Impact of treatment on the prognosis of childhood in hepatoblastoma: A SEER based analysis.

Background: The prognosis of patients with hepatoblastoma has been unsatisfactory. This study analyzed the effects of different treatment methods on cancer-specific survival (CSS) in children with hepatoblastoma. Method: From 2000 to 2018, patients with hepatoblastoma were included in the Surveillance, Epidemiology, and End Results (SEER) database. CSS was estimated using the Kaplan-Meier method. Cox regression analysis assessed prognostic factors. The predictive models were validated using the concordance index (C-index), calibration curve and receiver operating characteristic (ROC) curve. Result: Of the 785 included patients, 730 (93.0 %) underwent chemotherapy, 516 (65.7 %) underwent liver tumour resection and 129 (16.4 %) underwent liver transplantation. Both chemotherapy and surgery could significantly improve the CSS rate (all p < 0.001). However, there was no difference in CSS rate between the two surgical methods (liver tumour resection and liver transplantation) (p = 0.613). Further subgroup analysis revealed that children who underwent liver tumour resection or liver transplantation based on chemotherapy (all p > 0.05) had a similar prognosis. Multivariate analysis revealed that age (p = 0.003), race (p = 0.001), operative method (p < 0.001), chemotherapy (p < 0.001), distant metastasis (p < 0.001) and tumour size (p < 0.001) were independent factors related to CSS. The C-index of the new nomogram was 0.759, and its consistency was good. The ROC curves verified that the nomogram had a better prediction ability for 1-, 3- and 5-year CSS rates. Conclusion: In children with hepatoblastoma, there was no statistically significant difference in CSS between chemotherapy combined with liver transplantation and liver tumour resection. The nomogram we constructed demonstrated satisfactory CSS prediction ability.

Dynamics of peripheral blood inflammatory index predict tumor pathological response and survival among patients with locally advanced non-small cell lung cancer who underwent neoadjuvant immunochemotherapy: a multi-cohort retrospective study.

Background: Static tumor features before initiating anti-tumor treatment were insufficient to distinguish responding from non-responding tumors under the selective pressure of immuno-therapy. Herein we investigated the longitudinal dynamics of peripheral blood inflammatory indexes (dPBI) and its value in predicting major pathological response (MPR) in non-small cell lung cancer (NSCLC). Methods: A total of 147 patients with NSCLC who underwent neoadjuvant immunochemotherapy were retrospectively reviewed as training cohort, and 26 NSCLC patients from a phase II trial were included as validation cohort. Peripheral blood inflammatory indexes were collected at baseline and as posttreatment status; their dynamics were calculated as their posttreatment values minus their baseline level. Least absolute shrinkage and selection operator algorithm was utilized to screen out predictors for MPR, and a MPR score was integrated. We constructed a model incorporating this MPR score and clinical predictors for predicting MPR and evaluated its predictive capacity via the area under the curve (AUC) of the receiver operating characteristic and calibration curves. Furthermore, we sought to interpret this MPR score in the context of micro-RNA transcriptomic analysis in plasma exosomes for 12 paired samples (baseline and posttreatment) obtained from the training cohort. Results: Longitudinal dynamics of monocyte-lymphocyte ratio, platelet-to-lymphocyte ratio, platelet-to-albumin ratio, and prognostic nutritional index were screened out as significant indicators for MPR and a MPR score was integrated, which was further identified as an independent predictor of MPR. Then, we constructed a predictive model incorporating MPR score, histology, and differentiated degree, which discriminated MPR and non-MPR patients well in both the training and validation cohorts with an AUC value of 0.803 and 0.817, respectively. Furthermore, micro-RNA transcriptomic analysis revealed the association between our MPR score and immune regulation pathways. A significantly better event-free survival was seen in subpopulations with a high MPR score. Conclusion: Our findings suggested that dPBI reflected responses to neoadjuvant immuno-chemotherapy for NSCLC. The MPR score, a non-invasive biomarker integrating their dynamics, captured the miRNA transcriptomic pattern in the tumor microenvironment and distinguished MPR from non-MPR for neoadjuvant immunochemotherapy, which could support the clinical decisions on the utilization of immune checkpoint inhibitor-based treatments in NSCLC patients.

