DNA methylation signatures provide novel diagnostic biomarkers and predict responses of immune therapy for breast cancer.

Breast cancer (BRCA) is one of the most common malignant tumors affecting women worldwide. DNA methylation modifications can influence oncogenic pathways and provide potential diagnostic and therapeutic targets for precision oncology. In this study, we used non-parametric permutation tests to identify differentially methylated positions (DMPs) between paired tumor and normal BRCA tissue samples from the Cancer Genome Atlas (TCGA) database. Then, we applied non-negative matrix factorization (NMF) to the DMPs to derive eight distinct DNA methylation signatures. Among them, signatures Hyper-S3 and Hypo-S4 signatures were associated with later tumor stages, while Hyper-S1 and Hypo-S3 exhibited higher methylation levels in earlier stages. Signature Hyper-S3 displayed an effect on overall survival. We further validated the four stage-associated signatures using an independent BRCA DNA methylation dataset from peripheral blood samples. Results demonstrated that 24 commonly hypomethylated sites in Hypo-S4 showed lower methylation in BRCA patients compared to healthy individuals, suggesting its potential as an early diagnostic biomarker. Furthermore, we found that methylation of 23 probes from four stage-related signatures exhibited predictive power for immune therapy response. Notably, methylation levels of all three probes from the Hypo-S4 and activity of the Hypo-S4 demonstrated highly positive relevance to PD-L1 gene expression, implying their significant predictive values for immunotherapy outcomes. GO and KEGG pathway enrichment analysis revealed that genes with these 23 immunotherapy-related methylation probes are mainly involved in glycan degradation or protein deglycosylation. These methylation signatures and probes may serve as novel epigenetic biomarkers for predicting tumor immunotherapy response. Our findings provide new insights into precision oncology approaches for BRCA.

Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention and intervention.

Identifying risk protein targets and their therapeutic drugs is crucial for effective cancer prevention. Here, we conduct integrative and fine-mapping analyses of large genome-wide association studies data for breast, colorectal, lung, ovarian, pancreatic, and prostate cancers, and characterize 710 lead variants independently associated with cancer risk. Through mapping protein quantitative trait loci (pQTL) for these variants using plasma proteomics data from over 75,000 participants, we identify 365 proteins associated with cancer risk. Subsequent colocalization analysis identifies 101 proteins, including 74 not reported in previous studies. We further characterize 36 potential druggable proteins for cancers or other disease indications. Analyzing >3.5 million electronic health records, we uncover five drugs (Haloperidol, Trazodone, Tranexamic Acid, Haloperidol, and Captopril) associated with increased cancer risk and two drugs (Caffeine and Acetazolamide) linked to reduced colorectal cancer risk. This study offers novel insights into therapeutic drugs targeting risk proteins for cancer prevention and intervention.

Large-scale alternative polyadenylation-wide association studies to identify putative cancer susceptibility genes.

Alternative polyadenylation (APA) modulates mRNA processing in the 3' untranslated regions (3' UTR), affecting mRNA stability and translation efficiency. Research into genetically regulated APA has the potential to provide insights into cancer risk. Herein, we conducted large alternative polyadenylation-wide association studies (APA-WAS) to investigate associations of APA levels with cancer risk. Genetic models were built to predict APA levels in multiple tissues using genotype and RNA-sequencing data from 1,337 samples from the Genotype-Tissue Expression Project. Associations of genetically predicted APA levels with cancer risk were assessed by applying the prediction models to data from large genome-wide association studies of six common cancers among European-ancestry populations, including breast, ovary, prostate, colorectum, lung, and pancreas. A total of 58 risk genes (corresponding to 76 APA sites) were associated with at least one type of cancer, including 25 genes previously not linked to cancer susceptibility. Of the identified risk APAs, 97.4% and 26.3% were supported by 3' UTR APA quantitative trait loci and co-localization analyses, respectively. Luciferase reporter assays for four selected putative regulatory 3' UTR variants demonstrated that the risk alleles of 3' UTR variants, rs324015 (STAT6), rs2280503 (DIP2B), rs1128450 (FBXO38), and rs145220637 (LDHA), significantly increased the post-transcriptional activities of their target genes compared to reference alleles. Furthermore, knockdown of the target genes confirmed their ability to promote proliferation and migration. Overall, this study provides insights into the role of APA in the genetic susceptibility to common cancers.

Study on the Effect of Additives on the Performance of Cement-Based Composite Anti-Corrosion Coatings for Steel Bars in Prefabricated Construction.

