Development and validation of a gene expression-based signature to predict distant metastasis in locoregionally advanced nasopharyngeal carcinoma: a retrospective, multicentre, cohort study

The prognostic value of pretreatment 18F-FDG PET-CT parameters with peripheral blood markers in patients with de novo metastatic nasopharyngeal carcinoma

BACKGROUND AND PURPOSE

To develop and validate a prognostic nomogram based on pretreatment 18F-fluorodeoxyglucose positron emission tomography/computed tomography (PET-CT)radiomics parameters and peripheral blood markers for risk stratification in patients with de novo metastatic nasopharyngeal carcinoma (dmNPC).

MATERIALS AND METHODS

A total of 558 patients with dmNPC were retrospectively enrolled between 2011 and 2019. Eligible patients were randomly divided into training and validation cohorts (7:3 ratio). A Cox regression model was used to identify prognostic factors for overall survival (OS). The predictive accuracy and discriminative ability of the prognostic nomogram were determined using the concordance index (C-index) and calibration curve.

RESULTS

Independent factors derived from multivariable analysis of the training cohort to predict death were lactate dehydrogenase levels, pretreatment Epstein-Barr virus DNA, total lesion glycolysis of locoregional lesions, number of metastatic lesions, and age, all of which were assembled into a nomogram with (nomogram B) or without PET-CT parameters (nomogram A). The C-index of nomogram B for predicting death was 0.70, which was significantly higher than the C-index values for nomogram A. Patients were then stratified into low- and high-risk groups based on the scores calculated using nomogram B for OS. The median OS was significantly higher in the low-risk group than in the high-risk group (69.60 months [95 % CI: 58.50-108.66] vs. 21.40 months [95 % CI: 19.20-23.90]; p＜0.01). All the results were confirmed in the validation cohort.

CONCLUSION

The proposed nomogram including PET-CT parameters yielded accurate prognostic predictions for patients with dmNPC, enabling effective risk stratification for these patients.

Using the genomic adjusted radiation dose (GARD) to personalize the radiation dose in nasopharyngeal cancer

BACKGROUND

Locally advanced nasopharyngeal cancer (NPC) patients undergoing radiotherapy are at risk of treatment failure, particularly locoregional recurrence. To optimize the individual radiation dose, we hypothesize that the genomic adjusted radiation dose (GARD) can be used to correlate with locoregional control.

METHODS

A total of 92 patients with American Joint Committee on Cancer / International Union Against Cancer stage III to stage IVB recruited in a randomized phase III trial were assessed (NPC-0501) (NCT00379262). Patients were treated with concurrent chemo-radiotherapy plus (neo) adjuvant chemotherapy. The primary endpoint is locoregional failure free rate (LRFFR).

RESULTS

Despite the homogenous physical radiation dose prescribed (Median: 70 Gy, range 66-76 Gy), there was a wide range of GARD values (median: 50.7, range 31.1-67.8) in this cohort. In multivariable analysis, a GARD threshold (GARDT) of 45 was independently associated with LRFFR (p = 0.008). By evaluating the physical dose required to achieve the GARDT (RxRSI), three distinct clinical subgroups were identified: (1) radiosensitive tumors that RxRSI at dose < 66 Gy (N = 59, 64.1 %) (b) moderately radiosensitive tumors that RxRSI dose within the current standard of care range (66-74 Gy) (N = 20, 21.7 %), (c) radioresistant tumors that need a significant dose escalation above the current standard of care (>74 Gy) (N = 13, 14.1 %).

CONCLUSION

GARD is independently associated with locoregional control in radiotherapy-treated NPC patients from a Phase 3 clinical trial. GARD may be a potential framework to personalize radiotherapy dose for NPC patients.

Impact of magnetic resonance imaging-derived skeletal muscle index in locoregionally advanced nasopharyngeal carcinoma

PURPOSE

To evaluate the clinical implication of magnetic resonance imaging (MRI)-derived skeletal muscle index (SMI) in locoregionally advanced nasopharyngeal carcinoma (LANPC) patients undergoing induction chemotherapy (IC) followed by concurrent chemoradiotherapy (CCRT) and further to develop a nomogram for predicting survival prognosis.

METHODS

SMI was determined through baseline MRI at the third cervical level. The nomogram was based on a training cohort involving 409 LANPC patients. We validated the prognostic accuracy of this prognostic model in an internal validation cohort (n = 204) and an external independent cohort (n = 272).

RESULTS

SMI was an independent risk factor for OS. A prognostic model comprising age, TNM stage and SMI for individual survival prediction was developed and graphically represented as a nomogram. The model showed favorable discrimination (C-index: 0.686), predictive accuracy [time dependent area under the curve (tAUC) at 5 years: 0.70], and calibration, and was further validated in the internal and external validation datasets. A risk stratification derived from the model stratified these patients into three prognostic subgroups with significantly different survival.

CONCLUSIONS

Low SMI accessed by MRI was significantly associated with poor overall survival in LANPC patients undergoing IC + CCRT. Moreover, we established and validated a novel nomogram involving age, TNM stage and SMI that could provide accurate prognostic stratification among this population.

Application of machine learning for high-throughput tumor marker screening

High-throughput sequencing and multiomics technologies have allowed increasing numbers of biomarkers to be mined and used for disease diagnosis, risk stratification, efficacy assessment, and prognosis prediction. However, the large number and complexity of tumor markers make screening them a substantial challenge. Machine learning (ML) offers new and effective ways to solve the screening problem. ML goes beyond mere data processing and is instrumental in recognizing intricate patterns within data. ML also has a crucial role in modeling dynamic changes associated with diseases. Used together, ML techniques have been included in automatic pipelines for tumor marker screening, thereby enhancing the efficiency and accuracy of the screening process. In this review, we discuss the general processes and common ML algorithms, and highlight recent applications of ML in tumor marker screening of genomic, transcriptomic, proteomic, and metabolomic data of patients with various types of cancers. Finally, the challenges and future prospects of the application of ML in tumor therapy are discussed.

Enhancing efficacy and reducing toxicity: Therapeutic optimization in locoregionally advanced nasopharyngeal carcinoma

When applied as the standard therapeutic modality, intensity-modulated radiotherapy (IMRT) improves local control and survival rates in patients with nasopharyngeal carcinoma (NPC). However, distant metastasis continues to be the leading cause of treatment failure. Here, we review the most recent optimization strategies for combining chemotherapy with IMRT in high-risk patients with locoregionally advanced NPC. We focus on major clinical trials on induction chemotherapy and metronomic adjuvant chemotherapy, emphasizing their efficacy in mitigating distant metastasis and prognosis. We also highlight innovations in reducing toxicity in low-risk patients, particularly through approaches of excluding chemotherapy, adopting equivalent low-toxicity drugs, or selectively exempting lymph nodes with low metastatic risk from irradiation. These approaches have provided positive treatment outcomes and significantly enhanced patients' quality of life. Finally, we provide an overview of the evolving immunotherapy landscape, with a focus on the ongoing trials and future potential of immune checkpoint inhibitors in advanced NPC treatment.

Development and validation of a LASSO prediction model for cisplatin induced nephrotoxicity: a case-control study in China

BACKGROUND

Early identification of high-risk individuals with cisplatin-induced nephrotoxicity (CIN) is crucial for avoiding CIN and improving prognosis. In this study, we developed and validated a CIN prediction model based on general clinical data, laboratory indications, and genetic features of lung cancer patients before chemotherapy.

METHODS

We retrospectively included 696 lung cancer patients using platinum chemotherapy regimens from June 2019 to June 2021 as the traing set to construct a predictive model using Absolute shrinkage and selection operator (LASSO) regression, cross validation, and Akaike's information criterion (AIC) to select important variables. We prospectively selected 283 independent lung cancer patients from July 2021 to December 2022 as the test set to evaluate the model's performance.

RESULTS

The prediction model showed good discrimination and calibration, with AUCs of 0.9217 and 0.8288, sensitivity of 79.89% and 45.07%, specificity of 94.48% and 94.81%, in the training and test sets respectively. Clinical decision curve analysis suggested that the model has value for clinical use when the risk threshold ranges between 0.1 and 0.9. Precision-Recall (PR) curve shown in recall interval from 0.5 to 0.75: precision gradually declines with increasing Recall, up to 0.9.

CONCLUSIONS

Predictive models based on laboratory and demographic variables can serve as a beneficial complementary tool for identifying high-risk populations with CIN.

Early prediction of long-term survival of patients with nasopharyngeal carcinoma by multi-parameter MRI radiomics

Purpose

The objective is to create a comprehensive model that integrates clinical, semantic, and radiomics features to forecast the 5-year progression-free survival (PFS) of individuals diagnosed with non-distant metastatic Nasopharyngeal Carcinoma (NPC).

Methods

In a retrospective analysis, we included clinical and MRI data from 313 patients diagnosed with primary NPC. Patient classification into progressive and non-progressive categories relied on the occurrence of recurrence or distant metastasis within a 5-year timeframe. Initial screening comprised clinical features and statistically significant image semantic features. Subsequently, MRI radiomics features were extracted from all patients, and optimal features were selected to formulate the Rad-Score.Combining Rad-Score, image semantic features, and clinical features to establish a combined model Evaluation of predictive efficacy was conducted using ROC curves and nomogram specific to NPC progression. Lastly, employing the optimal ROC cutoff value from the combined model, patients were dichotomized into high-risk and low-risk groups, facilitating a comparison of 10-year overall survival (OS) between the groups.

Results

The combined model showcased superior predictive performance for NPC progression, reflected by AUC values of 0.84, an accuracy rate of 81.60%, sensitivity at 0.77, and specificity at 0.81 within the training group. In the test set, the AUC value reached 0.81, with an accuracy of 74.6%, sensitivity at 0.82, and specificity at 0.66.

Conclusion

The amalgamation of Rad-Score, clinical, and imaging semantic features from multi-parameter MRI exhibited significant promise in prognosticating 5-year PFS for non-distant metastatic NPC patients. The combined model provided quantifiable data for informed and personalized diagnosis and treatment planning.

