Development and Validation of a Nomogram for Preoperative Prediction of Perineural Invasion in Colorectal Cancer

Predictive Study of Machine Learning-Based Multiparametric MRI Radiomics Nomogram for Perineural Invasion in Rectal Cancer: A Pilot Study

This study aimed to establish and validate the efficacy of a nomogram model, synthesized through the integration of multi-parametric magnetic resonance radiomics and clinical risk factors, for forecasting perineural invasion in rectal cancer. We retrospectively collected data from 108 patients with pathologically confirmed rectal adenocarcinoma who underwent preoperative multiparametric MRI at the First Affiliated Hospital of Bengbu Medical College between April 2019 and August 2023. This dataset was subsequently divided into training and validation sets following a ratio of 7:3. Both univariate and multivariate logistic regression analyses were implemented to identify independent clinical risk factors associated with perineural invasion (PNI) in rectal cancer. We manually delineated the region of interest (ROI) layer-by-layer on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences and extracted the image features. Five machine learning algorithms were used to construct radiomics model with the features selected by least absolute shrinkage and selection operator (LASSO) method. The optimal radiomics model was then selected and combined with clinical features to formulate a nomogram model. The model performance was evaluated using receiver operating characteristic (ROC) curve analysis, and its clinical value was assessed via decision curve analysis (DCA). Our final selection comprised 10 optimal radiological features and the SVM model showcased superior predictive efficiency and robustness among the five classifiers. The area under the curve (AUC) values of the nomogram model were 0.945 (0.899, 0.991) and 0.846 (0.703, 0.99) for the training and validation sets, respectively. The nomogram model developed in this study exhibited excellent predictive performance in foretelling PNI of rectal cancer, thereby offering valuable guidance for clinical decision-making. The nomogram could predict the perineural invasion status of rectal cancer in early stage.

Development and External Validation of a CT-Based Radiomics Nomogram to Predict Perineural Invasion and Survival in Gastric Cancer: A Multi-institutional Study

RATIONALE AND OBJECTIVES

To develop and validate a radiomics nomogram utilizing CT data for predicting perineural invasion (PNI) and survival in gastric cancer (GC) patients.

MATERIALS AND METHODS

A retrospective analysis of 408 GC patients from two institutions: 288 patients from Institution I were divided 7:3 into a training set (n = 203) and a testing set (n = 85); 120 patients from Institution II served as an external validation set. Radiomics features were extracted and screened from CT images. Independent radiomics, clinical, and combined models were constructed to predict PNI. Model discrimination, calibration, clinical utility, and prognostic significance were evaluated using area under the curve (AUC), calibration curves, decision curves analysis, and Kaplan-Meier curves, respectively.

RESULTS

15 radiomics features and three clinical factors were included in the final analysis. The AUCs of the radiomics model in the training, testing, and external validation sets were 0.843 (95% CI: 0.788-0.897), 0.831 (95% CI: 0.741-0.920), and 0.802 (95% CI: 0.722-0.882), respectively. A nomogram was developed by integrating significant clinical factors with radiomics features. The AUCs of the nomogram in the training, testing, and external validation sets were 0.872 (95% CI: 0.823-0.921), 0.862 (95% CI: 0.780-0.944), and 0.837 (95% CI: 0.767-0.908), respectively. Survival analysis revealed that the nomogram could effectively stratify patients for recurrence-free survival (Hazard Ratio: 4.329; 95% CI: 3.159-5.934; P < 0.001).

CONCLUSION

The radiomics-derived nomogram presented a promising tool for predicting PNI in GC and held significant prognostic implications.

IMPORTANT FINDINGS

The nomogram functioned as a non-invasive biomarker for determining the PNI status. The predictive performance of the nomogram surpassed that of the clinical model (P < 0.05). Furthermore, patients in the high-risk group stratified by the nomogram had a significantly shorter RFS (P < 0.05).

Perineural Invasion in Cervical Cancer: A Hidden Trail for Metastasis

Perineural invasion (PNI), the neoplastic invasion of nerves, is an often overlooked pathological phenomenon in cervical cancer that is associated with poor clinical outcomes. The occurrence of PNI in cervical cancer patients has limited the promotion of Type C1 surgery. Preoperative prediction of the PNI can help identify suitable patients for Type C1 surgery. However, there is a lack of appropriate preoperative diagnostic methods for PNI, and its pathogenesis remains largely unknown. Here, we dissect the neural innervation of the cervix, analyze the molecular mechanisms underlying the occurrence of PNI, and explore suitable preoperative diagnostic methods for PNI to advance the identification and treatment of this ominous cancer phenotype.

