Mining and exploration of rehabilitation nursing targets for colorectal cancer.

BACKGROUND: There are often subtle early symptoms of colorectal cancer, a common malignancy of the intestinal tract. However, it is not yet clear how MYC and NCAPG2 are involved in colorectal cancer. METHOD: We obtained colorectal cancer datasets GSE32323 and GSE113513 from the Gene Expression Omnibus (GEO). After downloading, we identified differentially expressed genes (DEGs) and performed Weighted Gene Co-expression Network Analysis (WGCNA). We then undertook functional enrichment assay, gene set enrichment assay (GSEA) and immune infiltration assay. Protein-protein interaction (PPI) network construction and analysis were undertaken. Survival analysis and Comparative Toxicogenomics Database (CTD) analysis were conducted. A gene expression heat map was generated. We used TargetScan to identify miRNAs that are regulators of DEGs. RESULTS: 1117 DEGs were identified. Their predominant enrichment in activities like the cellular phase of the cell cycle, in cell proliferation, in nuclear and cytoplasmic localisation and in binding to protein-containing complexes was revealed by Gene Ontology (GO). When the enrichment data from GSE32323 and GSE113513 colon cancer datasets were merged, the primary enriched DEGs were linked to the cell cycle, protein complex, cell cycle control, calcium signalling and P53 signalling pathways. In particular, MYC, MAD2L1, CENPF, UBE2C, NUF2 and NCAPG2 were identified as highly expressed in colorectal cancer samples. Comparative Toxicogenomics Database (CTD) demonstrated that the core genes were implicated in the following processes: colorectal neoplasia, tumour cell transformation, inflammation and necrosis. CONCLUSIONS: High MYC and NCAPG2 expression has been observed in colorectal cancer, and increased MYC and NCAPG2 expression correlates with worse prognosis.

SMARCA4 is a haploinsufficient B cell lymphoma tumor suppressor that fine-tunes centrocyte cell fate decisions.

SMARCA4 encodes one of two mutually exclusive ATPase subunits in the BRG/BRM associated factor (BAF) complex that is recruited by transcription factors (TFs) to drive chromatin accessibility and transcriptional activation. SMARCA4 is among the most recurrently mutated genes in human cancer, including ∼30% of germinal center (GC)-derived Burkitt lymphomas. In mice, GC-specific Smarca4 haploinsufficiency cooperated with MYC over-expression to drive lymphomagenesis. Furthermore, monoallelic Smarca4 deletion drove GC hyperplasia with centroblast polarization via significantly increased rates of centrocyte recycling to the dark zone. Mechanistically, Smarca4 loss reduced the activity of TFs that are activated in centrocytes to drive GC-exit, including SPI1 (PU.1), IRF family, and NF-κB. Loss of activity for these factors phenocopied aberrant BCL6 activity within murine centrocytes and human Burkitt lymphoma cells. SMARCA4 therefore facilitates chromatin accessibility for TFs that shape centrocyte trajectories, and loss of fine-control of these programs biases toward centroblast cell-fate, GC hyperplasia and lymphoma.

A prognostic matrix code defines functional glioblastoma phenotypes and niches.

Interactions among tumor, immune and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, we characterize functional and clinical relevance of genes encoding CMPs in GBM at bulk, single cell, and spatial anatomical resolution. We identify a "matrix code" for genes encoding CMPs whose expression levels categorize GBM tumors into matrisome-high and matrisome-low groups that correlate with worse and better patient survival, respectively. The matrisome enrichment is associated with specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells and immune checkpoint gene expression. Anatomical and single cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative anatomic structures that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene matrisome signature that retains and further refines the prognostic value of genes encoding CMPs and, importantly, potentially predicts responses to PD1 blockade in clinical trials for GBM. The matrisome gene expression profiles provide potential biomarkers of functionally relevant GBM niches that contribute to mesenchymal-immune cross talk and patient stratification which could be applied to optimize treatment responses.