Prognostic value of tumor deposits and positive lymph node ratio in stage III colorectal cancer: a retrospective cohort study

BACKGROUND

In colorectal cancer (CRC), tumor deposits (TD) have been used to guide the N staging only in node-negative patients. It remains unknown about the prognostic value of TD in combination with positive lymph node ratio (LNR) in stage III CRC.

PATIENTS AND METHODS

The authors analyzed data from 31 139 eligible patients diagnosed with stage III CRC, including 30 230 from the Surveillance, Epidemiology, and End Results (SEER) database as a training set and 909 from two Chinese hospitals as a validation set. The associations of TD and LNR with cancer-specific survival (CSS) and overall survival (OS) were evaluated using the Kaplan-Meier method and Cox regression models.

RESULTS

Both TD-positive and high LNR (value ≥0.4) were associated with worse CSS in the training [multivariable hazard ratio (HR), 1.50; 95% CI: 1.43-1.58 and HR, 1.74; 95% CI: 1.62-1.86, respectively] and validation sets (HR, 1.90; 95% CI: 1.41-2.54 and HR, 2.01; 95% CI: 1.29-3.15, respectively). Compared to patients with TD-negative and low LNR (value<0.4), those with TD-positive and high LNR had a 4.09-fold risk of CRC-specific death in the training set (HR, 4.09; 95% CI: 3.54-4.72) and 4.60-fold risk in the validation set (HR, 4.60; 95% CI: 2.88-7.35). Patients with TD-positive/H-LNR CRC on the right side had the worst prognosis ( P <0.001). The combined variable of TD and LNR contributed the most to CSS prediction in the training (24.26%) and validation (32.31%) sets. A nomogram including TD and LNR showed satisfactory discriminative ability, and calibration curves indicated favorable consistency in both the training and validation sets.

CONCLUSIONS

TD and LNR represent independent prognostic predictors for stage III CRC. A combination of TD and LNR could be used to identify those at high-risk of CRC deaths.

Radiomics-based lymph nodes prognostic models from three MRI regions in nasopharyngeal carcinoma

Accurate prediction of the prognosis of nasopharyngeal carcinoma (NPC) is important for treatment. Lymph nodes metastasis is an important predictor for distant failure and regional recurrence in patients with NPC. Traditionally, subjective radiological evaluation increases concerns regarding the accuracy and consistency of predictions. Radiomics is an objective and quantitative evaluation algorithm for medical images. This retrospective analysis was conducted based on the data of 729 patients newly diagnosed with NPC without distant metastases to evaluate the performance of radiomics pretreatment using magnetic resonance imaging (MRI)-determined metastatic lymph nodes models to predict NPC prognosis with three delineation methods. Radiomics features were extracted from all lymph nodes (ALN), largest lymph node (LLN), and largest slice of the largest lymph node (LSLN) to generate three radiomics signatures. The radiomics signatures, clinical model, and radiomics-clinic merged models were developed in training cohort for predicting overall survival (OS). The results showed that LSLN signature with clinical factors predicted OS with high accuracy and robustness using pretreatment MR-determined metastatic lymph nodes (C-index [95 % confidence interval]: 0.762[0.760-0.763]), providing a new tool for treatment planning in NPC.

Significance of Pre-Treatment CALLY Score Combined with EBV-DNA Levels for Prognostication in Non-Metastatic Nasopharyngeal Cancer Patients: A Clinical Perspective

Background

The C-reactive protein-albumin-lymphocyte (CALLY) score is a novel indicator associated with inflammation, immunity, and nutrition, utilized for cancer prognostic stratification. This study aimed to evaluate the integrated prognostic significance of the pre-treatment CALLY score and Epstein-Barr virus (EBV) DNA levels in nasopharyngeal carcinoma (NPC) patients and to develop prognostic models.

Patients and Methods

A total of 1707 NPC patients from September 2015 to December 2017 were retrospectively enrolled. The cut-off point for the CALLY score, determined by maximum selected rank statistics, integrates with the published cut-off point for pre-EBV DNA to develop a comprehensive index. Subsequently, patients were randomly allocated in a 1:1 ratio into training and validation cohorts. Survival analysis was conducted using the Kaplan-Meier method with Log rank tests, and the Cox proportional hazards model was applied to identify independent prognostic factors for constructing predictive nomograms. The predictive ability of the nomograms were assessed through the concordance index (C-index), calibration curves, and decision curve analysis.

Results

By integrating CALLY scores and EBV-DNA levels, patients were categorized into three risk clusters. Kaplan-Meier curves reveal significant differences in overall survival (OS), distant metastasis-free survival (DMFS), and locoregional relapse-free survival (LRRFS) outcomes among different risk groups (all P values < 0.05). Multivariate analysis revealed that CALLY-EBV DNA index serves as an independent prognostic factor for the OS, DMFS, and LRRFS. The prognostic nomograms based on the CALLY-EBV DNA index provided accurate predictions for 1-year, 3-year, and 5-year OS, DMFS, and LRRFS. Additionally, compared to the traditional TNM staging system, the nomograms exhibited enhanced discriminatory power, calibration capability, and clinical applicability. All results were in agreement with the validation cohort.

Conclusion

The CALLY-EBV DNA index is an independent prognostic biomarker. The nomogram prediction models, constructed based on the CALLY-EBV DNA index, demonstrates superior predictive performance compared to the traditional TNM staging.

Embolic stroke of undetermined source: Focusing on atrial cardiopathy and patent foramen ovale

BACKGROUND

Atrial cardiopathy(AC) and patent foramen ovale (PFO) are two etiologies of embolic strokes of undetermined source (ESUS). We aimed to explore the relationship between them in ESUS.

METHODS

A total of 1146 participants were included from January 2019 to June 2022, which included the ESUS group and non-embolic stroke which includes LAA(large arterial atherosclerosis) + SAO(small artery occlusion) group. AC was defined as the presence of at least one of the following: PTFV1(P-wave terminal force in lead V1) > 4000 μV*ms in the electrocardiograms, NT-proBNP(N-terminal probrain natriuretic peptide) > 250 pg/mL in laboratory tests or LAD(left atrial diameter) > 3.8 cm for women and > 4.0 cm for men in cardiac ultrasound. The presence of PFO was assessed by transthoracic echocardiography, transcranial Doppler ultrasound, transesophageal echocardiography or cardiac MRI. PFO was considered pathogenic if the RoPE score was 7 to 10.

RESULTS

The prevalence of AC and PFO was higher in the ESUS group than the LAA + SAO group. The prevalence of AC was lower in ESUS patients with pathogenic PFO (37.9%) than those without PFO (68.4%) and with incidental PFO (64.0%) (p = 0.006). The prevalence of pathogenic PFO was lower in ESUS patients with AC than those without AC (6.0% vs. 17.8%, p = 0.006). The AUC(area under the curve) of PTFV1 for predicting ESUS was 0.724 [95%CI (0.686-0.762), p < 0.05)], indicating that PTFV1 the most valuable AC biomarker.

CONCLUSIONS

The prevalence of AC is inversely related to the prevalence of pathogenic PFO in ESUS patients. PTFV1 was the most valuable index to predict ESUS among the AC biomarkers.

Prediction of early recovery of graft function after living donor liver transplantation in children

For end-stage liver disease in children, living donor liver transplantation (LDLT) is often the important standard curative treatment. However, there is a lack of research on early recovery of graft function after pediatric LDLT. This is a single-center, ambispective cohort study. We collected the demographic and clinicopathological data of donors and recipients, and determined the risk factors of postoperative delayed recovery of hepatic function (DRHF) by univariate and multivariate Logistic analyses. 181 cases were included in the retrospective cohort and 50 cases in the prospective cohort. The incidence of DRHF after LDLT in children was 29.4%, and DRHF could well evaluate the early recovery of graft function after LDLT. Through Logistic analyses and AIC score, preoperative liver function of donors, ischemia duration level of the liver graft, Ln (Cr of recipients before operation) and Ln (TB of recipients on the 3rd day after operation) were predictive indicators for DRHF after LDLT in children. Using the above factors, we constructed a predictive model to evaluate the incidence of postoperative DRHF. Self-verification and prospective internal verification showed that this prediction model had good accuracy and clinical applicability. In conclusion, we pointed many risk factors for early delayed recovery of graft function after LDLT in children, and developed a visual and personalized predictive model for them, offering valuable insights for clinical management.

Identification of nomogram associated with durable clinical benefit gene for advanced non-small cell lung cancer with sensitivity to responsive to immunotherapy

Background

Immunotherapy has become the standard treatment for advanced non-small cell lung cancer (NSCLC). However, a subset of the most advanced NSCLC patients fails to respond adequately to Immune checkpoint inhibitors (ICIs). Developing new nomograms and integrating prognostic factors are crucial for improving the clinical predictability of NSCLC patients undergoing ICIs.

Methods

Clinical information and genomic data of NSCLC patients undergoing ICIs were retrieved from cBioPortal. Gene alterations associated with durable clinical benefit (DCB) were compared to those linked to no durable benefit (NDB). The Kaplan-Meier plot method was employed for survival analysis, and a novel nomogram was formulated by selecting pertinent clinical variables.

Results

For the NSCLC patients receiving immunotherapy, three subgroups were identified based on the treatment regimen, including anti-PD-1 monotherapy, anti-PD-1 combination with anti-CTLA-4, and first-line treatment. The mutation status of TP53, PGR, PTPRT, RELN, MUC19, LRP1B, and FAT3 was found to be associated with progression-free survival (PFS). Using the clinicopathological parameters and genomic data of the patients, we developed three novel nomograms to predict the prognosis of ICI treatment in different subgroups.

Conclusion

Our study revealed that PGR, PTPRD, RELN, MUC19, LRP1B, and FAT3 mutation could serve as predictive biomarkers. Our systematic nomograms demonstrate significant potential in predicting the prognosis for NSCLC patients with sensitivity to different ICI treatment strategies.

