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

Deep Learning-Based Prediction of Future Extrahepatic Metastasis and Macrovascular Invasion in Hepatocellular Carcinoma

Purpose

For timely treatment of extrahepatic metastasis and macrovascular invasion (aggressive progressive disease [PD]) in hepatocellular carcinoma, models aimed at stratifying the risks of subsequent aggressive PD should be constructed.

Patients and Methods

After dividing 332 patients from five hospitals into training (n = 236) and validation (n = 96) datasets, non-invasive models, including clinical/semantic factors (Model^CS), deep learning radiomics (Model^D), and both (Model^CSD), were constructed to stratify patients according to the risk of aggressive PD. We examined the discrimination and calibration; similarly, we plotted a decision curve and devised a nomogram. Furthermore, we performed analyses of subgroups who received different treatments or those in different disease stages and compared time to aggressive PD and overall survival in the high- and low-risk subgroups.

Results

Among the constructed models, Model^CSD, combining clinical/semantic factors and deep learning radiomics, outperformed Model^CS and Model^D (areas under the curve [AUCs] for the training dataset: 0.741, 0.815, and 0.856; validation dataset: 0.780, 0.836, and 0.862), with statistical difference per the net reclassification improvement, the integrated discrimination improvement, and/or the DeLong test in both datasets. Besides, Model^CSD had the best calibration and decision curves. The performance of Model^CSD was not affected by treatment types (AUC: resection = 0.839; transarterial chemoembolization = 0.895; p = 0.183) or disease stages (AUC: BCLC [Barcelona Clinic Liver Cancer] stage 0 and A = 0.827; BCLC stage AB &B = 0.861; p = 0.537). Moreover, the high-risk group had a significantly shorter median time to aggressive PD than the low-risk group (training dataset hazard ratio [HR] = 0.108, p < 0.001; validation dataset HR = 0.058, p < 0.001) and poorer overall survival (training dataset HR = 0.357, p < 0.001; validation dataset HR = 0.204, p < 0.001).

Conclusion

Our deep learning-based model successfully stratified the risks of aggressive PD. In the high-risk population, current guideline indicates that first-line treatments are insufficient to prevent extrahepatic metastasis and macrovascular invasion and ensure survival benefits, so more therapies may be explored for these patients.

CDKN1C as a prognostic biomarker correlated with immune infiltrates and therapeutic responses in breast cancer patients

Breast cancer (BC) prognosis and therapeutic sensitivity could not be predicted efficiently. Previous evidence have shown the vital roles of CDKN1C in BC. Therefore, we aimed to construct a CDKN1C‐based model to accurately predicting overall survival (OS) and treatment responses in BC patients. In this study, 995 BC patients from The Cancer Genome Atlas database were selected. Kaplan‐Meier curve, Gene set enrichment and immune infiltrates analyses were executed. We developed a novel CDKN1C‐based nomogram to predict the OS, verified by the time‐dependent receiver operating characteristic curve, calibration curve and decision curve. Therapeutic response prediction was followed based on the low‐ and high‐nomogram score groups. Our results indicated that low‐CDKN1C expression was associated with shorter OS and lower proportion of naïve B cells, CD8 T cells, activated NK cells. The predictive accuracy of the nomogram for 5‐year OS was superior to the tumour‐node‐metastasis stage (area under the curve: 0.746 vs. 0.634, p < 0.001). The nomogram exhibited excellent predictive performance, calibration ability and clinical utility. Moreover, low‐risk patients were identified with stronger sensitivity to therapeutic agents. This tool can improve BC prognosis and therapeutic responses prediction, thus guiding individualized treatment decisions.

Curcumol inhibits EBV-positive Nasopharyngeal carcinoma migration and invasion by targeting nucleolin

Metastasis is a major cause of recurrence and death in patients with EBV-positive Nasopharyngeal carcinoma (NPC). Previous reports documented that curcumol has both anti-cancer and anti-viral effects, but there is little literature systematically addressing the mechanism of curcumol in EBV-positive tumors. Previously we found that nucelolin (NCL) is a target protein of curcumol in CNE2 cells, an EBV-negative NPC, and in this experiment, we reported a critical role for NCL in promoting migration and invasion of C666-1 cells, an EBV-positive NPC, and found that the expression of NCL determined the level of curcumol's efficacy. Mechanistically, NCL interacted with Epstein-Barr Virus Nuclear Antigen 1 (EBNA1) to activate VEGFA/VEGFR1/PI3K/AKT signaling pathway, which in turn promoted NPC cell invasion and metastasis. Moreover, further study showed that the differential expression of NCL and curcumol intervention only had a regulatory effect on the nuclear accumulation of VEGFR1, which strengthened the anti-cancer effect of curcumol mediated through NCL. Our findings indicated that curcumol exerted anti EBV-positive NPC invasion and metastasis by downregulating EBNA1 and inhibiting VEGFA/VEGFR1/PI3K/AKT signaling by targeting NCL, which provides a novel pharmacological basis for curcumol's clinical use in treating patients with EBV-positive NPC.

A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study

Background

Induction chemotherapy (ICT) plus concurrent chemoradiotherapy (CCRT) and CCRT alone were the optional treatment regimens in locoregionally advanced nasopharyngeal carcinoma (NPC) patients. Currently, the choice of them remains equivocal in clinical practice. We aimed to develop a deep learning-based model for treatment decision in NPC.

Methods

A total of 1872 patients with stage T3N1M0 NPC were enrolled from four Chinese centres and received either ICT+CCRT or CCRT. A nomogram was constructed for predicting the prognosis of patients with different treatment regimens using multi-task deep learning radiomics and pre-treatment MR images, based on which an optimal treatment regimen was recommended. Model performance was assessed by the concordance index (C-index) and the Kaplan-Meier estimator.

Findings

The nomogram showed excellent prognostic ability for disease-free survival in both the CCRT (C-index range: 0.888-0.921) and ICT+CCRT (C-index range: 0.784-0.830) groups. According to the prognostic difference between treatments using the nomogram, patients were divided into the ICT-preferred and CCRT-preferred groups. In the ICT-preferred group, patients receiving ICT+CCRT exhibited prolonged survival over those receiving CCRT in the internal and external test cohorts (hazard ratio [HR]: 0.17, p<0.001 and 0.24, p=0.02); while the trend was opposite in the CCRT-preferred group (HR: 6.24, p<0.001 and 12.08, p<0.001). Similar results for treatment decision using the nomogram were obtained in different subgroups stratified by clinical factors and MR acquisition parameters.

Interpretation

Our nomogram could predict the prognosis of T3N1M0 NPC patients with different treatment regimens and accordingly recommend an optimal treatment regimen, which may serve as a potential tool for promoting personalized treatment of NPC.

Funding

National Key R&D Program of China, National Natural Science Foundation of China, Beijing Natural Science Foundation, Strategic Priority Research Program of CAS, Project of High-Level Talents Team Introduction in Zhuhai City, Beijing Natural Science Foundation, Beijing Nova Program, Youth Innovation Promotion Association CAS.

A comprehensive model based on temporal dynamics of peripheral T cell repertoire for predicting post-treatment distant metastasis of nasopharyngeal carcinoma

Many nasopharyngeal carcinoma (NPC) patients develop distant metastases after treatment, leading to poor outcomes. To date, there are no peripheral biomarkers suitable for all NPC patients to predict distant metastasis. Hence, we purposed to develop a noninvasive comprehensive model for predicting post-treatment distant metastasis of all NPC. Since T-cell receptor β chain (TCRB) repertoire has achieved prognostic prediction in many cancers, the clinical characteristics and parameters of TCRB repertoire of 71 cases of peripheral blood samples (pairwise pre-treatment and post-treatment samples from 40 NPC patients who without (nM, n = 21) or with (M, n = 19) post-treatment distant metastasis) were collected. The least absolute shrinkage and selection operator algorithm was used to construct a distant metastasis prediction model. In terms of TCRB repertoire parameters, the diversity of TCRB repertoire was significantly decreased in M group after treatment but not in nM group. Ascending TCRB diversity and higher similarity between pre- and post-treatment samples showed better distant metastasis-free survival (DMFS). The similarity still had robust DMFS prediction in patients with reduced TCRB diversity. More importantly, the 5-factor comprehensive model consisting of basic clinical characteristics and TCRB repertoire indices showed a higher prognostic accuracy than any one individual factor in DMFS predicting. In conclusion, treatment had different effects on the composition of TCRB repertoire in patients without and with post-treatment distant metastasis. The dynamics of TCRB diversity, the similarity of TCRB repertoires, and combinations of these factors with basic clinical characteristics could serve as noninvasive DMFS predictors for all NPC patients.

Nomogram predict relapse-free survival of patients with thymic epithelial tumors after surgery

Background

Hematological indicators and clinical characteristics play an important role in the evaluation of the progression and prognosis of thymic epithelial tumors. Therefore, we aimed to combine these potential indicators to establish a prognostic nomogram to determine the relapse-free survival (RFS) of patients with thymic epithelial tumors undergoing thymectomy.

Methods

This retrospective study was conducted on 156 patients who underwent thymectomy between May 2004 and August 2015. Cox regression analysis were performed to determine the potential indicators related to prognosis and combine these indicators to create a nomogram for visual prediction. The prognostic predictive ability of the nomogram was evaluated using the consistency index (C-index), receiver operating characteristic (ROC) curve, and risk stratification. Decision curve analysis was used to evaluate the net benefits of the model.

Results

Preoperative albumin levels, neutrophil-to-lymphocyte ratio (NLR), T stage, and WHO histologic types were included in the nomogram. In the training cohort, the nomogram showed well prognostic ability (C index: 0.902). Calibration curves for the relapse-free survival (RFS) were in good agreement with the standard lines in training and validation cohorts.

