The Surgical clinics of North America · 2023
Matthew Jacobsson、Vitas Wagner、Shalini Kanneganti
Colorectal cancer remains the third leading cause of cancer death in the United States. Colorectal cancer screening allows for prevention and early detection of precancerous and cancerous lesions, and screening has been shown to be effective in preventing colorectal cancer deaths. Screening recommendations vary by patient risk profile. A variety of screening modalities exist.
Frontiers in Oncology · 2026
S. Murthy、Anna Schuh
Early-onset colorectal cancer (EOCRC), defined as colorectal cancer diagnosed before age 50, is rising across high- and middle-income settings whilst organised screening stays anchored to older age thresholds. Blood-based liquid biopsy, combined with machine learning, is the most plausible route to early detection in this group because it does not depend on bowel preparation, endoscopy capacity, or adherence to stool-based testing. The gap is structural: incidence climbs fastest in the population below the age at which any guideline-endorsed modality is offered. The analytical challenge is that early-stage tumour-derived signals in plasma are low in abundance and distributed across heterogeneous molecular layers: circulating tumour DNA mutations, aberrant methylation, cfDNA fragmentomics, and small non-coding RNA. Machine learning converts these into a single calibrated probability. This review examines where artificial intelligence (AI)-driven liquid biopsy genuinely adds diagnostic value in EOCRC, distinguishes components in which learned models are decorative from those in which they are mechanistically necessary, and identifies the validation deficit separating research cohorts from deployable clinical tools. It summarises the first-generation tools used clinically for early detection and post-treatment monitoring, then considers analytes from exosome-bound microRNAs to long-read whole-genome sequencing of circulating plasma DNA, which reads cytosine modification natively, resolves methylation and fragmentation on single molecules, and characterises structural events short reads cannot anchor. Any analyte can feed a learned model, but more diverse input yields better discrimination. The central argument is that approved, guideline-included blood tests were validated in populations aged 45 and above, and their performance in younger patients cannot be assumed.
Trends in cancer · 2025
Zimeng Wu、Zhe Hu、Qinlan Li 等
The global incidence of endometrial cancer is on the rise, marked by a notable surge in early-onset endometrial cancer (EOEC; age at diagnosis <50 years). By contrast to late-onset cases, EOEC displays distinct clinical, pathological, and molecular characteristics. The enhanced understanding of the disease's pathophysiology, enabling a more precise differentiation between low-risk and high-risk patients, could facilitate the establishment of risk-stratified treatments that preserve ovarian function and fertility in low-risk EOEC cases. In this review, we delve into the distinctive epidemiological, molecular, and clinical characteristics of EOEC, as well as early noninvasive screening and fertility preservation treatments.
PeerJ · 2026
Li-Fang Huang、Run-Tao Zhong、Kang-Kang Li 等
Background The incidence of early-onset colorectal cancer (EO-CRC; age <50 years) has been increasing worldwide. This single-center retrospective study aimed to compare the clinical characteristics of EO-CRC and late-onset CRC (LO-CRC; age ≥ 50 years) and to identify age-specific prognostic factors for overall survival (OS). Methods A total of 1,148 CRC patients were retrospectively analyzed and categorized into EO-CRC (n = 247) and LO-CRC (n = 901) groups. Clinical characteristics were compared using the Mann–Whitney U test and Chi-square test. Prognostic factors associated with OS were identified using Least absolute shrinkage and selection operator (LASSO) Cox regression followed by multivariate Cox modeling. Model performance was evaluated using the C-index, calibration curves, and time-dependent Receiver Operating Characteristic (ROC) analysis. Variable importance was further validated using a random survival forest (RSF) model. Results EO-CRC patients showed higher proportions of family history, concurrent polyps, and Programmed Cell Death Ligand 1 (PD-L1) expression >10%, whereas LO-CRC patients exhibited higher rates of hypertension, diabetes, and elevated carcinoembryonic antigen (CEA) levels. Although OS did not differ significantly between groups (P = 0.460), their prognostic determinants varied markedly. In EO-CRC, distant metastasis, family history, Tumor, Node, and Metastasis (TNM) stage, PMS1 homolog 2, mismatch repair system component (PMS2), MutS Homolog 6 (MSH6), tumor size, concurrent polyps, and Ki-67 were major predictors. In LO-CRC, age, BRAF gene V600E mutation (BRAF V600E) mutation, elevated Carbohydrate antigen 19-9 (CA19-9), Ki-67, low hemoglobin, vascular invasion, MutL Homolog 1 (MLH1), and pathological type were significant contributors. The C-index values for the EO-CRC and LO-CRC models were 0.829 (SE = 0.023) and 0.751 (SE = 0.018), respectively, and all time-dependent ROC curves demonstrated Area Under the Curve (AUCs) above 0.70, indicating good predictive performance. RSF analyses further confirmed that distant metastasis and family history as the strongest predictors, while age and BRAF V600E are the strongest predictors for LO-CRC. Conclusion This study suggests that EO-CRC and LO-CRC have fundamentally different prognostic determinants: the former emphasizes genetic susceptibility and tumor invasiveness, indicating that this group of patients may benefit from early genetic counseling, MMR/MSI testing, and immune checkpoint inhibitor therapy. The latter highlights age, acquired molecular changes, and chronic systemic factors, supporting the inclusion of metabolic and geriatric assessments in routine tumor care.