Development and internal validation of a nomogram based on peripheral blood inflammatory markers for predicting prognosis in nasopharyngeal carcinoma

BACKGROUND

Inflammatory markers, including the product of neutrophil count, platelet count, and monocyte count divided by lymphocyte count (PIV) and the platelet-to-white blood cell ratio (PWR), have not been previously reported as prognostic factors in nasopharyngeal carcinoma (NPC) patients. In order to predict overall survival (OS) in NPC patients, our goal was to create and internally evaluate a nomogram based on inflammatory markers (PIV, PWR).

METHODS

A retrospective study was done on patients who received an NPC diagnosis between January 2015 and December 2018. After identifying independent prognostic indicators linked to OS using Cox proportional hazards regression analysis, we created a nomogram with the factors we had chosen.

RESULTS

A total of 630 NPC patients in all were split into training (n = 441) and validation sets (n = 189) after being enrolled in a population-based study in 2015-2018 and monitored for a median of 5.9 years. In the training set, the age, PIV, and PWR, selected as independent predictors for OS via multivariate Cox's regression model, were chosen to develop a nomogram. Both training and validation cohorts had C-indices of 0.850 (95% confidence interval [CI]: 0.768-0.849) and 0.851 (95% CI: 0.765-0.877). Furthermore, compared with traditional TNM staging, our nomogram demonstrated greater accuracy in predicting patient outcomes. The risk stratification model derived from our prediction model may facilitate personalized treatment strategies for NPC patients.

CONCLUSION

Our findings confirmed the prognostic significance of the PWR and PIV in NPC. High PIV levels (>363.47) and low PWR (≤36.42) values are associated with worse OS in NPC patients.

ADAM22 acts as a novel predictive biomarker for unfavorable prognosis and facilitates metastasis via PI3K/AKT signaling pathway in nasopharyngeal carcinoma

BACKGROUND

Nasopharyngeal carcinoma (NPC) is a type of epithelial malignancy known for its high likelihood of metastasizing to distant organs, which remains the primary obstacle in the treatment of NPC. The present study aimed to identify potential intervention target for NPC metastasis.

METHODS

The differentially expressed genes (DEGs) were firstly analyzed and intersected across various NPC related datasets in the Gene Expression Omnibus database. Subsequently, various techniques including quantitative polymerase chain reaction (qPCR), western blotting, immunohistochemistry, migration and invasion assays, in conjunction with bioinformatics and prognostic modeling, were utilized to elucidate the role of candidate genes in NPC metastasis.

RESULTS

We discerned the gene a disintegrin and metalloprotease 22 (ADAM22) as a distinct and significant factor in the progression and metastasis of NPC through five datasets. The elevated expression of ADAM22 was observed in clinical tissue and plasma samples with advanced NPC, as well as in high metastatic cells. Furthermore, we highlighted its essential role in a prognostic model that demonstrated strong prediction performance for NPC. Notably, overexpression of ADAM22 was found to enhance the aggressiveness and epithelial-mesenchymal transition (EMT) of low metastatic NPC cells, whereas the downregulation of ADAM22 resulted in suppressed effect in high metastatic cells. Delving into the mechanism, ADAM22 activated the PI3K/Akt signaling pathway through the mediation of Rac Family Small GTPase 2 (RAC2), thereby facilitating EMT and metastasis in NPC.

CONCLUSIONS

The study provided pioneering insights that ADAM22 had the potential to act as an oncogene by promoting EMT and metastasis of NPC through the RAC2-mediated PI3K/Akt signaling pathway. Thus, ADAM22 could serve as a novel prognostic indicator in NPC.

Clinical Subtypes of Neutrophilic Asthma: A Cluster Analysis From Australasian Severe Asthma Network

BACKGROUND

Clinical heterogeneity may exist within asthma subtypes defined by inflammatory markers. However, the heterogeneity of neutrophilic asthma (NA) remains largely unexplored.

OBJECTIVE

To explore potential clusters and the stability of NA.

METHODS

Participants with NA from the Australasian Severe Asthma Network underwent a multidimensional assessment. They were then asked to participate in a 12-month longitudinal cohort study. We explored potential clusters using a hierarchical cluster analysis and validated the differential future risk of asthma exacerbations in the identified clusters. A decision tree analysis was developed to predict cluster assignments. Finally, the stability of prespecified clusters was examined within 1 month.

RESULTS

Three clusters were identified in 149 patients with NA. Cluster 1 (n = 99; 66.4%) was characterized by female-predominant nonsmokers with well-controlled NA, cluster 2 (n = 16; 10.7%) by individuals with comorbid anxiety/depressive symptoms with poorly controlled NA, and cluster 3 by older male smokers with late-onset NA. Cluster 2 had a greater proportion of participants with severe exacerbations (P = .005), hospitalization (P = .010), and unscheduled visits (P = .013) and a higher number of emergency room visits (P = .039) than that of the other two clusters. The decision tree assigned 92.6% of participants correctly. Most participants (87.5%; n = 7) in cluster 2 had a stable NA phenotype, whereas participants of clusters 1 and 3 had variable phenotypes.

CONCLUSIONS

We identified three clinical clusters of NA, in which cluster 2 represents an uncontrolled and stable NA subtype with an elevated risk of exacerbations. These findings have clinical implications for the management of NA.

Potential pre-COPD indicators in association with COPD development and COPD prediction models in Chinese: a prospective cohort study

Background

Lung injury might take place before chronic obstructive pulmonary disease (COPD) occurs. A clearer definition of "pre-COPD" based on the effects of potential indicators on increasing risk of COPD development and a prediction model involving them are lacking.

Methods

A total of 3526 Chinese residents without COPD aged 40 years or older derived from the national cross-sectional survey of COPD surveillance in 2014-2015 were followed up for a mean of 3.59 years. We examined the associations of chronic bronchitis, preserved ratio impaired spirometry (PRISm), low peak expiratory flow (PEF), spirometric small airway dysfunction (sSAD), low maximal mid-expiratory flow (MMEF), low forced expiratory flow 50% of pulmonary volume (FEF50), and low FEF75 with subsequent COPD and constructed a prediction model with LASSO-Cox regression.

Findings

235 subjects in the cohort developed COPD during the follow-up. Subjects with PRISm, low PEF, sSAD, low MMEF, low FEF50, and low FEF75 had an increased risk of developing COPD (adjusted hazard ratio [HR] ranging from 1.57 to 3.01). Only chronic bronchitis (HR 2.84 [95% CI 1.38-5.84] and 2.94 [1.43-6.04]) and sSAD/low MMEF (HR 2.74 [2.07-3.61] and 2.38 [1.65-3.43]) showed effects independent of the other indicators and their concurrence had the strongest effect (HR 5.89 and 4.80). The prediction model including age, sex, low MMEF, low FEF50, and indoor exposure to biomass had good performance both internally and temporally. The corrected C-index was 0.77 (0.72-0.81) for discrimination in internal validation. For temporal validation, the area under the receiver operating characteristic curve was 0.73 (0.63-0.83). Good calibration was indicated in plot for internal validation and by Hosmer-Lemeshow test for temporal validation.

Interpretation

Individuals with concurrent chronic bronchitis and sSAD/low MMEF indicating pre-COPD optimally require more high attention from physicians. Our prediction model could serve as a multi-dimension tool to predict COPD comprehensively.

Funding

The Ministry of Finance and the Ministry of Science and Technology of the People's Republic of China and the National Natural Science Foundation of China.

Stiffness promotes cell migration, invasion, and invadopodia in nasopharyngeal carcinoma by regulating the WT-CTTN level

Matrix stiffness potently promotes the malignant phenotype in various biological contexts. Therefore, identification of gene expression to participate in mechanical force signals transduced into downstream biochemical signaling will contribute substantially to the advances in nasopharyngeal carcinoma (NPC) treatment. In the present study, we detected that cortactin (CTTN) played an indispensable role in matrix stiffness-induced cell migration, invasion, and invadopodia formation. Advances in cancer research have highlighted that dysregulated alternative splicing contributes to cancer progression as an oncogenic driver. However, whether WT-CTTN or splice variants (SV1-CTTN or SV2-CTTN) regulate matrix stiffness-induced malignant phenotype is largely unknown. We proved that alteration of WT-CTTN expression modulated matrix stiffness-induced cell migration, invasion, and invadopodia formation. Considering that splicing factors might drive cancer progression through positive feedback loops, we analyzed and showed how the splicing factor PTBP2 and TIA1 modulated the production of WT-CTTN. Moreover, we determined that high stiffness activated PTBP2 expression. Taken together, our findings showed that the PTBP2-WT-CTTN level increases upon stiffening and then promotes cell migration, invasion, and invadopodia formation in NPC.

Precision Medicine for Nasopharyngeal Cancer-A Review of Current Prognostic Strategies

Nasopharyngeal carcinoma (NPC) is an Epstein-Barr virus (EBV) driven malignancy arising from the nasopharyngeal epithelium. Current treatment strategies depend on the clinical stage of the disease, including the extent of the primary tumour, the extent of nodal disease, and the presence of distant metastasis. With the close association of EBV infection with NPC development, EBV biomarkers have shown promise in predicting treatment outcomes. Among the omic technologies, RNA and miRNA signatures have been widely studied, showing promising results in the research setting to predict treatment response. The transformation of radiology images into measurable features has facilitated the use of radiomics to generate predictive models for better prognostication and treatment selection. Nonetheless, much of this work remains in the research realm, and challenges remain in clinical implementation.

Camrelizumab and apatinib plus induction chemotherapy and concurrent chemoradiotherapy in stage N3 nasopharyngeal carcinoma: a phase 2 clinical trial

The antiangiogenic agent apatinib has been shown to clinically improve responses to immune checkpoint inhibitors in several cancer types. Patients with N3 nasopharyngeal carcinoma have a high risk of distant metastasis, however, if the addition of immunotherapy to standard treatment could improve efficacy is unclear. In this phase II clinical trial (ChiCTR2000032317), 49 patients with stage TanyN3M0 nasopharyngeal carcinoma were enrolled and received the combination of three cycles of induction chemotherapy, camrelizumab and apatinib followed by chemoradiotherapy. Here we report on the primary outcome of distant metastasis-free survival and secondary end points of objective response rate, failure-free survival, locoregional recurrence-free survival, overall survival and toxicity profile. After induction therapy, all patients had objective response, including 13 patients (26.5%) with complete response. After a median follow-up of 28.7 months, the primary endpoint of 1-year distant metastasis-free survival was met for the cohort (1-year DMFS rate: 98%). Grade≥3 toxicity appeared in 32 (65.3%) patients, with the most common being mucositis (14[28.6%]) and nausea/vomiting (9[18.4%]). In this work, camrelizumab and apatinib in combination with induction chemotherapy show promising distant metastasis control with acceptable safety profile in patients with stage TanyN3M0 nasopharyngeal carcinoma.

