Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Although epigenetic alterations are common in CRC, the epigenetic changes that occur during colorectal carcinogenesis remain unclear. Thus, we sought to elucidate the role of NOS1 methylation in colorectal carcinogenesis, which is essential for understanding disease pathology. We used the UCSC Xena database to comparatively analyze NOS1 expression and methylation status between tumour and adjacent normal tissues across 33 cancer types. Low expression and hypermethylation of NOS1 have been identified in 12 cancer types, with CRC demonstrating this characteristic epigenetic regulation. NOS1 promoter methylation status was examined using genomic DNA extraction and MassARRAY EpiTYPER methylation analysis. NOS1 hypermethylation was confirmed among CRCs in in-house dataset 1 (p < 0.001), and among CRCs and advanced adenomas in in-house dataset 2 (p < 0.001). An upward trend in methylation changes was identified from non-advanced adenoma to advanced adenoma to CRC (p for trend < 0.001). Quantitative real-time PCR was used to analyze NOS1 expression in in-house Dataset 3, and significantly low NOS1 expression was also identified in CRCs (p < 0.001). Kaplan-Meier estimator and Cox proportional hazard models were used to assess the prognostic and predictive roles of NOS1. NOS1 hypermethylation was significantly associated with better disease-specific survival (DSS) in CRC patients. NOS1 hypermethylation is an epigenetic driver of colorectal tumorigenesis and is associated with better survival.

BackgroundGrowing evidence supports the importance of characterizing the organizational patterns of various cellular constituents in the tumor microenvironment in precision oncology. Most existing data on immune cell infiltrates in tumors, which are based on immune cell counts or nearest neighbor-type analyses, have failed to fully capture the cellular organization and heterogeneity.MethodsWe introduce a computational algorithm, termed Tumor-Immune Partitioning and Clustering (TIPC), that jointly measures immune cell partitioning between tumor epithelial and stromal areas and immune cell clustering versus dispersion. As proof-of-principle, we applied TIPC to a prospective cohort incident tumor biobank containing 931 colorectal carcinoma cases. TIPC identified tumor subtypes with unique spatial patterns between tumor cells and T lymphocytes linked to certain molecular pathologic and prognostic features. T lymphocyte identification and phenotyping were achieved using multiplexed (multispectral) immunofluorescence. In a separate hepatocellular carcinoma cohort, we replaced the stromal component with specific immune cell types-CXCR3+CD68+ or CD8+-to profile their spatial relationships with CXCL9+CD68+ cells.ResultsSix unsupervised TIPC subtypes based on T lymphocyte distribution patterns were identified, comprising two cold and four hot subtypes. Three of the four hot subtypes were associated with significantly longer colorectal cancer (CRC)-specific survival compared to a reference cold subtype. Our analysis showed that variations in T-cell densities among the TIPC subtypes did not strictly correlate with prognostic benefits, underscoring the prognostic significance of immune cell spatial patterns. Additionally, TIPC revealed two spatially distinct and cell density-specific subtypes among microsatellite instability-high colorectal cancers, indicating its potential to upgrade tumor subtyping. TIPC was also applied to additional immune cell types, eosinophils and neutrophils, identified using morphology and supervised machine learning; here two tumor subtypes with similarly low densities, namely 'cold, tumor-rich' and 'cold, stroma-rich', exhibited differential prognostic associations. Lastly, we validated our methods and results using The Cancer Genome Atlas colon and rectal adenocarcinoma data (n = 570). Moreover, applying TIPC to hepatocellular carcinoma cases (n = 27) highlighted critical cell interactions like CXCL9-CXCR3 and CXCL9-CD8.ConclusionsUnsupervised discoveries of microgeometric tissue organizational patterns and novel tumor subtypes using the TIPC algorithm can deepen our understanding of the tumor immune microenvironment and likely inform precision cancer immunotherapy.

