IKBIP might be a potential prognostic biomarker for glioblastoma multiforme.

BACKGROUND: Due to the negative association between inhibitor of nuclear factor-kB kinase-interacting protein (IKBIP) and survival in gliomas, this study aimed to comprehensively analyze the potential function of IKBIP in glioblastoma multiforme (GBM). METHODS: GBM samples were retrieved from The Cancer Genome Atlas and Chinese Glioma Genome Atlas as training and validation cohorts, respectively, and survival and Cox regression analyses were conducted. Based on clinical indicators and IKBIP, three prognostic models were established and then verified using the validation dataset. Infiltrating immune cell analysis and single-sample gene set enrichment analysis were also conducted to explore the underlying mechanisms. Finally, the key findings were validated through molecular biology experiments. RESULTS: Patients in the high IKBIP score group had poorer survival. Based on Cox regression and subgroup analyses, IKBIP was identified as an independent prognostic factor. Among the three models constructed, the model combining the IKBIP signature and clinical features displayed good performance in terms of discrimination, calibration, and model improvement capability in the training cohort. This model was also successfully validated in an external cohort from the CGGA. Further analysis revealed that many immune cells and related pathways were involved in the high-risk group. In vitro experiments revealed that the knockdown of IKBIP inhibited cell invasion and proliferation, and promoted their senescence. CONCLUSIONS: The prognostic value of IKBIP and its positive impact on the invasiveness of GBM were identified, indicating that IKBIP may serve as an underlying target for the treatment of GBM.

Radiomic model to predict the expression of PD-1 and overall survival of patients with ovarian cancer.

BACKGROUND: Programmed cell death 1 (PD-1), encoded by programmed cell death protein 1 (PDCD1), is widely investigated in clinical trials. We aimed to develop a radiomic model to discriminate its expression levels patients with ovarian cancer (OC) and explore its prognostic value. METHODS: Computed tomography (CT) images with the corresponding sequencing data and clinicopathological features were used. The volumes of interest were manually delineated. After extraction and normalization, the radiomic features were screened using repeat least absolute shrinkage and selection operator. A radiomic model for PD-1 prediction, radiomic score (rad_score), was developed using logistic regression and validated via internal 5-fold cross-validation. The Kaplan-Meier curves, COX proportional hazards model, and landmark analysis were used for survival analysis. RESULTS: The mRNA level of PDCD1 significantly affects the overall survival (OS) of OC patients. The rad_score for PDCD1 prediction was based on four features and was significantly correlated with other genes involved in T-cell exhaustion and immune checkpoint molecules. The areas under the receiver operating characteristic curves reached 0.810 and 0.772 in the training and validation datasets, respectively. The calibration curves and decision curve analysis proved the model's fitness and clinical benefits. Patients with higher rad_score had poorer OS (P < 0.001, 0.031, 0.014, 0.01, and < 0.001, after landmark of 12 months, before and after landmark of 36 months, and before and after landmark of 60 months, respectively). CONCLUSIONS: The radiomic signature from CT images can discriminate the PD-1 expression status and OC prognosis, which is correlated with T-cell exhaustion.

Prognostic Value of mRNAsi/Corrected mRNAsi Calculated by the One-Class Logistic Regression Machine-Learning Algorithm in Glioblastoma Within Multiple Datasets.

Glioblastoma (GBM) is the most common glial tumour and has extremely poor prognosis. GBM stem-like cells drive tumorigenesis and progression. However, a systematic assessment of stemness indices and their association with immunological properties in GBM is lacking. We collected 874 GBM samples from four GBM cohorts (TCGA, CGGA, GSE4412, and GSE13041) and calculated the mRNA expression-based stemness indices (mRNAsi) and corrected mRNAsi (c_mRNAsi, mRNAsi/tumour purity) with OCLR algorithm. Then, mRNAsi/c_mRNAsi were used to quantify the stemness traits that correlated significantly with prognosis. Additionally, confounding variables were identified. We used discrimination, calibration, and model improvement capability to evaluate the established models. Finally, the CIBERSORTx algorithm and ssGSEA were implemented for functional analysis. Patients with high mRNAsi/c_mRNAsi GBM showed better prognosis among the four GBM cohorts. After identifying the confounding variables, c_mRNAsi still maintained its prognostic value. Model evaluation showed that the c_mRNAsi-based model performed well. Patients with high c_mRNAsi exhibited significant immune suppression. Moreover, c_mRNAsi correlated negatively with infiltrating levels of immune-related cells. In addition, ssGSEA revealed that immune-related pathways were generally activated in patients with high c_mRNAsi. We comprehensively evaluated GBM stemness indices based on large cohorts and established a c_mRNAsi-based classifier for prognosis prediction.