Using machine learning algorithms to predict the prognosis of advanced nasopharyngeal carcinoma after intensity-modulated radiotherapy

BACKGROUND

The prognosis of advanced nasopharyngeal carcinoma (NPC) patients after intensity-modulated radiotherapy (IMRT) has not been well studied. We aimed to construct prognostic models for advanced NPC patients with stage III-IV after their first treatment with IMRT by using machine learning algorithms and to identify the most important predictors.

METHODS

A total of 427 patients treated in Meizhou People's Hospital in Guangdong province, China from January 1, 2013 to December 12, 2018 were enrolled in this study, with an average follow-up period of 7.16 years from July 2020 to March 2021. Candidate predictors were selected from demographics, clinical features, medical examinations and test results. Three machine learning algorithms were applied to construct advanced NPC prognostic models: logistic regression (LR), decision tree (DT), and random forest (RF). Area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. The important predictors of the optimal model for unfavourable prognosis were identified and ranked.

RESULTS

There were 50 (11.7%) NPC-related deaths observed in this study. The mean age of all participants was 49.39±11.29 years, of whom 299 (70.0%) were males. In general, RF showed the best predictive performance with the highest AUC (0.753, 95% CI: 0.609, 0.896), compared to LR (0.736, 95% confidence interval (CI): 0.590, 0.881), and DT (0.720, 95% CI: 0.520, 0.921). The six most important predictors identified by RF were Epstein-Barr virus deoxyribonucleic acid, aspartate aminotransferase, body mass index, age, blood glucose level, and alanine aminotransferase.

CONCLUSIONS

We proposed RF as a simple and accurate tool for the evaluation of the prognosis of advanced NPC patients after the treatment with IMRT in clinical settings.

Elevation of H3K27me3 level contributes to the radioresistance of nasopharyngeal carcinoma by inhibiting OAS1 expression

Radiotherapy has long been a main treatment option for nasopharyngeal carcinoma (NPC). However, during clinical treatment, NPC is prone to developing radioresistance, resulting in treatment failure. This study aims to examine the role of histone methylation in the induction of radioresistance. It was found that the radioresistance of NPC cells was related to the increase of the level of histone H3 lysine 27 trimethylation (H3K27me3). Treatment of cells with histone methyltransferase inhibitor GSK126 increased the radiosensitivity of NPC cells by triggering Bcl2 apoptosis regulator/BCL2-associated X, apoptosis regulator (Bcl2/BAX) signaling pathway. Bioinformatics analysis indicated that the expression of 2'-5'-oligoadenylate synthetase 1 (OAS1) was reduced in the radioresistant cells but increased in the GSK126-treated cells. Chromatin immunoprecipitation assay confirmed that the decrease of OAS1 expression in radioresistant cells was mainly due to the enrichment of H3K27me3 in its promoter region. Furthermore, downregulation of OAS1 reduced apoptosis due to the inhibition of Bcl2/BAX pathway after irradiation, while OAS1 overexpression increased radiosensitivity. Our findings revealed for the first time that the increase of H3K27me3 level was associated with the decrease of OAS1 expression, leading to the inhibition of apoptosis and ultimately contributing to the radioresistance of NPC cells. Moreover, the histone methyltransferase inhibitor GSK126 could overcome the radioresistance and thus might be a potential therapeutic strategy for NPC.NEW & NOTEWORTHY Our findings revealed for the first time that the increase of H3K27me3 level was associated with the decrease of OAS1 expression, leading to the inhibition of apoptosis and ultimately contributing to the radioresistance of NPC cells. Moreover, we demonstrated that the histone methyltransferase inhibitor GSK126 could be a promising therapeutic strategy for NPC by overcoming radioresistance, providing valuable insights into the clinical treatment of NPC.

Nomograms containing body dose parameters for predicting survival in patients with nasopharyngeal carcinoma

PURPOSE

To assess the impact of body dose on survival outcomes in nasopharyngeal carcinoma (NPC) patients and to create novel nomograms incorporating body dose parameters for predicting survival.

METHODS

594 of non-metastasis NPC patients (training group, 396; validation group, 198) received intensity-modulated radiation therapy at our institution from January 2012 to December 2016. Patient characteristics, body dose parameters in dose-volume histogram (DVH) and hematology profiles were collected for predicting overall survival (OS) and progression-free survival (PFS). Nomograms for OS and PFS were developed using the selected predictors. Each nomogram was evaluated based on its C-index and calibration curve.

RESULTS

Body dose-based risk score for OS (RSOS), N stage, age, and induction chemotherapy were independent predictors for OS, with a C-index of 0.784 (95% CI 0.749-0.819) in the training group and 0.763 (95% CI 0.715-0.810) in the validation group for the nomogram. As for PFS, the most important predictors were the body dose-based risk score for PFS (RSPFS), N stage, and induction chemotherapy. C-index of PFS nomogram was 0.706 (95% CI 0.681-0.720) in the training group and 0.691 (95% CI 0.662-0.711) in the validation group. The two models outperformed the TNM staging system in predicting outcomes.

CONCLUSIONS

Body dose coverage is a useful predictor of prognosis in clinical routine patients. The novel nomograms integrating body dose parameters can precisely predict OS and PFS in NPC patients.

Development and validation of an individualized angiogenesis and tumor-infiltrating lymphocytes prognostic signature in nasopharyngeal carcinoma

In recent years, targeted therapy and immunotherapy have become ideal choices for the treatment of advanced, metastatic, recurrent, and drug-resistant nasopharyngeal carcinoma (NPC), but the lack of understanding of the relationship and mechanism between TILs and angiogenic factors hinders therapeutic development and optimization. In this study, the expression of angiogenesis-related markers (VEGF-A,VEGFR-2) and TILs (CD4+T,CD8+T) was studied by using immunohistochemistry (IHC). Then we constructed an immunohistochemical scoring model for the co-expression of angiogenesis-related markers and TILs (COV+TIL score)in the training (n = 124) and validated the accuracy and reliability of the scoring system in the validation cohorts (n = 114), respectively We established the COV+TIL score model and stratified patients into different risk level in the training cohorts according to COV+TIL score (cut-off value=28). Patients in the high-risk group had worse prognosis in the training cohorts five-year overall survival (OS), progression-free survival (PFS), locoregional relapse-free survival (LRRFS), and distant metastasis-free survival (DMFS) was lower than that of patients in the low-risk group, and this result was validated in the validation cohorts ( 5-year OS in the high-risk and the low-risk group 46.8% vs. 83.4%, HR: 3.42, 95%CI: 1.77-6.61, p < 0.001); ( 5-year PFS 45.9% vs. 81.2%, HR: 3.22, 95%CI: 1.71-6.06, p < 0.001); ( 5-year LRRFS 74.6% vs. 87.5%, HR: 3.22, 95%CI: 1.16-8.93, p = 0.027); and ( 5-year DMFS79.2% vs. 93.2%, HR: 2.22, 95%CI: 0.91-5.39, p = 0.086). Upon multivariable analysis, COV+TIL score emerged as an independent prognostic indicator for defining survival in the training cohorts and the validation cohorts. Combining the COV+TIL score and TNM stage improved the prediction ability of the survival. In conclusion, NPC patients with high COV+TIL score showed worse prognosis.

Recent Advances in Assessing the Clinical Implications of Epstein-Barr Virus Infection and Their Application to the Diagnosis and Treatment of Nasopharyngeal Carcinoma

Reports about the oncogenic mechanisms underlying nasopharyngeal carcinoma (NPC) have been accumulating since the discovery of Epstein-Barr virus (EBV) in NPC cells. EBV is the primary causative agent of NPC. EBV-host and tumor-immune system interactions underlie the unique representative pathology of NPC, which is an undifferentiated cancer cell with extensive lymphocyte infiltration. Recent advances in the understanding of immune evasion and checkpoints have changed the treatment of NPC in clinical settings. The main EBV genes involved in NPC are LMP1, which is the primary EBV oncogene, and BZLF1, which induces the lytic phase of EBV. These two multifunctional genes affect host cell behavior, including the tumor-immune microenvironment and EBV behavior. Latent infections, elevated concentrations of the anti-EBV antibody and plasma EBV DNA have been used as biomarkers of EBV-associated NPC. The massive infiltration of lymphocytes in the stroma suggests the immunogenic characteristics of NPC as a virus-infected tumor and, at the same time, also indicates the presence of a sophisticated immunosuppressive system within NPC tumors. In fact, immune checkpoint inhibitors have shown promise in improving the prognosis of NPC patients with recurrent and metastatic disease. However, patients with advanced NPC still require invasive treatments. Therefore, there is a pressing need to develop an effective screening system for early-stage detection of NPC in patients. Various modalities, such as nasopharyngeal cytology, cell-free DNA methylation, and deep learning-assisted nasopharyngeal endoscopy for screening and diagnosis, have been introduced. Each modality has its advantages and disadvantages. A reciprocal combination of these modalities will improve screening and early diagnosis of NPC.

A Rulefit-based prognostic analysis using structured MRI report to select potential beneficiaries from induction chemotherapy in advanced nasopharyngeal carcinoma: A dual-centre study

BACKGROUND AND PURPOSE

Structured MRI report facilitate prognostic prediction for nasopharyngeal carcinoma (NPC). However, the intrinsic association among structured variables is not fully utilised. This study aimed to investigate the performance of a Rulefit-based model in feature integration behind structured MRI report and prognostic prediction in advanced NPC.