A prediction nomogram for perineural invasion in colorectal cancer patients: a retrospective study

BACKGROUND

Perineural invasion (PNI), as the fifth recognized pathway for the spread and metastasis of colorectal cancer (CRC), has increasingly garnered widespread attention. The preoperative identification of whether colorectal cancer (CRC) patients exhibit PNI can assist clinical practitioners in enhancing preoperative decision-making, including determining the necessity of neoadjuvant therapy and the appropriateness of surgical resection. The primary objective of this study is to construct and validate a preoperative predictive model for assessing the risk of perineural invasion (PNI) in patients diagnosed with colorectal cancer (CRC).

MATERIALS AND METHODS

A total of 335 patients diagnosed with colorectal cancer (CRC) at a single medical center were subject to random allocation, with 221 individuals assigned to a training dataset and 114 to a validation dataset, maintaining a ratio of 2:1. Comprehensive preoperative clinical and pathological data were meticulously gathered for analysis. Initial exploration involved conducting univariate logistic regression analysis, with subsequent inclusion of variables demonstrating a significance level of p < 0.05 into the multivariate logistic regression analysis, aiming to ascertain independent predictive factors, all while maintaining a p-value threshold of less than 0.05. From the culmination of these factors, a nomogram was meticulously devised. Rigorous evaluation of this nomogram's precision and reliability encompassed Receiver Operating Characteristic (ROC) curve analysis, calibration curve assessment, and Decision Curve Analysis (DCA). The robustness and accuracy were further fortified through application of the bootstrap method, which entailed 1000 independent dataset samplings to perform discrimination and calibration procedures.

RESULTS

The results of multivariate logistic regression analysis unveiled independent risk factors for perineural invasion (PNI) in patients diagnosed with colorectal cancer (CRC). These factors included tumor histological differentiation (grade) (OR = 0.15, 95% CI = 0.03-0.74, p = 0.02), primary tumor location (OR = 2.49, 95% CI = 1.21-5.12, p = 0.013), gross tumor type (OR = 0.42, 95% CI = 0.22-0.81, p = 0.01), N staging in CT (OR = 3.44, 95% CI = 1.74-6.80, p < 0.001), carcinoembryonic antigen (CEA) level (OR = 3.13, 95% CI = 1.60-6.13, p = 0.001), and platelet-to-lymphocyte ratio (PLR) (OR = 2.07, 95% CI = 1.08-3.96, p = 0.028).These findings formed the basis for constructing a predictive nomogram, which exhibited an impressive area under the receiver operating characteristic (ROC) curve (AUC) of 0.772 (95% CI, 0.712-0.833). The Hosmer-Lemeshow test confirmed the model's excellent fit (p = 0.47), and the calibration curve demonstrated consistent performance. Furthermore, decision curve analysis (DCA) underscored a substantial net benefit across the risk range of 13% to 85%, reaffirming the nomogram's reliability through rigorous internal validation.

CONCLUSION

We have formulated a highly reliable nomogram that provides valuable assistance to clinical practitioners in preoperatively assessing the likelihood of perineural invasion (PNI) among colorectal cancer (CRC) patients. This tool holds significant potential in offering guidance for treatment strategy formulation.

Nomogram predicts risk of perineural invasion based on serum biomarkers for pancreatic cancer

BACKGROUND

Pancreatic cancer is a fatal tumor, and the status of perineural invasion (PNI) of pancreatic cancer was positively related to poor prognosis including overall survival and recurrence-free survival. This study aims to develop and validate a predictive model based on serum biomarkers to accurately predict the perineural invasion.

MATERIALS AND METHODS

The patients from No.924 Hospital of PLA Joint Logistic Support Force were included. The predictive model was developed in the training cohort using logistic regression analysis, and then tested in the validation cohort. The area under curve (AUC), calibration curves and decision curve analysis were used to validate the predictive accuracy and clinical benefits of nomogram.