A single-cell atlas of CD19 chimeric antigen receptor T cells.

Li et al. present a resource of single-cell RNA sequencing (scRNA-seq) data from the infusion products of relapsed or refractory large B cell lymphoma (rrLBCL) patients treated with standard-of-care axicabtagene ciloleucel and identify features that are significantly different between products from responders and non-responders at 3-month followup by PET/CT, an important landmark for long-term outcomes.

Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study.

Introduction: Lenvatinib plus an anti-PD-1 antibody has shown promising antitumor effects in patients with advanced hepatocellular carcinoma (HCC), but with clinical benefit limited to a subset of patients. We developed and validated a radiomic-based model to predict objective response to this combination therapy in advanced HCC patients. Methods: Patients (N = 170) who received first-line combination therapy with lenvatinib plus an anti-PD-1 antibody were retrospectively enrolled from 9 Chinese centers; 124 and 46 into the training and validation cohorts, respectively. Radiomic features were extracted from pretreatment contrast-enhanced MRI. After feature selection, clinicopathologic, radiomic, and clinicopathologic-radiomic models were built using a neural network. The performance of models, incremental predictive value of radiomic features compared with clinicopathologic features and relationship between radiomic features and survivals were assessed. Results: The clinicopathologic model modestly predicted objective response with an AUC of 0.748 (95% CI: 0.656-0.840) and 0.702 (95% CI: 0.547-0.884) in the training and validation cohorts, respectively. The radiomic model predicted response with an AUC of 0.886 (95% CI: 0.815-0.957) and 0.820 (95% CI: 0.648-0.984), respectively, with good calibration and clinical utility. The incremental predictive value of radiomic features to clinicopathologic features was confirmed with a net reclassification index of 47.9% (p < 0.001) and 41.5% (p = 0.025) in the training and validation cohorts, respectively. Furthermore, radiomic features were associated with overall survival and progression-free survival both in the training and validation cohorts, but modified albumin-bilirubin grade and neutrophil-to-lymphocyte ratio were not. Conclusion: Radiomic features extracted from pretreatment MRI can predict individualized objective response to combination therapy with lenvatinib plus an anti-PD-1 antibody in patients with unresectable or advanced HCC, provide incremental predictive value over clinicopathologic features, and are associated with overall survival and progression-free survival after initiation of this combination regimen.

Prolonged cytopenia following CD19 CAR T cell therapy is linked with bone marrow infiltration of clonally expanded IFNγ-expressing CD8 T cells.

Autologous anti-CD19 chimeric antigen receptor T cell (CAR T) therapy is highly effective in relapsed/refractory large B cell lymphoma (rrLBCL) but is associated with toxicities that delay recovery. While the biological mechanisms of cytokine release syndrome and neurotoxicity have been investigated, the pathophysiology is poorly understood for prolonged cytopenia, defined as grade ≥3 cytopenia lasting beyond 30 days after CAR T infusion. We performed single-cell RNA sequencing of bone marrow samples from healthy donors and rrLBCL patients with or without prolonged cytopenia and identified significantly increased frequencies of clonally expanded CX3CR1hi cytotoxic T cells, expressing high interferon (IFN)-γ and cytokine signaling gene sets, associated with prolonged cytopenia. In line with this, we found that hematopoietic stem cells from these patients expressed IFN-γ response signatures. IFN-γ deregulates hematopoietic stem cell self-renewal and differentiation and can be targeted with thrombopoietin agonists or IFN-γ-neutralizing antibodies, highlighting a potential mechanism-based approach for the treatment of CAR T-associated prolonged cytopenia.

Recurrent composite markers of cell types and states.