MATERIALS AND METHODS

We retrospectively enrolled 1207 patients diagnosed with non-metastatic advanced NPC from two centres, and divided into training (N = 544), internal testing (N = 367), and external testing (N = 296) cohorts. Machine learning algorithms including multivariate analysis, deep learning, Lasso, and Rulefit were used to establish corresponding prognostic models. The concordance indices (C- indices) of three clinical and six combined models with different algorithms for overall survival (OS) prediction were compared. Survival benefits of induction chemotherapy (IC) were calculated among risk groups stratified by different models. A website was established for individualised survival visualisation.

RESULTS

Incorporating structured variables into Stage model significantly improved the prognostic prediction performance. Six prognostic rules with structured variables were identified by Rulefit. OS prediction of Rules model was comparable to Lasso model in internal testing cohort (C-index: 0.720 vs. 0.713, P = 0.100) and achieved the highest C-index of 0.711 in external testing cohort, indicating better generalisability. The Rules model stratified patients into risk groups with significant 5-year OS differences in each cohort, and revealed significant survival benefits from additional IC in high-risk group.

CONCLUSION

The Rulefit-based Rules model, with the revelation of intrinsic associations behind structured variables, is promising in risk stratification and guiding individualised IC treatment for advanced NPC.

Diagnostic value of aldo‑keto reductase family 1 member B10 in human nasopharyngeal carcinoma

Aldo-keto reductase family 1 member B10 (AKR1B10) is a potential marker of several types of cancer; however, the role of AKR1B10 in nasopharyngeal carcinoma (NPC) remains unclear. In the present study, AKR1B10 RNA-seq data and clinical information were obtained from The Cancer Genome Atlas head and neck squamous cell carcinoma (HNSCC) database to evaluate the role of AKR1B10 in HNSCC. There was no statistically significant difference in the expression of AKR1B10 between HNSCC tissues and adjacent normal tissues, and high AKR1B10 expression was not associated with poor overall survival according to the public database. The present study further examined the role of AKR1B10 in patients with NPC using data obtained from the Gene Expression Omnibus database. Analysis of the GSE53819 and GSE61218 datasets showed that the there were no significant differences in the expression levels of AKR1B10 between NPC tissues and normal tissues. However, analysis of the GSE103611 dataset indicated that AKR1B10 may be associated with distance metastasis following radical treatment in NPC. Finally, serum samples from patients with NPC and healthy controls were collected and analyzed. The results revealed that AKR1B10 levels were significantly increased in samples from patients with NPC compared with those from healthy controls, and the area under the receiver operating characteristic curve was 0.909. In conclusion, unlike tissue AKR1B10 expression, serum AKR1B10 levels may be a promising biomarker for the diagnosis of NPC.

Construction and validation of a novel prognostic nomogram for predicting overall survival in lung adenocarcinoma patients with different patterns of metastasis

OBJECTIVE

Metastasis of lung cancer is an important factor affecting survival. The present study proposed to establish and verify a nomogram for predicting overall survival (OS) in lung adenocarcinoma (LUAD) patients with different patterns of metastasis.

METHODS

A total of 9727 patients diagnosed with metastatic LUAD patients from 2010 to 2015 were enrolled based on surveillance, epidemiology and end results (SEER) Database and then randomly divided into training and validation cohorts, and 136 patients in our Cancer Center were enrolled as the external validation cohort. Univariate and multivariate analyses were performed to evaluate the prognostic impact on OS. A prognostic nomogram was constructed and evaluated by C-index, calibration curve, decision curve analysis (DCA), and risk stratification system.

RESULTS

Ultimately, 6809 and 2918 patients diagnosed with metastatic LUAD in the training and validation cohorts were enrolled in the study, respectively. A male sex, a later T and N stage, a larger tumor size, treatment including no surgery, no chemotherapy and no radiotherapy, metastasis sites were found to be independent risk factors in LUAD patients for worse OS, and then incorporated into the nomogram. The frequency of bone metastasis was the highest, and in single site metastasis, the prognosis of liver metastasis was the worst. Two-site metastasis is more common than three-site and four-site metastasis, and co-metastasis eventually leads to a worse survival outcome. The C-index value of nomogram for predicting OS were 0.798, 0.703 and 0.698 in the internal training, validation and external validation cohorts, separately. The calibration curves for the 6-months, 1-year and 2-year showed significant agreement between nomogram models and actual observations. The DCA curves indicated nomogram was more beneficial than the AJCC TNM stage. Patients were further divided into low-risk and high-risk groups according to nomogram predicted scores and developed a survival risk classification system.

CONCLUSIONS

Our prognostic nomogram is expected to be an accurate and individualized clinical predictive tool for predicting OS in LUAD patients with different patterns of metastasis.

Development and validation of a nomogram for predicting deep venous thrombosis in patients with pelvic and acetabular fractures: a retrospective cohort study : Predictive model for pelvic/acetabular fractures

PURPOSE

To construct a novel nomogram model that can predict DVT and avoid unnecessary examination.

METHODS

Patients admitted to the hospital with pelvis/acetabular fractures were included between July 2014 and July 2018. The potential predictors associated with DVT were analyzed using Univariate and multivariable logistic regression analysis. The predictive nomogram was constructed and internally validated.

RESULTS

230 patients were finally enrolled. There were 149 individuals in the non-DVT group and 81 in the DVT group. Following analysis, we obtained the final nomogram model. The risk factors included age (OR, 1.037; 95% CI, 1.013-1.062; P = 0.002), body mass index (BMI) (OR, 1.253; 95% CI, 1.120-1.403; P < 0.001); instant application of anticoagulant after admission (IAA) (OR, 2.734; 95% CI, 0.847-8.829; P = 0.093), hemoglobin (HGB) (OR, 0.970; 95% CI, 0.954-0.986; P < 0.001), D-Dimer(OR, 1.154; 95% CI, 1.016-1.310; P = 0.027) and fibrinogen (FIB) (OR, 1.286; 95% CI, 1.024-1.616; P = 0.002). The apparent C-statistic was 0.811, and the adjusted C-statistic was 0.777 after internal validations, demonstrating good discrimination. Hosmer and Lemeshow's goodness of fit (GOF) test of the predictive model showed a good calibration for the probability of prediction and observation (χ2 = 3.285, P = 0.915; P > 0.05). The decision curve analysis (DCA) and Clinical impact plot (CIC) demonstrated superior clinical use of the nomogram.

CONCLUSIONS

An easy-to-calculate nomogram model for predicting DVT in patients with pelvic-acetabular fractures were developed. It could help clinicians to reduce DVT and avoid unnecessary examinations.

C2orf48 promotes the progression of nasopharyngeal carcinoma by regulating high mobility group AT-hook 2

Recurrence and metastasis are the major factors affecting the survival of nasopharyngeal carcinoma (NPC), and the mechanism remains unclear. Long non-coding RNA chromosome 2 open reading frame 48 (C2orf48) has been shown to influence the prognosis of many cancers. However, C2orf48's function in NPC has not been clarified. In this investigation, C2orf48 expression in NPC was measured by quantitative real-time PCR (qRT-PCR) at the cellular and tissue levels, and the association between C2orf48 expression and the prognosis of patients with NPC was examined. Additionally, the effects of C2orf48 and high mobility group AT-hook 2 (HMGA2) upon NPC proliferation, migration, and invasion were examined employing the MTT assay, colony formation assay, and transwell assay, respectively. Furthermore, the association between C2orf48 and HMGA2 in NPC was investigated. Our research demonstrated that C2orf48 was overexpressed in NPC tissues and cell lines, and compared to patients with lower levels of C2orf48 expression, those with higher levels had poorer 5-year overall survival and progression-free survival. Functionally, C2orf48 overexpression accelerated NPC cells proliferation, migration, and invasion. Besides, the tandem mass tag (TMT) quantitative proteomic analysis indicated that HMGA2 may be a target of C2orf48. Moreover, upregulation of C2orf48 could increase HMGA2 expression, and HMGA2 silencing could counteract the proliferation, migration, and invasion changes induced by C2orf48 in NPC cells. These results reveal that overexpression of C2orf48 can promote NPC cells proliferation, migration, and invasion via regulating the expression of HMGA2 and C2orf48 may be a potentially important prognostic marker for NPC.

A novel necroptosis related gene signature and regulatory network for overall survival prediction in lung adenocarcinoma

We downloaded the mRNA expression profiles of patients with LUAD and corresponding clinical data from The Cancer Genome Atlas (TCGA) database and used the Least Absolute Shrinkage and Selection Operator Cox regression model to construct a multigene signature in the TCGA cohort, which was validated with patient data from the GEO cohort. Results showed differences in the expression levels of 120 necroptosis-related genes between normal and tumor tissues. An eight-gene signature (CYLD, FADD, H2AX, RBCK1, PPIA, PPID, VDAC1, and VDAC2) was constructed through univariate Cox regression, and patients were divided into two risk groups. The overall survival of patients in the high-risk group was significantly lower than of the patients in the low-risk group in the TCGA and GEO cohorts, indicating that the signature has a good predictive effect. The time-ROC curves revealed that the signature had a reliable predictive role in both the TCGA and GEO cohorts. Enrichment analysis showed that differential genes in the risk subgroups were associated with tumor immunity and antitumor drug sensitivity. We then constructed an mRNA-miRNA-lncRNA regulatory network, which identified lncRNA AL590666. 2/let-7c-5p/PPIA as a regulatory axis for LUAD. Real-time quantitative PCR (RT-qPCR) was used to validate the expression of the 8-gene signature. In conclusion, necroptosis-related genes are important factors for predicting the prognosis of LUAD and potential therapeutic targets.

Amide proton transfer (APT) and magnetization transfer (MT) in predicting short-term therapeutic outcome in nasopharyngeal carcinoma after chemoradiotherapy: a feasibility study of three-dimensional chemical exchange saturation transfer (CEST) MRI

BACKGROUND

The three-dimensional chemical exchange saturation transfer (3D CEST) technique is a novel and promising magnetic resonance sequence; however, its application in nasopharyngeal carcinoma (NPC) lacks sufficient evaluation. This study aimed to assess the feasibility of the 3D CEST technique in predicting the short-term treatment outcomes for chemoradiotherapy (CRT) in NPC patients.