RESULTS

A nomogram was developed using preoperative total bilirubin, preoperative blood glucose, preoperative CA19-9. It achieved good AUC values of 0.753 and 0.737 in predicting PNI in training and validation cohorts, respectively. Calibration curves showed nomogram had good uniformity of the practical probability of PNI. Decision curve analyses revealed that the nomogram provided higher diagnostic accuracy and superior net benefit compared to single indicators.

CONCLUSION

The present study constructed and validate a novel nomogram predicted the PNI of resectable PHAC patients with high stability and accuracy. Besides, it could better screen high-risk probability of PNI in these patients, and optimize treatment decision-making.

Combining perineural invasion with staging improve the prognostic accuracy in colorectal cancer: a retrospective cohort study

BACKGROUND

Current guidelines only propose the importance of perineural invasion(PNI) on prognosis in stage II colon cancer. However, the prognostic value of PNI in other stages of colorectal cancer (CRC) is ambiguous.

METHODS

This single-center retrospective cohort study included 3485 CRC patients who underwent primary colorectal resection between January 2013 and December 2016 at the Sixth Affiliated Hospital of Sun Yat-sen University. Associations of PNI with overall survival (OS) and disease-free survival (DFS) were evaluated using multivariable Cox proportional hazards regression models. In addition, interaction analyses were performed to explore the prognostic effects of PNI in different clinical subgroups.

RESULTS

After median follow-up of 61.9 months, we found PNI was associated with poorer OS (adjusted hazard ratio [aHR], 1.290; 95% CI, 1.087-1.531) and DFS (aHR, 1.397; 95% CI, 1.207-1.617), irrespective of tumor stage. Interestingly, the weight of PNI was found second only to incomplete resection in the nomogram for risk factors of OS and DFS in stage II CRC patients. Moreover, OS and DFS were insignificantly different between stage II patients with PNI and stage III patients (both P > 0.05). PNI was found to be an independent prognostic factor of DFS in stage III CRC (aHR: 1.514; 95% CI, 1.211-1.892) as well. Finally, the adverse effect of PNI on OS was more significant in female, early-onset, and diabetes-negative patients than in their counterparts (interaction P = 0.0213, 0.0280, and 0.0186, respectively).

CONCLUSION

PNI was an important prognostic factor in CRC, more than in stage II. The survival of patients with stage II combined with perineural invasion is similar with those with stage III. PNI in stage III CRC also suggests a worse survival.

The complementary role of lymphovascular invasion and perineural invasion in the TNM staging process of rectal cancer

The aim of this study is to clarify the association between lymphovascular invasion (LVI) and/or perineural invasion (PNI) and the clinical characteristics and prognostic importance of rectal cancer, to provide a basis for early adjuvant treatment of rectal cancer. We retrospectively analyzed patients diagnosed with rectal cancer. This study involved rectal cancer tissue samples were obtained by surgical methods. Data on histological form, tumor classification, tumor size, gross growth pattern, blood and lymphatic vessel invasion, and PNI of the slice by HE staining were obtained from pathological examination. Immunohistochemical analysis of tissue samples was performed to determine p53 and EGFR expressions. There were 330 rectal cancer patients included in the study. LVI and/or PNI can be used as a high-risk factor for the prognosis of rectal cancer, predict prognostic survival, and guide adjuvant therapy. The detection rates of LVI and PNI were 32.1% and 16.1%. Differentiation grade, Union for International Cancer Control staging, tumor-lymph node-metastasis staging are significantly related to LVI or PNI. Multivariate logistic regression analysis shows that poor differentiation and N ≥ 1 can be used as independent risk factors and predictive factors for LVI. At the same time, poor differentiation and T > 3 is an independent risk factor for PNI. Only poor differentiation is the risk factor for poor prognosis in Cox risk regression analysis. In addition, the simultaneous occurrence of LVI and PNI is an independent prognostic factor.

Preoperative Prediction of Perineural Invasion Status of Rectal Cancer Based on Radiomics Nomogram of Multiparametric Magnetic Resonance Imaging

Objectives

To compare the predictive performance of different radiomics signatures from multiparametric magnetic resonance imaging (mpMRI), including four sequences when used individually or combined, and to establish and validate an optimal nomogram for predicting perineural invasion (PNI) in rectal cancer (RC) patients.