Conclusions

Combining clinical and hematologic factors, the nomogram performed well in predicting the prognosis and the relapse-free survival of this patient population. And it has potential to identify high-risk patients at an early stage. This is a relatively novel approach for the prediction of RFS in this patient population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08585-y.

Genetic Polymorphisms of Long Non-coding RNA Linc00312 Are Associated With Susceptibility and Predict Poor Survival of Nasopharyngeal Carcinoma

Background

Linc00312 is dysregulated in nasopharyngeal carcinoma (NPC) and participates in the initiation and progression of NPC. Our previous studies suggested that linc00312 was able to enhance the sensitivity of NPC cells to irradiation and NPC patients with higher expression of linc00312 was associated with better short-term curative effect and overall survival. The single nucleotide polymorphisms (SNPs) of lncRNAs may influence the disease course and outcome by affecting the expression, secondary structure or function of lncRNAs. However, the role of SNPs in linc00312 on the occurrence and survival of NPC remains unknown.

Methods

We recruited 684 NPC patients and 823 healthy controls to evaluate the association between linc00312 SNPs and NPC susceptibility by using multivariate logistic regression analysis. Kaplan-Meier analysis and Cox proportional hazards regression were applied to assess the effect of linc00312 SNPs on the survival of NPC patients. The relative expression of linc00312 in NPC tissues was determined by real-time PCR. The interaction between linc00312 and mir-411-3p was explored by luciferase reporter assay. In silico prediction of the changes on linc00312 folding structure was conducted by RNAfold WebServer.

Result

We demonstrated that rs12497104 (G > A) GA genotype carriers had a higher risk than others for suffering from NPC (GA vs GG, OR = 1.437, P = 0.003). Besides, patients with rs12497104 AA genotype showed a poorer overall survival in contrast to GG genotype (AA vs GG, HR = 2.117, P = 0.011). In addition, the heterozygous carriers of rs15734 (G > A) and rs164966 (A > G) were correlated with decreased risk of NPC (GA vs GG, OR = 0.778, P = 0.031; GA vs AA, OR = 0.781, P = 0.033, respectively). We found that the three SNPs might influence the expression of linc00312 in a genotype specific feature. The local centroid secondary structure as well as the minimum free energy of linc00312 were changed following the candidate SNPs alterations. Besides, we revealed that the G to A alteration at rs12497104 disrupted the binding between mir-411-3p and linc00312.

Conclusion

Our results indicated genetic polymorphisms of linc00312 might serve as potential biomarkers for NPC carcinogenesis and prognosis.

Development and Validation of Predicting Nomograms for Craniopharyngioma: A Retrospective, Multiple-Center, Cohort Study

Craniopharyngiomas (CPs) are benign tumors arising from the sellar region. However, little is known about their clinical features and long-term recurrence due to low morbidity and the lack of large cohort studies. Thus, we aimed to develop nomograms to accurately predict the extent of resection and tumor recurrence using clinical parameters. A total of 545 patients diagnosed with CP between 2009 and 2019 were examined: 381 in the development cohort and 164 in the validation cohort. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were performed to establish two nomograms. Receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA) and Kaplan-Meier (KM) curves were used to evaluate their predictive performance and discriminative power, respectively, in the two cohorts. In addition, the EORTC QLQ-BN20 questionnaire was used to assess neuropsychological status in the follow-up. In the development cohort, the area under the curve (AUC) and C-index were 0.760 and 0.758, respectively, for predicting the extent of resection and 0.78 and 0.75, respectively, for predicting 3-year progression-free survival (PFS) and 5-year PFS. Additionally, the model had a predictive accuracy of 0.785. Both nomograms showed acceptable discrimination in the two cohorts. Moreover, DCA demonstrated excellent clinical benefits from the two nomograms. Finally, participants were classified into two distinct risk groups according to the risk score, and an online calculator was created for convenient clinical use. During long term follow-up, hypothyroidism (77.61%) and hypocortisolism (76.70%) were the most common endocrine dysfunction after surgery and significant deficits were observed concerning visual disorder, motor dysfunction and seizures in the recurrent groups. In particular, better quality of life was associated with gross total resection (GTR), postoperative radiation, anterior interhemispheric (AI) approach and transsphenoidal approach. To our knowledge, these are the first nomograms based on a very large cohort of patients with CP that show potential benefits for guiding treatment decisions and long-term surveillance. The current study demonstrated the online calculator serve as the practical tool for individual strategies based on the patient’s baseline characteristics to achieve a better prognosis.

Current Status and Future Perspectives about Molecular Biomarkers of Nasopharyngeal Carcinoma

Simple Summary

Nasopharyngeal carcinoma is a serious major public health problem in its endemic countries. Up to 80% of NPC patients with locally advanced disease or distant metastasis at diagnosis were associated with poor prognosis and with median survival less than 4 months. The mortality rate of NPC metastasis is up to 91%. To date, there is no available curative treatment or reliable early diagnosis or prognosis for NPC. Discovery and development of reliable early diagnosis and prognosis biomarkers for nasopharyngeal carcinoma are urgent needed. Hence, we have here listed the potential early diagnosis and prognosis biomarker candidates for nasopharyngeal carcinoma. This review will give an insight to readers on the progress of NPC biomarker discovery to date, as well as future prospective biomarker development and their translation to clinical use.

Abstract

Nasopharyngeal carcinoma (NPC) is an epithelial malignancy that shows a remarkable ethnic and geographical distribution. It is one of the major public health problems in some countries, especially Southern China and Southeast Asia, but rare in most Western countries. Multifactorial interactions such as Epstein–Barr virus infection, individual’s genetic susceptibility, as well as environmental and dietary factors may facilitate the pathogenesis of this malignancy. Late presentation and the complex nature of the disease have led it to become a major cause of mortality. Therefore, an effective, sensitive, and specific molecular biomarker is urgently needed for early disease diagnosis, prognosis, and prediction of metastasis and recurrence after treatment. In this review, we discuss the recent research status of potential biomarker discovery and the problems that need to be explored further for better NPC management. By studying the aberrant pattern of these candidate biomarkers that promote NPC development and progression, we are able to understand the complexity of this malignancy better, hence positing our stands better towards strategies that may provide a way forward to the discovery of more reliable and specific biomarkers for diagnosis and targeted therapeutic development.

Serum EA-IgA and D-dimer, but not VCA-IgA, are associated with prognosis in patients with nasopharyngeal carcinoma: a meta-analysis

Background

Patients with nasopharyngeal cancer (NPC) differ in prognosis, even at the same stage; therefore, new biomarkers are urgently required to identify early-stage NPC patients at high risk of poor prognosis. Although Epstein–Barr virus (EBV) DNA has been used for prognosis, the value of many other biomarkers expressed during the infection cycle of EBV remains unclarified. This study aimed to evaluate the prognostic potential of EA-IgA, VCA-IgA and d-dimer in patients with NPC.

Methods

Electronic databases, including PubMed, Embase and Web of Science, were searched up to February 1, 2021. Pooled data were extracted from studies that evaluated the relationship between NPC and overall survival (OS), distant metastasis-free survival (DMFS) or disease-free survival (DFS) and then were subjected to a meta-analysis.

Results

Nine studies with 5729 patients were included in this meta-analysis. In patients with NPC, EA-IgA levels significantly predicted OS (HR = 1.63, 95% CI 1.07–2.48). d-Dimer levels significantly predicted OS (HR = 1.75, 95% CI 1.24–2.47) and DMFS (HR = 1.91, 95% CI 1.31–2.79). However, high levels of VCA-IgA were not associated with OS (HR = 1.24, 95% CI 0.95–1.60), DMFS (HR = 1.41, 95% CI 0.92–2.17) or DFS (HR = 2.39, 95% CI 0.78–7.26).

Conclusions

The present findings reveal that EA-IgA and d-dimer, but not VCA-IgA, can be used as prognostic biomarkers in NPC.

Combination of computed tomography imaging-based radiomics and clinicopathological characteristics for predicting the clinical benefits of immune checkpoint inhibitors in lung cancer

BACKGROUND

In this study, we tested whether a combination of radiomic features extracted from baseline pre-immunotherapy computed tomography (CT) images and clinicopathological characteristics could be used as novel noninvasive biomarkers for predicting the clinical benefits of non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs).

METHODS

The data from 92 consecutive patients with lung cancer who had been treated with ICIs were retrospectively analyzed. In total, 88 radiomic features were selected from the pretreatment CT images for the construction of a random forest model. Radiomics model 1 was constructed based on the Rad-score. Using multivariate logistic regression analysis, the Rad-score and significant predictors were integrated into a single predictive model (radiomics nomogram model 1) to predict the durable clinical benefit (DCB) of ICIs. Radiomics model 2 was developed based on the same Rad-score as radiomics model 1.Using multivariate Cox proportional hazards regression analysis, the Rad-score, and independent risk factors, radiomics nomogram model 2 was constructed to predict the progression-free survival (PFS).

RESULTS

The models successfully predicted the patients who would benefit from ICIs. For radiomics model 1, the area under the receiver operating characteristic curve values for the training and validation cohorts were 0.848 and 0.795, respectively, whereas for radiomics nomogram model 1, the values were 0.902 and 0.877, respectively. For the PFS prediction, the Harrell's concordance indexes for the training and validation cohorts were 0.717 and 0.760, respectively, using radiomics model 2, whereas they were 0.749 and 0.791, respectively, using radiomics nomogram model 2.

CONCLUSIONS

CT-based radiomic features and clinicopathological factors can be used prior to the initiation of immunotherapy for identifying NSCLC patients who are the most likely to benefit from the therapy. This could guide the individualized treatment strategy for advanced NSCLC.