Carbon dioxide gas-induced pneumothorax versus one-lung ventilation in minimally invasive esophagectomy: a multicenter propensity score matching cohort study.

BACKGROUND: Carbon dioxide gas-induced pneumoperitoneum might be the reason for the shorter postoperative survival of patients with malignant tumors. Whether CO 2 gas-induced pneumothorax has unfavorable impacts on the surgical and oncological outcomes of minimally invasive esophagectomy remains unclear. METHODS: Between 2010 and 2016, a total of 998 patients with squamous cell carcinoma of the esophagus who received video-assisted surgery were registered from three large-volume medical centers. The overall survival (OS) and disease-free survival (DFS) were compared after using propensity score-matched and inverse probability-weighted methods. In addition, the tumor-relapse state was evaluated, and the relapse pattern was compared. RESULTS: A total of 422 and 576 minimally invasive esophagectomies with intraoperative one-lung ventilation and CO 2 -induced pneumothorax were enrolled, respectively. The 5-year OS and DFS were similar between the CO 2 -induced pneumothorax (64.2% and 64.7%) and one-lung ventilation (65.3% and 62.4%) groups following propensity matching. The inverse probability weighting revealed similarly equal survival results in the two groups. The 5-year relapse rates were 35.1% and 30.6% in the one-lung ventilation and CO 2 -induced pneumothorax groups, respectively. Moreover, the relapse patterns were not significantly different between the two groups. CONCLUSION: The results of this study suggested that the use of intraoperative one-lung ventilation and CO 2 -induced pneumothorax have similar oncological outcomes; therefore, the two methods are both viable options in esophagectomy.

Risk factors and novel predictive models for metastatic neuroblastoma in children.

INTRODUCTION: Neuroblastoma (NB) with distant metastasis (DM) is a high-risk condition with a poor prognosis. Early identify the risk and prognostic differences of DM in children, which is helpful for the development of clinical diagnosis and treatment. METHODS: The study cohort included patients with NB in surveillance, epidemiological, and final outcome databases between 2010 and 2018. To identify the risk and prognostic factors for DM, both univariate and multivariate logistic and Cox regression analyses were conducted. In addition, we created and verified three online clinical prediction models. Finally, we assess the performance of the proposed predictive model. RESULTS: Among the 1224 children with NB included in the study, 599 developed DM. Primary site is the most important factor affecting metastasis and prognosis. The training and validation groups of the diagnostic nomograms had area under curves (AUC) values of 0.872 and 0.824, respectively. In addition, in the training group, the AUC values at 12, 36, and 60 months were 0.68, 0.71, and 0.75 for the OS nomogram and 0.70, 0.72, and 0.75 for the CSS nomogram. In the validation group, the AUC values at 12, 36, and 60 months were 0.68, 0.72, and 0.70 for the OS nomogram and 0.67, 0.71, and 0.69 for the CSS nomogram, respectively. Calibration curve and decision curve analyses revealed good performance of the nomogram. CONCLUSIONS: The nomogram developed in this study could appropriately predict DM and assess its prognosis in patients with NB.

The Effects of Drug Exposure and Single Nucleotide Polymorphisms on Aaptinib-Induced Severe Toxicities in Solid Tumors.

