A Nomogram to Predict Benign/Malignant Mediastinal Lymph Nodes Based on EBUS Sonographic Features.

Background: Endobronchial ultrasound (EBUS) sonographic features help identify benign/malignant lymph nodes while conducting transbronchial needle aspiration (TBNA). This study aims to identify risk factors for malignancy based on EBUS sonographic features and to estimate the risk of malignancy in lymph nodes by constructing a nomogram. Methods: 1082 lymph nodes from 625 patients were randomly enrolled in training (n = 760) and validation (n = 322) sets. The subgroup of EBUS-TBNA postoperative negative lymph nodes (n = 317) was randomly enrolled in a training (n = 224) set and a validation (n = 93) set. Logistic regression analysis was used to identify the EBUS features of malignant lymph nodes. A nomogram was formulated using the EBUS features in the training set and later validated in the validation set. Results: Multivariate analysis revealed that long-axis, short-axis, echogenicity, fusion, and central hilar structure (CHS) were the independent predictors of malignant lymph nodes. Based on these risk factors, a nomogram was constructed. Both the training and validation sets of 5 EBUS features nomogram showed good discrimination, with area under the curve values of 0.880 (sensitivity = 0.829 and specificity = 0.807) and 0.905 (sensitivity = 0.819 and specificity = 0.857). Subgroup multivariate analysis revealed that long-axis, echogenicity, and CHS were the independent predictors of malignancy outcomes of EBUS-TBNA postoperative negative lymph nodes. Based on these risk factors, a nomogram was constructed. Both the training and validation sets of 3 EBUS features nomogram showed good discrimination, with the area under the curve values of 0.890 (sensitivity = 0.882 and specificity = 0.786) and 0.834 (sensitivity = 0.930 and specificity = 0.636). Conclusions: Our novel scoring system based on two nomograms can be utilized to predict malignant lymph nodes.

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.

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

BACKGROUND In colorectal cancer (CRC), perineural invasion (PNI) is usually identified histologically in biopsy or resection specimens and is considered a high-risk feature for recurrence of CRC and is an indicator for adjuvant therapy. Preoperative identification of PNI could help determine the need for adjuvant therapy and the approach to surgical resection. This study aimed to develop and validate a nomogram for the preoperative prediction of PNI in patients with CRC. MATERIAL AND METHODS A total of 664 patients with CRC from a single center were classified into a training dataset (n=468) and a validation dataset (n=196). The least absolute shrinkage and selection operator (LASSO) regression model was used to select potentially relevant features. Multivariate logistic regression analysis was used to develop the nomogram. The performance of the nomogram was assessed based on its calibration, discrimination, and clinical utility. RESULTS The nomogram consisted of five clinical features and provided good calibration and discrimination in the training dataset, with an area under the curve (AUC) of 0.704 (95% CI, 0.657-0.751). Application of the nomogram in the validation cohort showed acceptable discrimination, with the AUC of 0.692 (95% CI, 0.617-0.766) and good calibration. Decision curve analysis (DCA) showed that the nomogram was clinically useful. CONCLUSIONS The nomogram developed in this study might allow clinicians to predict the risk of PNI in patients with CRC preoperatively. The nomogram showed favorable discrimination and calibration values, which may help optimize preoperative treatment decision-making for patients with CRC.

Preoperative Neutrophil-BMI Ratio As a Promising New Marker for Predicting Tumor Outcomes in Colorectal Cancer.