The influence of polymer emulsion, pigment filler, and dispersant on the corrosion resistance of polymer cement-based composite anti-corrosion coatings were investigated in this study. Adhesion loss rate tests and electrochemical tests were conducted on samples. The research results show that optimal corrosion resistance can be achieved with a 45 wt% dosage of emulsion, a 6 wt% dosage of pigment filler, and a 0.30 wt% dosage of dispersant. The bonding properties of bare steel bars, epoxy-coated steel bars, and polymer cement-based composite anti-corrosion coated steel bars with grout were compared. The results show that the polymer cement-based composite anti-corrosion coating can enhance the bonding properties of the samples. Furthermore, the microscopic analysis was conducted on the samples. The results demonstrate that the appropriate addition of emulsion can fill internal pores of the coating, tightly bonding hydration products with unhydrated cement particles. Moreover, incorporating a suitable dosage of functional additives enhances the stability of the coating system and leads to a denser microstructure.

Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes.

Genome-wide association studies (GWAS) have identified more than 200 common genetic variants independently associated with colorectal cancer (CRC) risk, but the causal variants and target genes are mostly unknown. We sought to fine-map all known CRC risk loci using GWAS data from 100,204 cases and 154,587 controls of East Asian and European ancestry. Our stepwise conditional analyses revealed 238 independent association signals of CRC risk, each with a set of credible causal variants (CCVs), of which 28 signals had a single CCV. Our cis-eQTL/mQTL and colocalization analyses using colorectal tissue-specific transcriptome and methylome data separately from 1299 and 321 individuals, along with functional genomic investigation, uncovered 136 putative CRC susceptibility genes, including 56 genes not previously reported. Analyses of single-cell RNA-seq data from colorectal tissues revealed 17 putative CRC susceptibility genes with distinct expression patterns in specific cell types. Analyses of whole exome sequencing data provided additional support for several target genes identified in this study as CRC susceptibility genes. Enrichment analyses of the 136 genes uncover pathways not previously linked to CRC risk. Our study substantially expanded association signals for CRC and provided additional insight into the biological mechanisms underlying CRC development.

Review of Animal Models of Colorectal Cancer in Different Carcinogenesis Pathways.

Colorectal cancer (CRC) is a common malignant tumor of the gastrointestinal tract with increasing morbidity and mortality. Exploring the factors affecting colorectal carcinogenesis and controlling its occurrence at its root is as important as studying post-cancer treatment and management. Establishing ideal animal models of CRC is crucial, which can occur through various pathways, such as adenoma-carcinoma sequence, inflammation-induced carcinogenesis, serrated polyp pathway and de-novo pathway. This article aims to categorize the existing well-established CRC animal models based on different carcinogenesis pathways, and to describe their mechanisms, methods, advantages and limitations using domestic and international literature sources. This will provide suggestions for the selection of animal models in early-stage CRC research.

Large-Scale Cancer Genomic Analysis Reveals Significant Disparities between Microsatellite Instability and Tumor Mutational Burden.

BACKGROUND: Microsatellite instability (MSI) and tumor mutational burden (TMB) are predictive biomarkers for pan-cancer immunotherapy. The interrelationship between MSI-high (MSI-H) and TMB-high (TMB-H) in human cancers and their predictive value for immunotherapy in lung cancer remain unclear. METHODS: We analyzed somatic mutation data from the Genomics Evidence Neoplasia Information Exchange (n = 46,320) to determine the relationship between MSI-H and TMB-H in human cancers using adjusted multivariate regression models. Patient survival was examined using the Cox proportional hazards model. The association between MSI and genetic mutations was assessed. RESULTS: Patients (31-89%) with MSI-H had TMB-low phenotypes across 22 cancer types. Colorectal and stomach cancers showed the strongest association between TMB and MSI. TMB-H patients with lung cancer who received immunotherapy exhibited significantly higher overall survival [HR, 0.61; 95% confidence interval (CI), 0.44-0.86] and progression-free survival (HR, 0.65; 95% CI, 0.47-0.91) compared to the TMB-low group; no significant benefit was observed in the MSI-H group. Patients with TMB and MSI phenotypes showed further improvement in overall survival and PFS. We identified several mutated genes associated with MSI-H phenotypes, including known mismatch repair genes and novel mutated genes, such as ARID1A and ARID1B. CONCLUSIONS: Our results demonstrate that TMB-H and/or a combination of MSI-H can serve as biomarkers for immunotherapies in lung cancer. IMPACT: These findings suggest that distinct or combined biomarkers should be considered for immunotherapy in human cancers because notable discrepancies exist between MSI-H and TMB-H across different cancer types.

Novel insights into genetic susceptibility for colorectal cancer from transcriptome-wide association and functional investigation.

