A Novel Nomogram for Predicting the Overall Survival in Patients with Unresectable HCC after TACE plus Hepatic Arterial Infusion Chemotherapy

Background and aimTransarterial chemoembolization combined with hepatic arterial infusion chemotherapy (TACE-HAIC) has shown encouraging efficacy in the treatment of unresectable hepatocellular carcinoma (HCC). We aimed to develop a novel nomogram to predict overall survival (OS) of patients with unresectable HCC treated with TACE-HAIC.MethodsA total of 591 patients with unresectable HCC treated with TACE-HAIC between May 2009 and September 2020 were enrolled. These patients were randomly divided into training and validation cohorts. The independent prognostic factors were identified with Cox proportional hazards model. The model's discriminative ability and accuracy were validated using concordance index (C-index), calibration plots, the area under the time-dependent receiver operating characteristic curve (AUC) and decision curve analyses (DCAs).ResultsThe median OS was 15.6 months. A nomogram was established based on these factors, including tumor size, vein invasion, extrahepatic metastasis, tumor number, alpha fetoprotein (AFP), and albumin-bilirubin (ALBI), to predict OS for patients with unresectable HCC treated with TACE-HAIC. The C-index of the nomogram were 0.717 in the training cohort and 0.724 in validation cohort. The calibration plots demonstrated good agreement between the predicted outcomes and the actual observations. The AUC values were better than those of three conventional staging systems. The results of DCA indicated that the nomogram may have clinical usefulness. The patients in the low-risk group had a longer OS than those in intermediate-risk and high-risk groups (P<0.001).ConclusionA prognostic nomogram was developed and validated to assist clinicians in accurately predicting the OS of patients with unresectable HCC after TACE-HAIC.     

Development and validation of two nomograms for predicting overall survival and cancer-specific survival in gastric cancer patients with liver metastases: A retrospective cohort study from SEER database

BackgroundGastric cancer is heterogeneous and aggressive, especially with liver metastasis. This study aims to develop two nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of gastric cancer with liver metastasis (GCLM) patients.MethodsFrom January 2000 to December 2018, a total of 1936 GCLM patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database. They were further divided into a training cohort and a validation cohort, with the OS and CSS serving as the study's endpoints. The correlation analyses were used to determine the relationship between the variables. The univariate and multivariate Cox analyses were used to confirm the independent prognostic factors. To discriminate and calibrate the nomogram, calibration curves and the area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used. DCA curves were used to examine the accuracy and clinical benefits. The clinical utility of the nomogram and the AJCC Stage System was compared using net reclassification improvement (NRI) and integrated differentiation improvement (IDI) (IDI). Finally, the nomogram and the AJCC Stage System risk stratifications were compared.ResultsThere was no collinearity among the variables that were screened. The results of multivariate Cox regression analysis showed that six variables (bone metastasis, lung metastasis, surgery, chemotherapy, grade, age) and five variables (lung metastasis, surgery, chemotherapy, grade, N stage) were identified to establish the nomogram for OS and CSS, respectively. The calibration curves, time-dependent AUC curves, and DCA revealed that both nomograms had pleasant predictive power. Furthermore, NRI and IDI confirmed that the nomogram outperformed the AJCC Stage System.ConclusionBoth nomograms had satisfactory accuracy and were validated to assist clinicians in evaluating the prognosis of GCLM patients.     

Identification and validation of autophagy-related prognostic signature for head and neck squamous cell carcinoma