Background: Inflammation and nutritional status are highly associated with colorectal cancer (CRC) prognosis. This study aimed to evaluate the prognostic value of the preoperative neutrophil-BMI ratio (NBR) in patients with CRC. Methods: A retrospective analysis was performed on 2471 patients with CRC who underwent surgical resection between 2004 and 2019. Patients were divided into two groups based on the cutoff value for NBR. Cox regression and Kaplan-Meier curves were used to evaluate overall survival (OS). Results: High NBR was associated with female sex, low BMI, colon, right-sided CRC, poor differentiation, T3 to 4 stage, M1 to 2 stage, high carcinoembryonic antigen (CEA) level, III-IV stage, microsatellite instability (MSI), and no adjuvant chemotherapy (all P < .05). The high NBR group had a shorter OS than the low NBR group. Female and right sided patients with CRC and with high NBR had a worse prognosis. Univariate Cox regression suggested that NBR was significantly associated with poor prognosis. Multivariate analysis confirmed that age (P = .019,HR:1.012), differentiation (P = .001,HR:1.306), TNM stage (P < .001,HR:2.432), CEA (P = .014,HR:1.001), and NBR (P < .001, HR: 3.309) were independent poor prognostic factors for OS. Subgroup univariate analysis indicated that female patients with high NBR had a worse prognosis. A nomogram composed of TNM stage, CEA, and NBR was developed, and internal validation was based on female patients with CRC. The nomogram provided good discrimination for both the training and validation sets, with area under the curve values of 0.79 and 0.769, respectively. Conclusions: High preoperative levels of NBR are indicators of poor prognosis in patients with CRC.

Profiles of alternative splicing events in the diagnosis and prognosis of Gastric Cancer.

Background: Gastric cancer (GC) is a heterogeneous disease, and alternative splicing (AS) is a powerful universal transcriptional regulatory mechanism that contributes to the occurrence and development of cancer. However, the systematic analysis of AS events in GC is lacking; therefore, further studies are needed. Methods: Genome-wide analysis of AS events was performed using RNA-Seq data to evaluate the difference between GC and adjacent tissues at the AS level. Prognostic signatures based on differentially expressed alternative splicing (DEAS) events and a correlation network between DEAS and genes were built. Results: We identified 48,141 AS events, of which 2325 showed differential expression patterns. The parental genes before DEAS events play an essential role in regulating GC-related processes such as ribosome (FDR < 0.0001) and thermogenesis (FDR = 0.0002). There were 76 survival-associated DEAS cases. Stratifying patients according to the percent spliced in index value of six types of splicing patterns formed significant Kaplan-Meier curves in the overall survival analysis. A prognostic feature based on DEAS performed well for stratification in patients with GC. Conclusion: The present study will enrich our understanding regarding the distinction of GC and provide a generous amount of biomarkers and potential targets for the treatment of GC.

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.

Immune infiltration and immune gene signature predict the response to fluoropyrimidine-based chemotherapy in colorectal cancer 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.

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.

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.

A nomogram to predict vascular invasion before resection of colorectal cancer.

Vascular invasion (VI) is an important feature for systemic recurrence and an indicator for the application of adjuvant therapy in colorectal cancer (CRC). Preoperative knowledge of VI is important in determining whether adjuvant therapy is necessary, as well as the adequacy of surgical resection. In the present study, a predictive nomogram for VI in patients with CRC was constructed. The prediction model consisted of 664 eligible patients with CRC, who were divided into a training set (n=468) and a validation set (n=196). Data were collected between August 2013 and April 2018. The feature selection model was established using the least absolute shrinkage and selection operator regression model. Multivariable logistic regression analysis was used to construct the predictive nomogram. The performance of the nomogram was evaluated by calibration, discrimination and clinical usefulness. Differentiation, computed tomography (CT)-based on N stage (CT N stage), hemameba and tumor distance from the anus (cm) were integrated into the nomogram. The nomogram exhibited good discrimination, with an area under the curve (AUC) of 0.731 and good calibration. Application of the nomogram in the validation cohort showed acceptable discrimination, with an AUC of 0.710 and good calibration. Decision curve analysis revealed that the nomogram was clinically useful. These findings suggests, to the best of our knowledge, that this may be the first nomogram for individual preoperative prediction of VI in patients with CRC, which may promote preoperative optimization strategies for this selected group of patients.