Development and Validation of a Nomogram based on cell growth-related Biomarkers for Oral Squamous Cell Carcinoma

Purpose: We aimed to develop a prognostic nomogram based on immunohistochemistry (IHC) biomarkers of patients with oral squamous cell carcinoma (OSCC). Methods: A total of 294 patients were enrolled in the study. The least absolute shrinkage and selection operator (LASSO) Cox regression model was performed to develop a combined IHC score (IHCs) classifier. Results: Five biomarkers, specifically c-Met, Vimentin, HIF-2α, VEGF-c, and Bcl-2 were extracted. Then, an IHCs classifier was developed, and patients were stratified into high- and low-IHCs groups. In the training cohort, the 5-year overall survival (OS) was 62.1% in low-IHCs group and 28.2% in high-IHCs group (P<0.001). The 5-year OS was 68.6% for the low-IHCs group and 28.4% for the high-IHCs group in the validation cohort (P<0.001). The area under the ROC curve (AUROC) of the combination of the IHCs classifier and TNM stage was 0.746 (95% CI: 0.658-0.833) in the training cohort and 0.735 (95% CI: 0.651-0.818) in the validation cohort, respectively. Conclusions: The nomogram could effectively predict the prognosis for patients with OSCC and may be employed as a potential tool to guide the individual decision-making process.

Development and Validation of a Prognostic Nomogram for Postoperative Recurrence-Free Survival of Ameloblastoma

Purpose

Ameloblastoma is a benign odontogenic neoplasm with a high local recurrence rate if the operation is not thorough. However, a useful clinical tool for the quantitative assessment of the prognosis and risk of postoperative recurrence of ameloblastoma has not yet been constructed. This study aims to develop a prognostic nomogram model for ameloblastoma of the jaw to assist surgeons in surgical decision-making.

Patients and Methods

Patients who underwent initial surgery for ameloblastoma in our department from October 2004 to March 2020 were enrolled and randomly divided into training and validation sets. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify the independent prognostic factors, from which a nomogram for predicting 3-, 5- and 10-year recurrence-free survival (RFS) of ameloblastoma was constructed using the training set and internally validated using the validation set. The model performance was assessed by Harrell's concordance index (C-index) and calibration curves.

Results

A total of 302 eligible patients with ameloblastoma were enrolled, 54 of whom were confirmed to relapse during the follow-up period of 6 to 191 months. Four independent predictors, including cortical bone perforation, root(s) resorption, WHO classification, and treatment pattern, were identified and included in the construction of a nomogram for recurrence-free survival (RFS), which showed promising calibration performance and discrimination in the training set (C-index 0.790, 95% confidence interval [CI] 0.735–0.845) and the validation set (C-index 0.734, 95% CI 0.599–0.869).

Conclusion

A favorable nomogram was developed that accurately predicted the RFS of patients with ameloblastoma based on individual characteristics. Risk stratification using the nomogram could optimize tailored therapy and follow-up.

Human/eukaryotic ribosomal protein L14 (RPL14/eL14) overexpression represses proliferation, migration, invasion and EMT process in nasopharyngeal carcinoma

Although human/eukaryotic ribosomal protein L14 (RPL14/eL14) is known to be associated with a variety of cancers, its role in nasopharyngeal carcinoma (NPC) remains unclear. The aim of this study was to explore the impact of RPL14(eL14) in NPC. The results of quantitative real-time polymerase chain reaction (qRT-PCR), western blot, and immunohistochemical staining revealed that the expression of RPL14(eL14) significantly reduced in NPC tissues and cells. Furthermore, the protein expression of RPL14(eL14) was linked to NPC-related clinical pathological features, including the T and N classification of Tumor Node Metastasis (TNM) staging (all p < 0.05). Cell counting kit-8 (CCK-8) assay and colony formation assay revealed that RPL14(eL14) overexpression repressed NPC cell proliferation. In cell cycle assay, RPL14(eL14) overexpression significantly blocked NPC cells in S phase. Overexpression of RPL14(eL14) repressed cell migration and invasion in NPC as shown by transwell assay and cell scratch healing assay. In addition, RPL14(eL14) was closely correlated with the expression of epithelial–mesenchymal transition (EMT) biomarkers, including E-cadherin, N-cadherin, and vimentin as detected by western blot. In conclusion, our results revealed that RPL14(eL14) may be considered as an antioncogene in NPC, which greatly suppresses cancer progression.

Tenascin-C promotes epithelial-to-mesenchymal transition and the mTOR signaling pathway in nasopharyngeal carcinoma

Tenascin-C (TNC) is a large extracellular matrix glycoprotein that promotes cell adhesion and tissue remodeling, and is involved in the transduction of cellular signaling pathways. The present study aimed to investigate the role of TNC and determine its effect in nasopharyngeal carcinoma (NPC). TNC gene transcription and expression were analyzed using the NPC dataset and immunohistochemistry analysis of NPC tissues. Weighted gene co-expression network and gene enrichment analyses were performed to determine the potential molecular mechanisms underlying the effects of TNC in NPC. TNC expression was suppressed in NPC cells, and the effects were determined both in vitro and in vivo. The results demonstrated that TNC gene transcription and expression were high in NPC tissues compared with normal tissues. Notably, TNC knockdown inhibited NPC cell proliferation, migration and invasion. In addition, TNC knockdown inhibited tumor growth in mice. In vitro, TNC knockdown inhibited epithelial-to-mesenchymal transition (EMT) and decreased activity of the PI3K/AKT/mTOR signaling pathway in NPC cells. Taken together, these results suggest that TNC promotes cell proliferation, EMT and activity of the PI3K/AKT/mTOR signaling pathway in NPC cells, and thus functions as an oncogene.

Apatinib for the treatment of metastatic or locoregionally recurrent nasopharyngeal carcinoma after failure of chemotherapy: A multicenter, single-arm, prospective phase 2 study

BACKGROUND

The authors aimed to investigate the efficacy and safety of apatinib in patients with metastatic or locoregionally recurrent nasopharyngeal carcinoma (NPC).

METHODS

A multicenter, single-arm, prospective phase 2 study was conducted on patients (18-70 years of age) with metastatic or recurrent NPC who had failed chemotherapy. Patients with recurrent disease involving vascular structure invasion were excluded. All enrolled patients received apatinib (500 mg daily) in continuous 4-week cycles until disease progression or development of unacceptable toxicity. The primary end point of this study was objective response rate (ORR), and the secondary end points were progression-free survival (PFS), overall survival (OS), and toxicity. This study was registered with ClinicalTrials.gov (NCT03130270).

RESULTS

Between January 2017 and June 2018, 33 patients were enrolled. At the end of the data collection (May 20, 2020), the 33 patients had completed a total of 261.2 cycles of apatinib. Although 12 patients achieved a partial response, no patient achieved a complete response; thus, the ORR in the 33 patients was 36.4% (95% CI, 19.0%-53.7%). At the end of follow-up (median, 30 months; 95% CI, 24.9-35.1), median OS and median PFS were 16 months (95% CI, 14.6-17.4 months) and 5.0 months (95% CI, 3.6-6.4 months), respectively. The most common adverse events (grade 1/2) were hand-foot syndrome (18 [54.5%]), hypertension (14 [42.4%]), oral ulcer (8 [24.2%]), and proteinuria (4 [12.1%]). Two patients (1 with diabetes and 1 with hypertension) developed cerebral infarction. Grade 3/4 toxicities were uncommon.

CONCLUSIONS

Apatinib shows promising activity, with manageable toxicities, in patients with metastatic or locoregionally recurrent NPC. Further evaluation of apatinib in large-scale studies is warranted.

LAY SUMMARY

Clinical studies on vascular endothelial growth factor receptor (VEGFR)-targeted therapy for recurrent or metastatic nasopharyngeal carcinoma (NPC) are limited. A recent preclinical study that evaluated apatinib in models of NPC showed a high objective response rate and a favorable safety profile. Our data further confirmed good efficacy in patients with lung metastasis. Further studies of the efficacy and safety of apatinib combined with immune checkpoint inhibitors or chemotherapy in NPC is warranted.

Predictive nomogram for leprosy using genetic and epidemiological risk factors in Southwestern China: Case-control and prospective analyses

Background

There is a high incidence of leprosy among house-contacts compared with the general population. We aimed to establish a predictive model using these genetic factors along with epidemiological factors to predict leprosy risk of leprosy household contacts (HHCs).

Methods

Weighted genetic risk score (wGRS) encompassing genome wide association studies (GWAS) variants and five non-genetic factors were examined in a case–control design associated with leprosy risk including 589 cases and 647 controls from leprosy HHCs. We constructed a risk prediction nomogram and evaluated its performance by concordance index (C-index) and calibration curve. The results were validated using bootstrap resampling with 1000 resamples and a prospective design including 1100 HHCs of leprosy patients.

Finding

The C-index for the risk model was 0·792 (95% confidence interval [CI] 0·768-0·817), and was confirmed to be 0·780 through bootstrapping validation. The calibration curve for the probability of leprosy showed good agreement between the prediction of the nomogram and actual observation. HHCs were then divided into the low-risk group (nomogram score ≤ 81) and the high-risk group (nomogram score > 81). In prospective analysis, 12 of 1100 participants had leprosy during 63 months’ follow-up. We generated the nomogram for leprosy in the validation cohort (C-index 0·773 [95%CI 0·658-0·888], sensitivity75·0%, specificity 66·8%). Interpretation The nomogram achieved an effective prediction of leprosy in HHCs. Using the model, the risk of an individual contact developing leprosy can be determined, which can lead to a rational preventive choice for tracing higher-risk leprosy contacts.

Funding

The ministry of health of China, ministry of science and technology of China, Chinese academy of medical sciences, Jiangsu provincial department of science and technology, Nanjing municipal science and technology bureau.