Methods

Our retrospective study included 279 RC patients without preoperative antitumor therapy (194 in the training dataset and 85 in the test dataset) who underwent preoperative mpMRI scan between January 2017 and January 2021. Among them, 72 cases were PNI-positive. Then, clinical and radiological variables were collected, including carcinoembryonic antigen (CEA), radiological tumour stage (T_1-4), lymph node stage (N_0-2) and so on. Quantitative radiomics features were extracted and selected from oblique axial T₂-weighted imaging (T₂WI), T₁-weighted imaging (T₁WI), apparent diffusion coefficient (ADC), and enhanced T₁WI (T₁CE) sequences. The clinical model was constructed by integrating the final selected clinical and radiological variables. The radiomics signatures included four single-sequence signatures and one fusion signature were built using the respective remaining optimized features. And the nomogram was constructed based on the independent predictors by using multivariable logistic regression. The area under curve (AUC), DeLong test, calibration curve, and decision curve analysis (DCA) were used to evaluate the performance.

Results

Ultimately, 20 radiomics features were retained from the four sequences—T₁WI (n = 4), T₂WI (n = 5), ADC (n = 5), and T₁CE (n = 6)—to construct four single-sequence radiomics signatures and one fusion radiomics signature. The fusion radiomics signature performed better than four single-sequence radiomics signatures and clinical model (AUCs of 0.835 and 0.773 vs. 0.680-0.737 and 0.666-0.709 in the training and test datasets, respectively). The nomogram constructed by incorporating CEA, tumour stage and rad-score performed best, with AUCs of 0.869 and 0.864 in the training and test datasets, respectively. Delong test showed that the nomogram was significantly different from the clinical model and four single-sequence radiomics signatures (P < 0.05). Moreover, calibration curves demonstrated good agreement, and DCA highlighted benefits of the nomogram.

Conclusions

The comprehensive nomogram can preoperatively and noninvasively predict PNI status, provide a convenient and practical tool for treatment strategy, and help optimize individualized clinical decision-making in RC patients.

Development and Validation of a Nomogram for Predicting the Unresolved Risk of Parents of Adolescents With Psychiatric Diagnoses

Evaluating the resolution of parents of ill children can help in taking measures to alleviate their distress in a timely manner and promote children's rehabilitation. This study aims to develop and validate a nomogram for predicting the unresolved risk of parents of adolescents with psychiatric diagnoses. The data for 130 parents (modeling dataset = 90; validation dataset = 40) were collected. A nomogram was first developed to predict the unresolved risk for parents based on the logistic regression analysis in the modeling dataset. The internal and external validation then were conducted through quantifying the performance of the nomogram with respect to discrimination and calibration, respectively, in the modeling and validation datasets. Finally, the clinical use was evaluated through decision curve analyses (DCA) in the overall dataset. In the results, the nomogram consisted of six risk factors and provided a good discrimination with areas under the curve of 0.920 (95% CI, 0.862-0.978) in internal validation and 0.886 (95% CI, 0.786-0.986) in external validation. The calibration with good consistency between the observed probability and predicted probability was also found in both internal and external validation. DCA showed that the nomogram had a good clinical utility. In conclusion, the proposed nomogram exhibited a favorable performance with regard to its predictive accuracy, discrimination capability, and clinical utility, and, thus, can be used as a convenient and reliable tool for predicting the unresolved risk of parents of children with psychiatric diagnoses.

Preoperative Evaluation of Perineural Invasion in Cervical Cancer: Development and Independent Validation of a Novel Predictive Nomogram

BACKGROUND

Perineural invasion (PNI) is associated with a poor prognosis for cervical cancer and influences surgical strategies. However, a preoperative evaluation that can determine PNI in cervical cancer patients is lacking.

METHODS

After 1:1 propensity score matching, 162 cervical cancer patients with PNI and 162 cervical cancer patients without PNI were included in the training set. Forty-nine eligible patients were enrolled in the validation set. The PNI-positive and PNI-negative groups were compared. Multivariate logistic regression was performed to build the PNI prediction nomogram.