To investigate the value of drug exposure and host germline genetic factors in predicting apatinib (APA)-related toxicities. METHOD: In this prospective study, plasma APA concentrations were quantified using liquid chromatography with tandem mass spectrometry, and 57 germline mutations were genotyped in 126 advanced solid tumor patients receiving 250 mg daily APA, a vascular endothelial growth factor receptor II inhibitor. The correlation between drug exposure, genetic factors, and the toxicity profile was analyzed. RESULTS: Non-small cell lung cancer (NSCLC) was more prone to APA-related toxicities and plasma concentrations of APA, and its main metabolite M1-1 could be associated with high-grade adverse events (AEs) (P < 0.01; M1-1, P < 0.01) and high-grade antiangiogenetic toxicities (APA, P = 0.034; P < 0.05), including hypertension, proteinuria, and hand-foot syndrome, in the subgroup of NSCLC. Besides, CYP2C9 rs34532201 TT carriers tended to have higher levels of APA (P < 0.001) and M1-1 (P < 0.01), whereas CYP2C9 rs1936968 GG carriers were predisposed to higher levels of M1-1 (P < 0.01). CONCLUSION: Plasma APA and M1-1 exposures were able to predict severe AEs in NSCLC patients. Dose optimization and drug exposure monitoring might need consideration in NSCLC patients with CYP2C9 rs34532201 TT and rs1936968 GG. SIGNIFICANCE STATEMENT: Apatinib is an anti-VEGFR2 inhibitor for the treatment of multiple cancers. Though substantial in response, apatinib-induced toxicity has been a critical issue that is worth clinical surveillance. Few data on the role of drug exposure and genetic factors in apatinib-induced toxicity are available. Our study demonstrated a distinct drug-exposure relationship in NSCLC but not other tumors and provided invaluable evidence of drug exposure levels and single nucleotide polymorphisms as predictive biomarkers in apatinib-induced severe toxicities.

Pan-Immune-Inflammatory Value in Patients with Non-Small-Cell Lung Cancer Undergoing Neoadjuvant Immunochemotherapy.

Background: We aimed to investigate the predictive value of a systematic serum inflammation index, pan-immune-inflammatory value (PIV), in pathological complete response (pCR) of patients treated with neoadjuvant immunotherapy to further promote ideal patients' selection. Methods: The clinicopathological and baseline laboratory information of 128 NSCLC patients receiving neoadjuvant immunochemotherapy between October 2019 and April 2022 were retrospectively reviewed. We performed least absolute shrinkage and selection operator (LASSO) algorithm to screen candidate serum biomarkers for predicting pCR, which further entered the multivariate logistic regression model to determine final biomarkers. Accordingly, a diagnostic model for predicting individual pCR was established. Kaplan-Meier method was utilized to estimate curves of disease-free survival (DFS), and the Log rank test was analyzed to compare DFS differences between patients with and without pCR. Results: Patients with NSCLC heterogeneously responded to neoadjuvant immunotherapy, and those with pCR had a significant longer DFS than patients without pCR. Through LASSO and the multivariate logistic regression model, PIV was identified as a predictor for predicting pCR of patients. Subsequently, a diagnostic model integrating with PIV, differentiated degree and histological type was constructed to predict pCR, which presented a satisfactory predictive power (AUC, 0.736), significant agreement between actual and our nomogram-predicted pathological response. Conclusion: Baseline PIV was an independent predictor of pCR for NSCLC patients receiving neoadjuvant immunochemotherapy. A significantly longer DFS was achieved in patients with pCR rather than those without pCR; thus, the PIV-based diagnostic model might serve as a practical tool to identify ideal patients for neoadjuvant immunotherapeutic guidance.

miR-4739 promotes epithelial-mesenchymal transition and angiogenesis in "driver gene-negative" non-small cell lung cancer via activating the Wnt/ß-catenin signaling.

PURPOSE: "Driver gene-negative" non-small cell lung cancer (NSCLC) currently has no approved targeted drug, due to the lack of common actionable driver molecules. Even though miRNAs play crucial roles in various malignancies, their roles in "driver gene-negative" NSCLC keep unclear. METHODS: miRNA expression microarrays were utilized to screen miRNAs associated with "driver gene-negative" NSCLC malignant progression. Quantitative real-time PCR (RT-qPCR) and in situ hybridization (ISH) were employed to validate the expression of miR-4739, and its correlation with clinicopathological characteristics was analyzed in tumor specimens using univariate and multivariate analyses. The biological functions and underlying mechanisms of miR-4739 were investigated both in vitro and in vivo. RESULTS: our research demonstrated, for the first time, that miR-4739 was substantially increased in "driver gene-negative" NSCLC tumor tissues and cell lines, and overexpression of miR-4739 was related to clinical staging, metastasis, and unfavorable outcomes. Functional experiments discovered that miR-4739 dramatically enhanced tumor cell proliferation, migration, and metastasis by promoting the epithelial-to-mesenchymal transition (EMT). Meanwhile, miR-4739 can be transported from cancer cells to the site of vascular epithelial cells through exosomes, consequently facilitating the proliferation and migration of vascular epithelial cells and inducing angiogenesis. Mechanistically, miR-4739 can activate Wnt/ß-catenin signaling both in tumor cells and vascular epithelial cells by targeting Wnt/ß-catenin signaling antagonists APC2 and DKK3, respectively. CONCLUSION: Our work identifies a valuable oncogene, miR-4739, that accelerates malignant progression in "driver gene-negative" NSCLC and serves as a potential therapeutic target for this group of tumors.