METHODS

Forty NPC patients and fourteen healthy volunteers were enrolled and underwent the pre-treatment 3D CEST magnetic resonance imaging and diffusion-weighted imaging (DWI). The reliability of 3D CEST was assessed in healthy volunteers by calculating the intra- and inter-observer correlation coefficient (ICC) for amide proton transfer weighted-signal intensity (APTw-SI) and magnetization transfer ratio (MTR) values. NPC patients were divided into residual and non-residual groups based on short-term treatment outcomes after CRT. Whole-tumor regions of interest (ROIs) were manually drawn to measure APTw-SI, MTR and apparent diffusion coefficient (ADC) values. Multivariate analysis and the receiver operating characteristic curve (ROC) were used to evaluate the prediction performance of clinical characteristics, APTw-SI, MTR, ADC values, and combined models in predicting short-term treatment outcomes in NPC patients.

RESULTS

For the healthy volunteer group, all APTw-SI and MTR values exhibited good to excellent intra- and inter-observer agreements (0.736-0.910, 0.895-0.981, all P > 0.05). For NPC patients, MTR values showed a significant difference between the non-residual and residual groups (31.24 ± 5.21% vs. 34.74 ± 1.54%, P = 0.003) while no significant differences were observed for APTw-SI and ADC values (P > 0.05). Moreover, the diagnostic power of MTR value was superior to APTw-SI (AUC: 0.818 vs. 0.521, P = 0.017) and comparable to ADC values (AUC: 0.818 vs. 0.649, P > 0.05) in predicting short-term treatment outcomes for NPC patients. The prediction performance did not improve even when combining MTR values with APTw-SI and/or ADC values (P > 0.05).

CONCLUSIONS

The pre-treatment MTR value acquired through 3D CEST demonstrated superior predictive performance for short-term treatment outcomes compared to APTw-SI and ADC values in NPC patients after CRT.

Development and validation of a risk prediction model for cage subsidence after instrumented posterior lumbar fusion based on machine learning: a retrospective observational cohort study

Background

Interbody cage subsidence is a common complication after instrumented posterior lumbar fusion surgery, several previous studies have shown that cage subsidence is related to multiple factors. But the current research has not combined these factors to predict the subsidence, there is a lack of an individualized and comprehensive evaluation of the risk of cage subsidence following the surgery. So we attempt to identify potential risk factors and develop a risk prediction model that can predict the possibility of subsidence by providing a Cage Subsidence Score (CSS) after surgery, and evaluate whether machine learning-related techniques can effectively predict the subsidence.

Methods

This study reviewed 59 patients who underwent posterior lumbar fusion in our hospital from 2014 to 2019. They were divided into a subsidence group and a non-subsidence group according to whether the interbody fusion cage subsidence occurred during follow-up. Data were collected on the patient, including age, sex, cage segment, number of fusion segments, preoperative space height, postoperative space height, preoperative L4 lordosis Angle, postoperative L4 lordosis Angle, preoperative L5 lordosis Angle, postoperative PT, postoperative SS, postoperative PI. The conventional statistical analysis method was used to find potential risk factors that can lead to subsidence, then the results were incorporated into stepwise regression and machine learning algorithms, respectively, to build a model that could predict the subsidence. Finally the diagnostic efficiency of prediction is verified.

Results

Univariate analysis showed significant differences in pre-/postoperative intervertebral disc height, postoperative L4 segment lordosis, postoperative PT, and postoperative SS between the subsidence group and the non-subsidence group (p < 0.05). The CSS was trained by stepwise regression: 2 points for postoperative disc height > 14.68 mm, 3 points for postoperative L4 segment lordosis angle >16.91°, and 4 points for postoperative PT > 22.69°. If the total score is larger than 0.5, it is the high-risk subsidence group, while less than 0.5 is low-risk. The score obtains the area under the curve (AUC) of 0.857 and 0.806 in the development and validation set, respectively. The AUC of the GBM model based on the machine learning algorithm to predict the risk in the training set is 0.971 and the validation set is 0.889. The AUC of the avNNet model reached 0.931 in the training set and 0.868 in the validation set, respectively.

Conclusion

The machine learning algorithm has advantages in some indicators, and we have preliminarily established a CSS that can predict the risk of postoperative subsidence after lumbar fusion and confirmed the important application prospect of machine learning in solving practical clinical problems.

Prognostic significance of the novel nutrition-inflammation marker of lymphocyte-C-reactive protein ratio in patients with nasopharyngeal carcinoma receiving concurrent chemoradiotherapy

Background

Recent studies indicate that the novel lymphocyte-C-reactive protein ratio (LCR) is strongly associated with the survival of various tumors, but its prognostic value in nasopharyngeal carcinoma (NPC) is understudied. This study aimed to explore the relationship between LCR and overall survival (OS) in NPC and develop a predictive model.

Methods

A total of 841 NPC patients who received concurrent chemoradiotherapy (CCRT) between January 2010 and December 2014 were retrospectively enrolled and randomly divided into a training cohort (n = 589) and a validation cohort (n = 252), and 122 patients between January 2015 and March 2015 were included as an additional validation cohort. Univariate and multivariate Cox analyses were performed to identify variables associated with OS and construct a predictive nomogram. The predictive accuracy of the nomogram was evaluated and independently validated.

Results

The LCR score differentiated NPC patients into two groups with distinct prognoses (HR = 0.53; 95% CI: 0.32-0.89, P = 0.014). Multivariate analysis showed that age, T stage, N stage, EBV-DNA status, and LCR score were independently associated with OS, and a predictive nomogram was developed. The nomogram had a good performance for the prediction of OS [C-index = 0.770 (95% CI: 0.675-0.864)]. and outperformed the traditional staging system [C-index = 0.589 (95% CI: 0.385-0.792)]. The results were internally and additionally validated using independent cohorts.

Conclusion

The pretreatment LCR could independently predict the overall survival in NPC patients. A novel LCR-based prognostic model of an easy-to-use nomogram was established, and it outperformed the conventional staging system in terms of predictive power. Further external verification remains necessary.

Multimodal recurrence scoring system for prediction of clear cell renal cell carcinoma outcome: a discovery and validation study

BACKGROUND

Improved markers for predicting recurrence are needed to stratify patients with localised (stage I-III) renal cell carcinoma after surgery for selection of adjuvant therapy. We developed a novel assay integrating three modalities-clinical, genomic, and histopathological-to improve the predictive accuracy for localised renal cell carcinoma recurrence.

METHODS

In this retrospective analysis and validation study, we developed a histopathological whole-slide image (WSI)-based score using deep learning allied to digital scanning of conventional haematoxylin and eosin-stained tumour tissue sections, to predict tumour recurrence in a development dataset of 651 patients with distinctly good or poor disease outcome. The six single nucleotide polymorphism-based score, which was detected in paraffin-embedded tumour tissue samples, and the Leibovich score, which was established using clinicopathological risk factors, were combined with the WSI-based score to construct a multimodal recurrence score in the training dataset of 1125 patients. The multimodal recurrence score was validated in 1625 patients from the independent validation dataset and 418 patients from The Cancer Genome Atlas set. The primary outcome measured was the recurrence-free interval (RFI).

FINDINGS

The multimodal recurrence score had significantly higher predictive accuracy than the three single-modal scores and clinicopathological risk factors, and it precisely predicted the RFI of patients in the training and two validation datasets (areas under the curve at 5 years: 0·825-0·876 vs 0·608-0·793; p<0·05). The RFI of patients with low stage or grade is usually better than that of patients with high stage or grade; however, the RFI in the multimodal recurrence score-defined high-risk stage I and II group was shorter than in the low-risk stage III group (hazard ratio [HR] 4·57, 95% CI 2·49-8·40; p<0·0001), and the RFI of the high-risk grade 1 and 2 group was shorter than in the low-risk grade 3 and 4 group (HR 4·58, 3·19-6·59; p<0·0001).

INTERPRETATION

Our multimodal recurrence score is a practical and reliable predictor that can add value to the current staging system for predicting localised renal cell carcinoma recurrence after surgery, and this combined approach more precisely informs treatment decisions about adjuvant therapy.

FUNDING

National Natural Science Foundation of China, and National Key Research and Development Program of China.

The application of clinical variable-based nomogram in predicting overall survival in malignant phyllodes tumors of the breast

Background: We aimed to explore prognostic risk factors in patients with malignant phyllodes tumors (PTs) of the breast and construct a survival prediction model. Methods: The Surveillance, Epidemiology, and End Results database was used to collect information on patients with malignant breast PTs from 2004 to 2015. The patients were randomly divided into training and validation groups using R software. Univariate and multivariate Cox regression analyses were used to screen out independent risk factors. Then, a nomogram model was developed in the training group and validated in the validation group, and the prediction performance and concordance were evaluated. Results: The study included 508 patients with malignant PTs of the breast, including 356 in the training group and 152 in the validation group. Univariate and multivariate Cox proportional hazard regression analyses showed that age, tumor size, tumor stage, regional lymph node metastasis (N), distant metastasis (M) and tumor grade were independent risk factors for the 5-year survival rate of patients with breast PTs in the training group (p < 0.05). These factors were used to construct the nomogram prediction model. The results showed that the C-indices of the training and validation groups were 0.845 (95% confidence interval [CI] 0.802-0.888) and 0.784 (95% CI 0.688-0.880), respectively. The calibration curves of the two groups were close to the ideal 45° reference line and showed good performance and concordance. Receiver operating characteristic and decision curve analysis curves showed that the nomogram has better predictive accuracy than other clinical factors. Conclusion: The nomogram prediction model constructed in this study has good predictive value. It can effectively assess the survival rates of patients with malignant breast PTs, which will aid in the personalized management and treatment of clinical patients.

