and Overall Survival (OS). The combined effect sizes for DFS were 2.72 (95% CI 1.81-3.62) and 2.53 (95% CI 1.80-3.26) for OS. Seven gene pathways, particularly those involving EGFR resistance, drug metabolism, and immune system, showed relevance to survival outcomes. Despite methodological variability, consistent evidence highlighted the prognostic value of specific germline variants. A significant lack of population diversity was observed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis systematic review underscores the prognostic relevance of germline DNA variations in breast cancer. However, further large-scale, longitudinal studies in diverse populations must validate and integrate these associations into clinical practice.\u003c/p\u003e","manuscriptTitle":"Germline Genetic Variations and Breast Cancer Prognosis: A Systematic Review and Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 11:49:01","doi":"10.21203/rs.3.rs-6823944/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":"fd2876dc-4cd7-49d8-ad2e-5bff71699de3","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-24T13:39:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 11:49:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6823944","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6823944","identity":"rs-6823944","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}