A novel lncRNA SNHG29 regulates EP300- related histone acetylation modification and inhibits FLT3-ITD AML development.

Internal tandem duplication (ITD) mutations within the FMS-like tyrosine kinase-3 (FLT3) occur in up to 25% of acute myeloid leukemia (AML) patients and indicate a very poor prognosis. The role of long noncoding RNAs (lncRNAs) in FLT3-ITD AML progression remains unexplored. We identified a novel lncRNA, SNHG29, whose expression is specifically regulated by the FLT3-STAT5 signaling pathway and is abnormally down-regulated in FLT3-ITD AML cell lines. SNHG29 functions as a tumor suppressor, significantly inhibiting FLT3-ITD AML cell proliferation and decreasing sensitivity to cytarabine in vitro and in vivo models. Mechanistically, we demonstrated that SNHG29's molecular mechanism is EP300-binding dependent and identified the EP300-interacting region of SNHG29. SNHG29 modulates genome-wide EP300 genomic binding, affecting EP300-mediated histone modification and consequently influencing the expression of varies downstream AML-associated genes. Our study uncovers a novel molecular mechanism for SNHG29 in mediating FLT3-ITD AML biological behaviors through epigenetic modification, suggesting that SNHG29 could be a potential therapeutic target for FLT3-ITD AML.

Metastasis pattern and prognosis in children with neuroblastoma.

BACKGROUND: We aimed to investigate the different metastases and prognoses of neuroblastoma (NB) and determine the risk factors of metastasis. METHOD: Data of 1224 patients with NB were obtained from the Surveillance, Epidemiology and End Results database (2010-2018). Pearson's chi-square test, Kaplan-Meier analysis, multivariable logistic regression and Cox regression analysis were used to determine the factors associated with prognosis. RESULTS: The overall incidence of NB was an age-adjusted rate of 8.2 patients per 1,000,000 children. In total, 1224 patients were included in our study, with 599 patients (48.9%) exhibiting distant metastases. Compared to patients with non-metastatic NB, a greater proportion of patients with metastatic NB were under 1 year, male, had an adrenal primary site, unilateral tumour, a tumour size > 10 cm, neuroblastoma-not otherwise specified (NB-NOS), second malignant neoplasms and were more likely to choose radiotherapy and chemotherapy. Multivariate Cox regression showed that metastasis was an independent risk factor for overall survival (OS) and cancer-specific survival (CSS). The survival rate of non-metastatic patients with NB was better than those with metastasis (OS: hazard ratio (HR): 0.248, P < 0.001; CSS: HR: 0.267, P < 0.001). The bone and liver were the two most common isolated metastatic sites in NB. However, no statistical difference was observed in OS and CSS between the only bone metastasis group, only liver metastasis group and bone metastasis combined with liver metastasis group (all P > 0.05). Additionally, age at diagnosis > 1 year (odds ratio (OR): 3.295, P < 0 .001), grades III-IV (OR: 26.228, P < 0 .001) and 5-10 cm tumours (OR: 1.781, P < 0 .001) increased the risk of bone metastasis of NB. Moreover, no surgical treatment (OR: 2.441, P < 0 .001) increased the risk of liver metastasis of NB. CONCLUSION: Metastatic NB has unique clinicopathological features, with the bone and liver as the most common single metastatic sites of NB. Therefore, more aggressive treatment is recommended for high-risk children with NB displaying distant metastases.

Invasiveness assessment by artificial intelligence against intraoperative frozen section for pulmonary nodules ≤ 3 cm.