Determining concise sets of genomic markers that identify cell types and states within tissue ecosystems remains challenging. To address this challenge, we developed Recurrent Composite Markers for Biological Identities with Neighborhood Enrichment (RECOMBINE). Validations of RECOMBINE with simulation and transcriptomics data in bulk, single-cell and spatial resolutions demonstrated the method's ability for unbiased selection of composite markers that characterize biological subpopulations. RECOMBINE captured markers of mouse visual cortex from single-cell RNA sequencing data and provided a gene panel for targeted spatial transcriptomics profiling. RECOMBINE identified composite markers of CD8 T cell states including GZMK + HAVCR2 - effector memory cells associated with anti-PD1 therapy response. The method outperformed differential gene expression analysis in characterizing a rare cell subpopulation within mouse intestine. Using RECOMBINE, we uncovered hierarchical gene programs of inter- and intra-tumoral heterogeneity in breast and skin tumors. In conclusion, RECOMBINE offers a data-driven approach for unbiased selection of composite markers, resulting in improved interpretation, discovery, and validation of cell types and states.

A prognostic matrix code defines functional glioblastoma phenotypes and niches.

Interactions among tumor, immune and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, we characterize functional and clinical relevance of genes encoding CMPs in GBM at bulk, single cell, and spatial anatomical resolution. We identify a "matrix code" for genes encoding CMPs whose expression levels categorize GBM tumors into matrisome-high and matrisome-low groups that correlate with worse and better survival, respectively, of patients. The matrisome enrichment is associated with specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells and immune checkpoint gene expression. Anatomical and single cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative anatomic structures that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene matrisome signature that retains and further refines the prognostic value of genes encoding CMPs and, importantly, potentially predicts responses to PD1 blockade in clinical trials for GBM. The matrisome gene expression profiles may provide biomarkers of functionally relevant GBM niches that contribute to mesenchymal-immune cross talk and patient stratification to optimize treatment responses.

Computed tomography-based radiomics machine learning classifiers to differentiate type I and type II epithelial ovarian cancers.

OBJECTIVES: To compare computed tomography (CT)-based radiomics for preoperatively differentiating type I and II epithelial ovarian cancers (EOCs) using different machine learning classifiers and to construct and validate the best diagnostic model. METHODS: A total of 470 patients with EOCs were included retrospectively. Patients were divided into a training dataset (N = 329) and a test dataset (N = 141). A total of 1316 radiomics features were extracted from the portal venous phase of contrast-enhanced CT images for each patient, followed by dimension reduction of the features. The support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), naïve Bayes (NB), logistic regression (LR), and eXtreme Gradient Boosting (XGBoost) classifiers were trained to obtain the radiomics signatures. The performance of each radiomics signature was evaluated and compared by the area under the receiver operating characteristic curve (AUC) and relative standard deviation (RSD). The best radiomics signature was selected and combined with clinical and radiological features to establish a combined model. The diagnostic value of the combined model was assessed. RESULTS: The LR-based radiomics signature performed well in the test dataset, with an AUC of 0.879 and an accuracy of 0.773. The combined model performed best in both the training and test datasets, with AUCs of 0.900 and 0.934 and accuracies of 0.848 and 0.823, respectively. CONCLUSION: The combined model showed the best diagnostic performance for distinguishing between type I and II EOCs preoperatively. Therefore, it can be a useful tool for clinical individualized EOC classification. KEY POINTS: • Radiomics features extracted from computed tomography (CT) could be used to differentiate type I and II epithelial ovarian cancers (EOCs). • Machine learning can improve the performance of differentiating type I and II EOCs. • The combined model exhibited the best diagnostic capability over the other models in both the training and test datasets.

A Multiparametric Fusion Radiomics Signature Based on Contrast-Enhanced MRI for Predicting Early Recurrence of Hepatocellular Carcinoma.

