Background: Colorectal cancer (CRC) is one of the most prevalent cancer types globally. A survival paradox exists due to the inherent heterogeneity in stage II/III CRC tumor biology. Ferroptosis is closely related to the progression of tumors, and ferroptosis-related genes can be used as a novel biomarker in predicting cancer prognosis. Methods: Ferroptosis-related genes were retrieved from the FerrDb and KEGG databases. A total of 1,397 samples were enrolled in our study from nine independent datasets, four of which were integrated as the training dataset to train and construct the model, and validated in the remaining datasets. We developed a machine learning framework with 83 combinations of 10 algorithms based on 10-fold cross-validation (CV) or bootstrap resampling algorithm to identify the most robust and stable model. C-indice and ROC analysis were performed to gauge its predictive accuracy and discrimination capabilities. Survival analysis was conducted followed by univariate and multivariate Cox regression analyses to evaluate the performance of identified signature. Results: The ferroptosis-related gene (FRG) signature was identified by the combination of Lasso and plsRcox and composed of 23 genes. The FRG signature presented better performance than common clinicopathological features (e.g., age and stage), molecular characteristics (e.g., BRAF mutation and microsatellite instability) and several published signatures in predicting the prognosis of the CRC. The signature was further stratified into a high-risk group and low-risk subgroup, where a high FRG signature indicated poor prognosis among all collected datasets. Sensitivity analysis showed the FRG signature remained a significant prognostic factor. Finally, we have developed a nomogram and a decision tree to enhance prognosis evaluation. Conclusion: The FRG signature enabled the accurate selection of high-risk stage II/III CRC population and helped optimize precision treatment to improve their clinical outcomes.

Background: Individualized recurrence risk prediction in patients with stage II/III colorectal cancer (CRC) is crucial for making postoperative treatment decisions. However, there is still a lack of effective approaches for identifying patients with stage II and III CRC at a high risk of recurrence. In this study, we aimed to establish a credible gene model for improving the risk assessment of patients with stage II/III CRC. Methods: Recurrence-free survival (RFS)-related genes were screened using Univariate Cox regression analysis in GSE17538, GSE39582, and GSE161158 cohorts. Common prognostic genes were identified by Venn diagram and subsequently subjected to least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis for signature construction. Kaplan-Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were used to assess the predictive accuracy and superiority of our risk model. Single-sample gene set enrichment analysis (ssGSEA) was employed to investigate the relationship between the infiltrative abundances of immune cells and risk scores. Genes significantly associated with the risk scores were identified to explore the biological implications of the 9-gene signature. Results: Survival analysis identified 347 RFS-related genes. Using these genes, a 9-gene signature was constructed, which was composed of MRPL41, FGD3, RBM38, SPINK1, DKK1, GAL3ST4, INHBB, CTB-113P19.1, and FAM214B. K-M curves verified the survival differences between the low- and high-risk groups classified by the 9-gene signature. The area under the curve (AUC) values of this signature were close to or no less than the previously reported prognostic signatures and clinical factors, suggesting that this model could provide improved RFS prediction. The ssGSEA algorithm estimated that eight immune cells, including regulatory T cells, were aberrantly infiltrated in the high-risk group. Furthermore, the signature was associated with multiple oncogenic pathways, including cell adhesion and angiogenesis. Conclusion: A novel RFS prediction model for patients with stage II/III CRC was constructed using multicohort validation. The proposed signature may help clinicians better manage patients with stage II/III CRC.

Approximately 30% of stage II and 50-60% of stage III colorectal cancer (CRC) patients who have undergone surgery will develop recurrence within 5 years. Thus, more reliable prognostic biomarkers are urgently needed to identify the high-risk subset of patients who will benefit from postoperative adjuvant therapy.
We retrospectively analyzed 911 stage II/III CRC patients in multiple cohorts. Using a series of bioinformatic and statistical approaches, an individualized prognostic signature was established in the training cohort and validated in four other independent cohorts. An integrated decision tree was generated to improve risk stratification, and a nomogram was built to quantify risk assessment for individual patients.
Epithelial-mesenchymal transition (EMT) was identified as a dominant risk factor for recurrence-free survival (RFS) in stage II/III CRC patients. The EMT-related gene signature could discriminate high-risk subsets in a training cohort and four independent validation cohorts (with 473, 89, 130, 74 and 145 patients, respectively). Survival analyses demonstrated that the EMT-related gene signature served as an independent risk factor for RFS in different subgroups. The decision tree could optimize the risk stratification, and the nomogram could predict the 5-year RFS probability accurately.
The proposed EMT-related prognostic signature is a useful biomarker to predict RFS and identify the high-risk subset in stage II/III CRC patients.