PURPOSE: To investigate the performance of an artificial intelligence (AI) algorithm for assessing the malignancy and invasiveness of pulmonary nodules in a multicenter cohort. METHODS: A previously developed deep learning system based on a 3D convolutional neural network was used to predict tumor malignancy and invasiveness. Dataset of pulmonary nodules no more than 3 cm was integrated with CT images and pathologic information. Receiver operating characteristic curve analysis was used to evaluate the performance of the system. RESULTS: A total of 466 resected pulmonary nodules were included in this study. The areas under the curves (AUCs) of the deep learning system in the prediction of malignancy as compared with pathological reports were 0.80, 0.80, and 0.75 for all, subcentimeter, and solid nodules, respectively. Additionally, the AUC in the AI-assisted prediction of invasive adenocarcinoma (IA) among subsolid lesions (n = 184) was 0.88. Most malignancies that were misdiagnosed by the AI system as benign diseases with a diameter measuring greater than 1 cm (26/250, 10.4%) presented as solid nodules (19/26, 73.1%) on CT. In an exploratory analysis involving nodules underwent intraoperative pathologic examination, the concordance rate in identifying IA between the AI model and frozen section examination was 0.69, with a sensitivity of 0.50 and specificity of 0.97. CONCLUSION: The deep learning system can discriminate malignant diseases for pulmonary nodules measuring no more than 3 cm. The AI model has a high positive predictive value for invasive adenocarcinoma with respect to intraoperative frozen section examination, which might help determine the individualized surgical strategy.

Risk stratification and prognosis prediction based on inflammation-related gene signature in lung squamous carcinoma.

BACKGROUND: Inflammation is known to have an intricate relationship with tumorigenesis and tumor progression while it is also closely related to tumor immune microenvironment. Whereas the role of inflammation-related genes (IRGs) in lung squamous carcinoma (LUSC) is barely understood. Herein, we recognized IRGs associated with overall survival (OS), built an IRGs signature for risk stratification and explored the impact of IRGs on immune infiltration landscape of LUSC patients. METHODS: The RNA-sequencing and clinicopathological data of LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, which were defined as training and validation cohorts. Cox regression and least absolute shrinkage and selection operator analyses were performed to build an IRG signature. CIBERSORT, microenvironment cell populations-counter and tumor immune dysfunction and rejection (TIDE) algorithm were used to perform immune infiltration analysis. RESULTS: A two-IRG signature consisting of KLF6 and SGMS2 was identified according to the training set, which could categorize patients into two different risk groups with distinct OS. Patients in the low-risk group had more anti-tumor immune cells infiltrated while patient with high-risk had lower TIDE score and higher levels of immune checkpoint molecules expressed. The IRG signature was further identified as an independent prognostic factor of OS. Subsequently, a prognostic nomogram including IRG signature, age, and cancer stage was constructed for predicting individualized OS, whose concordance index values were 0.610 (95% CI: 0.568-0.651) in the training set and 0.652 (95% CI: 0.580-0.724) in validation set. Time-dependent receiver operator characteristic curves revealed that the nomogram had higher prediction accuracy compared with the traditional tumor stage alone. CONCLUSION: The IRG signature was a predictor for patients with LUSC and might serve as a potential indicator of the efficacy of immunotherapy. The nomogram based on the IRG signature showed a relatively good predictive performance in survival.

Comprehensive 18F-FDG PET-based radiomics in elevating the pathological response to neoadjuvant immunochemotherapy for resectable stage III non-small-cell lung cancer: A pilot study.