Prognostic and Predictive Value of Circulating Inflammation Signature in Non-Metastatic Nasopharyngeal Carcinoma: Potential Role for Individualized Induction Chemotherapy

Purpose

We sought to assess the prognostic and predictive value of a circulating inflammation signature (CISIG) and develop CISIG-based tools for predicting prognosis and guiding individualized induction chemotherapy (ICT) in non-metastatic nasopharyngeal carcinoma (NPC).

Patients and Methods

We retrospectively collected a candidate inflammatory biomarker panel from patients with NPC treated with definitive radiotherapy between 2012 and 2017. We developed the CISIG using candidate biomarkers identified by a least absolute shrinkage and selection operator (LASSO) Cox regression model. The Cox regression analyses were used to evaluate the CISIG prognostic value. A CISIG-based prediction model was constructed, validated, and assessed. Potential stratified ICT treatment effects were examined.

Results

A total of 1149 patients were analyzed. Nine biomarkers selected by LASSO regression in the training cohort were used to construct the CISIG, including hyaluronidase, laminin, procollagen III, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, high-density lipoprotein, lactate dehydrogenase, and C-reactive protein-to-albumin ratio. CISIG was an independent prognostic factor for disease-free survival (DFS; hazard ratio: 2.65, 95% confidence interval: 1.93–3.64; P < 0.001). High CISIG group (>−0.2) was associated with worse 3-year DFS than low CISIG group in both the training (67.5% vs 88.3%, P < 0.001) and validation cohorts (72.3% vs 85.1%, P < 0.001). We constructed and validated a CISIG-based nomogram, which showed better performance than the clinical stage and Epstein–Barr virus DNA classification methods. A significant interaction between CISIG and the ICT treatment effect was observed (P for interaction = 0.036). Patients with high CISIG values did not benefit from ICT, whereas patients with low CISIG values significantly benefited from ICT.

Conclusion

The developed CISIG, based on a circulating inflammatory biomarker panel, adds prognostic information for patients with NPC. The proposed CISIG-based tools offer individualized risk estimation to facilitate suitable ICT candidate identification.

An interpretable machine learning prognostic system for locoregionally advanced nasopharyngeal carcinoma based on tumor burden features

OBJECTIVES

We aimed to build a survival system by combining a highly-accurate machine learning (ML) model with explainable artificial intelligence (AI) techniques to predict distant metastasis in locoregionally advanced nasopharyngeal carcinoma (NPC) patients using magnetic resonance imaging (MRI)-based tumor burden features.

MATERIALS AND METHODS

1643 patients from three hospitals were enrolled according to set criteria. We employed ML to develop a survival model based on tumor burden signatures and all clinical factors. Shapley Additive exPlanations (SHAP) was utilized to explain prediction results and interpret the complex non-linear relationship among features and distant metastasis. We also constructed other models based on routinely used cancer stages, Epstein-Barr virus (EBV) DNA, or other clinical features for comparison. Concordance index (C-index), receiver operating curve (ROC) analysis and decision curve analysis (DCA) were executed to assess the effectiveness of the models.

RESULTS

Our proposed system consistently demonstrated promising performance across independent cohorts. The concordance indexes were 0.773, 0.766 and 0.760 in the training, internal validation and external validation sets. SHAP provided personalized protective and risk factors for each NPC patient and uncovered some novel non-linear relationships between features and distant metastasis. Furthermore, high-risk patients who received induction chemotherapy (ICT) and concurrent chemoradiotherapy (CCRT) had better 5-year distant metastasis-free survival (DMFS) than those who only received CCRT, whereas ICT + CCRT and CCRT had similar DMFS in low-risk patients.

CONCLUSIONS

The interpretable machine learning system demonstrated superior performance in predicting metastasis in locoregionally advanced NPC. High-risk patients might benefit from ICT.

Prognosis prediction of extremity and trunk wall soft-tissue sarcomas treated with surgical resection with radiomic analysis based on random survival forest

Many researches have applied machine learning methods to find associations between radiomic features and clinical outcomes. Random survival forests (RSF), as an accurate classifier, sort all candidate variables as the rank of importance values. There was no study concerning on finding radiomic predictors in patients with extremity and trunk wall soft-tissue sarcomas using RSF. This study aimed to determine associations between radiomic features and overall survival (OS) by RSF analysis. To identify radiomic features with important values by RSF analysis, construct predictive models for OS incorporating clinical characteristics, and evaluate models' performance with different method. We collected clinical characteristics and radiomic features extracted from plain and contrast-enhanced computed tomography (CT) from 353 patients with extremity and trunk wall soft-tissue sarcomas treated with surgical resection. All radiomic features were analyzed by Cox proportional hazard (CPH) and followed RSF analysis. The association between radiomics-predicted risks and OS was assessed by Kaplan-Meier analysis. All clinical features were screened by CPH analysis. Prognostic clinical and radiomic parameters were fitted into RSF and CPH integrative models for OS in the training cohort, respectively. The concordance indexes (C-index) and Brier scores of both two models were evaluated in both training and testing cohorts. The model with better predictive performance was interpreted with nomogram and calibration plots. Among all 86 radiomic features, there were three variables selected with high importance values. The RSF on these three features distinguished patients with high predicted risks from patients with low predicted risks for OS in the training set (P < 0.001) using Kaplan-Meier analysis. Age, lymph node involvement and grade were incorporated into the combined models for OS (P < 0.05). The C-indexes in both two integrative models fluctuated above 0.80 whose Brier scores maintained less than 15.0 in the training and testing datasets. The RSF model performed little advantages over the CPH model that the calibration curve of the RSF model showed favorable agreement between predicted and actual survival probabilities for the 3-year and 5-year survival prediction. The multimodality RSF model including clinical and radiomic characteristics conducted high capacity in prediction of OS which might assist individualized therapeutic regimens. Level III, prognostic study.

Comprehensive Characterization of Cachexia-Inducing Factors in Diffuse Large B-Cell Lymphoma Reveals a Molecular Subtype and a Prognosis-Related Signature

Background

Cachexia is defined as an involuntary decrease in body weight, which can increase the risk of death in cancer patients and reduce the quality of life. Cachexia-inducing factors (CIFs) have been reported in colorectal cancer and pancreatic adenocarcinoma, but their value in diffuse large B-cell lymphoma (DLBCL) requires further genetic research.

Methods

We used gene expression data from Gene Expression Omnibus to evaluate the expression landscape of 25 known CIFs in DLBCL patients and compared them with normal lymphoma tissues from two cohorts [GSE56315 (n = 88) and GSE12195 (n = 136)]. The mutational status of CIFs were also evaluated in The Cancer Genome Atlas database. Based on the expression profiles of 25 CIFs, a single exploratory dataset which was merged by the datasets of GSE10846 (n = 420) and GSE31312 (n = 498) were divided into two molecular subtypes by using the method of consensus clustering. Immune microenvironment between different subtypes were assessed via single-sample gene set enrichment analysis and the CIBERSORT algorithm. The treatment response of commonly used chemotherapeutic drugs was predicted and gene set variation analysis was utilized to reveal the divergence in activated pathways for distinct subtypes. A risk signature was derived by univariate Cox regression and LASSO regression in the merged dataset (n = 882), and two independent cohorts [GSE87371 (n = 221) and GSE32918 (n = 244)] were used for validation, respectively.

Results

Clustering analysis with CIFs further divided the cases into two molecular subtypes (cluster A and cluster B) associated with distinct prognosis, immunological landscape, chemosensitivity, and biological process. A risk-prognostic signature based on CCL2, CSF2, IL15, IL17A, IL4, TGFA, and TNFSF10 for DLBCL was developed, and significant differences in overall survival analysis were found between the low- and high-risk groups in the training dataset and another two independent validation datasets. Multivariate regression showed that the risk signature was an independently prognostic factor in contrast to other clinical characteristics.

Conclusion

This study demonstrated that CIFs further contribute to the observed heterogeneity of DLBCL, and molecular classification and a risk signature based on CIFs are both promising tools for prognostic stratification, which may provide important clues for precision medicine and tumor-targeted therapy.

A Nomogram for Predicting Multiple Metastases in Metastatic Colorectal Cancer Patients: A Large Population-Based Study

OBJECTIVES

The present study aims to discover the risk factors of multiple metastases and develop a functional nomogram to forecast multiple metastases in metastatic colorectal cancer (mCRC) patients.

METHODS

mCRC cases were retrospectively collected from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016. Survival times between multiple metastases and single metastasis were compared using Kaplan-Meier analysis and log-rank tests. Risk factors for multiple metastases were determined by univariate and multivariate logistic regression analyses, and a nomogram was developed to forecast the probability of multiple metastases in mCRC patients. We assessed the nomogram performance in terms of discrimination and calibration, including concordance index (C-index), area under the curve (AUC), and decision curve analysis (DCA). Bootstrap resampling was used as an internal verification method, and at the same time we select external data from Renmin Hospital of Wuhan University as independent validation sets.

RESULTS

A total of 5,302 cases were included in this study as training group, while 120 cases were as validation group. The patients with single metastasis and multiple metastases were 3,531 and 1,771, respectively. The median overall survival (OS) and cancer-specific survival (CSS) for patients with multiple metastases or single metastasis were 19 vs. 31 months, and 20 vs. 33 months, respectively. Based on the univariate and multivariate analyses, clinicopathological characteristics were associated with number of metastasis and were used to establish nomograms to predict the risk of multiple metastases. The C-indexes and AUC for the forecast of multiple metastases were 0.715 (95% confidence interval (CI), 0.707-0.723), which showed the nomogram had good discrimination and calibration curves of the nomogram showed no significant bias from the reference line, indicating a good degree of calibration. In the validation group, the AUC was 0.734 (95% CI, 0.653-0.834), and calibration curve also showed no significant bias, indicating the favorable effects of our nomogram.

