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Purpose The overall survival (OS) of patients diagnosed with stage II‐III colorectal cancer (CRC) can vary greatly, even between patients with the same tumor stage. We aimed to design a nomogram to predict OS in resected, stage II‐III CRC and stratify patients with CRC into different risk groups. Patients and Methods Based on data from 873 patients with CRC, we used univariate Cox regression analysis to select the significant prognostic features, which were subjected to the least absolute shrinkage and selection operator (LASSO) regression algorithm for feature selection. Cross‐validation was used to confirm suitable tuning parameters (λ) for LASSO logistic regression. Then, the nomogram was used to estimate 3‐ and 5‐year OS based on the multivariable Cox regression model. The survival curves of the two groups were produced using the Kaplan‐Meier method. Risk group stratification was performed to assess the predictive capacity of the nomogram. Results Preoperative mean platelet volume, preoperative platelet distribution width, monocytes, and postoperative adjuvant chemotherapy were identified as independent prognostic factors by LASSO regression and integrated for the construction of the nomogram. The nomogram provided good discrimination, with C‐indices of 0.67 and 0.69 for the training and validation sets, respectively. Calibration plots illustrated excellent agreement between the nomogram predictions and actual observations for 3‐ and 5‐year OS. Moreover, a significant difference in OS was shown between patients stratified into different risk groups (P, Four points are allocated for preoperative mean platelet volume, preoperative platelet distribution width, monocytes, and postoperative adjuvant chemotherapy. Nomogram for predicting 3‐ and 5‐year probabilities of colorectal cancer patients was established. Draw a vertical straight line from the variable value to the axis labeled “Points”. Then calculate all variables’ points. The total points on the bottom scales that correspond to the 3‐ and 5‐year survival were showed apparently.

Introduction: Circulating tumor cells (CTCs) undergo epithelial-mesenchymal transition (EMT), and the heterogeneity of EMT possibly affects colorectal cancer metastasis (mCRC) evolution. The aim of this study was to identify novel metastasis-related proteins and EMT-related pathways.Methods: The EMT status of the CTCs derived from CRC patients with liver metastasis was determined on the basis of surface markers. Comparative proteomic analysis was then performed on matched pairs of primary tumors, adjacent para-tumor and liver metastases tissues. An optimized proteomic workflow including data independent acquisition (DIA) and parallel reaction monitoring (PRM) was used to screen for novel EMT-related protein clusters. Results: The proportion of the unstable epithelial/mesenchymal (E/M)-type CTCs correlated significantly with distant metastases. We screened 105 proteins related to EMT from 4,752 proteins identified in all samples, of which 40 proteins were differentially expressed across the different tissues. We identified a novel EMT-related protein cluster (e.g., GNG2, COL6A1, COL6A2, DCN, COL6A3, LAMB2, TNXB, CAVIN1) and found that the expression levels of the core EMT-related proteins KRAS and ERBB2 were altered during metastasis progression. The proteomics data indicated that KRAS, ERBB2, COL6A1 and CAVIN1 are promising EMT-related metastatic biomarkers.Conclusions: The plasticity of EMT phenotypes in the CTCs are key to CRC metastasis, prognosis and treatment outcome. Therapies targeting this aggressive CTC subset and the related proteins may suppress metastatic evolution.

Background KRAS gene is the most common type of mutation reported in colorectal cancer (CRC). KRAS mutation-mediated regulation of immunophenotype and immune pathways in CRC remains to be elucidated. Methods 535 CRC patients were used to compare the expression of immune-related genes (IRGs) and the abundance of tumor-infiltrating immune cells (TIICs) in the tumor microenvironment between KRAS-mutant and KRAS wild-type CRC patients. An independent dataset included 566 cases of CRC and an in-house RNA sequencing dataset were served as validation sets. An in-house dataset consisting of 335 CRC patients were used to analyze systemic immune and inflammatory state in the presence of KRAS mutation. An immue risk (Imm-R) model consist of IRG and TIICs for prognostic prediction in KRAS-mutant CRC patients was established and validated. Results NF-κB and T-cell receptor signaling pathways were significantly inhibited in KRAS-mutant CRC patients. Regulatory T cells (Tregs) was increased while macrophage M1 and activated CD4 memory T cell was decreased in KRAS-mutant CRC. Prognosis correlated with enhanced Tregs, macrophage M1 and activated CD4 memory T cell and was validated. Serum levels of hypersensitive C-reactive protein (hs-CRP), CRP, and IgM were significantly decreased in KRAS-mutant compared to KRAS wild-type CRC patients. An immune risk model composed of VGF, RLN3, CT45A1 and TIICs signature classified CRC patients with distinct clinical outcomes. Conclusions KRAS mutation in CRC was associated with suppressed immune pathways and immune infiltration. The aberrant immune pathways and immune cells help to understand the tumor immune microenvironments in KRAS-mutant CRC patients.