BACKGROUND:The impact of sleep patterns on gastrointestinal cancer mortality has not been comprehensively explored. Moreover, the interaction between sleep patterns and genetic susceptibility with gastrointestinal cancer mortality remains uncertain. METHODS:The study included a total of 379 845 participants from the UK Biobank. A poor sleep pattern was defined by short sleep (< 7 h/day) or long sleep (> 9 h/day), late chronotype, frequent insomnia, snoring, and frequent daytime dozing. The outcomes were mortality of any gastrointestinal cancers and six site-specific gastrointestinal cancers. Polygenic risk score was generated to characterize genetic risk. Multivariable cox proportional hazards regression models were use to analyze the associations of sleep patterns and genetic susceptibility with gastrointestinal cancer mortality. RESULTS:A poor sleep pattern was associated with the increased risk of mortality from overall gastrointestinal cancer (HR 1.32, 95% CI 1.16-1.49), esophagus cancer (HR 1.37, 95% CI 1.02-1.85) and liver cancer (HR 2.01, 95% CI 1.48-2.74). Participants with a poor sleep pattern and high genetic risk combination had the highest mortality risks for esophagus, stomach, colorectal, and liver cancer. Significant multiplicative interactions (HR 1.51, 95% CI 1.05-2.18) and additive interactions (RERI 0.54, 95% CI 0.13-0.95; AP 0.34, 95% CI 0.10-0.58) of an intermediate sleep pattern and high genetic risk were observed on esophagus cancer mortality. CONCLUSIONS:A poor sleep pattern is associated with increased risks of gastrointestinal cancer mortality independent of the conventional risk factors, and the association is modified by genetic susceptibility.

Epithelial-mesenchymal transition (EMT) is critical in tumor progression and metastasis, with long non-coding RNAs (lncRNAs) as key regulatory elements. This study explored the association between genetic variants in EMT-related lncRNAs and colorectal cancer (CRC) risk in a Chinese population. A case-control study was conducted involving 1,888 untreated CRC cases and 1,888 cancer-free controls. Multivariate logistic regression models were used to assess effects of SNPs on CRC risk, while expression quantitative trait loci (eQTL) analysis used data from the Genotype-Tissue Expression (GTEx) project, and gene expression was evaluated using The Cancer Genome Atlas (TCGA) database. Four functionally relevant SNPs (AC106786.1 rs76180806, rs2277930, LINC00578 rs28711160, and RP1-193H18.2 rs17823238) were significantly associated with CRC risk (OR = 1.52, 95

Obesity is a chronic metabolic disorder and a growing global public health challenge, affecting hundreds of millions of individuals worldwide. While diet and physical activity are well-established contributors, increasing evidence underscores the critical role of epigenetic mechanisms in mediating obesity-related processes. Epigenetic modifications—such as DNA methylation, RNA methylation (particularly N6-methyladenosine), histone modifications, non-coding RNAs, and chromatin remodeling—modulate gene expression without altering the DNA sequence. This review aims to provide an overview of the epigenetic mechanisms involved in obesity, with an emphasis on their molecular functions and regulatory networks. Integrating findings from relevant studies, we discuss how these modifications influence obesity-related outcomes through regulating key processes such as adipocyte differentiation and energy metabolism. Advancing our understanding of epigenetic regulation may pave the way for novel, targeted strategies in the prevention and treatment of obesity.