BACKGROUND: Transcriptome-wide association studies have been successful in identifying candidate susceptibility genes for colorectal cancer (CRC). To strengthen susceptibility gene discovery, we conducted a large transcriptome-wide association study and an alternative splicing transcriptome-wide association study in CRC using improved genetic prediction models and performed in-depth functional investigations. METHODS: We analyzed RNA-sequencing data from normal colon tissues and genotype data from 423 European descendants to build genetic prediction models of gene expression and alternative splicing and evaluated model performance using independent RNA-sequencing data from normal colon tissues of the Genotype-Tissue Expression Project. We applied the verified models to genome-wide association studies (GWAS) summary statistics among 58 131 CRC cases and 67 347 controls of European ancestry to evaluate associations of genetically predicted gene expression and alternative splicing with CRC risk. We performed in vitro functional assays for 3 selected genes in multiple CRC cell lines. RESULTS: We identified 57 putative CRC susceptibility genes, which included the 48 genes from transcriptome-wide association studies and 15 genes from splicing transcriptome-wide association studies, at a Bonferroni-corrected P value less than .05. Of these, 16 genes were not previously implicated in CRC susceptibility, including a gene PDE7B (6q23.3) at locus previously not reported by CRC GWAS. Gene knockdown experiments confirmed the oncogenic roles for 2 unreported genes, TRPS1 and METRNL, and a recently reported gene, C14orf166. CONCLUSION: This study discovered new putative susceptibility genes of CRC and provided novel insights into the biological mechanisms underlying CRC development.

LncRNA SEMA3B-AS1 suppresses the tumor-initiating characteristics of triple negative breast cancer via engaging in MLL4-mediated H3K4 trimethylation.

Long noncoding RNAs (lncRNAs) are crucial regulators of tumor-initiating cells (TICs) and hold particular importance in triple negative breast cancer (TNBC). Yet, the precise mechanisms by which TIC-associated lncRNAs influence TNBC remain unclear. Our research utilized The Cancer Genome Atlas Breast Cancer (BC) data set to identify prognostic lncRNAs. We then conducted extensive assays to explore their impact on the tumor-initiating phenotype of TNBC cells and the underlying mechanisms. Notably, we found that low expression of lncRNA SEMA3B-AS1 correlated with unfavorable survival in BC patients. SEMA3B-AS1 was also downregulated in TNBC and linked to advanced tumor stage. Functional experiments confirmed its role as a TIC-suppressing lncRNA, curtailing mammosphere formation, ALDH + TIC cell proportion, and impairing clonogenicity, migration, and invasion. Mechanistic insights unveiled SEMA3B-AS1's nuclear localization and interaction with MLL4 (mixed-lineage leukemia 4), triggering H3K4 methylation-associated transcript activation and thus elevating the expression of SEMA3B, a recognized tumor suppressor gene. Our findings emphasize SEMA3B-AS1's significance as a TNBC-suppressing lncRNA that modulates TIC behavior. This study advances our comprehension of lncRNA's role in TNBC progression, advocating for their potential as therapeutic targets in this aggressive BC subtype.

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

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

Large-scale alternative polyadenylation (APA)-wide association studies to identify putative susceptibility genes in human common cancers.

Alternative polyadenylation (APA) modulates mRNA processing in the 3' untranslated regions (3'UTR), which affect mRNA stability and translation efficiency. Here, we build genetic models to predict APA levels in multiple tissues using sequencing data of 1,337 samples from the Genotype-Tissue Expression, and apply these models to assess associations between genetically predicted APA levels and cancer risk with data from large genome-wide association studies of six common cancers, including breast, ovary, prostate, colorectum, lung, and pancreas among European-ancestry populations. At a Bonferroni-corrected P □<□0.05, we identify 58 risk genes, including seven in newly identified loci. Using luciferase reporter assays, we demonstrate that risk alleles of 3'UTR variants, rs324015 ( STAT6 ), rs2280503 ( DIP2B ), rs1128450 ( FBXO38 ) and rs145220637 ( LDAH ), could significantly increase post-transcriptional activities of their target genes compared to reference alleles. Further gene knockdown experiments confirm their oncogenic roles. Our study provides additional insight into the genetic susceptibility of these common cancers.

Combined signature of N7-methylguanosine regulators with their related genes and the tumor microenvironment: a prognostic and therapeutic biomarker for breast cancer.