CONCLUSIONS

We developed a new nomogram to predict the risk of multiple metastases. The nomogram shows the good prediction effect and can provide assistance for clinical diagnosis and treatment.

Nomogram to differentiate between aortic dissection and non-ST segment elevation acute coronary syndrome: a retrospective cohort study

Background

Aortic dissection (AD) and non-ST segment elevation acute coronary syndrome (ACS) are two of the most life-threatening diseases encountered in the emergency department (ED), but there are no rapid and reliable tools for differentiation. The purpose of this study is to develop and validate a nomogram that incorporates both the clinical characteristics and bedside laboratory tests available to differentiate between AD and non-ST segment elevation ACS (NSTE-ACS).

Methods

Between January 2016 and July 2018, patients with AD and NSTE-ACS were enrolled and divided into training and validation groups. The least absolute shrinkage and selection operator (LASSO) regression model was used to select the factors with significant value of predicting the diagnosis of AD. A nomogram was built on the basis of multivariable logistic regression analysis. Area under the curve (AUC) of receiver operating characteristic (ROC) curve and the calibration curve were used to assess the performance of the nomogram. Decision curve analysis was performed to assess the clinical utility of the nomogram.

Results

A final cohort of 263 patients (94 patients with AD and 169 patients with NSTE-ACS) were enrolled. Six variables were incorporated in the nomogram: pain severity, tearing pain, pulse asymmetry, electrocardiogram (ECG), D-dimer level and troponin I level. The AUC of the nomogram to predict the probability of AD was 0.919 (95% CI, 0.876-0.962) in the training group and 0.938 (95% CI, 0.888-0.989) in the validation group. The calibration curve demonstrated a good consistency between the actual clinical results and the predicted outcomes. The decision curve analysis indicated that the nomogram had higher overall net benefits in predicting AD in both the training group and the validation group.

Conclusions

We developed and validated a predictive nomogram that could be used as a tool to differentiate AD from NSTE-ACS rapidly and accurately.

Establishment and validation of a novel nomogram to predict overall survival in nasopharyngeal carcinoma with lymph node metastasis

BACKGROUND

The aim of the present study was to establish and validate a nomogram to predict the overall survival (OS) in nasopharyngeal carcinoma (NPC) patients with lymph node metastasis (LNM).

METHODS

A novel nomogram was constructed using 863 patients with LNM-positive NPC from the Surveillance, Epidemiology, and End Results (SEER) database. Significant prognostic factors in the nomograms were determined using multivariate Cox risk analysis. The predictive capability was evaluated using calibration curves and decision curve analysis (DCA).

RESULTS

Multivariate analysis identified seven factors that could be used to construct the nomogram: age, pathological type, T stage, M stage, surgery of primary site, radiotherapy, and chemotherapy. The calibration curves and DCA demonstrated optimal agreement. Based on the nomogram, all patients could be stratified into three risk groups: low, middle, and high.

CONCLUSIONS

The novel nomogram demonstrated its potential as an individualized tool to predict OS.

Prognostic Nomograms for Nonelderly Adults with Gastric Signet Ring Cell Carcinoma

Background

Nomograms were established to predict the survival for gastric signet ring cell carcinoma (GSRC) in young and middle-aged adults. Material and Methods. Eligible patients with GSRC from 2004 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database and then divided into a training and a testing cohort in proportion. Independent prognostic factors were picked by univariate and multivariate Cox regression analysis to set up nomograms. The predictive effect and clinical value of nomograms were evaluated by the concordance index (C-index), calibration curves, and receiver operating characteristic curve (ROC).

Results

A total of 1686 GSRC patients were subsumed into this case for analysis, including a training (n = 1180) and a testing cohort (n = 506). Independent risk factors related to overall survival (OS) and cancer-specific survival (CSS) comprised of race, TNM stage, tumor size, number of positive lymph nodes (PLNE), and chemotherapy. For OS, the C-indexes of the training and testing cohorts were 0.737 and 0.752, while for CSS, C-indexes were, respectively, 0.749 and 0.751. These revealed that nomograms accurately predicted OS and CSS. Calibration curves and ROC demonstrated the apparent superiority of nomograms.

Conclusion

We built a well-understood and comprehensive prognostic assessment model for GSRC, which provided an individualized survival prediction in the form of a quantitative score that can be considered for clinical practice.

A Clinical-Radiomics Nomogram Based on Computed Tomography for Predicting Risk of Local Recurrence After Radiotherapy in Nasopharyngeal Carcinoma

Purpose: We aimed to establish a nomogram model based on computed tomography (CT) imaging radiomic signature and clinical factors to predict the risk of local recurrence in nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). Methods: This was a retrospective study consisting of 156 NPC patients treated with IMRT. Radiomics features were extracted from the gross tumor volume for nasopharynx (GTVnx) in pretreatment CT images for patients with or without local recurrence. Discriminative radiomics features were selected after t-test and the least absolute shrinkage and selection operator (LASSO) analysis. The most stable model was obtained to generate radiomics signature (Rad_Score) by using machine learning models including Logistic Regression, K-Nearest neighbor, Naive Bayes, Decision Tree, Stochastic Gradient Descent, Gradient Booting Tree and Linear Support Vector Classification. A nomogram for local recurrence was established based on Rad_Score and clinical factors. The predictive performance of nomogram was evaluated by discrimination ability and calibration ability. Decision Curve Analysis (DCA) was used to evaluate the clinical benefits of the multi-factor nomogram in predicting local recurrence after IMRT. Results: Local recurrence occurred in 42 patients. A total of 1,452 radiomics features were initially extracted and seven stable features finally selected after LASSO analysis were used for machine learning algorithm modeling to generate Rad_Score. The nomogram showed that the greater Rad_Score was associated with the higher risk of local recurrence. The concordance index, specificity and sensitivity in the training cohort were 0.931 (95%CI:0.8765-0.9856), 91.2 and 82.8%, respectively; whereas, in the validation cohort, they were 0.799 (95%CI: 0.6458-0.9515), 79.4, and 69.2%, respectively. Conclusion: The nomogram based on radiomics signature and clinical factors can predict the risk of local recurrence after IMRT in patients with NPC and provide evidence for early clinical intervention.

YBX3 Mediates the Metastasis of Nasopharyngeal Carcinoma via PI3K/AKT Signaling

The metastasis of nasopharyngeal carcinoma (NPC) is a complex process associated with oncogenic dysfunction, the deciphering of which remains a challenge and requires more in-depth studies. Y-box protein 3 (YBX3) is a DNA/RNA binding protein associated with gene transcription, DNA repair, and the progression of various diseases. However, whether and how YBX3 affects the metastasis of NPC remains unknown. Thus, in this study, we aimed to investigate the role of YBX3 in the metastasis of NPC and determine its underlying mechanism. Interestingly, it was found that the expression of YBX3, which was associated with NPC metastasis, was upregulated in the clinical NPC tissues and cell lines. Moreover, we found that knockdown of YBX3 expression by lentivirus shRNA significantly suppressed NPC cells migration in vitro and metastasis in vivo. Mechanistically, RNA sequencing results suggested that the genes regulated by YBX3 were significantly enriched in cell adhesion molecules, cAMP signaling pathway, calcium signaling pathway, focal adhesion, PI3K/AKT signaling pathway, Ras signaling pathway, Rap1 signaling pathway, NF-κB signaling pathway, and Chemokine signaling pathway. Of these, PI3K/AKT signaling pathway contained the most genes. Accordingly, YBX3 knockdown decreased the activation of PI3K/AKT signaling pathway, thereby inhibit epithelial-to-mesenchymal transition (EMT) and MMP1. These results have demonstrated that YBX3 are involved in the metastasis of NPC through regulating PI3K/AKT signaling pathway, and serve as a potential therapeutic target for patients with NPC.

Prognostic models for 1-year survival of NPC after radiotherapy in different ages

PURPOSE

Previous studies have shown that approximately 10% of nasopharyngeal cancer (NPC) patients die within a year of disease onset, and that age is an independent predictor. However, no predictive model has been developed. We aimed to establish novel prognostic models to predict the 1-year cancer-specific survival (CSS) of young, middle-aged, and older patients with NPC after radiotherapy.

METHODS

The data of 2822 NPC patients who underwent radiotherapy between 2004 and 2015 were reviewed from the surveillance, epidemiology, and end results database. We divided them into young, middle-aged, and older people groups according to age (< 44 years, 45-59 years, and ≥ 60 years, respectively). Multivariate analyses were performed, and prognostic models were constructed.

RESULTS

Multivariate analyses indicated that age, ethnicity, histological subtype, T, and M stage were independent predictors of 1-year CSS in the older people group. In contrast, ethnicity and age were not found to have predictive value in the young and middle-aged groups, respectively. Accordingly, three prognostic models with excellent predictive values were established for the three groups (C-indices: 0.791 [95% CI 0.722-0.859], 0.763 [95% CI 0.721-0.806] and 0.723 [95% CI 0.683-0.763], respectively). These predictive values are higher than those of the eighth edition American joint committee cancer tumor-node-metastasis (TNM) classification system.

CONCLUSION

Three prognostic models for predicting the 1-year CSS of young, middle-aged, and older NPC patients after radiotherapy showed better predictive power than the TNM classification system. These models may guide treatment strategies and clinical decision-making in different cohorts.

Cancer-associated fibroblasts promote the survival of irradiated nasopharyngeal carcinoma cells via the NF-κB pathway

Background

Irradiation has emerged as a valid tool for nasopharyngeal carcinoma (NPC) in situ treatment; however, NPC derived from tissues treated with irradiation is a main cause cancer-related death. The purpose of this study is to uncover the underlying mechanism regarding tumor growth after irradiation and provided potential therapeutic strategy.