RESULTS

Age [odds ratio (OR), 1.028; 95% confidence interval (CI), 0.999-1.058], adenocarcinoma (OR, 1.169; 95% CI, 0.675-2.028), tumor size (OR, 1.216; 95% CI, 0.927-1.607), neoadjuvant chemotherapy (OR, 0.544; 95% CI, 0.269-1.083), lymph node enlargement (OR, 1.953; 95% CI, 1.086-3.550), deep stromal invasion (OR, 1.639; 95% CI, 0.977-2.742), and full-layer invasion (OR, 5.119; 95% CI, 2.788-9.799) were integrated in the PNI prediction nomogram based on multivariate logistic regression. The PNI prediction nomogram exhibited satisfactory performance, with areas under the curve of 0.763 (95% CI, 0.712-0.815) for the training set and 0.860 (95% CI, 0.758-0.961) for the validation set. Moreover, after reviewing the pathological slides of patients in the validation set, four patients initially diagnosed as PNI-negative were recognized as PNI-positive. All these four patients with false-negative PNI were correctly predicted to be PNI-positive (predicted p > 0.5) by the nomogram, which improved the PNI detection rate.

CONCLUSION

The nomogram has potential to assist clinicians when evaluating the PNI status, reduce misdiagnosis, and optimize surgical strategies for patients with cervical cancer.

Radiomics for predicting perineural invasion status in rectal cancer

BACKGROUND

Perineural invasion (PNI), as a key pathological feature of tumor spread, has emerged as an independent prognostic factor in patients with rectal cancer (RC). The preoperative stratification of RC patients according to PNI status is beneficial for individualized treatment and improved prognosis. However, the preoperative evaluation of PNI status is still challenging.

AIM

To establish a radiomics model for evaluating PNI status preoperatively in RC patients.

METHODS

This retrospective study enrolled 303 RC patients in a single institution from March 2018 to October 2019. These patients were classified as the training cohort (n = 242) and validation cohort (n = 61) at a ratio of 8:2. A large number of intra- and peritumoral radiomics features were extracted from portal venous phase images of computed tomography (CT). After deleting redundant features, we tested different feature selection (n = 6) and machine-learning (n = 14) methods to form 84 classifiers. The best performing classifier was then selected to establish Rad-score. Finally, the clinicoradiological model (combined model) was developed by combining Rad-score with clinical factors. These models for predicting PNI were compared using receiver operating characteristic curve (ROC) analysis and area under the ROC curve (AUC).

RESULTS

One hundred and forty-four of the 303 patients were eventually found to be PNI-positive. Clinical factors including CT-reported T stage (cT), N stage (cN), and carcinoembryonic antigen (CEA) level were independent risk factors for predicting PNI preoperatively. We established Rad-score by logistic regression analysis after selecting features with the L1-based method. The combined model was developed by combining Rad-score with cT, cN, and CEA. The combined model showed good performance to predict PNI status, with an AUC of 0.828 [95% confidence interval (CI): 0.774-0.873] in the training cohort and 0.801 (95%CI: 0.679-0.892) in the validation cohort. For comparison of the models, the combined model achieved a higher AUC than the clinical model (cT + cN + CEA) achieved (P < 0.001 in the training cohort, and P = 0.045 in the validation cohort).

CONCLUSION

The combined model incorporating Rad-score and clinical factors can provide an individualized evaluation of PNI status and help clinicians guide individualized treatment of RC patients.

The Heterogeneity of Skewness in T2W-Based Radiomics Predicts the Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

Our study aimed to investigate whether radiomics on MRI sequences can differentiate responder (R) and non-responder (NR) patients based on the tumour regression grade (TRG) assigned after surgical resection in locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT). Eighty-five patients undergoing primary staging with MRI were retrospectively evaluated, and 40 patients were finally selected. The ROIs were manually outlined in the tumour site on T2w sequences in the oblique-axial plane. Based on the TRG, patients were grouped as having either a complete or a partial response (TRG = (0,1), n = 15). NR patients had a minimal or poor nCRT response (TRG = (2,3), n = 25). Eighty-four local first-order radiomic features (RFs) were extracted from tumour ROIs. Only single RFs were investigated. Each feature was selected using univariate analysis guided by a one-tailed Wilcoxon rank-sum. ROC curve analysis was performed, using AUC computation and the Youden index (YI) for sensitivity and specificity. The RF measuring the heterogeneity of local skewness of T2w values from tumour ROIs differentiated Rs and NRs with a p-value ≈ 10⁻⁵; AUC = 0.90 (95%CI, 0.73–0.96); and YI = 0.68, corresponding to 80% sensitivity and 88% specificity. In conclusion, higher heterogeneity in skewness maps of the baseline tumour correlated with a greater benefit from nCRT.