Objectives: The postoperative early recurrence (ER) rate of hepatocellular carcinoma (HCC) is 50%, and no highly reliable predictive tool has been developed yet. The aim of this study was to develop and validate a predictive model with radiomics analysis based on multiparametric magnetic resonance (MR) images to predict early recurrence of HCC. Methods: In total, 302 patients (training dataset: n = 211; validation dataset: n = 91) with pathologically confirmed HCC who underwent preoperative MR imaging were enrolled in this study. Three-dimensional regions of interest of the entire lesion were accessed by manually drawing along the tumor margins on the multiple sequences of MR images. Least absolute shrinkage and selection operator Cox regression were then applied to select ER-related radiomics features and construct radiomics signatures. Univariate analysis and multivariate Cox regression analysis were used to identify the significant clinico-radiological factors and establish a clinico-radiological model. A predictive model of ER incorporating the fusion radiomics signature and clinico-radiological risk factors was constructed. The diagnostic performance and clinical utility of this model were measured by receiver-operating characteristic (ROC), calibration curve, and decision curve analyses. Results: The fusion radiomics signature consisting of 6 radiomics features achieved good prediction performance (training dataset: AUC = 0.85, validation dataset: AUC = 0.79). The predictive model of ER integrating clinico-radiological risk factors and the fusion radiomics signature improved the prediction efficacy with AUCs of 0.91 and 0.87 in the training and validation datasets, respectively. Furthermore, the nomogram and ER risk stratification system based on the predictive model demonstrated encouraging predictions of the individualized risk of ER and gave three risk groups with low, intermediate, or high risk of ER. Conclusions: The proposed predictive model incorporating clinico-radiological factors and the fusion radiomics signature derived from multiparametric MR images may be an effective tool for the individualized prediction of postoperative ER in patients with HCC.

BET inhibition induces vulnerability to MCL1 targeting through upregulation of fatty acid synthesis pathway in breast cancer.

Therapeutic options for treatment of basal-like breast cancers remain limited. Here, we demonstrate that bromodomain and extra-terminal (BET) inhibition induces an adaptive response leading to MCL1 protein-driven evasion of apoptosis in breast cancer cells. Consequently, co-targeting MCL1 and BET is highly synergistic in breast cancer models. The mechanism of adaptive response to BET inhibition involves the upregulation of lipid synthesis enzymes including the rate-limiting stearoyl-coenzyme A (CoA) desaturase. Changes in lipid synthesis pathway are associated with increases in cell motility and membrane fluidity as well as re-localization and activation of HER2/EGFR. In turn, the HER2/EGFR signaling results in the accumulation of and vulnerability to the inhibition of MCL1. Drug response and genomics analyses reveal that MCL1 copy-number alterations are associated with effective BET and MCL1 co-targeting. The high frequency of MCL1 chromosomal amplifications (>30%) in basal-like breast cancers suggests that BET and MCL1 co-targeting may have therapeutic utility in this aggressive subtype of breast cancer.

Follicular Lymphoma Microenvironment Characteristics Associated with Tumor Cell Mutations and MHC Class II Expression.

Follicular lymphoma (FL) is a B-cell malignancy with a complex tumor microenvironment that is rich in nonmalignant immune cells. We applied single-cell RNA sequencing to characterize the diverse tumor and immune cell populations of FL and identified major phenotypic subsets of FL T cells, including a cytotoxic CD4 T-cell population. We characterized four major FL subtypes with differential representation or relative depletion of distinct T-cell subsets. By integrating exome sequencing, we observed that somatic mutations are associated with, but not definitive for, reduced MHC expression on FL cells. In turn, expression of MHCII genes by FL cells was associated with significant differences in the proportions and targetable immunophenotypic characteristics of T cells. This provides a classification framework of the FL microenvironment in association with FL genotypes and MHC expression, and informs different potential immunotherapeutic strategies based upon tumor cell MHCII expression. SIGNIFICANCE: We have characterized the FL-infiltrating T cells, identified cytotoxic CD4 T cells as an important component that is associated with tumor cell-intrinsic characteristics, and identified sets of targetable immune checkpoints on T cells that differed from FLs with normal versus low MHC expression. See related commentary by Melnick, p. 374. This article is highlighted in the In This Issue feature, p. 369.