Purpose: To develop a comprehensive PET radiomics model to predict the pathological response after neoadjuvant toripalimab with chemotherapy in resectable stage III non-small-cell lung cancer (NSCLC) patients. Methods: Stage III NSCLC patients who received three cycles of neoadjuvant toripalimab with chemotherapy and underwent 18F-FDG PET/CT were enrolled. Baseline 18F-FDG PET/CT was performed before treatment, and preoperative 18F-FDG PET/CT was performed three weeks after the completion of neoadjuvant treatment. Surgical resection was performed 4-5 weeks after the completion of neoadjuvant treatment. Standardized uptake value (SUV) statistics features and radiomics features were derived from baseline and preoperative PET images. Delta features were derived. The radiologic response and metabolic response were assessed by iRECIST and iPERCIST, respectively. The correlations between PD-L1 expression, driver-gene status, peripheral blood biomarkers, and the pathological responses (complete pathological response [CPR]; major pathological response [MPR]) were assessed. Associations between PET features and pathological responses were evaluated by logistic regression. Results: Thirty patients underwent surgery and 29 of them performed preoperative PET/CT. Twenty patients achieved MPR and 16 of them achieved CPR. In univariate analysis, five SUV statistics features and two radiomics features were significantly associated with pathological responses. In multi-variate analysis, SUVmax, SUVpeak, SULpeak, and End-PET-GLDM-LargeDependenceHighGrayLevelEmphasis (End-GLDM-LDHGLE) were independently associated with CPR. SUVpeak and SULpeak performed better than SUVmax and SULmax for MPR prediction. No significant correlation, neither between the radiologic response and the pathological response, nor among PD-L1, driver gene status, and baseline PET features was found. Inflammatory response biomarkers by peripheral blood showed no difference in different treatment responses. Conclusion: The logistic regression model using comprehensive PET features contributed to predicting the pathological response after neoadjuvant toripalimab with chemotherapy in resectable stage III NSCLC patients.

Identifying the long-term survival beneficiary of chemotherapy for stage N1c sigmoid colon cancer.

Sigmoid colon cancer often has an unsatisfactory prognosis. This study explored the effect of tumor deposits (TDs) on survival, and whether their presence/absence influence individualized treatment. Data of postoperative patients with sigmoid colon cancer were extracted from the Surveillance, Epidemiology, and End Results database. Overall survival (OS) was calculated using the Kaplan-Meier method and prognostic factors were identified using Cox regression analysis and random forest (RF). The nomogram's discrimination performance was evaluated using a concordance index (C-index), integrated discrimination improvement (IDI), calibration curves, and decision-curve analysis. The N1c group showed a worse prognosis than the N0 group. For N1c patients, a combination of surgery and chemotherapy prolonged survival, compared to surgery alone; however, the chemotherapy-surgery combination did not affect the OS of patients younger than 70 years, in stage T1-2, and/or of black race. Multivariable analysis and RF presented Age, T stage, and N stage were the most important predictors for OS. The novel nomogram had superiority to the TNM staging system with improved C-index and IDI, as well as good consistency and higher clinical benefit. TDs are associated with poor survival from sigmoid colon cancer, and considering TDs can inform the formulation of individual treatment regimens. The nomogram shows satisfactory prediction ability for OS.

Role of a lipid metabolism-related lncRNA signature in risk stratification and immune microenvironment for colon cancer.

BACKGROUND: Energy metabolism disorder, especially lipid metabolism disorder, is an important biological characteristic of colon cancer. This research sought to examine the association between lipid metabolism-related long non-coding RNAs (lncRNAs) and prognoses among colon cancer patients. METHODS: The transcriptome profile and clinical data of patients with colon cancer were retrieved from The Cancer Genome Atlas database. Using consensus clustering, cases were divided into two clusters and Kaplan-Meier analysis was executed to analyze differences in their prognoses. The gene set enrichment analysis (GSEA) was used to discover biological processes and signaling pathways. A lipid metabolism-related lncRNA prognostic model (lipid metabolism-LncRM) was created utilizing the least absolute shrinkage and selection operator (LASSO) regression. The tumor microenvironment was evaluated on the basis of the composition of immune and stromal cells. RESULTS: The patients in Cluster 2 were found to have a better prognosis and higher expression of programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) relative to Cluster 1. The results of GSEA showed the enrichment of energy metabolism pathways in Cluster 2. LASSO regression was used to identify the five LncRNAs that were shown to be most substantially linked to patient prognosis. These were NSMCE1-DT, LINC02084, MYOSLID, LINC02428, and MRPS9-AS1. Receiver operating characteristic (ROC) curves and survival analysis illustrated that the lipid metabolism-LncRM had a significant prognostic value. Further analysis showed that high- and low-risk groups were significantly different in terms of clinical characteristics and immune cells infiltration. CONCLUSIONS: Lipid metabolism-related lncRNAs could predict the prognoses and tumor microenvironment of colon cancer and might be important biomarkers relevant to immunotherapy.