BackgroundMany studies have demonstrated that autophagy plays a significant role in regulating tumor growth and progression. However, the effect of autophagy-related genes (ARGs) on the prognosis have rarely been analyzed in head and neck squamous cell carcinoma (HNSCC).MethodsWe obtained differentially expressed ARGs from HNSCC mRNA data in The Cancer Genome Atlas (TCGA) database. And then we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to explore the autophagy-related biological functions. The overall survival (OS)-related and disease specific survival (DSS)-related ARGs were identified by univariate Cox regression analyses. With these genes, we established OS-related and DSS-related risk signature by LASSO regression method, respectively. We validated the reliability of the risk signature with receiver operating characteristic (ROC) analysis, Kaplan-Meier survival curves, clinical correlation analysis, and nomogram. Then we analyzed relationships between risk signature and immune cell infiltration.ResultsWe established the prognostic signatures based on 14 ARGs for OS and 12 ARGs for DSS. The ROC curves, survival analysis, and nomogram validated the predictive accuracy of the models. Clinic correlation analysis showed that the risk group was closely related to Stage, pathological T stage, pathological N stage and human papilloma virus (HPV) subtype. Cox regression demonstrated that the risk score was an independent predictor for the prognosis of HNSCC patients. Furthermore, patients in low-risk score group exhibited higher immunescore and distinct immune cell infiltration than high-risk score group. And we further analysis revealed that the copy number alterations (CNAs) of ARGs-based signature affected the abundance of tumor-infiltrating immune cells.ConclusionIn this study, we identified novel autophagy-related signature for the prediction of OS and DSS in patients with HNSCC. Meanwhile, our study provides a novel sight to understand the role of autophagy and elucidate the important role of autophagy in tumor immune microenvironment (TIME) of HNSCC.     

A high-quality model for predicting the prognosis of breast neuroendocrine carcinoma to help clinicians decide on appropriate treatment methods: A population-based analysis

BackgroundBreast neuroendocrine carcinoma (NEC) is a rare malignancy with unclear treatment options and prognoses. This study aimed to construct a high-quality model to predict overall survival (OS) and breast cancer-specific survival (BCSS) and help clinicians choose appropriate breast NEC treatments.Patients and methodsA total of 378 patients with breast NEC and 349,736 patients with breast invasive ductal carcinoma (IDC) were enrolled in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2018. Propensity score matching (PSM) was performed to balance the clinical baseline. Prognostic factors determined by multivariate Cox analysis were included in the nomogram. C-index and calibration curves were used to verify the performance of the nomogram.ResultsNomograms were constructed for the breast NEC and breast IDC groups after PSM. The C–index of the nomograms ranged from 0.834 to 0.880 in the internal validation and 0.818–0.876 in the external validation, indicating that the nomogram had good discrimination. The risk stratification system showed that patients with breast NEC had worse prognoses than those with breast IDC in the low-risk and intermediate-risk groups but had a similar prognosis that those in the high-risk group. Moreover, patients with breast NEC may have a better prognosis when undergoing surgery plus chemotherapy than when undergoing surgery alone or chemotherapy alone.ConclusionsWe established nomograms with a risk stratification system to predict OS and BCSS in patients with breast NEC. This model could help clinicians evaluate prognosis and provide individualized treatment recommendations for patients with breast NEC.     

Identification of an autophagy-related gene signature predicting overall survival for hepatocellular carcinoma

The poor prognosis of hepatocellular carcinoma (HCC) calls for the development of accurate prognostic models. The growing number of studies indicating a correlation between autophagy activity and HCC indicates there is a commitment to finding solutions for the prognosis of HCC from the perspective of autophagy. We used a cohort in The Cancer Genome Atlas (TCGA) to evaluate the expression of autophagy-related genes in 371 HCC samples using univariate Cox and lasso Cox regression analysis, and the prognostic features were identified. A prognostic model was established by combining the expression of selected genes with the multivariate Cox regression coefficient of each gene. Eight autophagy-related genes were selected as prognostic features of HCC. We established the HCC prognostic risk model in TCGA dataset using these identified prognostic genes. The model’s stability was confirmed in two independent verification sets ({"type":"entrez-geo","attrs":{"text":"GSE14520","term_id":"14520"}}GSE14520 and {"type":"entrez-geo","attrs":{"text":"GSE36376","term_id":"36376"}}GSE36376). The model had a good predictive power for the overall survival (OS) of HCC (hazard ratio = 2.32, 95% confidence interval = 1.76–3.05, P<0.001). Moreover, the risk score computed by the model did not depend on other clinical parameters. Finally, the applicability of the model was demonstrated through a nomogram (C-index = 0.701). In the present study, we established an autophagy-related risk model having a high prediction accuracy for OS in HCC. Our findings will contribute to the definition of prognosis and establishment of personalized therapy for HCC patients.     