Supplementary Table S1. Analyses of MSI, DNA methylation, KRAS, BRAF, and PIK3CA mutations, and neoantigen load. Supplementary Table S2. List of antibodies and fluorophores used in the immunofluorescence procedure. Supplementary Table S3. Statistical methods. Supplementary Table S4. Macrophage density, M1-like macrophage density, M2-like macrophage density, and M1:M2 macrophage density ratio in overall tissue regions and patient survival with inverse probability weighting (IPW). Supplementary Table S5. Densities of macrophage populations defined by positivity for single polarization marker in tumor intraepithelial and stromal regions and patient survival with inverse probability weighting (IPW). Supplementary Table S6. Clinical, pathological, and molecular characteristics of colorectal cancer cases according to year of diagnosis. Supplementary Table S7. Macrophage densities and M1:M2 macrophage density ratio in tumor intraepithelial and stromal regions and patient survival in strata of MSI status with inverse probability weighting (IPW). Supplementary Table S8. Macrophage densities and M1:M2 macrophage density ratio in tumor intraepithelial and stromal regions and patient survival in strata of year of diagnosis with inverse probability weighting (IPW). Figure S1. Comparison of staining patterns between multiplex immunofluorescence and standard immunohistochemistry. Figure S2. Flowchart of the cyclic immunofluorescence procedure. Figure S3. Analysis of immunofluorescence slides to quantify tumor intraepithelial and stromal macrophage densities. Figure S4. Macrophage densities across 10 TMAs. Figure S5. Fluorescence signal intensities across 10 TMAs. Figure S6. t-SNE analysis of a random sample of 0.5% of all cells based on fluorophore signal intensities shows no clear clustering according to the TMAs. Figure S7. Core-to-core correlation of macrophage densities in two randomly chosen cores of tumors with two or more cores. Figure S8. Inverse probability weighting-adjusted Kaplan-Meier survival curves of colorectal cancer survival according to the ordinal quartile categories (Q1-Q4) of intraepithelial (A) and stromal (B) macrophage densities. Figure S9. Forest plots of inverse probability weighting-adjusted Cox regression models of colorectal cancer specific survival according to the densities of intraepithelial and stromal M1-like polarized macrophages, with M1-like macrophages defined using different cut-offs (10-50%) of the M1-end of the macrophage polarization (M1:M2) index distribution. Figure S10. Forest plots of inverse probability weighting-adjusted Cox regression models of colorectal cancer specific survival according to the densities of intraepithelial and stromal M2-like polarized macrophages, with M2-like macrophages defined using different cut-offs (10-50%) of the M2-end of the macrophage polarization (M1:M2) index distribution. Figure S11. Forest plots of inverse probability weighting-adjusted Cox regression models of colorectal cancer specific survival according to the intraepithelial and stromal M1:M2-density ratio with M1-like and M2-like macrophages defined using different cut-offs (10-50%) of both tails of the macrophage polarization (M1:M2) index distribution. Figure S12. Densities of M1-like and M2-like macrophages in relation to histologic lymphocytic reaction patterns.

Background Carcinogens in cigarette smoke may cause aberrant epigenomic changes. The hypomethylation of long interspersed nucleotide element-1 (LINE-1) in colorectal carcinoma has been associated with genomic instability and worse clinical outcome. We hypothesized that the association between smoking behavior and colorectal cancer mortality might be stronger in tumors with lower LINE-1 methylation levels. Findings To test our hypothesis, we examined the interaction of tumor LINE-1 methylation levels and smoking status at diagnosis using data of 1208 cases among 4420 incident colorectal cancer cases that were ascertained in two prospective cohort studies. We conducted multivariable Cox proportional hazards regression analyses, using inverse probability weighting with covariate data of the 4420 cases to control for potential confounders and selection bias due to data availability. The prognostic association of smoking status at diagnosis differed by tumor LINE-1 methylation levels ( P interaction = 0.050 for overall mortality and 0.017 for colorectal cancer-specific mortality; with an alpha level of 0.005). In cases with <60% LINE-1 methylation, current smoking (vs. never smoking) was associated with worse overall mortality (multivariable hazard ratio, 1.80; 95% confidence interval, 1.19–2.73). In contrast, smoking status was not associated with mortality in cases with ≥60% LINE-1 methylation. Conclusions Our findings suggest that the association between smoking status and mortality is stronger in colorectal cancer patients with lower tumor LINE-1 methylation levels. These results warrant further investigation into an interactive role of smoking and aberrant DNA methylation in colorectal cancer progression.