Pretreatment MR-based radiomics nomogram as potential imaging biomarker for individualized assessment of perineural invasion status in rectal cancer

PURPOSE

To investigate whether pretreatment magnetic resonance (MR)-based radiomics nomogram can individualize prediction of perineural invasion (PNI) status in rectal cancer (RC).

MATERIAL AND METHODS

A total of 122 RC patients with pathologically confirmed were classified as training cohort (n = 87) and test cohort (n = 35). 180 radiomics features were extracted from all lesions based on oblique axial T₂WI TSE images. The dimensionality reduction and feature selection in training cohort were realized by the maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) regression model. A predictive model combining radiomics features and clinical risk factors (pathological N stage, pathological LVI status) was established by multivariate logistic regression analysis. The performance of the model was assessed based on its receiver operating characteristic (ROC) curve, nomogram, and calibration.

RESULTS

The developed radiomics nomogram that integrated the radiomics signature and clinical risk factors could provide discrimination in the training and test cohorts. The accuracy and the area under the curve (AUC) for assessing PNI status were 0.82, 0.86, respectively, in the training cohort, while they were 0.71 and 0.85 in the test cohort. The goodness-of-fit of the nomogram was evaluated using the Hosmer-Lemeshow test (p = 0.52 in training cohort and p = 0.24 in test cohort). Decision curve analysis (DCA) showed that the radiomics nomogram was clinically useful.

CONCLUSION

The developed radiomics nomogram might be helpful in the individualized assessment PNI status in patients with RC. This stratification of RC patients according to their PNI status may provide the basis for individualized adjuvant therapy, especially for stage II patients.

Tumor microenvironment characterization in triple-negative breast cancer identifies prognostic gene signature

We aimed to elucidate the landscape of tumor microenvironment (TME) in triple-negative breast cancer (TNBC). Cohorts from Gene Expression Omnibus database (N = 107) and METABRIC (N = 299) were used as the training set and validation set, respectively. TME was evaluated via single-sample gene set enrichment analysis, and unsupervised clustering was used for cluster identification. Consequently, TNBC was classified into two distinct TME clusters (Cluster 1 and Cluster 2) according to predefined immune-related terms. Cluster 1 was characterized by low immune infiltration with poor prognosis; whereas, Cluster 2 was characterized by high immune infiltration with better survival probability. Further, Cluster 1 had larger tumor volumes, while Cluster 2 had smaller tumor volumes. Finally, a TME signature for prognosis stratification in TNBC was developed and validated. In summary, we comprehensively evaluated the TME of TNBC and constructed a TME signature that correlated with prognosis. Our results provide new insights for the immunotherapy of TNBC.

A Combined Epithelial Mesenchymal Transformation and DNA Repair Gene Panel in Colorectal Cancer With Prognostic and Therapeutic Implication

Background

Epithelial mesenchymal transformation (EMT) and DNA repair status represent intrinsic features of colorectal cancer (CRC) and are associated with patient prognosis and treatment responsiveness. We sought to develop a combined EMT and DNA repair gene panel with potential application in patient classification and precise treatment.

Methods

We comprehensively evaluated the EMT and DNA repair patterns of 1,652 CRC patients from four datasets. Unsupervised clustering was used for classification. The clinical features, genetic mutation, tumor mutation load, and chemotherapy as well as immunotherapy sensitivity among different clusters were systematically compared. The least absolute shrinkage and selection operator regression method was used to develop the risk model.

Results

Three distinct CRC clusters were determined. Clustet1 was characterized by down-regulated DNA repair pathways but active epithelial markers and metabolism pathway and had intermediate prognosis. Clustet2 was characterized by down-regulated both epithelial markers and DNA repair pathways and had poor outcome. Clustet3 presented with activation of DNA repair pathway and epithelial markers had favorable prognosis. Clustet1 might benefit form chemotherapy and Clustet3 had a higher response rate to immunotherapy. An EMT and DNA repair risk model related to prognosis and treatment response was developed.