Prognostic and predictive role of a metabolic rate‐limiting enzyme signature in hepatocellular carcinoma

ObjectivesAbnormal expression of metabolic rate‐limiting enzymes drives the occurrence and progression of hepatocellular carcinoma (HCC). This study aimed to elucidate the comprehensive model of metabolic rate‐limiting enzymes associated with the prognosis of HCC.Materials and MethodsHCC animal model and TCGA project were used to screen out differentially expressed metabolic rate‐limiting enzyme. Cox regression, least absolute shrinkage and selection operation (LASSO) and experimentally verification were performed to identify metabolic rate‐limiting enzyme signature. The area under the receiver operating characteristic curve (AUC) and prognostic nomogram were used to assess the efficacy of the signature in the three HCC cohorts (TCGA training cohort, internal cohort and an independent validation cohort).ResultsA classifier based on three rate‐limiting enzymes (RRM1, UCK2 and G6PD) was conducted and serves as independent prognostic factor. This effect was further confirmed in an independent cohort, which indicated that the AUC at year 5 was 0.715 (95% CI: 0.653‐0.777) for clinical risk score, whereas it was significantly increased to 0.852 (95% CI: 0.798‐0.906) when combination of the clinical with signature risk score. Moreover, a comprehensive nomogram including the signature and clinicopathological aspects resulted in significantly predict the individual outcomes.ConclusionsOur results highlighted the prognostic value of rate‐limiting enzymes in HCC, which may be useful for accurate risk assessment in guiding clinical management and treatment decisions.     

Development and validation of robust metabolism-related gene signature in the prognostic prediction of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumours worldwide. Given metabolic reprogramming in tumours was a crucial hallmark, several studies have demonstrated its value in the diagnostics and surveillance of malignant tumours. The present study aimed to identify a cluster of metabolism-related genes to construct a prediction model for the prognosis of HCC. Multiple cohorts of HCC cases (466 cases) from public datasets were included in the present analysis. (GEO cohort) After identifying a list of metabolism-related genes associated with prognosis, a risk score based on metabolism-related genes was formulated via the LASSO-Cox and LASSO-pcvl algorithms. According to the risk score, patients were stratified into low- and high-risk groups, and further analysis and validation were accordingly conducted. The results revealed that high-risk patients had a significantly worse 5-year overall survival (OS) than low-risk patients in the GEO cohort. (30.0% vs. 57.8%; hazard ratio [HR], 0.411; 95% confidence interval [95% CI], 0.302–0.651; p < 0.001) This observation was confirmed in the external TCGA-LIHC cohort. (34.5% vs. 54.4%; HR 0.452; 95% CI, 0.299–0.681; p < 0.001) To promote the predictive ability of the model, risk score, age, gender and tumour stage were integrated into a nomogram. According to the results of receiver operating characteristic curves and decision curves analysis, the nomogram score possessed a superior predictive ability than conventional factors, which indicate that the risk score combined with clinicopathological features was able to achieve a robust prediction for OS and improve the individualized clinical decision making of HCC patients. In conclusion, the metabolic genes related to OS were identified and developed a metabolism-based predictive model for HCC. Through a series of bioinformatics and statistical analyses, the predictive ability of the model was approved.     

Development and Validation of a Nomogram for Predicting Overall Survival in Pancreatic NeuroendocrineTumors

BACKGROUND: The objective of current study was to develop and validate a nomogram to predict overall survival in pancreatic neuroendocrine tumors (PNETs). METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was queried for patients with PNETs between 2004 and 2015. Patients were randomly separated into the training set and the validation set. Cox regression model was used in training set to obtain independent prognostic factors to develop a nomogram for predicting overall survival (OS). The discrimination and calibration plots were used to evaluate the predictive accuracy of the nomogram. RESULTS: A total of 3142 patients with PNETs were collected from the SEER database. Sex, age, marital status, primary site, TNM stage, tumor grade, and therapy were associated with OS in the multivariate models. A nomogram was constructed based on these variables. The nomogram for predicting OS displayed better discrimination power than the Tumor-Node-Metastasis (TNM) stage systems 7th edition in the training set and validation set. The calibration curve indicated that the nomogram was able to accurately predict 3- and 5-year OS. CONCLUSIONS: The nomogram which could predict 3- and 5-year OS were established in this study. Our nomogram showed a good performance, suggesting that it could be served as an effective tool for prognostic evaluation of patients with PNETs.     