Integrative Analyses and Verification of the Expression and Prognostic Significance for RCN1 in Glioblastoma Multiforme.

Glioblastoma multiform is a lethal primary brain tumor derived from astrocytic, with a poor prognosis in adults. Reticulocalbin-1 (RCN1) is a calcium-binding protein, dysregulation of which contributes to tumorigenesis and progression in various cancers. The present study aimed to identify the impact of RCN1 on the outcomes of patients with Glioblastoma multiforme (GBM). The study applied two public databases to require RNA sequencing data of Glioblastoma multiform samples with clinical data for the construction of a training set and a validation set, respectively. We used bioinformatic analyses to determine that RCN1 could be an independent factor for the overall survival of Glioblastoma multiform patients. In the training set, the study constructed a predictive prognostic model based on the combination of RCN1 with various clinical parameters for overall survival at 0.5-, 1.0-, and 1.5-years, as well as developed a nomogram, which was further validated by validation set. Pathways analyses indicated that RCN1 was involved in KEAS and MYC pathways and apoptosis. In vitro experiments indicated that RCN1 promoted cell invasion of Glioblastoma multiform cells. These results illustrated the prognostic role of RCN1 for overall survival in Glioblastoma multiform patients, indicated the promotion of RCN1 in cell invasion, and suggested the probability of RCN1 as a potential targeted molecule for treatment in Glioblastoma multiform.

Decreased Expression of NUSAP1 Predicts Poor Overall Survival in Cervical Cancer.

Background: Nucleolar and spindle-associated protein 1 (NUSAP1) was previously reported to be associated with poor prognosis in multiple cancers. In the present study, we comprehensively investigated the clinicopathological features and potential prognostic value of NUSAP1 in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). Methods: The expression profiles of the genes were extracted from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Cancer Cell Line Encyclopedia (CCLE), Gene Expression Profiling Interactive Analysis (GEPIA), and The Human Protein Atlas databases. The association between clinicopathological characteristics and NUSAP1 was analyzed using logistic regression in TCGA patients and receiver operating characteristic (ROC) curve analysis for GSE7803, GSE9750, and GSE63514 datasets. The prognostic value of NUSAP1 in TCGA patients was evaluated using the Kaplan-Meier method and Cox regression. Gene set enrichment analysis (GSEA) was conducted using TCGA dataset. Results: A total of 68 differentially expressed genes (DEGs) were identified in CESC. ROC analysis of NUSAP1 suggested that the area under the ROC curve was 0.968. Kaplan-Meier survival analysis indicated that CESC with low expression of NUSAP1 has a worse prognosis than CESC with high NUSAP1 expression (P = 0.005). The logistic regression revealed that low NUSAP1 expression in CESC was related to advanced tumor stage in TCGA database. Moreover, Cox regression analysis showed that NUSAP1 expression correlated significantly with prognosis in the case of patients in TCGA database. GSEA demonstrated that CESC patients with high expression of NUSAP1 were enriched in the G2M checkpoint, MYC targets, and breast cancer ZNF217. Conclusion: The results suggest that identification of DEGs might enhance our understanding of the causes and molecular mechanisms underlying the development of CESC. Moreover, NUSAP1 may play an important role in CESC progression and prognosis and may serve as a valuable indicator of poor survival in CESC.

Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples.