Fluoropyrimidine-based chemotherapy is an essential component of systemic chemotherapy for colorectal cancer (CRC). The immune response is implicated in chemotherapy-induced cytotoxicity. Here, we reported an immune risk (Imm-R) model for prognostic prediction in patients receiving fluoropyrimidine-based chemotherapy. Gene expression profiles and corresponding clinical information were collected from four data sets and divided into training set (n = 183) and validation set (validation set1: n = 34; validation set2: n = 99). The composition of 22 tumor-infiltrating immune cells (TIICs) populations was characterized with the CIBERSORT deconvolution algorithm. A prognostic Imm-R model for predicting overall survival was established by performing least absolute shrinkage and selection operator (LASSO) penalized COX regression analysis. T follicular helper cells and M0 macrophages were associated with better survival, while eosinophils were associated with worse survival. TIICs signature was constructed based on the above three immune cell types. Furthermore, a Imm-R model was created by integrating TIICs signature with immune-related genes (IRGs), which effectively in distinguishing CRC patients with poorer prognosis. The Imm-R model was associated with activation of the TGF-beta signaling and suppression of DNA damage. Results of this research provide new insights into the role of immunity for in fluoropyrimidine-based chemotherapy as well as a useful tools to predict the outcome of CRC patients receiving fluoropyrimidine-based chemotherapy.

Lymphatic infiltration (LI) is a key factor affecting the treatment of patients with colorectal cancer (CRC). Thus, the aim of this study was to develop and validate a nomogram for individual preoperative prediction of LI in patients with CRC. We conducted a retrospective analysis of 664 patients who received their initial diagnosis of CRC at our center. Those patients were allocated to a training dataset (n = 468) and a validation dataset (n = 196). The least absolute shrinkage and selection operator regression model was used for data dimension reduction and feature selection. The nomogram was constructed from the training dataset and internally verified using the concordance index (C-index), calibration, area under the receiver operating characteristic curve and decision curve analysis (DCA). The enhancement computed tomography reported N1/N2 classification, preoperative tumor differentiation, elevated carcinoembryonic antigen, and carbohydrate antigen19-9 level were selected as variables for the prediction nomogram. Encouragingly, the nomogram showed favorable calibration with C-index 0.757 in the training cohort and 0.725 in validation cohort. The DCA signified that the nomogram was clinically useful. The Kaplan–Meier survival curve showed that patients with LI had a worse prognosis and could benefit from postoperative adjuvant chemotherapy. Use common clinicopathologic factors, a non-invasive scale for individualized preoperative forecasting of LI was established conveniently. LI prediction has great significance for risk stratification of prognosis and treatment of resectable CRC.

Our objective was to explore the expression and clinical significance of Kelch-like epichlorohydrin-associated protein 1 (Keap1) in breast cancer tissue. Eighty-one breast cancer patients having undergone surgical treatment in our hospital between March 2002 and December 2008 were enrolled in this study. Normal tissue adjacent to tumors was used for the control samples. Diagnoses for all patients were confirmed by postoperative pathological examination. Immunohistochemical assays were used to measure the expression of Keap1 protein in breast cancer tissue and adjacent normal tissue, and its clinical significance was explored. We observed that 24.6% breast cancer tissue samples were positive for Keap1, a significantly lower proportion than that seen with adjacent normal tissue specimens (80.2%; P < 0.05). The presence of Keap1 expression did not correlate with age, tumor size, pathological classification, or degree of differentiation. However, it was found to be significantly associated with tumor-node-metastasis stage and the presence of lymphatic metastasis. Kaplan-Meier survival analysis showed a remarkably higher five-year survival rate among patients with positive Keap1 expression than in those lacking detectable levels of the protein (P = 0.032). Keap1 expression is significantly decreased in breast cancer tissue; therefore, the early detection of its expression might have great significance in determining prognosis for breast cancer patients.

OBJECTIVE Human epididymis protein 4 (HE4) has been approved for diagnosing ovarian cancer. The goal of this meta-analysis was to evaluate the clinical value of the serum HE4 in the diagnosis of ovarian cancer. METHODS The PubMed and Embase databases were searched to identify suitable studies. The sensitivity (SEN), specificity (SPE), and positive and negative likelihood ratios (PLR and NLR, respectively) of HE4 for the diagnosis of ovarian cancer were commonly used as bivariates. Summary receiver operating characteristic curves were used to summarize overall test performances. Meta-Disc 1.4 software was used to analyze the data. RESULTS A total of 6,269 patients from 31 trials were subjected to meta-analysis. The summary estimates of HE4 for ovarian cancer diagnosis were as follows: SEN 0.73 (95% confidence interval [CI] 0.71-0.75); SPE 0.89 (95% CI 0.88-0.90); PLR 7.30 (95% CI 5.42-9.84); and NLR 0.15 (95% CI 0.10-0.23). SEN 0.74 (95% CI 0.72-0.76); SPE 0.89 (95% CI 0.88-0.90); PLR 7.35 (95% CI 5.55-9.73); NLR 0.14 (95% CI 0.09-0.21). CONCLUSION Our study demonstrates that the sensitivity and specificity of HE4 was higher than that of cancer antigen 125. The results indicated that HE4 could be a useful tumor marker for ovarian cancer diagnosis. However, the results of this meta-analysis should be interpreted with caution, due to the heterogeneity among study designs. Further study should pay more attention to the possibility that HE4 can be a marker for monitoring recurrence of ovarian cancer.