The prognostic value of red cell distribution width (RDW), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) in radiotherapy for oropharyngeal cancer

BackgroundRed cell distribution width (RDW), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) are known inflammatory indices. Elevated values are found in many cancers and may be associated with a poor prognosis. The article aimed to assess the impact of RDW, NLR, and PLR on overall survival (OS) of patients with oropharyngeal cancer treated with radiotherapy (RT).Materials and methodsThis retrospective study includes 208 patients treated for oropharyngeal cancer with definitive RT or RT combined with neoadjuvant or concurrent systemic therapy, at one institution between 2004 and 2014. The receiver operating characteristic (ROC) method, log-rank testing, and Cox proportional hazards regression model were used for the analysis.ResultsThe OS was significantly higher in RDW ≤ 13.8% (p = 0.001) and NLR ≤ 2.099 (p = 0.016) groups. The RDW index was characterized by the highest discriminatory ability [area under the curve (AUC) = 0.59, 95% confidence interval (CI): 0.51–0.67], closely followed by NLR (AUC = 0.58, 95% CI: 0.50–0.65). In the univariate Cox regression analysis, RDW [hazard ratio (HR): 1.28, 95% CI: 1.12–1.47, p < 0.001] and NLR (HR: 1.11, 95% CI: 1.06–1.18, p < 0.001) were associated with an increased risk of death. In the multivariate analysis, among the analyzed indices, only NLR was significantly associated with survival (HR: 1.16, 95% CI: 1.03–1.29, p = 0.012).ConclusionsIn the study, only NLR proved to be an independent predictor of OS. However, its clinical value is limited due to the relatively low sensitivity and specificity.     

Development and validation of a nomogram to predict overall survival of T1 esophageal squamous cell carcinoma patients with lymph node metastasis

PurposeTo develop a nomogram for predicting the prognosis of T1 esophageal squamous cell carcinoma (ESCC) patients with positive lymph node.MethodsT1 ESCC patients with lymph node metastasis diagnosed between 2010 and 2015 were selected from the Surveillance, Epidemiology, and Final Results (SEER) database. The entire cohort was randomly divided in the ratio of 7:3 into a training group (n=457) and validation group (n=192), respectively. Prognostic factors were identified by univariate and multivariate Cox regression models. Harrell''s concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve were used to evaluate the discrimination and calibration of the nomogram. The accuracy and clinical net benefit of the nomogram compared with the 7^th AJCC staging system were evaluated using net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).ResultsThe nomogram consisted of eight factors: insurance, T stage, summary stage, primary site, radiation code, chemotherapy, surgery, and radiation sequence with surgery. In the training and validation cohorts, the AUCs exceeded 0.700, and the C-index scores were 0.749 and 0.751, respectively, indicating that the nomogram had good discrimination. The consistency between the survival probability predicted by the nomogram and the actual observed probability was indicated by the calibration curve in the training and validation cohorts. For NRI>0 and IDI>0, the predictive power of the nomogram was more accurate than that of the 7^th AJCC staging system. Furthermore, the DCA curve indicated that the nomogram achieved better clinical utility than the traditional system.ConclusionsUnlike the 7^th AJCC staging system, the developed and validated nomogram can help clinical staff to more accurately, personally and comprehensively predict the 1-year and 3-year OS probability of T1 ESCC patients with lymph node metastasis.     

