DNA damage has been extensively studied as a potentially helpful tool in assessing and preventing cancer, having been widely associated with the deregulation of DNA damage repair (DDR) genes and with an increased risk of cancer. Adipose tissue and tumoral cells engage in a reciprocal interaction to establish an inflammatory microenvironment that enhances cancer growth by modifying epigenetic and gene expression patterns. Here, we hypothesize that 8-oxoguanine DNA glycosylase 1 (OGG1)—a DNA repair enzyme—may represent an attractive target that connects colorectal cancer (CRC) and obesity. In order to understand the mechanisms underlying the development of CRC and obesity, the expression and methylation of DDR genes were analyzed in visceral adipose tissue from CRC and healthy participants. Gene expression analysis revealed an upregulation of OGG1 expression in CRC participants (p < 0.005) and a downregulation of OGG1 in normal-weight healthy patients (p < 0.05). Interestingly, the methylation analysis showed the hypermethylation of OGG1 in CRC patients (p < 0.05). Moreover, expression patterns of OGG1 were found to be regulated by vitamin D and inflammatory genes. In general, our results showed evidence that OGG1 can regulate CRC risk through obesity and may act as a biomarker for CRC.

Colorectal cancer (CRC) remains a major global health burden and a leading cause of cancer-related morbidity and mortality. Current blood-based biomarkers lack sufficient sensitivity and specificity, particularly for early detection. In this context, circulating immune-cell transcriptomic profiling has emerged as a promising minimally invasive approach. This systematic review was conducted following a PROSPERO-registered protocol (CRD42024604757) and PRISMA 2020 guidelines to evaluate the diagnostic and prognostic potential of circulating monocyte-related transcriptomic profiles in CRC. Of 295 records identified, six studies met the inclusion criteria. The available evidence consistently supports the diagnostic value of circulating transcriptomic profiles in distinguishing patients with CRC from healthy individuals and in reflecting tumour-associated immune alterations. Monocyte-related signatures, including CXCR2⁺ monocytes, were associated with disease stage and metastatic features. Epitranscriptomic modifications, such as m6A and m5C, further reinforced their diagnostic relevance, with some studies reporting higher diagnostic accuracy than classical biomarkers. In contrast, evidence for prognostic value remains limited, heterogeneous, and often indirect, largely due to small sample sizes, methodological variability, and reliance on public datasets. Overall, circulating immune-cell transcriptomic profiles are promising non-invasive biomarkers for CRC detection and characterization, although their prognostic utility remains unclear. Methodological heterogeneity limits clinical applicability, highlighting the need for standardized, CRC-specific studies with cell-type-resolved approaches.

Background/Objective: Periodontitis is a chronic inflammatory disease influenced by clinical, behavioral, and microbial determinants. However, the contribution of these factors to disease remains a topic of debate, particularly in untreated adult populations. This study aims to characterize the clinical, epidemiological, and microbial features associated with periodontitis in an adult cohort and to compare the discriminatory performance of microbiome-based predictive models with conventional clinical–behavioral models. Methods: A cross-sectional study was conducted in 943 adults. Periodontal status was determined by experienced clinicians according to the 2017 World Workshop on the Classification of Periodontal and Peri-implant Diseases and Conditions. Clinical variables, behavioral factors (smoking, bruxism, diet), intraoral conditions (caries and malocclusion), and systemic comorbidities were recorded. The oral microbiome was analyzed using targeted PCR for selected periodontal bacteria. Predictive models were constructed using logistic regression and least absolute shrinkage and selection operator (LASSO) variable selection. Results: Periodontitis was diagnosed in 47.2% of participants. Age, smoking, and bruxism were significantly associated with periodontitis. Malocclusion was the only significant intraoral predictor (OR = 2.00). Individuals with periodontitis exhibited increased levels of periodontopathogens, including P. gingivalis, T. forsythia, and E. corrodens, along with reduced levels of S. mutans. Microbiome-based models demonstrated superior discriminatory performance (AUC = 0.76, LASSO). E. corrodens and C. sputigena were independently associated with greater probing depth (p < 0.001). Conclusions: Microbiome-based predictive models, particularly at the species level, showed better discriminatory performance than conventional clinical–behavioral models. These findings support the potential utility of salivary microbial signatures as adjunctive, non-invasive biomarkers of periodontal inflammatory status.

Background/Objective: Periodontitis is a chronic inflammatory disease with a multifactorial etiology involving clinical, behavioral, and microbial determinants. However, the relative contribution of these factors to disease discrimination remains debated, particularly in untreated adult populations. This study is aimed to characterize the clinical, epidemiological, and microbial features associated with periodontitis in an adult cohort, and to compare the discriminatory performance of microbiome-based predictive models with conventional clini-cal-behavioral models. Methods: A cross-sectional study was conducted in 943 adult participants. Periodontal status was determined by experienced clinicians according to the diagnostic criteria established in the 2017 World Workshop on the Classification of Periodontal and Peri-implant Diseases and Conditions, based on clinical attachment loss (CAL), probing depth (PD), bleeding on probing (BOP), and radiographic bone loss. Clinical variables, behavioral factors (smoking, bruxism, diet), intraoral conditions (caries and malocclusion), and systemic comorbidities were recorded. The salivary microbiome was analyzed using targeted multiplex PCR for selected periodontal bacterial species. Predictive models were constructed using logistic regression and least absolute shrinkage and selection operator (LASSO) variable selection. Results: Periodontitis was diagnosed in 47.2% of participants. Age, smoking, and bruxism were significantly associated with periodontitis, although their combined discriminatory capacity was modest (AUC = 0.65). Malocclusion emerged as the only significant intraoral predictor (OR = 2.00). Microbiologically, individuals with periodontitis exhibited increased levels of recognized periodontopatho-gens, including P. gingivalis, T. forsythia, and E. corrodens, along with reduced levels of commensal species such as S. mutans. Microbiome-based models demonstrated superior discriminatory performance (AUC = 0.76, LASSO). E. corrodens and C. sputigena were independently associated with greater probing depth (p < 0.001). Additionally, E. corrodens levels were significantly higher in periodontitis patients with cardiovascular disease compared to controls. Conclusions: While conventional clinical and behavioral variables show limited capacity to discriminate periodontitis status, microbiome-based predictive models—particularly at the species level—demonstrate improved diagnostic performance. These findings support the potential role of salivary microbial signatures as adjunctive, non-invasive biomarkers reflecting periodontal inflammatory status.

Aberrant DNA methylation has been shown to be a fingerprint characteristic in human colorectal tumors. In this study, we hypothesize that investigating global DNA methylation could offer potential candidates for clinical application in CRC. The epigenome-wide association analysis was conducted in both the tumor area (N = 27) and the adjacent tumor-free (NAT) area (N = 15). We found 78,935 differentially methylated CpG sites (DMCs) (FDR < 0.05), 42,888 hypomethylated and 36,047 hypermethylation showing overall hypomethylation. Gene ontology and KEGG analysis of differentially methylated genes showed significant enrichment in developmental genes, as well as in genes involved in metabolic processes and the cell cycle, such as the TFGβ and cAMP signaling pathways. Through filtered analysis, we identified TNXB as the most epigenetically dysregulated gene, hypomethylated and downregulated in CRC (both with p < 0.001) and associated with poor overall survival. In the functional analysis, TNXB was epigenetically regulated in a dose-dependent manner, suggesting a potential role in CRC. The epigenetic dysregulation and functional role of TNXB in CRC could have clinical implications, serving as indicators of malignant potential, with adverse effects associated with disease origin and progression in CRC.