A novel nomogram for predicting overall survival in peripheral T cell lymphoma patients.. [DOI: 10.21203/rs.3.rs-2823604/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background The prognosis of peripheral T cell lymphomas (PTCLs) varies greatly. This study aimed at generating a prognostic nomogram based on differentially expressed genes (DEGs).Methods Firstly, we collected RNA transcripts from Gene Expression Omnibus and identified DEGs. Secondly we used univariate Cox regression, Least absolute shrinkage and selection operator (LASSO) to screen the independent risk factors to construct nomogram in the training cohort. Thirdly, we evaluate its prediction accuracy via decision curves analysis (DCA), receiver operating characteristic (ROC) and calibration rate to confirm its performance on survival in training and validation cohort. Then we carried out subgroup analysis in training and validation to eliminate the effects of age, gender, and pathological subtype. Lastly, to verify feasibility of nomogram in practice, we applied immunohistochemistry to clinical samples and analyzed the relationship between IHC scores and prognosis.Results The 702 DEGs between 40 PTCLs and 20 non-tumor patients were identified. Then ANGPTL2, CPSF4, CLIC4 and OTUD6B were screened out as independent risk factors via univariate Cox regression and LASSO. The DCA, ROC, Harrell’s concordance index (c-index) and calibration rate showed nomogram predicting more accurately than any single specific transcript. The results showed PTCLs with higher nomogram-score had a longer survival, regardless of age, gender and pathological subtype. Finally, the high expression level of ANGPTL2, CPSF4 and OTUD6B related to poor prognosis. Higher expression of CLIC4 related to longer survival.Conclusion This nomogram showed the favorable clinical applicability, regardless of age, gender and pathological subtype.

RNA-binding domain 2 of nucleolin is important for the autophagy induction of curcumol in nasopharyngeal carcinoma cells

BACKGROUND & AIMS

Excessive autophagy induces cell death and is regarded as the treatment of cancer therapy. We have confirmed that the anti-cancer mechanism of curcumol is related to autophagy induction. As the main target protein of curcumol, RNA binding protein nucleolin (NCL) interacted with many tumor promoters accelerating tumor progression. However, the role of NCL in cancer autophagy and in curcumol's anti-tumor effects haven't elucidated. The purpose of the study is to identify the role of NCL in nasopharyngeal carcinoma autophagy and reveal the immanent mechanisms of NCL played in cell autophagy.

METHODS & RESULTS

In the current study, we have found that NCL was markedly upregulated in nasopharyngeal carcinoma (NPC) cells. NCL overexpression effectively attenuated the level of autophagy in NPC cells, and NCL silence or curcumol treatment obviously aggravated the autophagy of NPC cells. Moreover, the attenuation of NCL by curcumol lead a significant suppression on PI3K/AKT/mTOR signaling pathway in NPC cells. Mechanistically, NCL was found to be directly interact with AKT and accelerate AKT phosphorylation, which caused the activation of the PI3K/AKT/mTOR pathway. Meanwhile, the RNA Binding Domain (RBD) 2 of NCL interacts with Akt, which was also influenced by curcumol. Notably, the RBDs of NCL delivered AKT expression was related with cell autophagy in the NPC.

CONCLUSION

The results demonstrated that NCL regulated cell autophagy was related with interaction of NCL and Akt in NPC cells. The expression of NCL play an important role in autophagy induction and further found that was associated with its effect on NCL RNA-binding domain 2. This study may provide a new perspective on the target protein studies for natural medicines and confirm the effect of curcumol not only regulating the expression of its target protein, but also influencing the function domain of its target protein.

Automatic tumor segmentation and metachronous single-organ metastasis prediction of nasopharyngeal carcinoma patients based on multi-sequence magnetic resonance imaging

Background

Distant metastases is the main failure mode of nasopharyngeal carcinoma. However, early prediction of distant metastases in NPC is extremely challenging. Deep learning has made great progress in recent years. Relying on the rich data features of radiomics and the advantages of deep learning in image representation and intelligent learning, this study intends to explore and construct the metachronous single-organ metastases (MSOM) based on multimodal magnetic resonance imaging.

Patients and methods

The magnetic resonance imaging data of 186 patients with nasopharyngeal carcinoma before treatment were collected, and the gross tumor volume (GTV) and metastatic lymph nodes (GTVln) prior to treatment were defined on T1WI, T2WI, and CE-T1WI. After image normalization, the deep learning platform Python (version 3.9.12) was used in Ubuntu 20.04.1 LTS to construct automatic tumor detection and the MSOM prediction model.

Results

There were 85 of 186 patients who had MSOM (including 32 liver metastases, 25 lung metastases, and 28 bone metastases). The median time to MSOM was 13 months after treatment (7–36 months). The patients were randomly assigned to the training set (N = 140) and validation set (N = 46). By comparison, we found that the overall performance of the automatic tumor detection model based on CE-T1WI was the best (6). The performance of automatic detection for primary tumor (GTV) and lymph node gross tumor volume (GTVln) based on the CE-T1WI model was better than that of models based on T1WI and T2WI (AP@0.5 is 59.6 and 55.6). The prediction model based on CE-T1WI for MSOM prediction achieved the best overall performance, and it obtained the largest AUC value (AUC = 0.733) in the validation set. The precision, recall, precision, and AUC of the prediction model based on CE-T1WI are 0.727, 0.533, 0.730, and 0.733 (95% CI 0.557–0.909), respectively. When clinical data were added to the deep learning prediction model, a better performance of the model could be obtained; the AUC of the integrated model based on T2WI, T1WI, and CE-T1WI were 0.719, 0.738, and 0.775, respectively. By comparing the 3-year survival of high-risk and low-risk patients based on the fusion model, we found that the 3-year DMFS of low and high MSOM risk patients were 95% and 11.4%, respectively (p &lt; 0.001).

Conclusion

The intelligent prediction model based on magnetic resonance imaging alone or combined with clinical data achieves excellent performance in automatic tumor detection and MSOM prediction for NPC patients and is worthy of clinical application.

An overall survival predictive nomogram to identify high-risk patients among locoregionally advanced nasopharyngeal carcinoma: Developed based on the SEER database and validated institutionally

Objective

Locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients, even at the same stage, have different prognoses. We aim to construct a prognostic nomogram for predicting the overall survival (OS) to identify the high-risk LA-NPC patients.

Materials and methods

Histologically diagnosed WHO type II and type III LA-NPC patients in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled as the training cohort (n= 421), and LA-NPC patients from Shantou University Medical College Cancer Hospital (SUMCCH) served as the external validation cohort (n= 763). Variables were determined in the training cohort through Cox regression to form a prognostic OS nomogram, which was verified in the validation cohort, and compared with traditional clinical staging using the concordance index (C-index), Kaplan–Meier curves, calibration curves and decision curve analysis (DCA). Patients with scores higher than the specific cut-off value determined by the nomogram were defined as high-risk patients. Subgroup analyses and high-risk group determinants were explored.

Results

Our nomogram had a higher C-index than the traditional clinical staging method (0.67 vs. 0.60, p&lt;0.001). Good agreement between the nomogram-predicted and actual survival were shown in the calibration curves and DCA, indicating a clinical benefit of the nomogram. High-risk patients identified by our nomogram had worse prognosis than the other groups, with a 5-year overall survival (OS) of 60.4%. Elderly patients at advanced stage and without chemotherapy had a tendency for high risk than the other patients.

Conclusions

Our OS predictive nomogram for LA-NPC patients is reliable to identify high-risk patients.

Chemotherapy-Induced Senescence Reprogramming Promotes Nasopharyngeal Carcinoma Metastasis by circRNA-Mediated PKR Activation

Senescence is associated with tumor metastasis and chemotherapy resistance, yet the mechanisms remain elusive. Here, it is identified that nasopharyngeal carcinoma (NPC) patients who developed distant metastasis are characterized by senescence phenotypes, in which circWDR37 is a key regulator. CircWDR37 deficiency limits cisplatin or gemcitabine-induced senescent NPC cells from proliferation, migration, and invasion. Mechanistically, circWDR37 binds to and dimerizes double-stranded RNA-activated protein kinase R (PKR) to initiate PKR autophosphorylation and activation. Independent of its kinase activity, phosphorylated PKR induces I-kappaB kinase beta (IKKβ) phosphorylation, binds to and releases RELA from NF-κB inhibitor alpha (IκBα) to trigger nuclear factor kappa B (NF-κB) activation, thereby stimulating cyclin D1 (CCND1) and senescence-associated secretory phenotype component gene transcription in a circWDR37-dependent manner. Low circWDR37 levels correlate with chemotherapy response and favorable survival in NPC patients treated with gemcitabine or cisplatin induction chemotherapy. This study uncovers a new mechanism of circWDR37 activated PKR in senescence-driven metastasis and provides appealing therapeutic targets in NPC.

hsa_circ_0136839 regulates the malignant phenotypes of nasopharyngeal carcinoma via the Wnt/β-catenin signaling pathway

With the development of RNA sequencing (RNA-seq) technology, circular RNA (circRNA), a new class of RNA, has received much attention in cancer research. However, information available on the biogenesis and functional value of circRNAs in nasopharyngeal carcinoma (NPC) is scarce. In the present study, we screened the circRNA profile of the NPC cell line C666-1 compared with that of the normal control NP69 by RNA-seq and identified a novel and relatively higher expressed circRNA, hsa_circ_0136839. Hsa_circ_0136839 was markedly downregulated in NPC tissues, as confirmed by quantitative reverse transcription polymerase chain reaction. Functional in vitro studies revealed that hsa_circ_0136839 knockdown in C666-1 cell notably promoted cell proliferation, migration, and invasion abilities, as well as affected cell cycle distribution with an S-phase arrest. However, hsa_circ_0136839 overexpression in CNE2 cells resulted in an opposite response. Mechanistically, we demonstrated that aberrant hsa_circ_0136839 expression might affect the malignant phenotypes of NPC cells by activating the wnt/β-catenin signaling pathway. Thus, our findings contribute to further the understanding of NPC pathogenesis and provide new ideas for NPC clinical diagnosis and treatment.