Methods

Fibroblasts were extracted from fresh NPC tissue and normal nasopharyngeal mucosa. Immunohistochemistry was conducted to measure the expression of α-SMA and FAP. Cytokines were detected by protein array chip and identified by real-time PCR. CCK-8 assay was used to detect cell proliferation. Radiation-resistant (IRR) 5-8F cell line was established and colony assay was performed to evaluate tumor cell growth after irradiation. Signaling pathways were acquired via gene set enrichment analysis (GSEA). Comet assay and γ-H2AX foci assay were used to measure DNA damage level. Protein expression was detected by western blot assay. In vivo experiment was performed subcutaneously.

Results

We found that radiation-resistant NPC tissues were constantly infiltrated with a greater number of cancer-associated fibroblasts (CAFs) compared to radiosensitive NPC tissues. Further research revealed that CAFs induced the formation of radioresistance and promoted NPC cell survival following irradiation via the IL-8/NF-κB pathway to reduce irradiation-induced DNA damage. Treatment with Tranilast, a CAF inhibitor, restricted the survival of CAF-induced NPC cells and attenuated the of radioresistance properties.

Conclusions

Together, these data demonstrate that CAFs can promote the survival of irradiated NPC cells via the NF-κB pathway and induce radioresistance that can be interrupted by Tranilast, suggesting the potential value of Tranilast in sensitizing NPC cells to irradiation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-021-01878-x.

Blood-based multi-tissue gene expression inference with Bayesian ridge regression

MOTIVATION

Gene expression profiling is widely used in basic and cancer research but still not feasible in many clinical applications because tissues, such as brain samples, are difficult and not ethnical to collect. Gene expression in uncollected tissues can be computationally inferred using genotype and expression quantitative trait loci. No methods can infer unmeasured gene expression of multiple tissues with single tissue gene expression profile as input.

RESULTS

Here, we present a Bayesian ridge regression-based method (B-GEX) to infer gene expression profiles of multiple tissues from blood gene expression profile. For each gene in a tissue, a low-dimensional feature vector was extracted from whole blood gene expression profile by feature selection. We used GTEx RNAseq data of 16 tissues to train inference models to capture the cross-tissue expression correlations between each target gene in a tissue and its preselected feature genes in peripheral blood. We compared B-GEX with least square regression, LASSO regression and ridge regression. B-GEX outperforms the other three models in most tissues in terms of mean absolute error, Pearson correlation coefficient and root-mean-squared error. Moreover, B-GEX infers expression level of tissue-specific genes as well as those of non-tissue-specific genes in all tissues. Unlike previous methods, which require genomic features or gene expression profiles of multiple tissues, our model only requires whole blood expression profile as input. B-GEX helps gain insights into gene expressions of uncollected tissues from more accessible data of blood.

AVAILABILITY AND IMPLEMENTATION

B-GEX is available at https://github.com/xuwenjian85/B-GEX.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Development and Validation of a Nomogram for Predicting Radiation-Induced Temporal Lobe Injury in Nasopharyngeal Carcinoma

Background

The purpose was to develop and validate a nomogram for prediction on radiation-induced temporal lobe injury (TLI) in patients with nasopharyngeal carcinoma (NPC).

Methods

The prediction model was developed based on a primary cohort that consisted of 194 patients. The data was gathered from January 2008 to December 2010. Clinical factors associated with TLI and dose–volume histograms for 388 evaluable temporal lobes were analyzed. Multivariable logistic regression analysis was used to develop the predicting model, which was conducted by R software. The performance of the nomogram was assessed with calibration and discrimination. An external validation cohort contained 197 patients from January 2011 to December 2013.

Results

Among the 391 patients, 77 patients had TLI. Prognostic factors contained in the nomogram were Dmax (the maximum point dose) of temporal lobe, D1cc (the maximum dose delivered to a volume of 1 ml), T stage, and neutrophil-to-lymphocyte ratios (NLRs). The Internal validation showed good discrimination, with a C-index of 0.847 [95%CI 0.800 to 0.893], and good calibration. Application of the nomogram in the external validation cohort still obtained good discrimination (C-index, 0.811 [95% CI, 0.751 to 0.870]) and acceptable calibration.

Conclusions

This study developed and validated a nomogram, which may be conveniently applied for the individualized prediction of TLI.

A Nomogram Combing Peripheral Parameters for Estimation of CRSwNP Recurrence

BACKGROUND

The preoperative prediction of the recurrence of chronic rhinosinusitis with nasal polyps (CRSwNP) remains difficult in clinical practice.

OBJECTIVE

We aimed to develop a nomogram that combined peripheral risk factors to clinically predict the recurrence of CRSwNP.

METHODS

Data from 158 CRSwNP patients who underwent endoscopic sinus surgery (ESS) from January 2012 to December 2016 were collected, and the patients were followed up for 3 years. Of these, 96 patients who underwent ESS in an earlier period formed the training cohort for nomogram development, and 62 patients who underwent ESS thereafter formed the validation cohort to confirm the model's performance. Risk factors for recurrence identified by univariate and multivariate logistic regression were used to create a nomogram.

RESULTS

The recurrence rate was 29.2% (28/96) for the training cohort and 35.5% (22/62) for the validation cohort. Univariate analysis identified blood eosinophils (Eos), serum IgE level, asthma comorbidity, and the number of previous ESSs as risk factors for recurrence. Among those four parameters, serum IgE level and a previous ESS surgery were identified as two independent risk factors. A nomogram consisting of blood Eos, total serum IgE level, asthma comorbidity, and the number of previous ESSs was constructed, demonstrating a C index of 0.81 (95% CI, 0.79-0.83) and 0.80 (95% CI, 0.77-0.83) for predicting recurrence in the training and validation cohorts, respectively. The nomogram had well-fitted calibration curves.

CONCLUSION

The nomogram might be able to preoperatively predict the recurrence of CRSwNP by using currently available and objective parameters. Further studies are required to validate its reliability and effectiveness.

Deep learning for risk prediction in patients with nasopharyngeal carcinoma using multi-parametric MRIs

BACKGROUND

Magnetic resonance images (MRI) is the main diagnostic tool for risk stratification and treatment decision in nasopharyngeal carcinoma (NPC). However, the holistic feature information of multi-parametric MRIs has not been fully exploited by clinicians to accurately evaluate patients.

OBJECTIVE

To help clinicians fully utilize the missed information to regroup patients, we built an end-to-end deep learning model to extract feature information from multi-parametric MRIs for predicting and stratifying the risk scores of NPC patients.

METHODS

In this paper, we proposed an end-to-end multi-modality deep survival network (MDSN) to precisely predict the risk of disease progression of NPC patients. Extending from 3D dense net, this proposed MDSN extracted deep representation from multi-parametric MRIs (T1w, T2w, and T1c). Moreover, deep features and clinical stages were integrated through MDSN to more accurately predict the overall risk score (ORS) of individual NPC patient.

RESULT

A total of 1,417 individuals treated between January 2012 and December 2014 were included for training and validating the end-to-end MDSN. Results were then tested in a retrospective cohort of 429 patients included in the same institution. The C-index of the proposed method with or without clinical stages was 0.672 and 0.651 on the test set, respectively, which was higher than the that of the stage grouping (0.610).

CONCLUSIONS

The C-index of the model which integrated clinical stages with deep features is 0.062 higher than that of stage grouping alone (0.672 vs 0.610). We conclude that features extracted from multi-parametric MRIs based on MDSN can well assist the clinical stages in regrouping patients.

Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature

Background

Accumulating evidence has demonstrated that immune-related lncRNAs (IRLs) are commonly aberrantly expressed in breast cancer (BC). Thus, we aimed to establish an IRL-based tool to improve prognosis prediction in BC patients.

Methods

We obtained IRL expression profiles in large BC cohorts (N = 911) from The Cancer Genome Atlas (TCGA) database. Then, in light of the correlation between each IRL and recurrence-free survival (RFS), we screened prognostic IRL signatures to construct a novel RFS nomogram via a Cox regression model. Subsequently, the performance of the IRL-based model was evaluated through discrimination, calibration ability, risk stratification ability and decision curve analysis (DCA).

Results

A total of 52 IRLs were obtained from TCGA. Based on multivariate Cox regression analyses, four IRLs (A1BG-AS1, AC004477.3, AC004585.1 and AC004854.2) and two risk parameters (tumor subtype and TNM stage) were utilized as independent indicators to develop a novel prognostic model. In terms of predictive accuracy, the IRL-based model was distinctly superior to the TNM staging system (AUC: 0.728 VS 0.673, P = 0.010). DCA indicated that our nomogram had favorable clinical practicability. In addition, risk stratification analysis showed that the IRL-based tool efficiently divided BC patients into high- and low-risk groups (P < 0.001).

Conclusions

A novel IRL-based model was constructed to predict the risk of 5-year RFS in BC. Our model can improve the predictive power of the TNM staging system and identify high-risk patients with tumor recurrence to implement more appropriate treatment strategies.

Two machine learning methods identify a metastasis-related prognostic model that predicts overall survival in medulloblastoma patients

Approximately 30% of medulloblastoma (MB) patients exhibit metastasis at initial diagnosis, which often leads to a poor prognosis. Here, by using univariate Cox regression analysis, two machine learning methods (Lasso-penalized Cox regression and random survival forest-variable hunting (RSF-VH)), and multivariate Cox regression analysis, we established two metastasis-related prognostic models, including the 47-mRNA-based model based on the Lasso method and the 21-mRNA-based model based on the RSF-VH method. In terms of the results of the receiver operating characteristic (ROC) curve analyses, we selected the 47-mRNA metastasis-associated model with the higher area under the curve (AUC). The 47-mRNA-based prognostic model could classify MB patients into two subgroups with different prognoses. The ROC analyses also suggested that the 47-mRNA metastasis-associated model may have a better predictive ability than MB subgroup. Multivariable Cox regression analysis demonstrated that the 47-mRNA-based model was independent of other clinical characteristics. In addition, a nomogram comprising the 47-mRNA-based model was built. The results of ROC analyses suggested that the nomogram had good discrimination ability. Our 47-mRNA metastasis-related prognostic model and nomogram might be an efficient and valuable tool for overall survival (OS) prediction and provide information for individualized treatment decisions in patients with MB.