Response of primary tumor and lymph node in non-small cell lung cancer after neoadjuvant immunotherapy: a pooled analysis.

The good pathological response of primary tumors (PTs) to neoadjuvant immunotherapy has been acknowledged in non-small cell lung cancer (NSCLC), however, it remains unclear whether neoadjuvant immunotherapy shows consistent effects in metastatic lymph nodes (LNs). We compared the pathological response of PT and nodal downstaging using a pooled analysis to assess the effect of neoadjuvant immunotherapy on LNs. Original articles reporting the tumor major pathological response (ypT(MPR)), pathological complete response (ypT0) and nodal downstaging following neoadjuvant immunotherapy in NSCLC were retrieved. The OR and 95% CI were calculated by Review Manager V.5.3. Subgroup analysis was performed according to the neoadjuvant therapy regimen used. A total of 209 patients from 6 studies were included in this analysis. The frequency of nodal downstaging was comparable to that of ypT(MPR) (OR 1.31; 95% CI 0.84 to 2.05; p=0.24). Interestingly, ypN0 was observed more frequently than ypT0 (OR 3.26; 95% CI 2.06 to 5.16; p<0.0001). However, this difference was not observed in the subgroup of cN2 patients who underwent immune checkpoint inhibitor monotherapy (OR 1.58; 95% CI 0.56 to 4.48; p=0.39). Neoadjuvant immunotherapy results in satisfactory response in metastatic LN. Patients had a high probability of node clearance when ypT0 was confirmed, especially in patients treated with immunochemotherapy.

Integrative Analysis of Bioinformatics and Machine Learning Algorithms Identifies a Novel Diagnostic Model Based on Costimulatory Molecule for Predicting Immune Microenvironment Status in Lung Adenocarcinoma.

Costimulatory molecules are an indispensable signal for activating immune cells. However, the features of many costimulatory molecule genes (CMGs) in lung adenocarcinoma (LUAD) are poorly understood. This study systematically explored expression patterns of CMGs in the tumor immune microenvironment (TIME) status of patients with LUAD. Their expression profiles were downloaded from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Two robust TIME subtypes ("hot" and "cold") were classified by K-means clustering and estimation of stromal and immune cells in malignant tumor tissues using expression data. The "hot" subtype presented higher infiltration in activated immune cells and enrichments in the immune cell receptor signaling pathway and adaptive immune response. Three CMGs (CD80, LTB, and TNFSF8) were screened as final diagnostic markers by means of Least Absolute Shrinkage Selection Operator and Support Vector Machine-Recursive Feature Elimination algorithms. Accordingly, the diagnostic nomogram for predicting individualized TIME status showed satisfactory diagnostic accuracy in The Cancer Genome Atlas training cohort as well as GSE31210 and GSE180347 validation cohorts. Immunohistochemistry staining of 16 specimens revealed an apparently positive correlation between the expression of CMG biomarkers and pathologic response to immunotherapy. Thus, this diagnostic nomogram provided individualized predictions in TIME status of LUAD patients with good predictive accuracy, which could serve as a potential tool for identifying ideal candidates for immunotherapy.

Postoperative locoregional recurrence pattern and treatment management of stage pT4 sigmoid colon cancer: a retrospective cohort study.

BACKGROUND: This study aimed to explore the pattern of locoregional recurrence after surgery in patients with non-metastatic stage pT4 sigmoid colon cancer and the role of adjuvant radiotherapy on survival. METHODS: We retrospectively analyzed data from 208 patients who underwent surgery in our hospital. The patients were randomly divided into training and validation groups at a 1:1 ratio. Patients at high risk for locoregional recurrence were screened using Cox regression analysis. Based on the data of 2,886 patients in the Surveillance, Epidemiology, and End Results (SEER) database, the effect of adjuvant radiotherapy on overall survival (OS) and cancer-specific survival (CSS) was evaluated by Kaplan-Meier curves. RESULTS: Of the 208 patients, 57 (27.4%) presented with locoregional recurrences (14 anastomotic and 43 abdominal or pelvic lymph node recurrences). Multivariate analysis showed that serum CEA, differentiation, lymph node dissection number, and N stage were independent predictors of locoregional recurrence-free survival (all p < 0.05). A risk-stratification model was constructed, and a total score of ≥ 6.5 points was considered the high-risk group for locoregional recurrence. Both the training and validation sets presented that the model had a good predictive ability (area under the curve = 0.828 and 0.724, respectively). Analysis of SEER data revealed that adjuvant radiotherapy significantly prolonged OS and CSS in the high-risk population (all p < 0.05, vs. no radiotherapy). CONCLUSIONS: Patients with a total risk score of 6.5 or more had a high likelihood of locoregional recurrence, and perhaps adjuvant radiotherapy could improve their survival.