BACKGROUND: Long non-coding RNAs (lncRNAs) have recently emerged as essential biomarkers of cancer progression. However, studies are limited regarding lncRNAs correlated with recurrence and fluorouracil-based adjuvant chemotherapy (ACT) in stage II/III colorectal cancer (CRC).
METHODS: 1640 stage II/III CRC patients were enrolled from 15 independent datasets and a clinical in-house cohort. 10 prevalent machine learning algorithms were collected and then combined into 76 combinations. 109 published transcriptome signatures were also retrieved. qRT-PCR assay was performed to verify our model.
RESULTS: We comprehensively identified 27 stably recurrence-related lncRNAs from multi-center cohorts. According to these lncRNAs, a consensus machine learning-derived lncRNA signature (CMDLncS) that exhibited best power for predicting recurrence risk was determined from 76 kinds of algorithm combinations. A high CMDLncS indicated unfavorable recurrence and mortality rates. CMDLncS not only could work independently of common clinical traits (e.g., AJCC stage) and molecular features (e.g., microsatellite state, KRAS mutation), but also presented dramatically better performance than these variables. qRT-PCR results from 173 patients further verified our in-silico findings and assessed its feasible in different centers. Comparisons of CMDLncS with 109 published transcriptome signatures further demonstrated its predictive superiority. Additionally, patients with high CMDLncS benefited more from fluorouracil-based ACT and were characterized by activation of stromal and epithelial-mesenchymal transition, while patients with low CMDLncS suggested the sensitivity to bevacizumab and displayed enhanced immune activation.
CONCLUSIONS: CMDLncS provides an attractive platform for identifying patient at high risk of recurrence and could optimize precision treatment to improve the clinical outcomes in stage II/III CRC.
BACKGROUND: This study was supported by the National Natural Science Foundation of China (81,972,663); Henan Province Young and Middle-Aged Health Science and Technology Innovation Talent Project (YXKC2020037); and Henan Provincial Health Commission Joint Youth Project (SB201902014).

A considerable number of patients with stage II/III colorectal cancer (CRC) will relapse within 5 years after surgery, which is a leading cause of death in early-stage CRC. The current TNM stage system is limited due to the heterogeneous clinical outcomes displayed in patients of same stage. Therefore, searching for a novel tool to identify patients at high recurrence-risk for improving post-operative individual management is an urgent need.
Using four independent public cohorts and qRT-PCR data from 66 tissues, we developed and validated a recurrence-associated immune signature (RAIS) based on global immune genes. The clinical and molecular features, tumor immune microenvironment landscape, and immune checkpoints profiles of RAIS were also investigated.
In five independent cohorts, this novel scoring system was proven to be an independent recurrent factor and displayed excellent discrimination and calibration in predicting the recurrence-risk at 1~5 years. Further analysis revealed that the high-risk group displayed high mutation rate of TP53, while the low-risk group had more abundance of activated CD4+/CD8+ T cells and high expression of PD-1/PD-L1.
The RAIS model is highly predictive of recurrence in patients with stage II/III CRC, which might serve as a powerful tool to further optimize decision-making in adjuvant chemotherapy and immunotherapy, as well as tailor surveillance protocol for individual patients.