BACKGROUND: Major depressive disorder (MDD) is a severe disease characterized by multiple pathological changes. However, there are no reliable diagnostic biomarkers for MDD. The aim of the current study was to investigate the gene network and biomarkers underlying the pathophysiology of MDD. METHODS: In this study, we conducted a comprehensive analysis of the mRNA expression profile of MDD using data from Gene Expression Omnibus (GEO). The MDD dataset (GSE98793) with 128 MDD and 64 control whole blood samples was divided randomly into two non-overlapping groups for cross-validated differential gene expression analysis. The gene ontology (GO) enrichment and gene set enrichment analysis (GSEA) were performed for annotation, visualization, and integrated discovery. Protein-protein interaction (PPI) network was constructed by STRING database and hub genes were identified by the CytoHubba plugin. The gene expression difference and the functional similarity of hub genes were investigated for further gene expression and function exploration. Moreover, the receiver operating characteristic curve was performed to verify the diagnostic value of the hub genes. RESULTS: We identified 761 differentially expressed genes closely related to MDD. The Venn diagram and GO analyses indicated that changes in MDD are mainly enriched in ribonucleoprotein complex biogenesis, antigen receptor-mediated signaling pathway, catalytic activity (acting on RNA), structural constituent of ribosome, mitochondrial matrix, and mitochondrial protein complex. The GSEA suggested that tumor necrosis factor signaling pathway, Toll-like receptor signaling pathway, apoptosis pathway, and NF-kappa B signaling pathway are all crucial in the development of MDD. A total of 20 hub genes were selected via the PPI network. Additionally, the identified hub genes were downregulated and show high functional similarity and diagnostic value in MDD. CONCLUSIONS: Our findings may provide novel insight into the functional characteristics of MDD through integrative analysis of GEO data, and suggest potential biomarkers and therapeutic targets for MDD.

Establishment of prognostic factors in recurrent nasopharyngeal carcinoma patients who received salvage intensity-modulated radiotherapy: A meta-analysis.

Local recurrence remains a major cause of therapeutic failure in patients with nasopharyngeal carcinoma (NPC) and the effective treatment of recurrent NPC (r-NPC) is still a challenge. Intensity-modulated radiotherapy (IMRT) is considered as a favorable technique in the management of r-NPC, especially for extensive lesions. However, local r-NPC is a highly heterogeneous disease and the survival outcome following salvage IMRT varies. Furthermore, due to varied samples and therapeutic protocols, no consensus has been reached in the establishment of prognostic values. Hence, we used Medline and Embase electronic databases to conducted a meta-analysis to generate the best estimation of the prognostic factors in local r-NPC following salvage IMRT. Finally, a total of 783 patients in seven studies were enrolled. Overall, the pooled HR for OS of recurrent T stage and recurrent tumor volume was 1.77 (95% CI = 1.15-2.39) and 2.12 (95% CI = 1.42-2.82), without any heterogeneity. In addition, despite a significant association was observed in the pooled HR of significant compliance for OS, however, significant heterogeneity was also observed (I2 = 76.6%, p = 0.039). Furthermore, no significant association was observed among the pooled HRs for OS in terms of age, gender, recurrent time interval, synchronous nodal recurrence, chemotherapy and total re-irradiation dose. Therefore, the present meta-analysis demonstrated that recurrent T stage and tumor volume may serve as the prognostic factors for OS in patients with r-NPC who received salvage IMRT. The other factors such as age, gender, and optimal re-irradiation dose warranted further investigation.

Integrating dynamic contrast-enhanced magnetic resonance imaging and diffusion kurtosis imaging for neoadjuvant chemotherapy assessment of nasopharyngeal carcinoma.