The Efficacy of Percutaneous Transhepatic Gallbladder Drainage Combined with Gallbladder-Preserving Cholecystolithotomy in High-Risk Patients with Acute Calculous Cholecystitis.

Objective: This study aimed to investigate the value of combining percutaneous transhepatic gallbladder drainage (PTGD) with gallbladder-preserving cholecystolithotomy (GPC) in high-risk patients with acute calculous cholecystitis. Methods: Clinical data from 74 high-risk patients with acute calculous cholecystitis, admitted to our hospital between October 2018 and September 2021, were analyzed retrospectively. All the patients underwent PTGD, and 59 of them underwent delayed cholecystectomy, while 14 patients were subjected to GPC 8-12 weeks after the PTGD; one patient, whose life expectancy was fewer than 6 months, was not treated for gallstones after PTGD. Results: In all 74 patients, symptom remission was achieved after the PTGD therapy, and the incidence of catheter-related complications was 10.8%. Among the 59 patients who underwent delayed cholecystectomy (DC) after PTGD, there was a complication incidence of 6.8%. Of the 14 patients who underwent GPC after the PTGD, 13 patients were subjected to the removal of drainage tubes, 1 patient received cholecystostomy catheter draining externally, and two patients (14.3%) had complications. There were no perioperative deaths. Conclusion: Percutaneous transhepatic gallbladder drainage, combined with GPC, is a safe and effective treatment that is suitable for high-risk patients with acute calculous cholecystitis who cannot receive DC. This combined method allows for early acute cholecystitis to settle, helps to remove gallstones at a later stage, and solves the problem of long-term tube drainage after PTGD.

AHCYL1 Is a Novel Biomarker for Predicting Prognosis and Immunotherapy Response in Colorectal Cancer.

Background: Colorectal cancer (CRC) is the third most frequent cancer worldwide. The AHCYL1 gene is required for CNV and has a close association with the tumor immune microenvironment. However, the predictive value of the AHCYL1 gene in patients with CRC remains unknown. Methods: AHCYL1 gene with prognostic potential was comprehensively analyzed. Next, using LASSO Cox regression, we fully examined and integrated the AHCYL1 and AHCYL1-related genes from TCGA database. Meanwhile, TCGA database was used to study the connection between AHCYL1 and the tumor immune microenvironment and tumor mutation burden (TMB) in CRC. The influence of AHCYL1 in tumor growth and the recruiting ability of CD8+ T cells were verified, respectively, in vivo and in tissues. To ascertain the connection between AHCYL1 and AHCYL1-related genes and the prognosis of CRC, a prognostic model was created and validated. Result: We demonstrated that AHCYL1 has a differential expression and patients with AHCYL1 deletion get shorter survival in CRC. Additionally, the tissues without AHCYL1 have a weaker ability to recruit the natural killer (NK) cell, CD8+ T cells, and tumor-infiltrating lymphocytes (TILs) and response to immunotherapy. Additionally, knockdown of AHCYL1 promoted tumor growth in the CRC mouse model and recruited lower CD8+ T cells in CRC tissues. TCGA database was used to classify patients into low- and high-risk categories based on the expression of four genes. Meanwhile, we discovered an association between the low-risk group and a lower TMB and a higher response to immunotherapy. Finally, a predictive nomogram based on these genes was developed and verified, yielding a C-index of 0.74. Conclusion: For CRC patients, the prognostic model based on AHCYL1 and AHCYL1-related genes showed a high predictive performance in terms of prognosis and immunotherapy response.

A Predictive Nomogram of Early Recurrence for Patients with AFP-Negative Hepatocellular Carcinoma Underwent Curative Resection.

