Development and validation of a transcriptomics-based gene signature to predict distant metastasis and guide induction chemotherapy in locoregionally advanced nasopharyngeal carcinoma

Prognostic value of prognostic nutritional index in patients with nasopharyngeal carcinoma treated with endostar and concurrent chemoradiotherapy

PURPOSE

This study aimed to evaluate the prognostic value of the pre-treatment prognostic nutritional index (PNI) in patients with locally advanced nasopharyngeal carcinoma (LANPC) treated with endostar combined with concurrent chemoradiotherapy (ECCRT).

METHODS

Clinical data from 92 patients with LANPC who underwent ECCRT between May 2015 and December 2020 were retrospectively analyzed. The PNI was calculated using peripheral blood samples taken 1 week before treatment. The optimal cut-off value for PNI was determined via receiver operating characteristic (ROC) curve analysis based on overall survival (OS). Patients were categorized into high PNI and low PNI groups. The Kaplan-Meier method assessed the impact of PNI on survival, while univariate and multivariate Cox regression analyses identified independent risk factors affecting patient survival.

RESULTS

The optimal cut-off value of PNI was 50.05. The 3-year OS, progression-free survival (PFS), distant metastasis-free survival (DMFS), and local recurrence-free survival (LRRFS) rates were 91.07% vs. 75.00% (P = 0.002), 83.93% vs. 66.67% (P = 0.015), 89.29% vs. 69.44% (P = 0.004), and 94.64% vs. 91.67% (P = 0.668) in the high PNI and low PNI groups, respectively. A low PNI was associated with shorter OS (HR = 3.592, P = 0.004), PFS (HR = 2.890, P = 0.017), and DMFS (HR = 3.826, P = 0.008). Multivariate analysis revealed that PNI was an independent prognostic factor for OS, PFS, and DMFS.

CONCLUSIONS

The PNI may serve as a valuable prognostic predictor for patients with LANPC receiving ECCRT, aiding clinicians in selectively providing multimodal interventions to optimize survival outcomes.

An individualized immune prognostic signature in nasopharyngeal carcinoma

BACKGROUND

Immune-related characteristics can serve as reliable prognostic biomarkers in various cancers. Herein, we aimed to construct an individualized immune prognostic signature in nasopharyngeal carcinoma (NPC).

METHODS

This study retrospectively included 455 NPC samples and 39 normal healthy nasopharyngeal tissue specimens. Samples from Gene Expression Omnibus (GEO) were obtained as discovery cohort to screen candidate prognostic immune-related gene pairs based on relative expression ordering of the genes. Quantitative real-time reverse transcription-PCR was used to detect the selected genes to construct an immune-related gene pair signature in training cohort, which comprised 118 clinical samples, and was then validated in validation cohort 1, comprising 92 clinical samples, and validation cohort 2, comprising 88 samples from GEO.

RESULTS

We identified 26 immune-related gene pairs as prognostic candidates in discovery cohort. A prognostic immune signature comprising 11 immune gene pairs was constructed in training cohort. In validation cohort 1, the immune signature could significantly distinguish patients with high or low risk in terms of progression-free survival (PFS) (hazard ratio [HR] 2.66, 95 % confidence interval (CI) 1.17-6.02, P=0.015) and could serve as an independent prognostic factor for PFS in multivariate analysis (HR 2.66, 95 % CI 1.17-6.02, P=0.019). Similar results were obtained using validation cohort 2, in which PFS was significantly worse in high risk group than in low risk group (HR 3.02, 95 % CI 1.12-8.18, P=0.022).

CONCLUSIONS

The constructed immune signature showed promise for estimating prognosis in NPC. It has potential for translation into clinical practice after prospective validation.

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.

Identification of important modules and biomarkers in tuberculosis based on WGCNA

Background

Tuberculosis (TB) is a significant public health concern, particularly in China. Long noncoding RNAs (lncRNAs) can provide abundant pathological information regarding etiology and could include candidate biomarkers for diagnosis of TB. However, data regarding lncRNA expression profiles and specific lncRNAs associated with TB are limited.

Methods

We performed ceRNA-microarray analysis to determine the expression profile of lncRNAs in peripheral blood mononuclear cells (PBMCs). Weighted gene co-expression network analysis (WGCNA) was then conducted to identify the critical module and genes associated with TB. Other bioinformatics analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and co-expression networks, were conducted to explore the function of the critical module. Finally, real-time quantitative polymerase chain reaction (qPCR) was used to validate the candidate biomarkers, and receiver operating characteristic analysis was used to assess the diagnostic performance of the candidate biomarkers.