Cancer Cells Enter an Adaptive Persistence to Survive Radiotherapy and Repopulate Tumor

Repopulation of residual tumor cells impedes curative radiotherapy, yet the mechanism is not fully understood. It is recently appreciated that cancer cells adopt a transient persistence to survive the stress of chemo- or targeted therapy and facilitate eventual relapse. Here, it is shown that cancer cells likewise enter a "radiation-tolerant persister" (RTP) state to evade radiation pressure in vitro and in vivo. RTP cells are characterized by enlarged cell size with complex karyotype, activated type I interferon pathway and two gene patterns represented by CST3 and SNCG. RTP cells have the potential to regenerate progenies via viral budding-like division, and type I interferon-mediated antiviral signaling impaired progeny production. Depleting CST3 or SNCG does not attenuate the formation of RTP cells, but can suppress RTP cells budding with impaired tumor repopulation. Interestingly, progeny cells produced by RTP cells actively lose their aberrant chromosomal fragments and gradually recover back to a chromosomal constitution similar to their unirradiated parental cells. Collectively, this study reveals a novel mechanism of tumor repopulation, i.e., cancer cell populations employ a reversible radiation-persistence by poly- and de-polyploidization to survive radiotherapy and repopulate the tumor, providing a new therapeutic concept to improve outcome of patients receiving radiotherapy.

Nasopharyngeal necrosis contributes to overall survival in nasopharyngeal carcinoma without distant metastasis: a comprehensive nomogram model

OBJECTIVES

This study focused on developing and validating a nomogram to predict the overall survival (OS) of patients with nasopharyngeal carcinoma (NPC) without distant metastasis based on their clinical characteristics, serum biomarkers, and presence of nasopharyngeal (NP) necrosis.

METHODS

This study included 9298 patients with NPC. Patients from January 2009 to December 2014 were randomly categorized into the training cohort and validation cohort A. Validation cohort B, whose data were collected from January 2015 to December 2017, was also included. OS was the primary endpoint of this study. Cox regression analysis was used to detect independent risk variables. Decision curve analysis, calibration curve, time-dependent receiver operating characteristic (ROC) curve, and concordance index (C-index) were used to evaluate the performance of the nomogram model.

RESULTS

A total of 267 patients developed NP necrosis after the first routine radiotherapy. After radiotherapy, patients with NP necrosis had significantly lower OS than other patients in all three cohorts (p < 0.001). Eleven factors, including NP necrosis, were involved in the nomogram, which had favorable discrimination and calibration with a C-index of 0.768 in the training cohort, 0.749 in validation cohort A, and 0.739 in validation cohort B. The nomogram exhibited a significantly larger area under the ROC curve for predicting OS than the TNM stage and Epstein-Barr virus (EBV) DNA (p < 0.001).

CONCLUSION

Compared with the TNM system and EBV DNA, we established a nomogram model with an accurate prognostic prediction for patients with NPC, which might help with patient management in NPC.

KEY POINTS

• This study included 9298 patients with NPC, and 11 factors were involved in the final model. • The nomogram had a significantly higher C-index and area under the ROC curve than the TNM stage and EBV DNA. • We established the first nomogram model for NPC involving the occurrence of NP necrosis, which was valuable for providing individual counseling and clinical assessments.

Can the prognosis of individual patients with nasopharyngeal carcinoma be predicted using a routine blood test at admission?

BACKGROUND AND PURPOSE

We sought to determine the prognostic value of a pre-treatment peripheral blood signature and the peripheral blood signature-based nomogram for patients with non-metastatic nasopharyngeal carcinoma (NPC).

MATERIALS AND METHODS

We retrospectively collected 21 peripheral blood indicators from patients with NPC between 2004 and 2015. Data were randomly divided into a training and a validation set (ratio: 6:4). The peripheral blood signature was constructed based on candidate biomarkers using the least absolute shrinkage and selection operator Cox regression model. Multivariable logistic regression was applied to identify the independent risk factors of overall survival to build the nomogram. The predictive value of the peripheral blood nomogram was evaluated using time-dependent area under the curve, decision curve analysis, and calibration curve.

RESULTS

In total, 6668 patients were enrolled with 4000 and 2668 in the training and validation cohorts, respectively. Four peripheral blood indicators, (white blood cell count, lymphocyte percentage, haemoglobin, and mean platelet volume), were included to construct the peripheral blood signature. Patients were divided into low- and high-risk groups using an optimal cut-off value of - 1.71142. Patients in the high-risk group had significantly lower overall, distant metastasis-free, and progression-free survival than patients in the low-risk group in both cohorts (P < 0.05). We constructed and validated a peripheral blood signature-based nomogram in combination with five vital clinical characteristics, (age, sex, tumour stage, nodal stage, and pre-treatment Epstein-Barr virus DNA), which showed favourable performance.

CONCLUSION

Patients with NPC with different outcomes could be distinguished based on their peripheral blood signature score; the proposed peripheral blood signature-based nomogram offers individualised risk estimation.

Nomogram to Predict Long-Term Overall Survival and Cancer-Specific Survival of Radiotherapy Patients with Nasopharyngeal Carcinoma

Objective

To establish and validate a nomogram to predict the overall survival (OS) and cancer-specific survival (CSS) in patients with nasopharyngeal carcinoma (NPC) receiving radiotherapy by integrating multiple independent prognostic factors.

Materials and Methods

Data from 5663 patients with NPC who received definite radiotherapy between 2004 and 2018 were included and divided into training and validation cohorts. Univariate and multivariate Cox regression analyses were performed to determine the independent prognostic factors of patients with NPC after radiotherapy. Thereafter, the predictive accuracy of the nomogram model was evaluated.

Results

Age, race, marital status, pathological type, tumor size, T stage, N stage, M stage, American Joint Committee on Cancer stage, and chemotherapy were independent factors affecting the prognosis of patients with NPC receiving radiotherapy. Nomograms with a concordance index of 0.726 (95% confidence interval (CI): 0.675-0.777) and 0.732 (95% CI: 0.680-0.785) were able to predict OS and CSS, respectively. The area under the curve showed excellent predictive performance. Additionally, the calibration curve indicated that the predicted survival rate was consistent with the actual survival rate, and the decision curve indicated its clinical value. The established risk stratification system was able to accurately stratify patients receiving radiotherapy for NPC into three risk subgroups with significant differences in prognosis (P < 0.05).

Conclusions

The constructed nomogram had good prognostic performance and could be used as an effective tool to evaluate the prognosis of patients with NPC after radiotherapy. This nomogram could be further used to guide clinical decisions and personalized treatment plans.

SEVs-mediated miR-6750 transfer inhibits pre-metastatic niche formation in nasopharyngeal carcinoma by targeting M6PR

Reliable detection of circulating small extracellular vesicles (SEVs) and their miRNA cargo has been needed to develop potential specific non-invasive diagnostic and therapeutic marker for cancer metastasis. Here, we detected miR-6750, the precise molecular function of which was largely unknown, was significantly enriched in serum-SEVs from normal volunteers vs. patients with nasopharyngeal carcinoma (NPC). And we determined that miR-6750-SEVs attenuated NPC metastasis. Subsequently, miR-6750-SEVs was proven to inhibit angiogenesis and activate macrophage toward to M1 phenotype to inhibit pre-metastatic niche formation. After analyzing the expression level of miR-6750 in NPC cells, HUVECs and macrophage, we found that once miR-6750 level in NPC cells was close to or higher than normal nasopharyngeal epithelial cells (NP69), miR-6750-SEVs would be transferred from NPC cells to macrophage and then to HUVECs to modulate metastatic niche. Moreover, in vitro assays and BALB/c mouse tumor models revealed that miR-6750 directly targeted mannose 6-phosphate receptor (M6PR). Taken together, our findings revealed that miR-6750-M6PR axis can mediate NPC metastasis by remodeling tumor microenvironment (TME) via SEVs, which give novel sights to pathogenesis of NPC.

Reduced Skeletal Muscle Mass Is Associated with an Increased Risk of Asthma Control and Exacerbation

BACKGROUND

Skeletal muscle mass (SMM) has been suggested to be associated with multiple health-related outcomes. However, the potential influence of SMM on asthma has not been largely explored.

OBJECTIVE

To study the association between SMM and clinical features of asthma, including asthma control and exacerbation, and to construct a model based on SMM to predict the risk of asthma exacerbation (AEx).

METHODS

In this prospective cohort study, we consecutively recruited patients with asthma (n = 334), classified as the SMM ^Normal group (n = 223), SMM ^Low group (n = 88), and SMM ^High group (n = 23). We investigated the association between SMM and clinical asthma characteristics and explored the association between SMM and asthma control and AEx within a 12-month follow-up period. Based on SMM, an exacerbation prediction model was developed, and the overall performance was externally validated in an independent cohort (n = 157).

RESULTS

Compared with the SMM ^Normal group, SMM ^Low group exhibited more airway obstruction and worse asthma control, while SMM ^High group had a reduced eosinophil percentage in induced sputum. Furthermore, SMM ^Low group was at a significantly increased risk of moderate-to-severe exacerbation compared with the SMM ^Normal group (relative risk _adjusted 2.02 [95% confidence interval (CI), 1.35-2.68]; p = 0.002). In addition, a model involving SMM was developed which predicted AEx (area under the curve: 0.750, 95% CI: 0.691-0.810).

CONCLUSIONS

Low SMM was an independent risk factor for future AEx. Furthermore, a model involving SMM for predicting the risk of AEx in patients with asthma indicated that assessment of SMM has potential clinical implications for asthma management.

Resistin Promotes Nasopharyngeal Carcinoma Metastasis through TLR4-Mediated Activation of p38 MAPK/NF-κB Signaling Pathway

NPC is a type of malignant tumor with a high risk of local invasion and early distant metastasis. Resistin is an inflammatory cytokine that is predominantly produced from the immunocytes in humans. Accumulating evidence has suggested a clinical association of circulating resistin with the risk of tumorigenesis and a relationship between blood resistin levels and the risk of cancer metastasis. In this study, we explored the blood levels and the role of resistin in NPC. High resistin levels in NPC patients were positively associated with lymph node metastasis, and resistin promoted the migration and invasion of NPC cells in vitro. These findings were also replicated in a mouse model of NPC tumor metastasis. We identified TLR4 as a functional receptor in mediating the pro-migratory effects of resistin in NPC cells. Furthermore, p38 MAPK and NF-κB were intracellular effectors that mediated resistin-induced EMT. Taken together, our results suggest that resistin promotes NPC metastasis by activating the TLR4/p38 MAPK/NF-κB signaling pathways.