初次肿瘤细胞减灭术与中间性肿瘤细胞减灭术对晚期卵巢癌患者远期生存的影响真实世界临床研究

摘要 目的：比较初次肿瘤细胞减灭术（PDS）与中间性肿瘤细胞减灭术（IDS）对晚期卵巢癌患者远期生存的影响。方法：收集自2018年1月至2018年6月于中国科学技术大学附属第一医院妇瘤科手术（PDS/IDS）的晚期上皮性卵巢癌（III-IVB期）患者，从其生存期（OS）、严重手术并发症发生率等方面对比两种术式。采用Kaplan-Meier法分析生存曲线，采用log-rank检验比较生存差异，采用Cox比例风险回归模型分析影响生存的危险因素。结果：共纳入76例患者，其中IDS组24例，PDS组52例。两组患者在年龄、营养评分、术前血红蛋白（Hb）水平、组织病理学类型、临床分期等方面无统计学差异（P>0.05）。IDS组术中出血量显著低于PDS组（1045.83±981.91 mL vs 1628.85±1168.72 mL，P<0.01）。IDS组严重手术并发症发生率显著低于PDS组（12.5% vs 36.5%，P<0.05）。随访期间，IDS组共9例死亡，PDS组共16例死亡。IDS组的中位OS为47.0个月，PDS组的中位OS为38.0个月，两组间的OS差异无统计学意义（P=0.17）。多因素Cox回归分析显示，术中出血量（HR=1.001，95%CI=1.000-1.002，P=0.03）和严重手术并发症（HR=2.345，95%CI=1.123-4.902，P=0.02）是影响OS的独立危险因素，而术式（PDS/IDS）不是影响OS的独立危险因素（HR=0.667，95%CI=0.302-1.473，P=0.32）。结论：对于晚期卵巢癌患者，IDS与PDS相比，可以减少术中出血量和严重手术并发症的发生率，但对远期生存无显著影响。术中出血量和严重手术并发症是影响远期生存的独立危险因素，应尽量避免。     

Lymph node ratio-based nomogram for prognosis evaluation and treatment optimization of non-metastatic oral cavity squamous cell carcinoma

BackgroundLymph node ratio (LNR) has been increasingly reported as a prognostic factor in oral cavity squamous cell carcinoma (OCSCC). This study aimed to develop and validate a prognostic nomogram integrating LNR and to further assess its role in guiding adjuvant therapy for OCSCC.MethodsA total of 8703 OCSCC patients treated primarily with surgery in the Surveillance, Epidemiology and End Results (SEER) database were retrieved and randomly divided into training and validation cohorts. The nomogram was created based on the factors identified by Cox model. The value of PORT and chemotherapy was respectively evaluated in each prognostic group according to nomogram-deduced individualized score.ResultsThe final nomogram included tumor site, grade, T stage, number of positive lymph nodes and LNR. Calibration plots demonstrated a good match between predicted and observed rates of overall survival (OS). The concordance indexes for training and validation cohorts were 0.720 (95% confidence interval (CI): 0.708, 0.732) and 0.711 (95% CI: 0.687, 0.735), both significantly higher than did TNM stage (p< 0.001). According to individualized nomogram score, patients were stratified into three subgroups with significantly distinct outcome. PORT presented survival benefit among medium- and high-risk groups whereas a near-detrimental effect in low-risk group. Chemotherapy was found to be beneficial only in high-risk group.ConclusionThis LNR-incorporated nomogram surpassed the conventional TNM stage in predicting prognosis of patients with non-metastatic OCSCC and identified sub-settings that could gain survival benefit from adjuvant thearpy.     

Role of cytokine gene polymorphisms on prognosis in hepatocellular carcinoma after radical surgery resection

This study aimed to determine whether SNPs of cytokine genes influence survival of hepatocellular carcinoma (HCC) patients after radical surgery resection. We evaluated 14 SNPs of eight cytokine genes in 263 patients treated with radical surgery resection of HCC. Categorical variables were compared by the χ² test and Fisher's exact test. The Kaplan–Meier methods with log-rank test and Cox regression models were used to compare survival of resected HCC patients according to the genotype. Among the 14 studied SNPs of cytokine genes, only the TNF-α-863 (CA + CC) genotypes were revealed to be significant independent predictors of prolonged overall survival (OS) after HCC radical surgery resection (HR: 0.586, 95% CI: 0.355–0.968), considering for other clinical factors in a Cox proportional hazard model. Meanwhile, no significant association was found between the 14 SNPs and relapse-free survival (RFS) of resected HCC patients. In addition, combination analysis with the Th1 cytokine (IFN-γ, IL-2, IL-12B, TGF-β1) or Th2 cytokine (IL-6, IL-10) genetic polymorphisms by the Kaplan–Meier method and Cox multivariate analysis did not reveal any significant association between OS and RFS of resected HCC patients.     