A Predictive Nomogram for Atypical Meningioma Based On Preoperative Magnetic Resonance Imaging and Routine Blood Tests.

OBJECTIVE: The objective of the study was to establish a 5-year progression-free survival prediction nomogram using preoperative routine blood tests and magnetic resonance imaging to guide postoperative treatment. METHODS: Our study was a retrospective analysis of patients with atypical meningioma admitted into our facility from January 31, 2010, to January 31, 2016. We used single-factor logistic analysis to extract valuable indicators from preoperative blood test results and 3D Slicer software to extract radiomic features from magnetic resonance imaging. The radiomics score was calculated by least absolute shrinkage and selection operator logistic regression analysis. We then combined blood indicators and radiomic signatures to construct a radiomic nomogram image. The performance of the model was evaluated comprehensively using the following three aspects: recognition ability, accuracy, and clinical value. RESULTS: Six significant radiological features were selected through least absolute shrinkage and selection operator logistic regression analysis. The radiometric label established by these six features has satisfactory predictive performance. The area under the curve in the training group was 0.885 (95% confidence interval, 0.8037-0.9659), and the area under the curve in the validation set was 0.789 (95% confidence interval, 0.6092-0.9686). We used the combined image tags and preoperative leukocyte and neutrophil count to construct a 5-year progression-free survival prediction nomogram. CONCLUSIONS: The analysis results of the calibration curve and the decision curve show that the nomogram constructed by combining radiomics and preoperative blood tests has a good predictive value for 5-year progression-free survival in atypical meningioma and can provide a reference for selecting postoperative treatment options.

An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Squamous Carcinoma.

Aging is an inevitable process characterized by a decline in many physiological activities, and has been known as a significant risk factor for many kinds of malignancies, but there are few studies about aging-related genes (ARGs) in lung squamous carcinoma (LUSC). We designed this study to explore the prognostic value of ARGs and establish an ARG-based prognosis signature for LUSC patients. RNA-sequencing and corresponding clinicopathological data of patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The ARG risk signature was developed on the basis of results of LASSO and multivariate Cox analysis in the TCGA training dataset (n = 492). Furthermore, the GSE73403 dataset (n = 69) validated the prognostic performance of this ARG signature. Immunohistochemistry (IHC) staining was used to verify the expression of the ARGs in the signature. A five ARG-based signature, including A2M, CHEK2, ELN, FOS, and PLAU, was constructed in the TCGA dataset, and stratified patients into low- and high-risk groups with significantly different overall survival (OS) rates. The ARG risk score remained to be considered as an independent indicator of OS in the multivariate Cox regression model for LUSC patients. Then, a prognostic nomogram incorporating the ARG risk score with T-, N-, and M-classification was established. It achieved a good discriminative ability with a C-index of 0.628 (95% confidence interval [CI]: 0.586-0.671) in the TCGA cohort and 0.648 (95% CI: 0.535-0.762) in the GSE73403 dataset. Calibration curves displayed excellent agreement between the actual observations and the nomogram-predicted survival. The IHC staining discovered that these five ARGs were overexpression in LUSC tissues. Besides, the immune infiltration analysis in the TCGA cohort represented a distinctly differentiated infiltration of anti-tumor immune cells between the low- and high-risk groups. We identified a novel ARG-related prognostic signature, which may serve as a potential biomarker for individualized survival predictions and personalized therapeutic recommendation of anti-tumor immunity for patients with LUSC.