BACKGROUND: Since neoadjuvant chemotherapy (NAC) has proven a benefit for locally advanced nasopharyngeal carcinoma (NPC), early response evaluation after chemotherapy is important to implement individualized therapy for NPC in the era of precision medicine. PURPOSE: To determine the combined and independent contribution between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion kurtosis imaging (DKI) in the early monitoring of NAC response for NPC. STUDY TYPE: Prospective. POPULATION: Fifty-three locally advanced NPC patients. FIELD STRENGTH/SEQUENCE: Four examinations before and at 4, 20, and 40 days after NAC initiation were performed at 3T MRI including DCE-MRI and DKI (b values = 0, 500, 1000, 1500 s/mm2 ). ASSESSMENT: DCE-MRI parameters (Ktrans [the volume transfer constant of Gd-DTPA], kep [rate constant], νe [the extracellular volume fraction of the imaged tissue], and νp [the blood volume fraction]) and DKI parameters (Dapp [apparent diffusion for non-Gaussian distribution] and Kapp [apparent kurtosis coefficient]) were analyzed using dedicated software. STATISTICAL TESTS: MRI parameters and their corresponding changes were compared between responders and nonresponders after one or two NAC cycles treatment using independent-samples Student's t-test or Mann-Whitney U-test depending on the normality contribution test and then followed by logistic regression and receiver operating characteristic curve (ROC) analyses. RESULTS: The responder group (RG) patients presented significantly higher mean Ktrans and Dapp values at baseline and larger Δ K ( 0 - 4 ) trans , Δvp(0-4) , and ΔDapp(0-4) values after either one or two NAC cycles compared with the nonresponder group (NRG) patients (all P < 0.05). ROC analyses demonstrated the higher diagnostic accuracy of combined DCE-MRI and DKI model to distinguish nonresponders from responders after two NAC cycles than using DCE-MRI (0.987 vs. 0.872, P = 0.033) or DKI (0.987 vs. 0.898, P = 0.047) alone. DATA CONCLUSION: Combined DCE-MRI and DKI models had higher diagnostic accuracy for NAC assessment compared with either model used independently. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1208-1216.

Role of diffusion-weighted imaging in the discrimination of benign and metastatic parotid area lymph nodes in patients with nasopharyngeal carcinoma.

To assess the utility of apparent diffusion coefficient (ADC) determined on diffusion-weighted MR imaging (DWI) to differentiate between benign and malignant parotid area lymph nodes (PLN) in nasopharyngeal carcinoma (NPC) patients. Thirty-nine consecutive NPC patients with a total of 40 enlarged, biopsied PLNs underwent DWI examination. ADC values for benign and malignant PLNs were measured and compared. Receiver operating characteristic (ROC) curve analysis was to evaluate the optimal threshold level of ADC values for metastatic PLNs. The kappa was to assess the degree of agreement between histopathological diagnoses and ADC values, or imaging features of PLNs on MRI. We found the mean ADC value for benign PLNs was markedly higher than malignant PLNs. A threshold ADC of 1.01 × 10-3 mm2/s was associated with a sensitivity of 85.7% and a specificity of 72.7% (area under the curve: 0.84). A moderate agreement was observed between the histopathological diagnosis and the threshold of ADC value (k value: 0.483). However, short axis diameter, necrosis, extranodal extension, and regional grouping of PLNs on MRI showed only a fair agreement with the histopathological diagnosis (k value: 0.257, 0.305, 0.276, and 0.205, respectively). Therefore, DWI may be a promising technique to differentiate metastatic from benign PLNs.

Early responses assessment of neoadjuvant chemotherapy in nasopharyngeal carcinoma by serial dynamic contrast-enhanced MR imaging.

PURPOSE: To evaluate the feasibility of utilizing serial dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) prospectively for early prediction of neoadjuvant chemotherapy (NAC) response in nasopharyngeal carcinoma (NPC) patients. MATERIALS AND METHODS: Sixty-three advanced NPC patients were recruited and received three DCE-MRI exams before treatment (Pre-Tx), 3days (Day3-Tx) and 20days (Day20-Tx) after initiation of chemotherapy (one NAC cycle). Early response to NAC was determined based on the third MRI scan and classified partial response (PR) as responders and stable disease (SD) as non-responders. After intensity-modulated radiotherapy (IMRT), complete response (CR) patients were classified as responders. The kinetic parameters (Ktrans, Kep, ve, and vp) derived from extended Tofts' model analysis and their corresponding changes ΔMetrics(0-X) (X=3 or 20days) were compared between the responders and non-responders using the Student's T-test or Mann-Whitney U test. RESULTS: Compared to the SD group, the PR group after one NAC cycle presented significantly higher mean Ktrans values at baseline (P=0.011) and larger ΔKtrans(0-3) and ΔKep(0-3) values (P=0.003 and 0.031). For the above parameters, we gained acceptable sensitivity (range: 66.8-75.0%) and specificity (range: 60.0-66.7%) to distinguish the non-responders from the responders and their corresponding diagnosis efficacy (range: 0.703-0.767). The PR group patients after one NAC cycle showed persistent inhibition of tumor perfusion by NAC as explored by DCE-MRI parameters comparing to the SD group (P<0.05) and presented a higher cure ratio after IMRT than those who did not (83.3% vs. 73.8%). CONCLUSIONS: This primarily DCE-MRI based study showed that the early changes of the kinetic parameters during therapy were potential imaging markers to predicting response right after one NAC cycle for NPC patients.