Background: Alpha-fetoprotein-negative (<20 ng/mL) hepatocellular carcinoma (AFP-NHCC) cannot be easily diagnosed in clinical practice, which may affect early treatment and prognosis. Furthermore, there are no reliable tools for the prediction of AFP-NHCC early recurrence that have been developed currently. The objective of this study was to identify the independent risk factors for AFP-NHCC and construct an individual prediction nomogram of early recurrence of these patients who underwent curative resection. Methods: A retrospective study of 199 patients with AFP-NHCC who had undergone curative resection and another 231 patients with AFP-positive HCC were included in case-controlled analyses. All AFP-NHCC patients were randomly divided into training and validation datasets at a ratio of 7:3. The univariate and multivariate Cox proportional hazards regression analyses were applied to identify the risk factors, based on which the predictive nomogram of early recurrence was constructed in the training dataset. The area under the curve (AUC), calibration curve, and decision curve was used to evaluate the predictive performance and discriminative ability of the nomogram, and the results were validated in the validation dataset. Results: Compared to AFP-positive patients, the AFP-negative group with lower values of laboratory parameters, lower tumor aggressiveness, and less malignant magnetic resonance (MR) imaging features. AST (HR = 2.200, p = 0.009), tumor capsule (HR = 0.392, p = 0.017), rim enhancement (HR = 2.825, p = 0.002) and TTPVI (HR = 5.511, p < 0.001) were independent predictors for early recurrence of AFP-NHCC patients. The nomogram integrated these independent predictors and achieved better predictive performance with AUCs of 0.89 and 0.85 in the training and validation datasets, respectively. The calibration curve and decision curve analysis both demonstrated better predictive efficacy and discriminative ability of the nomogram. Conclusions: The nomogram based on the multivariable Cox proportional hazards regression analysis presented accurate individual prediction for early recurrence of AFP-NHCC patients after surgery. This nomogram could assist physicians in personalized treatment decision-making for patients with AFP-NHCC.

Precision Combination Therapies Based on Recurrent Oncogenic Coalterations.

Cancer cells depend on multiple driver alterations whose oncogenic effects can be suppressed by drug combinations. Here, we provide a comprehensive resource of precision combination therapies tailored to oncogenic coalterations that are recurrent across patient cohorts. To generate the resource, we developed Recurrent Features Leveraged for Combination Therapy (REFLECT), which integrates machine learning and cancer informatics algorithms. Using multiomic data, the method maps recurrent coalteration signatures in patient cohorts to combination therapies. We validated the REFLECT pipeline using data from patient-derived xenografts, in vitro drug screens, and a combination therapy clinical trial. These validations demonstrate that REFLECT-selected combination therapies have significantly improved efficacy, synergy, and survival outcomes. In patient cohorts with immunotherapy response markers, DNA repair aberrations, and HER2 activation, we have identified therapeutically actionable and recurrent coalteration signatures. REFLECT provides a resource and framework to design combination therapies tailored to tumor cohorts in data-driven clinical trials and preclinical studies. SIGNIFICANCE: We developed the predictive bioinformatics platform REFLECT and a multiomics- based precision combination therapy resource. The REFLECT-selected therapies lead to significant improvements in efficacy and patient survival in preclinical and clinical settings. Use of REFLECT can optimize therapeutic benefit through selection of drug combinations tailored to molecular signatures of tumors. See related commentary by Pugh and Haibe-Kains, p. 1416. This article is highlighted in the In This Issue feature, p. 1397.

Simulation and Experimental Study on Roll-Forming Limit of Cup.

Roll forming can improve the material utilization rate and production efficiency of cups with a curved rotary profile, but there is no basis for the determination of forming limit. The DEFORM-3D software was used to simulate the roll forming of cups. The influence of the billet wall thickness and bottom thickness, coefficient of friction, radius of roller, and the fillet radius of the punch on the forming limit was studied, and the damage value and velocity vector were analyzed. The results showed that the forming limit of the billet's wall thickness in roll forming for a cup is about 62%. With the increase of the ratio of the formed cup's wall thickness to the billet's bottom thickness, the forming limit of wall thickness will be slightly reduced. A larger radius of roller, fillet radius of punch, and friction coefficient between punch and billet and a smaller friction coefficient between roller and billet are good for decreasing the damage value and improving the roll-forming limit. According to the numerical simulation results, the roll-forming limit diagram of cups is established, and the accuracy of the forming limit diagram is verified by experiments.