Results

Based on 8 TB patients and 9 healthy controls (HCs), a total of 1,372 differentially expressed lncRNAs were identified, including 738 upregulated lncRNAs and 634 downregulated lncRNAs. Among all lncRNAs and mRNAs in the microarray, the top 25% lncRNAs (3729) and top 25% mRNAs (2824), which exhibited higher median expression values, were incorporated into the WGCNA. The analysis generated 16 co-expression modules, among which the blue module was highly correlated with TB. GO and KEGG analyses showed that the blue module was significantly enriched in infection and immunity. Subsequently, considering module membership values (>0.85), gene significance values (>0.90) and fold-change value (>2 or < 0.5) as selection criteria, the top 10 upregulated lncRNAs and top 10 downregulated lncRNAs in the blue module were considered as potential biomarkers. The candidates were then validated in an independent validation sample set (31 TB patients and 32 HCs). The expression levels of 8 candidates differed significantly between TB patients and HCs. The lncRNAs ABHD17B (area under the curve [AUC] = 1.000) and ENST00000607464.1 (AUC = 1.000) were the best lncRNAs in distinguishing TB patients from HCs.

Conclusion

This study characterized the lncRNA profiles of TB patients and identified a significant module associated with TB as well as novel potential biomarkers for TB diagnosis.

Transpulmonary Expression of Exosomal microRNAs in Idiopathic and Congenital Heart Disease-Related Pulmonary Arterial Hypertension

BACKGROUND

Pulmonary artery hypertension (PAH) is a fatal disease characterized by a complex pathogenesis. Exosomes containing microRNAs (miRs) have emerged as a novel biomarker. Transpulmonary exosomal miRs offer valuable insights into pulmonary circulation microenvironments. Hereby, we aimed to explore the potentials of transpulmonary exosomal miRs as differentiating factors between idiopathic PAH and congenital heart disease (CHD)-related PAH.

METHODS AND RESULTS

During right heart catheterization, we collected exosomes at pulmonary arteries in 25 patients diagnosed with idiopathic PAH and 20 patients with CHD-related PAH. Next-generation sequencing identified several candidate exosomal miRs. Using quantitative polymerase chain reaction, we validated the expressions of these miRs and revealed significantly elevated expressions of miR-21, miR-139-5p, miR-155-5p, let-7f-5p, miR-328-3p, miR-330-3p, and miR-103a-3p in patients with CHD-related PAH, in contrast to patients with idiopathic PAH. Among these miRs, miR-21 exhibited the highest expression in patients with CHD-related PAH. These findings were further corroborated in an external cohort comprising 10 patients with idiopathic PAH and 8 patients with CHD-related PAH. Using an in vitro flow model simulating the shear stress experienced by pulmonary endothelial cells, we observed a significant upregulation of miR-21. Suppressing miR-21 rescued the shear stress-induced downregulation of the RAS/phosphatidylinositol 3-kinase/protein kinase B pathway, leading to a mitigation of apoptosis.

CONCLUSIONS

Our study identified a pronounced expression of transpulmonary exosomal miR-21, particularly in patients with CHD-related PAH, through next-generation sequencing analysis. Further investigation is warranted to elucidate the regulatory mechanisms involving miR-21 in the pathophysiology of PAH.

A Novel Prognostic Model Predicts Outcomes in Non-Metastatic Nasopharyngeal Carcinoma Based on Inflammation, Nutrition, and Coagulation Signature

Purpose

This study aimed to assess the prognostic and predictive value of a circulating hematological signature (CHS) and to develop a CHS-based nomogram for predicting prognosis and guiding individualized chemotherapy in non-metastatic nasopharyngeal carcinoma (NPC) patients.

Patients and Methods

NPC patients were recruited between January 2014 and December 2017 at the Jiangxi Cancer Hospital. The CHS was constructed based on a series of hematological indicators. The nomogram was developed by CHS and clinical factors.

Results

A total of 779 patients were included. Three biomarkers were selected by least absolute shrinkage and selection operator regression, including prognostic nutritional index, albumin-to-fibrinogen ratio, and prealbumin-to-fibrinogen ratio, were used to construct the CHS. The patients in the low-CHS group had better 5-year DMFS and OS than those in the high-CHS group in the training (DMFS: 85.0% vs 56.6%, p<0.001; OS: 90.3% vs 65.4%, p<0.001) and validation cohorts (DMFS: 92.3% vs 43.6%, p<0.001; OS: 92.1% vs 65.5%, p<0.001). The nomogram_CHS showed better performance than clinical stage in predicting distant metastasis (concordance index: 0.728 vs 0.646). In the low-TRS (total risk scores) group, the patients received RT alone, CCRT and IC plus CCRT had similar 5-year DMFS and OS (p>0.05). In the middle-TRS group, the patients received RT alone had worse 5-year DMFS (58.7% vs 80.8% vs 90.8%, p=0.002) and OS (75.0% vs 94.1% vs 95.0%, p=0.001) than those received CCRT or IC plus CCRT. In the high-TRS group, the patients received RT alone and CCRT had worse 5-year DMFS (18.6% vs 31.3% vs 81.5%, p<0.001) and OS (26.9% vs 53.2% vs 88.8%, p<0.001) than those received IC plus CCRT.

Conclusion

The developed nomogram_CHS had satisfactory prognostic accuracy in NPC patients and may individualize risk estimation to facilitate the identification of suitable IC candidates.

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.