BACKGROUND: A large number of patients with stage II/III colorectal cancer (CRC) have a high recurrence rate after radical resection. We aimed to develop a novel tool to stratify patients with different recurrence-risk for optimizing decision-making in post-operative surveillance and therapeutic regimens.
METHODS: We retrospectively enrolled four independent cohorts from the Gene Expression Omnibus and 66 CRC tissues from our hospital. The initial signature discovery was conducted in GSE143985 (n = 91). This was followed by independent validation of this signature in GSE17536 (n = 111), GSE29621 (n = 40), and GSE92921 (n = 59). Further experimental validation using qRT-PCR assays (n = 66) was performed to ensure the robustness and clinical feasible of this signature.
RESULTS: We developed a novel recurrence-related signature consisting of six genes. This signature was validated to be significantly associated with dismal recurrence-free survival in five cohorts GSE143985 (HR: 4.296 [2.612-7.065], P < 0.0001), GSE17536 (HR: 2.354 [1.662-3.334], P < 0.0001), GSE29621 (HR: 3.934 [1.622-9.539], P = 0.0024), GSE92921 (HR: 7.080 [2.011-24.924], P = 0.0023), and qPCR assays (HR: 3.654 [2.217-6.020], P < 0.0001). This signature was also proven to be an independent recurrent factor. More importantly, this signature displayed excellent discrimination and calibration in predicting the recurrence-risk at 1-5 years, with most AUCs were above 0.9, average C-index for the five cohorts was 0.8795, and near-perfect calibration.
CONCLUSIONS: We discovered and experimental validated a novel gene signature with stable and powerful performance for identifying patients at high recurrence-risk in stage II/III CRC.

Precise methods for postoperative risk stratification to guide the administration of adjuvant chemotherapy (ACT) in localized colorectal cancer (CRC) are still lacking. Here, we conducted a prospective, observational, and multicenter study to investigate the utility of circulating tumor DNA (ctDNA) in predicting the recurrence risk.
From September 2017 to March 2020, 276 patients with stage II/III CRC were prospectively recruited in this study and 240 evaluable patients were retained for analysis, of which 1290 serial plasma samples were collected. Somatic variants in both the primary tumor and plasma were detected via a targeted sequencing panel of 425 cancer-related genes. Patients were treated and followed up per standard of care.
Preoperatively, ctDNA was detectable in 154 of 240 patients (64.2%). At day 3-7 postoperation, ctDNA positivity was associated with remarkably high recurrence risk (hazard ratio [HR], 10.98; 95%CI, 5.31-22.72; P < 0.001). ctDNA clearance and recurrence-free status was achieved in 5 out of 17 ctDNA-positive patients who were subjected to ACT. Likewise, at the first sampling point after ACT, ctDNA-positive patients were 12 times more likely to experience recurrence (HR, 12.76; 95%CI, 5.39-30.19; P < 0.001). During surveillance after definitive therapy, ctDNA positivity was also associated with extremely high recurrence risk (HR, 32.02; 95%CI, 10.79-95.08; P < 0.001). In all multivariate analyses, ctDNA positivity remained the most significant and independent predictor of recurrence-free survival after adjusting for known clinicopathological risk factors. Serial ctDNA analyses identified recurrence with an overall accuracy of 92.0% and could detect disease recurrence ahead of radiological imaging with a mean lead time of 5.01 months.
Postoperative serial ctDNA detection predicted high relapse risk and identified disease recurrence ahead of radiological imaging in patients with stage II/III CRC. ctDNA may be used to guide the decision-making in postsurgical management.

A lack of caudal-type homeobox transcription factor 2 (CDX2) protein expression has been proposed as a prognostic biomarker for colorectal cancer (CRC). However, the relationship between CDX2 levels and the survival of patients with stage II/III CRC along with the relationship between microRNAs (miRs) and CDX2 expression are unclear. Tissue samples were collected from patients with stage II/III CRC surgically treated at Kyoto University Hospital. CDX2 expression was semi-quantitatively evaluated by immunohistochemistry (IHC). The prognostic impacts of CDX2 expression on overall survival (OS) and relapse-free survival (RFS) were evaluated by multivariable statistical analysis. The expression of miRs regulating CDX2 expression and their prognostic impacts were analyzed using The Cancer Genome Atlas Program for CRC (TCGA-CRC). Eleven of 174 CRC tissues lacked CDX2 expression. The five-year OS and RFS rates of patients with CDX2-negative CRC were significantly lower than those of CDX2-positive patients. Multivariate analysis of clinicopathological features revealed that CDX2-negative status is an independent marker of poor prognosis in stage II/III CRC. miR-9-5p was shown to regulate CDX2 expression. TCGA-CRC analysis showed that high miR-9-5p expression was significantly associated with poor patient prognosis in stage II/III CRC. In conclusion, CDX2, the post-transcriptional target of microRNA-9-5p, is a useful prognostic biomarker in patients with stage II/III CRC.