Conclusions

This work developed and validated a combined EMT and DNA repair gene panel for CRC classification, which may be an effective tool for survival prediction and treatment guidance in CRC patients.

Immune landscape and prognostic immune-related genes in KRAS-mutant colorectal cancer patients

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.

A Novel Nomogram for Individually Predicting of Vascular Invasion in Gastric Cancer

PURPOSE

Vascular invasion (VI) is associated with recurrence and is an indicator of poor prognosis in gastric cancer (GC). Pre-operative identification of VI may guide the selection of the optimal surgical approach and assess the requirement for neoadjuvant therapy.

METHODS

A total of 271 patients were retrospectively collected and randomly allocated into the training and validation datasets. The least absolute shrinkage and selection operator regression model was used to select potentially relevant features, and multivariable logistic regression analysis was used to develop the nomogram.

RESULTS

The nomogram consisted of pre-operative serum complement C3 levels, duration of symptoms, pre-operative computed tomography stage, abdominal distension and undifferentiated carcinoma. The nomogram provided good calibration for both the training and the validation set, with area under the curve values of 0.792 and 0.774. Decision curve analysis revealed that the nomogram was clinically useful.

CONCLUSION

The present study constructed a nomogram for the pre-operative prediction of VI in patients with GC. The nomogram may aid the identification of high-risk patients and aid the optimization of pre-operative decision-making.

Nomogram based on clinical characteristics for preoperative prediction of perineural invasion in gastric cancer

Purpose

Gastric cancer (GC) has a poor prognosis and high rate of recurrence. Perineural invasion (PNI) is a prognostic factor in GC that is associated with a high risk of systemic recurrence. Preoperative identification of PNI may facilitate patient stratification and optimal preoperative treatment. We therefore developed and validated a nomogram for the preoperative prediction of PNI.

Methods

We retrospectively collected clinical data from 261 GC patients, who were randomly assigned to training (n = 185) and validation (n = 76) sets. The least absolute shrinkage and selection operator regression model was used to identify potentially relevant clinical parameters, and multivariable logistic regression analysis was used to develop the nomogram.

Results

The nomogram consisted of body mass index, immunoglobulin A level, and computed tomography-based T- and N-stages. Good calibration was observed for both the training and validation sets, with areas under the curve of 0.77 and 0.79, respectively. Decision curve analysis revealed that the nomogram was clinically relevant.

Conclusion

We developed and validated a nomogram for the preoperative prediction of PNI in patients with GC. Our nomogram may facilitate the identification of high-risk patients and optimization of preoperative decision-making.

Development and Validation of a Novel Model for Predicting the 5-Year Risk of Type 2 Diabetes in Patients with Hypertension: A Retrospective Cohort Study

Background

Hypertension is now common in China. Patients with hypertension and type 2 diabetes are prone to severe cardiovascular complications and poor prognosis. Therefore, this study is aimed at establishing an effective risk prediction model to provide early prediction of the risk of new-onset diabetes for patients with a history of hypertension.

Methods

A LASSO regression model was used to select potentially relevant features. Univariate and multivariate Cox regression analyses were used to determine independent predictors. Based on the results of multivariate analysis, a nomogram of the 5-year incidence of T2D in patients with hypertension in mainland China was established. The discriminative capacity was assessed by Harrell's C-index, AUC value, calibration plot, and clinical utility.

Results

After random sampling, 1273 and 415 patients with hypertension were included in the derivation and validation cohorts, respectively. The prediction model included age, body mass index, FPG, and TC as predictors. In the derivation cohort, the AUC value and C-index of the prediction model are 0.878 (95% CI, 0.861-0.895) and 0.862 (95% CI, 0.830-0.894), respectively. In the validation cohort, the AUC value and C-index of the prediction model were 0.855 (95% CI, 0.836-0.874) and 0.841 (95% CI, 0.817-0.865), respectively. The calibration plots demonstrated good agreement between the estimated probability and the actual observation. Decision curve analysis shows that nomograms are clinically useful.

Conclusion

Our nomogram can be used as a simple, affordable, reasonable, and widely implemented tool to predict the 5-year T2D risk of hypertension patients in mainland China. This application helps timely intervention to reduce the incidence of T2D in patients with hypertension in mainland China.