A clinical nomogram and heat map for assessing survival in patients with stage I non-small cell lung cancer after complete resection

Background:

Assessing the prognosis of patients with early-stage non-small cell lung cancer (NSCLC) has become a major clinical issue. This study aimed to devise an effective clinical nomogram and heat map for assessing the survival of patients with stage I NSCLC receiving complete resection.

Methods:

Nomograms were established based on a retrospective study of 654 patients with stage I NSCLC who underwent radical resection at Sun Yat-Sen University Cancer Center between January 2009 and December 2014. The concordance index (C-index) and calibration curve were used to measure the accuracy and discriminative ability of the final nomogram. Heat maps were constructed with prognostic factors and survival probabilities. Survival curves were depicted using the Kaplan–Meier method, and the log-rank test was used to determine significance. Patients were classified into low- and high-risk subgroups using recursive partitioning analysis based on nomogram scores.

Results:

In univariate and multivariate analyses, the independent factors for overall survival (OS) and disease-free survival (DFS) were age, sex, tumor size, and visceral pleural invasion, which were all selected in the nomogram. The C-indices of the nomogram for predicting OS and DFS were 0.694 [95% confidence interval (CI) 0.651–0.737] and 0.653 (95% CI 0.61–0.696), respectively. The calibration curves for OS and DFS probabilities showed a good agreement between the nomogram prediction and actual observation. A heat map was generated using the above independent factors for OS and DFS. High-risk patients had shorter OS [hazard ratio (HR) = 3.535, 95% CI 2.444–5.113, p < 0.001] and DFS (HR = 2.607, 95% CI 1.922–3.537, p < 0.001) than low-risk patients.

Conclusion:

We established a prognostic nomogram and heat map that can be useful for evaluating survival in patients with stage I NSCLC after complete resection. The tools resulted in more accurate prediction and may guide clinicians in making treatment decisions.

Prognostic Impact of Osteopenia in Patients Who Underwent Living Donor Liver Transplantation for Hepatocellular Carcinoma

BACKGROUND

Osteopenia, loss of bone mineral density (BMD), was recently identified to be independently associated with early marker of deconditioning that precedes sarcopenia in patients with hepatocellular carcinoma (HCC). The aim of this study was to clarify the impact of osteopenia as the risk factor for mortality after living donor liver transplantation (LDLT) compared with already-reported biological markers.

METHODS

Data were collected retrospectively for all consecutive patients who underwent LDLT for HCC at our institution between January 1998 and December 2015. BMD was evaluated with computed tomographic measurement of pixel density in the midvertebral core of the 11th thoracic vertebra. Data related to clinicopathological parameters and prognosis were analyzed.

RESULTS

The median value of BMD was 163.6 Hounsfield units and osteopenia was identified in 103 (53.4%) of the 193 recipients, according to the age-specific formula. In addition to the other tumor burdens, such as tumor numbers ≥5 (HR 2.521, P = 0.027), DCP levels >200 mAU/mL (HR 2.678, P = 0.006), and neutrophil-to-lymphocyte ratio ≥3.01 (HR 2.068, P = 0.025), osteopenia (HR 2.106, P = 0.024) was independent risk factor for mortality by multivariate analysis. Overall survival of the patients who met the two risk factors and more was significantly lower than the others (HR 5.382, P < 0.001). Besides, the calibration plot for the 5-year overall survival using nomogram was predicted very well (C-index 0.746).

CONCLUSIONS

Preoperative osteopenia was independently associated with post-LDLT mortality among patients with HCC. Moreover, risk score and nomogram with calibration curve were developed to confirm the clinical usefulness of osteopenia for post-LDLT patients.

A novel DNA repair-related nomogram predicts survival in low-grade gliomas

Aims

We aimed to create a tumor recurrent‐based prediction model to predict recurrence and survival in patients with low‐grade glioma.

Methods

This study enrolled 291 patients (188 in the training group and 103 in the validation group) with clinicopathological information and transcriptome sequencing data. LASSO‐COX algorithm was applied to shrink predictive factor size and build a predictive recurrent signature. GO, KEGG, and GSVA analyses were performed for function annotations of the recurrent signature. The calibration curves and C‐Index were assessed to evaluate the nomogram's performance.

Results

This study found that DNA repair functions of tumor cells were significantly enriched in recurrent low‐grade gliomas. A predictive recurrent signature, built by the LASSO‐COX algorithm, was significantly associated with overall survival and progression‐free survival in low‐grade gliomas. Moreover, function annotations analysis of the predictive recurrent signature exhibited that the signature was associated with DNA repair functions. The nomogram, combining the predictive recurrent signature and clinical prognostic predictors, showed powerful prognostic ability in the training and validation groups.

Conclusion

An individualized prediction model was created to predict 1‐, 2‐, 3‐, 5‐, and 10‐year survival and recurrent rate of patients with low‐grade glioma, which may serve as a potential tool to guide postoperative individualized care.

Long Noncoding RNA TINCR-Mediated Regulation of Acetyl-CoA Metabolism Promotes Nasopharyngeal Carcinoma Progression and Chemoresistance

Frontier evidence suggests that dysregulation of long noncoding RNAs (lncRNA) is ubiquitous in all human tumors, indicating that lncRNAs might have essential roles in tumorigenesis. Therefore, an in-depth study of the roles of lncRNA in nasopharyngeal carcinoma (NPC) carcinogenesis might be helpful to provide novel therapeutic targets. Here we report that lncRNA TINCR was significantly upregulated in NPC and was associated positively with poor survival. Silencing TINCR inhibited NPC progression and cisplatin resistance. Mechanistically, TINCR bound ACLY and protected it from ubiquitin degradation to maintain total cellular acetyl-CoA levels. Accumulation of cellular acetyl-CoA promoted de novo lipid biosynthesis and histone H3K27 acetylation, which ultimately regulated the peptidyl arginine deiminase 1 (PADI1)-MAPK-MMP2/9 pathway. In addition, insulin-like growth factor 2 mRNA-binding protein 3 interacted with TINCR and slowed its decay, which partially accounted for TINCR upregulation in NPC. These findings demonstrate that TINCR acts as a crucial driver of NPC progression and chemoresistance and highlights the newly identified TINCR-ACLY-PADI1-MAPK-MMP2/9 axis as a potential therapeutic target in NPC. SIGNIFICANCE: TINCR-mediated regulation of a PADI1-MAPK-MMP2/9 signaling pathway plays a critical role in NPC progression and chemoresistance, marking TINCR as a viable therapeutic target in this disease.

Identification of a 9-gene prognostic signature for breast cancer

Breast cancer (BRCA) is the most common cancer among women and is the second leading cause of cancer death in women. In this study, we developed a 9‐gene prognostic signature to predict the prognosis of patients with BRCA. GSE20685, GSE42568, GSE20711, and GSE88770 were used as training sets. The Kaplan–Meier plot was constructed to assess survival differences and log‐rank test was performed to evaluate the statistical significance. The overall survival (OS) of patients in the low‐risk group was significantly higher than that in the high‐risk group. ROC analysis indicated that this 9‐gene signature shows good diagnostic efficiency both in OS and disease‐free survival (DFS). The 9‐gene signature was further validated through GSE16446, GSE7390, and TCGA‐BRCA datasets. We also established a nomogram that integrates clinicopathological features and 9‐gene signature. The analysis of the calibration plot showed that the nomogram has good prognostic performance. More convincingly, real‐time reverse transcription‐polymerase chain reaction (RT‐PCR) results indicated that the protective prognostic factors in BRCA patients were downregulated, whereas the dangerous prognostic factors were upregulated. The innovation of this article is not only constructing a prognostic gene signature, but also combining with clinical information to further establish a nomogram to better predict the survival probability of patients. It is worth mentioning that this signature also does not depend on other clinical factors or variables.

Tetrandrine inhibits cell migration and invasion in human nasopharyngeal carcinoma NPC-TW 039 cells through inhibiting MAPK and RhoA signaling pathways

The objective of this study was to investigate the effects of tetrandrine (TET) on cell migration and invasion of nasopharyngeal carcinoma NPC-TW 039 cells in vitro. TET at 1-10 μM did not change cell morphology and also did not decrease the total cell viability and proliferation in NPC-TW 039 cells. It decreased the cell mobility based on decreased wound closure in NPC-TW 039 cells by wound healing assay. TET suppressed the cell migration and invasion using transwell system. TET reduced MMP-2 activities at 1-10 μM and these effects are in dose-dependently. After exposed to various treatments, TET decreased the levels of p-ERK, p-JNK, p-p38, RhoA, and NF-κB at 48 hr. Based on these findings, we may suggest TET-inhibited cell migration and invasion of NPC-TW 039 cells via the suppression of MAPK and RhoA signaling pathways for inhibiting the MMP-2 and -9 expression in vitro. PRACTICAL APPLICATIONS: Tetrandrine (TET), a bis-benzylisoquinoline alkaloid, is obtained from the dried root of Stephania tetrandra. TET has been shown to induce cancer cell apoptosis on human cancer cells but its anti-metastasis effect on cell migration and invasion of nasopharyngeal carcinoma cells has not been investigated. Our results showed that TET significantly repressed the cell mobility, migration, and invasion of NPC-TW 039 cells in vitro that involved in inhibiting RhoA, Ras accompanying with p38/MAPK signaling pathway. We conclude that TET may be the anticancer agents for nasopharyngeal carcinoma therapy in the future.