Unidimensional Measurement May Evaluate Target Lymph Nodal Response After Induction Chemotherapy for Nasopharyngeal Carcinoma.

The aim of the study was to evaluate whether short axis and long axis on axial and coronal magnetic resonance imaging planes would reflect the tumor burden or alteration in size after induction chemotherapy in nasopharyngeal carcinoma. Patients with pathologically confirmed nasopharyngeal carcinoma (n = 37) with at least 1 positive cervical lymph node (axial short axis ≥15 mm) were consecutively enrolled in this prospective study. Lymph nodal measurements were performed along its short axis and long axis in both axial and coronal magnetic resonance imaging planes at diagnosis and after 2 cycles of induction chemotherapy. In addition, lymph nodal volumes were automatically calculated in 3D treatment-planning system, which were used as reference standard. Student's t test or nonparametric Mann-Whitney U test was used to compare the continuous quantitative variables. Meanwhile, the κ statistic and McNemar's test were used to evaluate the degree of agreement and discordance in response categorization among different measurements. Axial short axis was significantly associated with volumes at diagnosis (P < 0.001). A good agreement (κ=0.583) was found between axial short axis and volumetric criteria. However, the inconsistent lymph nodal shrinkage in 4 directions was observed. Axial short-axis shrinking was more rapid than the other 3 parameters. Interestingly, when utilizing the alternative planes for unidimensional measurements to assess tumor response, coronal short-axis showed the best concordance (κ=0.792) to the volumes. Axial short axis may effectively reflect tumor burden or change in tumor size in the assessment of target lymph nodal response after induction chemotherapy for nasopharyngeal carcinoma. However, it should be noted that axial short axis may amplify the therapeutic response. In addition, the role of coronal short axis in the assessment of tumor response needs further evaluation.

Diffusion kurtosis imaging predicts neoadjuvant chemotherapy responses within 4 days in advanced nasopharyngeal carcinoma patients.

PURPOSE: To explore the clinical value of diffusion kurtosis imaging (DKI) and monoexponential diffusion-weighted imaging (DWI) for predicting early response to neoadjuvant chemotherapy (NAC) in patients with nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS: Fifty-nine patients with stage III-IVb NPC underwent four 3.0T MR scans: prior to, and on the 4th, 21st, 42nd days after NAC initiation. The parameters of DKI (corrected diffusion coefficient, D; excess diffusion kurtosis coefficient, K) and monoexponential DWI (apparent diffusion coefficient, ADC) were obtained at the first three scans. Statistical methods included Student's t-test or Mann-Whitney U-test, receiver operating characteristic (ROC) curve analyses and paired X(2) test. RESULTS: D(pre) in responders group (RG) was significantly lower than nonresponders group (NRG) (1.029 ± 0.033 vs. 1.184 ± 0.055, ×10(-3) mm(2) /s, P = 0.020). ADC(day4) and ΔD(day4) were the most useful parameters of the two diffusional models to distinguish RG from NRG, respectively (area under the curve, 0.761 vs. 0.895). ΔD(day4) was more sensitive than ADC(day4) to predict treatment response to NAC (P = 0.006). CONCLUSION: Both DKI and monoexponential DWI showed potential to predict treatment response to NAC prior to morphological change. DKI may be superior to monoexponential DWI for predicting early response to NAC in patients with locally advanced NPC.