The Pan-Immune-Inflammation Value predicts the survival of patients with anaplastic lymphoma kinase-positive non-small cell lung cancer treated with first-line ALK inhibitor

BackgroundAnaplastic lymphoma kinase (ALK) tyrosine kinase inhibitors (TKIs) have significantly improved the clinical outcomes of patients with ALK-positive non-small cell lung cancer (NSCLC). However, reliable biomarkers to predict the prognostic role of this treatment are lacking. The Pan-Immune-Inflammation Value (PIV) has recently been demonstrated as a novel comprehensive biomarker to predict survival of patients with solid tumors. Our study aimed to evaluate the prognostic power of PIV in this group of patients.Patients and methods94 patients with advanced ALK-positive NSCLC who received first-line ALK inhibitors were enrolled in this study. PIV was calculated as the product of peripheral blood neutrophil, monocyte, and platelet counts divided by lymphocyte count. Kaplan-Meier method and Cox hazard regression models were used for survival analyses.ResultsThe 1-year progression-free survival (PFS) was 63.5%, and the 5-year overall survival (OS) rate was 55.1%. Patients with higher PIV, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune inflammation index (SII) had worse PFS in univariate analysis, but only the PIV (hazard ratio [HR] = 2.90, 95% confidence interval [CI]: 1.79–4.70, p < 0.001) was an independent prognostic factor in multivariate analysis. Similarly, patients with higher PIV, NLR, PLR, and SII had a worse OS in the univariate analysis, but only the PIV (HR = 4.70, 95% CI: 2.00–11.02, p < 0.001) was significantly associated with worse OS in multivariate analysis.ConclusionPIV is a comprehensive and convenient predictor of both PFS and OS in patients with ALK-positive advanced NSCLC who received first-line ALK TKIs. Prospective clinical trials are required to validate the value of this new parameter.     

Increased CYFRA 21-1, CEA and NSE are Prognostic of Poor Outcome for Locally Advanced Squamous Cell Carcinoma in Lung: A Nomogram and Recursive Partitioning Risk Stratification Analysis

OBJECTIVES: This study aimed to: (1) assess the prognostic significance of serum tumor markers in locally advanced squamous cell carcinoma in lung (LA-SCCL); (2) generate a nomogram to predict the overall survival (OS) and (3) identify a prognostic stratification to assist the therapeutic decision-making. METHODS: LA-SCCL patients receiving definitive radiotherapy and baseline tumor marker measurement were eligible for this retrospective study. Cox proportional hazards regression was used to determine independent factors associated with various survival indexes and a nomogram was created to estimate the 5-year OS probability for individual patient. The identified prognostic factors were recruited into a recursive partitioning analysis (RPA) for OS to stratify patients with distinct outcome. RESULTS: A total of 224 patients were eligible for analysis. Increased cytokeratin-19 fragment (CYFRA 21-1) was independently associated with inferior OS, progression free survival (PFS) and a borderline decreased local-regional progression free survival (LRPFS). Elevated carcino-embryonic antigen (CEA) served as an unfavorable determinant for OS and increased neuron-specific enolase (NSE) was predictive of poor distant metastasis free survival (DMFS). A nomogram integrating KPS, TNM stage, CEA and CYFRA 21-1 was created, resulting in a c-index of 0.62. RPA identified 4 prognostic classifications, with median OS of 27.6, 19.9, 17.3 and 10.9?months for low, intermediate, high and very-high risk groups, respectively. CONCLUSIONS: Baseline tumor marker panel including CYFRA 21-1, CEA and NSE can be prognostic of outcome for LA-SCCL receiving definitive radiotherapy. The RPA identified four prognostic subgroups, which could assist personalized therapy and clinical trial design in LA-SCCL.     