Preoperatively predicting the pathological types of acute appendicitis using machine learning based on peripheral blood biomarkers and clinical features: a retrospective study.

BACKGROUND: This study aimed to establish machine learning models for preoperative prediction of the pathological types of acute appendicitis. METHODS: Based on histopathology, 136 patients with acute appendicitis were included and divided into three types: acute simple appendicitis (SA, n=8), acute purulent appendicitis (PA, n=104), and acute gangrenous or perforated appendicitis (GPA, n=24). Patients with SA/PA and PA/GPA were divided into training (70%) and testing (30%) sets. Statistically significant features (P<0.05) for pathology prediction were selected by univariate analysis. According to clinical and laboratory data, machine learning logistic regression (LR) models were built. Area under receiver operating characteristic curve (AUC) was used for model assessment. RESULTS: Nausea and vomiting, abdominal pain time, neutrophils (NE), CD4+ T cell, helper T cell, B lymphocyte, natural killer (NK) cell counts, and CD4+/CD8+ ratio were selected features for the SA/PA group (P<0.05). Nausea and vomiting, abdominal pain time, the highest temperature, CD8+ T cell, procalcitonin (PCT), and C-reactive protein (CRP) were selected features for the PA/GPA group (P<0.05). By using LR models, the blood markers can distinguish SA and PA (training AUC =0.904, testing AUC =0.910). To introduce additional clinical features, the AUC for the testing set increased to 0.926. In the PA/GPA prediction model, AUC with blood biomarkers was 0.834 for the training and 0.821 for the testing set. Combining with clinical features, the AUC for the testing set increased to 0.854. CONCLUSIONS: Peripheral blood biomarkers can predict the pathological type of SA from PA and GPA. Introducing clinical symptoms could further improve the prediction performance.

CT-Based Radiomics Signature: A Potential Biomarker for Predicting Postoperative Recurrence Risk in Stage II Colorectal Cancer.

Objective: To evaluate whether a radiomics signature could improve stratification of postoperative risk and prediction of chemotherapy benefit in stage II colorectal cancer (CRC) patients. Material and Methods: This retrospective study enrolled 299 stage II CRC patients from January 2010 to December 2015. Based on preoperative portal venous-phase CT scans, radiomics features were generated and selected to build a radiomics score (Rad-score) using the Least Absolute Shrinkage and Selection Operator (LASSO) method. The minority group was balanced by the synthetic minority over-sampling technique (SMOTE). Predictive models were built with the Rad-score and clinicopathological factors, and the area under the curve (AUC) was used to evaluate their performance. A nomogram was also constructed for predicting 3-year disease-free survival (DFS). The performance of the nomogram was assessed with a concordance index (C-index) and calibration plots. Results: Overall, 114 features were selected to construct the Rad-score, which was significantly associated with the 3-year DFS. Multivariate analysis demonstrated that the Rad-score, CA724 level, mismatch repair status, and perineural invasion were independent predictors of recurrence. Results showed that the Rad-score can classify patients into high-risk and low-risk groups in the training cohort (AUC 0.886) and the validation cohort (AUC 0.874). On this basis, a nomogram that integrated the Rad-score and clinical variables demonstrated superior performance (AUC 0.954, 0.906) than the clinical model alone (AUC 0.765, 0.705) in the training and validation cohorts, respectively. The C-index of the nomogram was 0.872, and the performance was acceptable. Conclusion: Our radiomics-based model can reliably predict recurrence risk in stage II CRC patients and potentially provide complementary prognostic value to the traditional clinicopathological risk factors for better identification of patients who are most likely to benefit from adjuvant therapy. The proposed nomogram promises to be an effective tool for personalized postoperative surveillance for stage II CRC patients.