Sporadic colorectal cancer (CRC) develops principally through the adenoma-carcinoma sequence. Previous studies revealed that DNA methylation alterations play a significant role in colorectal neoplastic transformation. On the other hand, long noncoding RNAs (lncRNAs) have been identified to be associated with some critical tumorigenic processes of CRC. Accumulating evidence indicates more intricate regulatory relationships between DNA methylation and lncRNAs in CRC. Nevertheless, the methylation alterations of lncRNAs at different stages of colorectal carcinogenesis based on a genome-wide scale remain elusive. Therefore, in this study, we first used an Illumina MethylationEPIC BeadChip (850K array) to identify the methylation status of lncRNAs in 12 pairs of colorectal cancerous and adjacent normal tissues from cohort I, followed by cross-validation with The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Then, the abnormal hypermethylation of candidate genes in colorectal lesions was successfully confirmed by MassARRAY EpiTYPER in cohort II including 48 CRC patients, and cohort III including 286 CRC patients, 81 advanced adenoma (AA) patients and 81 nonadvanced adenoma (NAA) patients. DLX6-AS1 hypermethylation was detected at all stages of colorectal neoplasms and occurred as early as the NAA stage during colorectal neoplastic progression. The methylation levels were significantly higher in the comparisons of CRC vs. NAA (P < 0.001) and AA vs. NAA (P = 0.004). Moreover, the hypermethylation of DLX6-AS1 promoter was also found in cell-free DNA samples collected from CRC patients as compared to healthy controls (P adj = 0.003). Multivariate Cox proportional hazards regression analysis revealed DLX6-AS1 promoter hypermethylation was independently associated with poorer disease-specific survival (HR = 2.52, 95% CI: 1.35-4.69, P = 0.004) and overall survival (HR = 1.64, 95% CI: 1.02-2.64, P = 0.042) in CRC patients. Finally, a nomogram was constructed and verified by a calibration curve to predict the survival probability of individual CRC patients (C-index: 0.789). Our findings indicate DLX6-AS1 hypermethylation might be an early event during colorectal carcinogenesis and has the potential to be a novel biomarker for CRC progression and prognosis.

Epigenetics play an essential role in colorectal neoplasia process. There is a need to determine the appropriateness of epigenetic biomarkers for early detection as well as expand our understanding of the carcinogenic process. Therefore, the aim of the study was to assess how DNA methylation pattern of GALR1 gene evolves in a sample set representing colorectal neoplastic progression. The study was designed into three phases. Firstly, Methylation status of GALR1 was assessed with genome-wide DNA methylation beadchip and pyrosequencing assays in colorectal lesions and paired normal tissues. Then, linear mixed-effects modeling analyses were applied to describe the trend of DNA methylation during the progression of colorectal neoplasia. In the third phase, quantitative RT-PCR was used to examine GALR1 expression in patients with precursor lesion and colorectal cancer. We found that significant hypermethylation of GALR1 promoter was a widely existent modification in CRCs (P < 0.001). When further examined methylation pattern of GALR1 during neoplastic progression of CRC, we found that DNA methylation level of GALR1 showed a significant stepwise increase from normal to hyperplastic polyps, to adenomas and to carcinoma samples (P < 0.001). Besides, loss of mRNA expression is a common accompaniment to adenomas and carcinomas. Public omics data analyses showed an inverse correlation between gene expression and DNA methylation (P < 0.001). Our findings indicate that epigenetic alteration of GALR1 promoter is gradually accumulated during the colorectal neoplastic progression. It can potentially be a promising biomarker used for screening and surveillance of colorectal cancer.

Abstract Macrophages are among the most common cells in the colorectal cancer microenvironment, but their prognostic significance is incompletely understood. Using multiplexed immunofluorescence for CD68, CD86, IRF5, MAF, MRC1 (CD206), and KRT (cytokeratins) combined with digital image analysis and machine learning, we assessed the polarization spectrum of tumor-associated macrophages in 931 colorectal carcinomas. We then applied Cox proportional hazards regression to assess prognostic survival associations of intraepithelial and stromal densities of M1-like and M2-like macrophages while controlling for potential confounders, including stage and microsatellite instability status. We found that high tumor stromal density of M2-like macrophages was associated with worse cancer-specific survival, whereas tumor stromal density of M1-like macrophages was not significantly associated with better cancer-specific survival. High M1:M2 density ratio in tumor stroma was associated with better cancer-specific survival. Overall macrophage densities in tumor intraepithelial or stromal regions were not prognostic. These findings suggested that macrophage polarization state, rather than their overall density, was associated with cancer-specific survival, with M1- and M2-like macrophage phenotypes exhibiting distinct prognostic roles. These results highlight the utility of a multimarker strategy to assess the macrophage polarization at single-cell resolution within the tumor microenvironment.