No outcome disparities in patients with diffuse large B-cell lymphoma and a low socioeconomic status

IntroductionIn patients with diffuse large B-cell lymphoma (DLBCL) socioeconomic status (SES) is associated with outcome in several population-based studies. The aim of this study was to further investigate the existence of disparities in treatment and survival.MethodsA population-based cohort study was performed including 343 consecutive patients with DLBCL, diagnosed between 2005 and 2012, in the North-west of the Netherlands. SES was based on the socioeconomic position within the Netherlands by use of postal code and categorized as low, intermediate or high. With multivariable logistic regression and Cox proportional hazard models the association between SES and respectively treatment and overall survival (OS) was evaluated.ResultsTwo-third of patients was positioned in low SES. Irrespective of SES an equal proportion of patients received standard immunochemotherapy. SES was not a significant risk indicator for OS (intermediate versus low SES: hazard ratio (HR) 1.31 (95%CI 0.78–2.18); high versus low SES: HR 0.83 (95%CI 0.48–1.46)). The mortality risk remained significantly increased with higher age, advanced performance status, elevated LDH and presence of comorbidity.ConclusionWithin the setting of free access to health care, in this cohort of patients with DLBCL no disparities in treatment and survival were seen in those with lower SES.     

Sarcopenia and a 5-mRNA risk module as a combined factor to predict prognosis for patients with stomach adenocarcinoma

BackgroundSarcopenia is an important factor affecting the prognostic outcomes in adult cancer patients. Gastric cancer is considered an age-related disease and is one of the leading causes of global cancer mortality. We aimed to establish an effective age-related model at a molecular level to predict the prognosis of patients with gastric cancer.MethodsTCGA STAD (stomach adenocarcinoma) and NCBI GEO database were utilized in this study to explore the expression, clinical relevance and prognostic value of age-related mRNAs in stomach adenocarcinoma through an integrated bioinformatics analysis. WGCNA co-expression network, Univariate Cox regression analysis, LASSO regression and Multivariate Cox regression analysis were implemented to construct an age-related prognostic signature.ResultsAs a result, sarcopenia is not only an unfavorable factor for OS (overall survival) in patients with tumor of gastric (HR: 1.707, 95%CI: 1.437–2.026), but also increases the risk of postoperative complications in patients with gastric cancer (OR: 2.904, 95%CI: 2.150–3.922). A panel of 5 mRNAs (DCBLD1, DLC1, IGFBP1, RNASE1 and SPC24) were identified to dichotomize patients with significantly different OS and independently predicted the OS in TCGA STAD (HR = 3.044, 95%CI = 2.078–4.460, P < 0.001).ConclusionThe study provided novel insights to understand STAD at a molecular level and indicated that the 5 mRNAs might act as independent promising prognosis biomarkers for STAD. Sarcopenia and the 5-mRNA risk module as a combined factor to predict prognosis may play an important role in clinical diagnosis.     

Preoperative prediction of microvascular invasion in non-metastatic hepatocellular carcinoma based on nomogram analysis