Personalizing prognostic prediction in early-onset Colorectal Cancer

Accurately estimating prognosis based on clinicopathologic variables could improve risk stratification for patients with early-onset colorectal cancer (EOCRC). Our primary goal was to create and validate a survival nomogram with adequate performance for predicting overall survival (OS) in patients with EOCRC. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to identify clinical features statistically related to OS. Then we established and internally validated a survival nomogram based on surveillance, epidemiology and end results (SEER) database (N=23813). A cohort of 77 patients with EOCRC from Renmin Hospital of Wuhan University (RHWU) was employed to detect the external validity of the survival nomogram. Moreover, we compared the predictive accuracy of survival nomogram with TNM stage, and also compared the OS between endoscopy and surgery groups before and after propensity score matching (PSM) among EOCRC patients with early stage (Tis-T1N0M0). We selected seven informative indexes (N stage, M stage, perineural invasion, chemotherapy, surgery primary site, summary stage and tumor grade) for the construction of the survival nomogram. Then the survival nomogram exhibited good discrimination with C-index of 0.829, 0.841 and 0.796 in the SEER training, SEER validation and RHWU validation sets, respectively. Calibration curves showed good concordance between the survival nomogram predictions and actual outcomes for 1-year, 3-year and 5-year OS. Furthermore, the survival nomogram was superior to risk stratification by TNM stage in predicting OS among patients with EOCRC. Early-stage patients treated with endoscopy showed similar survival to those with surgery before and after PSM. We proposed a survival nomogram based on the extensively used parameters to precisely predict OS in EOCRC patients. This survival nomogram will contribute to aid oncologists better risk stratification and prognostication for patients with EOCRC.

Nomogram for predicting overall survival in stage II-III colorectal cancer

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 < .001).

CONCLUSION

We constructed and validated an original predictive nomogram for OS in patients with CRC after surgery, facilitating physicians to appraise the individual survival of postoperative patients accurately and identify high-risk patients who need more aggressive treatment and follow-up strategies.

A nomogram for preoperative prediction of lymphatic infiltration in colorectal cancer: A personalized approach to clinical decision making

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.

FJ, Liu Z, Tang W. Systematic profiling of alternative splicing events and splicing factors in left- and right-sided colon cancer

Left- and right-sided colon cancer (LC and RC) differ substantially in their molecular characteristics and prognoses, and are thus treated using different strategies. We systematically analyzed alternative splicing (AS) events and splicing factors in LC and RC. RNA-seq data were used for genome-wide profiling of AS events that could distinguish LC from RC. The Exon Skip splicing pattern was more common in RC, while the Retained Intron pattern was more common in LC. The AS events that were upregulated in RC were enriched for genes in the axon guidance pathway, while those that were upregulated in LC were enriched for genes in immune-related pathways. Prognostic models based on differentially expressed AS events were built, and a prognostic signature based on these AS events performed well for risk stratification in colon cancer patients. A correlation network of differentially expressed AS events and differentially expressed splicing factors was constructed, and RBM25 was identified as the hub gene in the network. In conclusion, large differences in AS events may contribute to the phenotypic differences between LC and RC. The differentially expressed AS events reported herein could be used as biomarkers and treatment targets for colon cancer.

Identifying gene modules of thyroid cancer associated with pathological stage by weighted gene co-expression network analysis

Thyroid cancer is the most common type of endocrine tumor. The TNM classification remains a standard for treatment determination and predicting prognosis in thyroid cancer. The genes modules associated with the progression of papillary thyroid carcinoma (PTC) were not clear. We applied a weighted gene co-expression network analysis (WGCNA) and differential expression analysis to systematically identified co-expressed gene modules and hub genes associated with PTC progression based on The Cancer Genome Atlas (TCGA) PTC transcriptome sequencing data. An independent validation cohort, GSE27155, was used to evaluate the preservation of gene modules. We identified two co-expressed genes modules associated with progression of PTC. Enrichment analysis indicated that the two modules were enriched in angiogenesis and extracellular matrix organization. DCN, COL1A1, COL1A2, COL5A2 and COL3A1 were hub genes in the co-expressed network. We systematically identified co-expressed gene modules and hub genes associated with PTC progression for the first time, which provided insights into the mechanisms underlying PTC progression and some potential targets for the treatment of PTC.