Background Cervical cancer is 1 of the most common cancers in females worldwide. Understanding the most recent global patterns and temporal trends of cervical cancer burden might be helpful for its prevention and control. Methods Data on cervical cancer (International Classification of Diseases, Tenth Revision, code C53) incidence and mortality in 2018 were extracted from the GLOBOCAN 2018 database and further analyzed for their correlations with the Human Development Index. Temporal trends were analyzed using the annual percent change with joinpoint analysis among 31 countries with highly qualified data from the Cancer Incidence in Five Continents Plus and World Health Organization mortality databases. Future trends for the next 15 years were predicted using an open-source age-period-cohort model. Results Cervical cancer incidence and mortality rates were both negatively correlated with the Human Development Index (r = -0.56 for incidence, r = -0.69 for mortality; P < .001) in cross-sectional analysis, and both remained stable in 12 countries or even decreased in 14 and 18 countries for incidence and mortality, respectively, during the most recent 10 data years. Similar findings were observed for the next 15 years. Conclusions Cervical cancer burden was correlated with socioeconomic development. An overwhelming majority of countries had stable or decreasing trends in incidence and mortality rates, especially in those with effective cervical cancer screening programs and human papillomavirus vaccination. Lay Summary The authors investigated the most up-to-date data from official databases released by the International Agency for Research on Cancer and found that cervical cancer incidence and mortality were negatively correlated with socioeconomic development. Among the 31 countries analyzed, most (26 countries were analyzed for incidence, and 30 were analyzed for mortality) had stable or even decreasing temporal trends over the most recent 10 years, especially in those with effective cervical cancer screening programs. In addition, the predicted trends for the next 15 years were basically consistent with the observed trends among most of the analyzed countries (19 countries for incidence and 26 countries for mortality).

Growing evidence supports the importance of quantifying tumor-immune cell interactions in the tumor microenvironment to enable precision cancer therapy. However, most existing methods rely solely upon immune cell density or nearest neighbor-type analyses and fail to fully characterize spatial heterogeneity. Herein, we describe a computational algorithm, termed Tumor-Immune Partitioning and Clustering (TIPC), that jointly measures immune cell partitioning between tumor epithelial and stromal areas and immune cell clustering versus dispersion. As proof of principle, we apply TIPC to two large colorectal cancer cohorts. TIPC identifies tumor subtypes with unique interaction signatures between tumor cells and T cells that harbor prognostic significance and are associated with distinct tumor molecular features. We extend our findings by applying TIPC to additional immune cell types identified using morphology and supervised machine learning. Spatial heterogeneity quantification and novel tumor subtype identification by TIPC may inform precision cancer immunotherapy and deepen our understanding of tumor immunobiology.

Abstract Purpose: Although high T-cell density is a well-established favorable prognostic factor in colorectal cancer, the prognostic significance of tumor-associated plasma cells, neutrophils, and eosinophils is less well-defined. Experimental Design: We computationally processed digital images of hematoxylin and eosin (H&E)–stained sections to identify lymphocytes, plasma cells, neutrophils, and eosinophils in tumor intraepithelial and stromal areas of 934 colorectal cancers in two prospective cohort studies. Multivariable Cox proportional hazards regression was used to compute mortality HR according to cell density quartiles. The spatial patterns of immune cell infiltration were studied using the GTumor:Immune cell function, which estimates the likelihood of any tumor cell in a sample having at least one neighboring immune cell of the specified type within a certain radius. Validation studies were performed on an independent cohort of 570 colorectal cancers. Results: Immune cell densities measured by the automated classifier demonstrated high correlation with densities both from manual counts and those obtained from an independently trained automated classifier (Spearman's ρ 0.71–0.96). High densities of stromal lymphocytes and eosinophils were associated with better cancer-specific survival [Ptrend < 0.001; multivariable HR (4th vs 1st quartile of eosinophils), 0.49; 95% confidence interval, 0.34–0.71]. High GTumor:Lymphocyte area under the curve (AUC0,20μm; Ptrend = 0.002) and high GTumor:Eosinophil AUC0,20μm (Ptrend < 0.001) also showed associations with better cancer-specific survival. High stromal eosinophil density was also associated with better cancer-specific survival in the validation cohort (Ptrend < 0.001). Conclusions: These findings highlight the potential for machine learning assessment of H&E-stained sections to provide robust, quantitative tumor-immune biomarkers for precision medicine.