Hematology/oncology clinics of North America · 2022
Heather Hampel、Matthew F Kalady、Rachel Pearlman 等
Around 10% to 16% of colorectal cancer patients have a pathogenic variant in a cancer susceptibility gene. Some of these variants are in cancer genes that are associated with colorectal cancer while others are not. The hereditary colorectal cancer syndromes can be divided into two major categories, the nonpolyposis and the polyposis conditions. The nonpolyposis conditions can be divided into those that lead to colorectal tumors with defective mismatch repair and those that do not. The polyposis conditions are further divided by predominant histology into the adenomatous, hamartomatous, serrated, and mixed polyposis conditions. All of these conditions are described in detail herein.
Cell · 2017
TaChung Yu、Fangfang Guo、Yanan Yu 等
Gut microbiota are linked to chronic inflammation and carcinogenesis. Chemotherapy failure is the major cause of recurrence and poor prognosis in colorectal cancer patients. Here, we investigated the contribution of gut microbiota to chemoresistance in patients with colorectal cancer. We found that Fusobacterium (F.) nucleatum was abundant in colorectal cancer tissues in patients with recurrence post chemotherapy, and was associated with patient clinicopathological characterisitcs. Furthermore, our bioinformatic and functional studies demonstrated that F. nucleatum promoted colorectal cancer resistance to chemotherapy. Mechanistically, F. nucleatum targeted TLR4 and MYD88 innate immune signaling and specific microRNAs to activate the autophagy pathway and alter colorectal cancer chemotherapeutic response. Thus, F. nucleatum orchestrates a molecular network of the Toll-like receptor, microRNAs, and autophagy to clinically, biologically, and mechanistically control colorectal cancer chemoresistance. Measuring and targeting F. nucleatum and its associated pathway will yield valuable insight into clinical management and may ameliorate colorectal cancer patient outcomes.
Functional & Integrative Genomics · 2025
Ying-Dong Hou、Hu-Bin Xia、Chen-Shan Xu 等
Emerging evidence has suggested a potential pathological association between early-onset left-sided colorectal cancer (EOLCC) and metabolic syndrome (MetS). However, the underlying genetic and molecular mechanisms remain insufficiently elucidated. This study aimed to identify and characterize key biomarkers associated with the progression and treatment response of MetS-related EOLCC. An in-hospital cohort was utilized to assess the clinical implications of primary tumor location in early-onset colorectal cancer (EOCRC). Differentially expressed genes (DEGs) and weighted gene coexpression network analysis (WGCNA) were employed to identify genes potentially associated with MetS-related EOLCC. Functional enrichment analyses were conducted to explore the underlying mechanisms. Candidate biomarkers were screened using random forest (RF) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Survival relevance, expression profiles, and diagnostic performance were analyzed to identify key biomarkers. Treatment responses were evaluated, and potential therapeutic compounds were identified through molecular docking. Single-cell RNA sequencing (scRNA-seq) data and in vitro experiments were used to validate gene expression and functional characteristics. The in-hospital cohort revealed a higher proportion of EOLCC among EOCRC patients. Using the edgeR package and WGCNA, we identified coexpressed genes common to both EOLCC and MetS, significantly enriched in pathways associated with stromal remodeling and metabolic regulation. Machine learning algorithms highlighted three candidate biomarkers. Among them, only CD151 was associated with prognosis and advanced disease stage. CD151 was strongly correlated with stromal remodeling and chemoresistance. Additionally, potential therapeutic compounds targeting MetS-related EOLCC were identified via molecular docking. scRNA-seq analysis confirmed the expression and functional patterns of CD151, particularly in tumor cells. The bioinformatics results were further validated through quantitative real-time PCR (qRT-PCR), western blotting, and immunohistochemical (IHC) staining. This study identified CD151 as a key biomarker in MetS-related EOLCC, offering valuable insights into prognosis, tumor biology, and personalized treatment strategies. CD151 may serve as a reference for future research and clinical applications targeting this disease subtype.