Dynamic Regulation Genes at Microtubule Plus Ends: A Novel Class of Glioma Biomarkers

Simple Summary

Microtubule plus-end-related genes (MPERGs) encode a group of proteins that specifically aggregate at the microtubule plus ends to play critical biological roles in the cell cycle, cell movement, ciliogenesis, and neuronal development by coordinating microtubule assembly and dynamics; however, the MPERG correlations and their clinical significance in glioma are not fully understood. This study is the first to systematically analyze and define a seven-gene signature (CTTNBP2, KIF18A, NAV1, SLAIN2, SRCIN1, TRIO, and TTBK2) and nomogram model closely associated with clinical factors and the tumor microenvironment as a reliable and independent prognostic biomarker to guide personalized choices of immunotherapy and chemotherapy for glioma patients.

Abstract

Glioma is the most prevalent and aggressive primary nervous system tumor with an unfavorable prognosis. Microtubule plus-end-related genes (MPERGs) play critical biological roles in the cell cycle, cell movement, ciliogenesis, and neuronal development by coordinating microtubule assembly and dynamics. This research seeks to systematically explore the oncological characteristics of these genes in microtubule-enriched glioma, focusing on developing a novel MPERG-based prognostic signature to improve the prognosis and provide more treatment options for glioma patients. First, we thoroughly analyzed and identified 45 differentially expressed MPERGs in glioma. Based on these genes, glioma patients were well distinguished into two subgroups with survival and tumor microenvironment infiltration differences. Next, we further screened the independent prognostic genes (CTTNBP2, KIF18A, NAV1, SLAIN2, SRCIN1, TRIO, and TTBK2) using 36 prognostic-related differentially expressed MPERGs to construct a signature with risk stratification and prognostic prediction ability. An increased risk score was related to the malignant progression of glioma. Therefore, we also designed a nomogram model containing clinical factors to facilitate the clinical use of the risk signature. The prediction accuracy of the signature and nomogram model was verified using The Cancer Genome Atlas and Chinese Glioma Genome Atlas datasets. Finally, we examined the connection between the signature and tumor microenvironment. The signature positively correlated with tumor microenvironment infiltration, especially immunoinhibitors and the tumor mutation load, and negatively correlated with microsatellite instability and cancer stemness. More importantly, immune checkpoint blockade treatment and drug sensitivity analyses confirmed that this prognostic signature was helpful in anticipating the effect of immunotherapy and chemotherapy. In conclusion, this research is the first study to define and validate an MPERG-based signature closely associated with the tumor microenvironment as a reliable and independent prognostic biomarker to guide personalized choices of immunotherapy and chemotherapy for glioma patients.

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.

Radiomics based on pretreatment MRI for predicting distant metastasis of nasopharyngeal carcinoma: A preliminary study

Objective

To explore the feasibility of predicting distant metastasis (DM) of nasopharyngeal carcinoma (NPC) patients based on MRI radiomics model.

Methods

A total of 146 patients with NPC pathologically confirmed, who did not exhibit DM before treatment, were retrospectively reviewed and followed up for at least one year to analyze the DM risk of the disease. The MRI images of these patients including T2WI and CE-T1WI sequences were extracted. The cases were randomly divided into training group (n=116) and validation group (n=30). The images were filtered before radiomics feature extraction. The least absolute shrinkage and selection operator (LASSO) regression was used to develop the dimension of texture parameters and the logistic regression was used to construct the prediction model. The ROC curve and calibration curve were used to evaluate the predictive performance of the model, and the area under curve (AUC), accuracy, sensitivity, and specificity were calculated.

Results

72 patients had DM and 74 patients had no DM. The AUC, accuracy, sensitivity and specificity of the model were 0. 80 (95% CI: 0.72~0. 88), 75.0%, 76.8%, 73.3%. and0.70 (95% CI: 0.51~0.90), 66.7%, 72.7%, 63.2% in training group and validation group, respectively.

Conclusion

The radiomics model based on logistic regression algorithm has application potential for evaluating the DM risk of patients with NPC.

Comprehensive Genetic Analysis of Tuberculosis and Identification of Candidate Biomarkers

Purpose: The purpose of this study is to use the data in the GEO database to analyze, screen biomarkers that can diagnose tuberculosis, and verification of candidate biomarkers.

Materials and methods: GSE158767 dataset were used to process WGCNA analysis, differential gene analysis, Gene ontology and KEGG analysis, protein-protein network analysis and hub genes analysis. Based on our previous study, the intersect between WGCNA and differential gene analysis could be used as candidate biomarkers. Then, the enzyme-linked immunosorbent assay was used to validate candidate biomarkers, and receiver operating characteristic was used to assess diagnose ability of candidate biomarkers.

Results: A total of 412 differential genes were screened. And we obtained 105 overlapping genes between DEGs and WGCNA. GO and KEGG analysis showed that most of the differential genes were significantly enriched in innate immunity. A total of 15 hub genes were screened, and four of them were verified by Enzyme-linked immunosorbent assay. CCL5 performed well in distinguishing the healthy group from the TB group (AUC = 0.723). And CCL19 performed well in distinguishing the TB group from the ORD groups (AUC = 0.811).

Conclusion: CCL19, C1Qb, CCL5 and HLA-DMB may play important role in tuberculosis, which indicated four genes may become effective biomarkers and could be conveniently used to facilitate the individual tuberculosis diagnosis in Chinese people.