Background: Smoking has been associated with worse colorectal cancer patient survival and may potentially suppress the immune response in the tumor microenvironment. We hypothesized that the prognostic association of smoking behavior at colorectal cancer diagnosis might differ by lymphocytic reaction patterns in cancer tissue. Methods: Using 1474 colon and rectal cancer patients within 2 large prospective cohort studies (Nurses' Health Study and Health Professionals Follow-up Study), we characterized 4 patterns of histopathologic lymphocytic reaction, including tumor-infiltrating lymphocytes (TILs), intratumoral periglandular reaction, peritumoral lymphocytic reaction, and Crohn's-like lymphoid reaction. Using covariate data of 4420 incident colorectal cancer patients in total, an inverse probability weighted multivariable Cox proportional hazards regression model was conducted to adjust for selection bias due to tissue availability and potential confounders, including tumor differentiation, disease stage, microsatellite instability status, CpG island methylator phenotype, long interspersed nucleotide element-1 methylation, and KRAS, BRAF, and PIK3CA mutations. Results: The prognostic association of smoking status at diagnosis differed by TIL status. Compared with never smokers, the multivariable-adjusted colorectal cancer-specific mortality hazard ratio for current smokers was 1.50 (95% confidence interval = 1.10 to 2.06) in tumors with negative or low TIL and 0.43 (95% confidence interval = 0.16 to 1.12) in tumors with intermediate or high TIL (2-sided P-interaction = .009). No statistically significant interactions were observed in the other patterns of lymphocytic reaction. Conclusions: The association of smoking status at diagnosis with colorectal cancer mortality may be stronger for carcinomas with negative or low TIL, suggesting a potential interplay of smoking and lymphocytic reaction in the colorectal cancer microenvironment.

Background: Tumour budding and poorly differentiated clusters (PDC) represent forms of tumour invasion. We hypothesised that T-cell densities (reflecting adaptive anti-tumour immunity) might be inversely associated with tumour budding and PDC in colorectal carcinoma. Methods: Utilising 915 colon and rectal carcinomas in two U.S.-wide prospective cohort studies, and multiplex immunofluorescence combined with machine learning algorithms, we assessed CD3, CD4, CD8, CD45RO (PTPRC), and FOXP3 co-expression patterns in lymphocytes. Tumour budding and PDC at invasive fronts were quantified by digital pathology and image analysis using the International tumour Budding Consensus Conference criteria. Using covariate data of 4,420 incident colorectal cancer cases, inverse probability weighting (IPW) was integrated with multivariable logistic regression analysis that assessed the association of T-cell subset densities with tumour budding and PDC while adjusting for selection bias due to tissue availability and potential confounders, including microsatellite instability status. Findings: Tumour budding counts were inversely associated with density of CD3+CD8+ [lowest vs. highest: multivariable odds ratio (OR), 0.50; 95% confidence interval (CI), 0.35–0.70; Ptrend < 0.001] and CD3+CD8+CD45RO+ cells (lowest vs. highest: multivariable OR, 0.44; 95% CI, 0.31–0.63; Ptrend < 0.001) in tumour epithelial region. Tumour budding levels were associated with higher colorectal cancer-specific mortality (multivariable hazard ratio, 2.13; 95% CI, 1.57–2.89; Ptrend < 0.001) in Cox regression analysis. There were no significant associations of PDC with T-cell subsets. Interpretation: Tumour epithelial naïve and memory cytotoxic T cell densities are inversely associated with tumour budding at invasive fronts, suggesting that cytotoxic anti-tumour immunity suppresses tumour microinvasion.