Background: Identifying predictive markers for breast cancer (BC) prognosis and immunotherapeutic responses remains challenging. Recent findings indicate that N7-methylguanosine (m7G) modification and the tumor microenvironment (TME) are critical for BC tumorigenesis and metastasis, suggesting that integrating m7G modifications and TME cell characteristics could improve the predictive accuracy for prognosis and immunotherapeutic responses. Methods: We utilized bulk RNA-sequencing data from The Cancer Genome Atlas Breast Cancer Cohort and the GSE42568 and GSE146558 datasets to identify BC-specific m7G-modification regulators and associated genes. We used multiple m7G databases and RNA interference to validate the relationships between BC-specific m7G-modification regulators (METTL1 and WDR4) and related genes. Single-cell RNA-sequencing data from GSE176078 confirmed the association between m7G modifications and TME cells. We constructed an m7G-TME classifier, validated the results using an independent BC cohort (GSE20685; n = 327), investigated the clinical significance of BC-specific m7G-modifying regulators by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis, and performed tissue-microarray assays on 192 BC samples. Results: Immunohistochemistry and RT-qPCR results indicated that METTL1 and WDR4 overexpression in BC correlated with poor patient prognosis. Moreover, single-cell analysis revealed relationships between m7G modification and TME cells, indicating their potential as indicators of BC prognosis and treatment responses. The m7G-TME classifier enabled patient subgrouping and revealed significantly better survival and treatment responses in the m7Glow+TMEhigh group. Significant differences in tumor biological functions and immunophenotypes occurred among the different subgroups. Conclusions: The m7G-TME classifier offers a promising tool for predicting prognosis and immunotherapeutic responses in BC, which could support personalized therapeutic strategies.

Enhancing Disease Risk Gene Discovery by Integrating Transcription Factor-Linked Trans-located Variants into Transcriptome-Wide Association Analyses.

Transcriptome-wide association studies (TWAS) have been successful in identifying putative disease susceptibility genes by integrating gene expression predictions with genome-wide association studies (GWAS) data. However, current TWAS models only consider cis-located variants to predict gene expression. Here, we introduce transTF-TWAS, which includes transcription factor (TF)-linked trans-located variants for model building. Using data from the Genotype-Tissue Expression project, we predict alternative splicing and gene expression and applied these models to large GWAS datasets for breast, prostate, and lung cancers. Our analysis revealed 887 putative cancer susceptibility genes, including 465 in regions not yet reported by previous GWAS and 137 in known GWAS loci but not yet reported previously, at Bonferroni-corrected P < 0.05. We demonstrate that transTF-TWAS surpasses other approaches in both building gene prediction models and identifying disease-associated genes. These results have shed new light on several genetically driven key regulators and their associated regulatory networks underlying disease susceptibility.

Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations.

Polygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher risk to undertake targeted screening. However, current PRS using European ancestry data have sub-optimal performance in non-European ancestry populations, limiting their utility among these populations. Towards addressing this deficiency, we expand PRS development for CRC by incorporating Asian ancestry data (21,731 cases; 47,444 controls) into European ancestry training datasets (78,473 cases; 107,143 controls). The AUC estimates (95% CI) of PRS are 0.63(0.62-0.64), 0.59(0.57-0.61), 0.62(0.60-0.63), and 0.65(0.63-0.66) in independent datasets including 1681-3651 cases and 8696-115,105 controls of Asian, Black/African American, Latinx/Hispanic, and non-Hispanic White, respectively. They are significantly better than the European-centric PRS in all four major US racial and ethnic groups (p-values < 0.05). Further inclusion of non-European ancestry populations, especially Black/African American and Latinx/Hispanic, is needed to improve the risk prediction and enhance equity in applying PRS in clinical practice.

Development and validation of a nomogram based on common biochemical indicators for survival prediction of children with high-risk neuroblastoma: A valuable tool for resource-limited hospitals.