A Prognostic Predictive System Based on Deep Learning for Locoregionally Advanced Nasopharyngeal Carcinoma

BACKGROUND

Images from magnetic resonance imaging (MRI) are crucial unstructured data for prognostic evaluation in nasopharyngeal carcinoma (NPC). We developed and validated a prognostic system based on the MRI features and clinical data of locoregionally advanced NPC (LA-NPC) patients to distinguish low-risk patients with LA-NPC for whom concurrent chemoradiotherapy (CCRT) is sufficient.

METHODS

This multicenter, retrospective study included 3444 patients with LA-NPC from January 1, 2010, to January 31, 2017. A 3-dimensional convolutional neural network was used to learn the image features from pretreatment MRI images. An eXtreme Gradient Boosting model was trained with the MRI features and clinical data to assign an overall score to each patient. Comprehensive evaluations were implemented to assess the performance of the predictive system. We applied the overall score to distinguish high-risk patients from low-risk patients. The clinical benefit of induction chemotherapy (IC) was analyzed in each risk group by survival curves.

RESULTS

We constructed a prognostic system displaying a concordance index of 0.776 (95% confidence interval [CI] = 0.746 to 0.806) for the internal validation cohort and 0.757 (95% CI = 0.695 to 0.819), 0.719 (95% CI = 0.650 to 0.789), and 0.746 (95% CI = 0.699 to 0.793) for the 3 external validation cohorts, which presented a statistically significant improvement compared with the conventional TNM staging system. In the high-risk group, patients who received induction chemotherapy plus CCRT had better outcomes than patients who received CCRT alone, whereas there was no statistically significant difference in the low-risk group.

CONCLUSIONS

The proposed framework can capture more complex and heterogeneous information to predict the prognosis of patients with LA-NPC and potentially contribute to clinical decision making.

Prognostic and Treatment Guiding Significance of MRI-Based Tumor Burden Features and Nodal Necrosis in Nasopharyngeal Carcinoma

We aimed to develop a nomogram integrating MRI-based tumor burden features (MTBF), nodal necrosis, and some clinical factors to forecast the distant metastasis-free survival (DMFS) of patients suffering from non-metastatic nasopharyngeal carcinoma (NPC). A total of 1640 patients treated at Sun Yat-sen University Cancer Center (Guangzhou, China) from 2011 to 2016 were enrolled, among which 1148 and 492 patients were randomized to a training cohort and an internal validation cohort, respectively. Additionally, 200 and 257 patients were enrolled in the Foshan and Dongguan validation cohorts, respectively, which served as independent external validation cohorts. The MTBF were developed from the stepwise regression of six multidimensional tumor burden variables, based on which we developed a nomogram also integrating nodal necrosis and clinical features. This model divided the patients into high- and low-risk groups by an optimal cutoff. Compared with those of patients in the low-risk group, the DMFS [hazard ratio (HR): 4.76, 95% confidence interval (CI): 3.39–6.69; p < 0.0001], and progression-free survival (PFS; HR: 4.11, 95% CI: 3.13–5.39; p < 0.0001) of patients in the high-risk group were relatively poor. Furthermore, in the training cohort, the 3-year DMFS of high-risk patients who received induction chemotherapy (ICT) combined with concurrent chemoradiotherapy (CCRT) was better than that of those who were treated with CCRT alone (p = 0.0340), whereas low-risk patients who received ICT + CCRT had a similar DMFS to those who only received CCRT. The outcomes we obtained were all verified in the three validation cohorts. The survival model can be used as a reliable prognostic tool for NPC patients and is helpful to determine patients who will benefit from ICT.

A gene-expression-based signature predicts survival in adults with T-cell lymphoblastic lymphoma: a multicenter study

We aimed to establish a discriminative gene-expression-based classifier to predict survival outcomes of T-cell lymphoblastic lymphoma (T-LBL) patients. After exploring global gene-expression profiles of progressive (n = 22) vs. progression-free (n = 28) T-LBL patients, 43 differentially expressed mRNAs were identified. Then an eleven-gene-based classifier was established using LASSO Cox regression based on NanoString quantification. In the training cohort (n = 169), high-risk patients stratified using the classifier had significantly lower progression-free survival (PFS: hazards ratio 4.123, 95% CI 2.565-6.628; p < 0.001), disease-free survival (DFS: HR 3.148, 95% CI 1.857-5.339; p < 0.001), and overall survival (OS: HR 3.790, 95% CI 2.237-6.423; p < 0.001) compared with low-risk patients. The prognostic accuracy of the classifier was validated in the internal testing (n = 84) and independent validation cohorts (n = 360). A prognostic nomogram consisting of five independent variables including the classifier, lactate dehydrogenase levels, ECOG-PS, central nervous system involvement, and NOTCH1/FBXW7 status showed significantly greater prognostic accuracy than each single variable alone. The addition of a five-miRNA-based signature further enhanced the accuracy of this nomogram. Furthermore, patients with a nomogram score ≥154.2 significantly benefited from the BFM protocol. In conclusion, our nomogram comprising the 11-gene-based classifier may make contributions to individual prognosis prediction and treatment decision-making.

An immune-related prognostic signature for predicting breast cancer recurrence

Breast cancer (BC) is the most common cancer among women worldwide and is the second leading cause of cancer‐related deaths in women. Increasing evidence has validated the vital role of the immune system in BC development and recurrence. In this study, we identified an immune‐related prognostic signature of BRCA that could help delineate risk scores of poor outcome for each patient. This prognostic signature comprised information on five danger genes—TSLP, BIRC5, S100B, MDK, and S100P—and three protect genes RARRES3, BLNK, and ACO1. Kaplan‐Meier survival curve showed that patients classified as low‐risk according to optimum cut‐off risk score had better prognosis than those identified within the high‐risk group. ROC analysis indicated that the identified prognostic signature had excellent diagnostic efficiency for predicting 3‐ and 5‐years relapse‐free survival (RFS). Multivariate Cox regression analysis proved that the prognostic signature is independent of other clinical parameters. Stratification analysis demonstrated that the prognostic signature can be used to predict the RFS of BC patients within the same clinical subgroup. We also developed a nomogram to predict the RFS of patients. The calibration plots exhibited outstanding performance. The validation sets (GSE21653, GSE20711, and GSE88770) were used to external validation. More convincingly, the real time RT‐PCR results of clinical samples demonstrated that danger genes were significantly upregulated in BC samples, whereas protect genes were downregulated. In conclusion, we developed and validated an immune‐related prognostic signature, which exhibited excellent diagnostic efficiency in predicting the recurrence of BC, and will help to make personalized treatment decisions for patients at different risk score.

Chromosomal Repositioning and Gene Regulation of Cells on a Micropillar Array

While various cell responses on material surfaces have been examined, relatively few reports are focused on significant self-deformation of cell nuclei and corresponding chromosomal repositioning. Herein, we prepared a micropillar array of poly(lactide-co-glycolide) (PLGA) and observed significant nuclear deformation of HeLa cells on the polymeric micropillars. In particular, we detected the territory positioning of chromosomes 18 and 19 and gene expression profiles of HeLa cells on the micropillar array using fluorescence in situ hybridization and a DNA microarray. Chromosome 18 was found to be translocated closer to the nuclear periphery than chromosome 19 on the micropillar array. With the repositioning of chromosomal territories, HeLa cells changed their gene expressions on the micropillar array with 180 genes upregulated and 255 genes downregulated for all of the 23 pairs of chromosomes under the experimental conditions and the employed Bioinformatics criteria. Hence, this work deepens the understanding on cell-material interactions by revealing that material surface topography can probably influence chromosomal repositioning in the nuclei and gene expressions of cells.

MRI-Based Deep-Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma

BACKGROUND

Distant metastasis is the primary cause of treatment failure in locoregionally advanced nasopharyngeal carcinoma (LANPC).

PURPOSE

To develop a model to evaluate distant metastasis-free survival (DMFS) in LANPC and to explore the value of additional chemotherapy to concurrent chemoradiotherapy (CCRT) for different risk groups.

STUDY TYPE

Retrospective.

POPULATION

In all, 233 patients with biopsy-confirmed nasopharyngeal carcinoma (NPC) from two hospitals.

FIELD STRENGTH

1.5T and 3T.

SEQUENCE

Axial T₂ -weighted (T₂ -w) and contrast-enhanced T₁ -weighted (CET₁ -w) images.

ASSESSMENT

Deep learning was used to build a model based on MRI images (including axial T₂ -w and CET₁ -w images) and clinical variables. Hospital 1 patients were randomly divided into training (n = 169) and validation (n = 19) cohorts; Hospital 2 patients were assigned to a testing cohort (n = 45). LANPC patients were divided into low- and high-risk groups according to their DMFS (P < 0.05). Kaplan-Meier survival analysis was performed to compare the DMFS of different risk groups and subgroup analysis was performed to compare patients treated with CCRT alone and treated with additional chemotherapy to CCRT in different risk groups, respectively.

STATISTICAL TESTS

Univariate analysis was performed to identify significant clinical variables. The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the model performance.

RESULTS

Our deep-learning model integrating the deep-learning signature, node (N) stage (from TNM staging), plasma Epstein-Barr virus (EBV)-DNA, and treatment regimens yielded an AUC of 0.796 (95% confidence interval [CI]: 0.729-0.863), 0.795 (95% CI: 0.540-1.000), and 0.808 (95% CI: 0.654-0.962) in the training, internal validation, and external testing cohorts, respectively. Low-risk patients treated with CCRT alone had longer DMFS than patients treated with additional chemotherapy to CCRT (P < 0.05).

DATA CONCLUSION

The proposed deep-learning model, based on MRI features and clinical variates, facilitated the prediction of DMFS in LANPC patients.

LEVEL OF EVIDENCE

3.

TECHNICAL EFFICACY STAGE

4.