Annals of oncology : official journal of the European Society for Medical Oncology · 2024
R S Laskar、C Qu、J R Huyghe 等
BACKGROUND: The incidence of early-onset colorectal cancer (EOCRC; diagnosed <50 years of age) is rising globally; however, the causes underlying this trend are largely unknown. CRC has strong genetic and environmental determinants, yet common genetic variants and causal modifiable risk factors underlying EOCRC are unknown. We conducted the first EOCRC-specific genome-wide association study (GWAS) and Mendelian randomization (MR) analyses to explore germline genetic and causal modifiable risk factors associated with EOCRC. PATIENTS AND METHODS: We conducted a GWAS meta-analysis of 6176 EOCRC cases and 65 829 controls from the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), the Colorectal Transdisciplinary Study (CORECT), the Colon Cancer Family Registry (CCFR), and the UK Biobank. We then used the EOCRC GWAS to investigate 28 modifiable risk factors using two-sample MR. RESULTS: We found two novel risk loci for EOCRC at 1p34.1 and 4p15.33, which were not previously associated with CRC risk. We identified a deleterious coding variant (rs36053993, G396D) at polyposis-associated DNA repair gene MUTYH (odds ratio 1.80, 95% confidence interval 1.47-2.22) but show that most of the common genetic susceptibility was from noncoding signals enriched in epigenetic markers present in gastrointestinal tract cells. We identified new EOCRC-susceptibility genes, and in addition to pathways such as transforming growth factor (TGF) β, suppressor of Mothers Against Decapentaplegic (SMAD), bone morphogenetic protein (BMP) and phosphatidylinositol kinase (PI3K) signaling, our study highlights a role for insulin signaling and immune/infection-related pathways in EOCRC. In our MR analyses, we found novel evidence of probable causal associations for higher levels of body size and metabolic factors-such as body fat percentage, waist circumference, waist-to-hip ratio, basal metabolic rate, and fasting insulin-higher alcohol drinking, and lower education attainment with increased EOCRC risk. CONCLUSIONS: Our novel findings indicate inherited susceptibility to EOCRC and suggest modifiable lifestyle and metabolic targets that could also be used to risk-stratify individuals for personalized screening strategies or other interventions.
JMIR Cancer · 2024
Chengkun Sun、E. Mobley、Michael Quillen 等
Abstract Background Colorectal cancer is now the leading cause of cancer-related deaths among young Americans. Accurate early prediction and a thorough understanding of the risk factors for early-onset colorectal cancer (EOCRC) are vital for effective prevention and treatment, particularly for patients below the recommended screening age. Objective Our study aims to predict EOCRC using machine learning (ML) and structured electronic health record data for individuals under the screening age of 45 years, with the aim of exploring potential risk and protective factors that could support early diagnosis. Methods We identified a cohort of patients under the age of 45 years from the OneFlorida+ Clinical Research Consortium. Given the distinct pathology of colon cancer (CC) and rectal cancer (RC), we created separate prediction models for each cancer type with various ML algorithms. We assessed multiple prediction time windows (ie, 0, 1, 3, and 5 y) and ensured robustness through propensity score matching to account for confounding variables including sex, race, ethnicity, and birth year. We conducted a comprehensive performance evaluation using metrics including area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Both linear (ie, logistic regression, support vector machine) and nonlinear (ie, Extreme Gradient Boosting and random forest) models were assessed to enable rigorous comparison across different classification strategies. In addition, we used the Shapley Additive Explanations to interpret the models and identify key risk and protective factors associated with EOCRC. Results The final cohort included 1358 CC cases with 6790 matched controls, and 560 RC cases with 2800 matched controls. The RC group had a more balanced sex distribution (2:3 male-to-female) compared to the CC group (2:5 male-to-female), and both groups showed diverse racial and ethnic representation. Our predictive models demonstrated reasonable results, with AUC scores for CC prediction of 0.811, 0.748, 0.689, and 0.686 at 0, 1, 3, and 5 years before diagnosis, respectively. For RC prediction, AUC scores were 0.829, 0.771, 0.727, and 0.721 across the same time windows. Key predictive features across both cancer types included immune and digestive system disorders, secondary malignancies, and underweight status. In addition, blood diseases emerged as prominent indicators specifically for CC. Conclusions Our findings demonstrate the potential of ML models leveraging electronic health record data to facilitate the early prediction of EOCRC in individuals under 45 years. By uncovering important risk factors and achieving promising predictive performance, this study provides preliminary insights that could inform future efforts toward earlier detection and prevention in younger populations.
medRxiv · 2024
C. Sun、E. Mobley、M. Quillen 等
Background: Colorectal cancer (CRC) is now the leading cause of cancer-related deaths among young Americans. Our study aims to predict early-onset CRC (EOCRC) using machine learning (ML) and structured electronic health record (EHR) data for individuals under the screening age of 45. Methods: We identified a cohort of patients under 45 from the OneFlorida+ Clinical Research Consortium. Given the distinct pathology of colon cancer (CC) and rectal cancer (RC), we created separate prediction models for each cancer type with various ML algorithms. We assessed multiple prediction time windows (0, 1, 3, and 5 years) and ensured robustness through propensity score matching (PSM) to account for confounding variables. Model performance was assessed using established metrics. Additionally, we employed the Shapley Additive exPlanations (SHAP) to identify risk factors for EOCRC. Results: Our study yielded results, with Area Under the Curve (AUC) scores of 0.811, 0.748, 0.689, and 0.686 for CC prediction, and 0.829, 0.771, 0.727, and 0.721 for RC prediction at 0, 1, 3, and 5 years, respectively. Notably, predictors included immune and digestive system disorders, along with secondary cancers and underweight, prevalent in both CC and RC groups. Blood diseases emerged as prominent indicators of CC. Conclusion: This study highlights the potential of ML techniques in leveraging EHR data to predict EOCRC, offering valuable insights for potential early diagnosis in patients who are below the recommended screening age.
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