BACKGROUND: Despite multiple attempts have been made to develop risk stratification within high-risk neuroblastoma (NB) patients (age of diagnosis ≥ 18 month-old with metastatic NB), the definition of "ultra high-risk NB" is still lack of consensus, and indicators for identifying this subgroup are still unclear. This study aimed to develop a nomogram based on easy-to-obtain blood-derived biofactors for identifying ultra high-risk NB patients with highest risk of death within 3 or 5 years. METHODS: One hundred sixty-seven NB patients who treated at Sun Yat-sen University Cancer Center between 2015 and 2023 were recruited and clustered randomly into training and validation cohorts (116 and 51 cases, respectively). Univariate and multivariate Cox analysis were performed in training set to screen independent prognostic indicators for constructing nomogram model of predicting 1-, 3- and 5-year overall survival (OS). The discrimination power of the nomogram in training and validation sets were assessed by concordance index (C-index) and calibration plot. Based on the risk score obtained from nomogram model, the prognostic accuracy of 1-, 3- and 5-year OS rates in training and validation cohorts were further evaluated using the area under receiver operating characteristic (ROC) curves (AUC). RESULTS: Through univariate and multivariate Cox analysis, independent prognostic indicators, including serum lactate dehydrogenase (LDH) and albumin (ALB), were identified in training set, and used to establish a nomogram model. The model showed good discrimination power with C-index in training cohort being 0.706 (95%CI: 0.633-0.788). According to the cut-point calculated based on the established nomogram, patients with a nomogram score > 34 points could be stratified to ultra high-risk NB subgroup, and this subgroup had poorer OS than those in non-ultra one (p < 0.001). AUC values of ROC curves for 3- and 5-year OS rates in the training set were 0.758 and 0.756, respectively. Moreover, based on the cut-point score (34 points) developed in training set, The model also showed good discrimination power with C-index of 0.773 (95%CI: 0.664-0.897) and powerful prognostic accuracy of AUC for 3- and 5-year OS rates being 0.825 and 0.826, respectively, in validation cohort. CONCLUSIONS: We developed a simple-to-use nomogram based on common laboratory indicators to identify the subgroup of ultra high-risk NB before treatment, providing these children even from developing countries or regions access to intensified multimodal treatments earlier and thus improving their long-term outcome.

Combining Asian-European Genome-Wide Association Studies of Colorectal Cancer Improves Risk Prediction Across Race and Ethnicity.

Polygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher risk to undertake targeted screening. However, current PRS using European ancestry data have sub-optimal performance in non-European ancestry populations, limiting their utility among these populations. Towards addressing this deficiency, we expanded PRS development for CRC by incorporating Asian ancestry data (21,731 cases; 47,444 controls) into European ancestry training datasets (78,473 cases; 107,143 controls). The AUC estimates (95% CI) of PRS were 0.63(0.62-0.64), 0.59(0.57-0.61), 0.62(0.60-0.63), and 0.65(0.63-0.66) in independent datasets including 1,681-3,651 cases and 8,696-115,105 controls of Asian, Black/African American, Latinx/Hispanic, and non-Hispanic White, respectively. They were significantly better than the European-centric PRS in all four major US racial and ethnic groups (p-values<0.05). Further inclusion of non-European ancestry populations, especially Black/African American and Latinx/Hispanic, is needed to improve the risk prediction and enhance equity in applying PRS in clinical practice.

Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries.

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

Integrating transcription factor occupancy with transcriptome-wide association analysis identifies susceptibility genes in human cancers.

Transcriptome-wide association studies (TWAS) have successfully discovered many putative disease susceptibility genes. However, TWAS may suffer from inaccuracy of gene expression predictions due to inclusion of non-regulatory variants. By integrating prior knowledge of susceptible transcription factor occupied elements, we develop sTF-TWAS and demonstrate that it outperforms existing TWAS approaches in both simulation and real data analyses. Under the sTF-TWAS framework, we build genetic models to predict alternative splicing and gene expression in normal breast, prostate and lung tissues from the Genotype-Tissue Expression project and apply these models to data from large genome-wide association studies (GWAS) conducted among European-ancestry populations. At Bonferroni-corrected P < 0.05, we identify 354 putative susceptibility genes for these cancers, including 189 previously unreported in GWAS loci and 45 in loci unreported by GWAS. These findings provide additional insight into the genetic susceptibility of human cancers. Additionally, we show the generalizability of the sTF-TWAS on non-cancer diseases.

Polygenic Risk Scores Associated with Tumor Immune Infiltration in Common Cancers.

It is largely unknown whether genetic susceptibility contributes to tumor immune infiltration in common cancers. We systematically investigated the association between polygenic risk scores (PRSs) and tumor immune infiltration in common cancers. First, we constructed a PRS for common cancers using the risk variants identified in previous genome-wide association studies. Then, we analyzed 139 immune traits predicted by previous studies by examining gene expression data in tumor tissues from The Cancer Genome Atlas (TCGA). We applied regression analyses to evaluate the associations between PRS and immune traits for each cancer overall and stratified by stage, including 2160 pathologically confirmed cases of breast, colorectal, lung, ovarian, pancreatic, and prostate cancers in the White population. At a nominal (p < 0.05) significance level, we identified 31 significant associations between PRS and immune traits. In the analyses stratified by stage for breast, colorectal, lung adenocarcinoma, and lung squamous cell carcinoma, we identified 65 significant associations, including 56 associations that were undetected by the overall analysis. This study provides evidence for genetic risk factors affecting immune infiltration and provides novel insights into the role of genetic susceptibility in immune responses, underlying cancer development, prognosis, and the potential role of an early diagnostic or therapeutic targeting strategy.