PurposeThe presence of microvascular invasion (MVI) is an unfavorable prognostic factor for hepatocellular carcinoma (HCC). This study aimed to construct a nomogram-based preoperative prediction model of MVI, thereby assisting to preoperatively select proper surgical procedures.MethodsA total of 714 non-metastatic HCC patients undergoing radical hepatectomy were retrospectively selected from Zhongshan Hospital between 2010 and 2018, followed by random assignment into training (N = 520) and validation cohorts (N = 194). Nomogram-based prediction model for MVI risk was constructed by incorporating independent risk factors of MVI presence identified from multivariate backward logistic regression analysis in the training cohort. The performance of nomogram was evaluated by calibration curve and ROC curve. Finally, decision curve analysis (DCA) was used to determine the clinical utility of the nomogram.ResultsIn total, 503 (70.4%) patients presented MVI. Multivariate analysis in the training cohort revealed that age (OR: 0.98), alpha-fetoprotein (≥400 ng/mL) (OR: 2.34), tumor size (>5 cm) (OR: 3.15), cirrhosis (OR: 2.03) and γ-glutamyl transpeptidase (OR: 1.61) were significantly associated with MVI presence. The incorporation of five risk factors into a nomogram-based preoperative estimation of MVI risk demonstrated satisfactory discriminative capacity, with C-index of 0.702 and 0.690 in training and validation cohorts, respectively. Calibration curve showed good agreement between actual and predicted MVI risks. Finally, DCA revealed the clinical utility of the nomogram.ConclusionThe nomogram showed a satisfactory discriminative capacity of MVI risk in HCC patients, and could be used to preoperatively estimate MVI risk, thereby establishing more rational therapeutic strategies.     

A predictive model with a risk-classification system for cancer-specific survival in patients with primary osteosarcoma of long bone

BackgroundOsteosarcoma (OS), most commonly occurring in long bone, is a group of malignant tumors with high incidence in adolescents. No individualized model has been developed to predict the prognosis of primary long bone osteosarcoma (PLBOS) and the current AJCC TNM staging system lacks accuracy in prognosis prediction. We aimed to develop a nomogram based on the clinicopathological factors affecting the prognosis of PLBOS patients to help clinicians predict the cancer-specific survival (CSS) of PLBOS patients.MethodWe studied 1199 PLBOS patients from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015 and randomly divided the dataset into training and validation cohorts at a proportion of 7:3. Independent prognostic factors determined by stepwise multivariate Cox analysis were included in the nomogram and risk-stratification system. C-index, calibration curve, and decision curve analysis (DCA) were used to verify the performance of the nomogram.ResultsAge, Histological type, Surgery of primary site, Tumor size, Local extension, Regional lymph node (LN) invasion, and Distant metastasis were identified as independent prognostic factors. C-indexes, calibration curves and DCAs of the nomogram indicating that the nomogram had good discrimination and validity. The risk-stratification system based on the nomogram showed significant differences (P < 0.05) in CSS among different risk groups.ConclusionWe established a nomogram with risk-stratification system to predict CSS in PLBOS patients and demonstrated that the nomogram had good performance. This model can help clinicians evaluate prognoses, identify high-risk individuals, and give individualized treatment recommendation of PLBOS patients.     

A preoperative model for predicting microvascular invasion and assisting in prognostic stratification in liver transplantation for HCC regarding empirical criteria

PurposeThe prediction of microvascular invasion (MVI) has increasingly been recognized to reflect prognosis involving local invasion and distant metastasis of hepatocellular carcinoma (HCC). The aim of this study was to assess a predictive model using preoperatively accessible clinical parameters and radiographic features developed and validated to predict MVI. This predictive model can distinguish clinical outcomes after liver transplantation (LT) for HCC patients.MethodsIn total, 455 HCC patients who underwent LT between January 1, 2015, and December 31, 2019, were retrospectively enrolled in two centers in China as a training cohort (ZFA center; n = 244) and a test cohort (SLA center; n = 211). Univariate and multivariate backward logistic regression analysis were used to select the significant clinical variables which were incorporated into the predictive nomogram associated with MVI. Receiver operating characteristic (ROC) curves based on clinical parameters were plotted to predict MVI in the training and test sets.ResultsUnivariate and multivariate backward logistic regression analysis identified four independent preoperative risk factors for MVI: α-fetoprotein (AFP) level (p < 0.001), tumor size ((p < 0.001), peritumoral star node (p = 0.003), and tumor margin (p = 0.016). The predictive nomogram using these predictors achieved an area under curve (AUC) of 0.85 and 0.80 in the training and test sets. Furthermore, MVI could discriminate different clinical outcomes within the Milan criteria (MC) and beyond the MC.ConclusionsThe nomogram based on preoperatively clinical variables demonstrated good performance for predicting MVI. MVI may serve as a supplement to the MC.