Developing a lncRNA Signature to Predict the Radiotherapy Response of Lower-Grade Gliomas Using Co-expression and ceRNA Network Analysis

Long Noncoding RNA VLDLR-AS1 Levels in Serum Correlate with Combat-Related Chronic Mild Traumatic Brain Injury and Depression Symptoms in US Veterans

More than 75% of traumatic brain injuries (TBIs) are mild (mTBI) and military service members often experience repeated combat-related mTBI. The chronic comorbidities concomitant with repetitive mTBI (rmTBI) include depression, post-traumatic stress disorder or neurological dysfunction. This study sought to determine a long noncoding RNA (lncRNA) expression signature in serum samples that correlated with rmTBI years after the incidences. Serum samples were obtained from Long-Term Impact of Military-Relevant Brain-Injury Consortium Chronic Effects of Neurotrauma Consortium (LIMBIC CENC) repository, from participants unexposed to TBI or who had rmTBI. Four lncRNAs were identified as consistently present in all samples, as detected via droplet digital PCR and packaged in exosomes enriched for CNS origin. The results, using qPCR, demonstrated that the lncRNA VLDLR-AS1 levels were significantly lower among individuals with rmTBI compared to those with no lifetime TBI. ROC analysis determined an AUC of 0.74 (95% CI: 0.6124 to 0.8741; p = 0.0012). The optimal cutoff for VLDLR-AS1 was ≤153.8 ng. A secondary analysis of clinical data from LIMBIC CENC was conducted to evaluate the psychological symptom burden, and the results show that lncRNAs VLDLR-AS1 and MALAT1 are correlated with symptoms of depression. In conclusion, lncRNA VLDLR-AS1 may serve as a blood biomarker for identifying chronic rmTBI and depression in patients.

Survival prediction for patients with glioblastoma multiforme using a Cox proportional hazards denoising autoencoder network

Objectives

This study aimed to establish and validate a prognostic model based on magnetic resonance imaging and clinical features to predict the survival time of patients with glioblastoma multiforme (GBM).

Methods

In this study, a convolutional denoising autoencoder (DAE) network combined with the loss function of the Cox proportional hazard regression model was used to extract features for survival prediction. In addition, the Kaplan-Meier curve, the Schoenfeld residual analysis, the time-dependent receiver operating characteristic curve, the nomogram, and the calibration curve were performed to assess the survival prediction ability.

Results

The concordance index (C-index) of the survival prediction model, which combines the DAE and the Cox proportional hazard regression model, reached 0.78 in the training set, 0.75 in the validation set, and 0.74 in the test set. Patients were divided into high- and low-risk groups based on the median prognostic index (PI). Kaplan-Meier curve was used for survival analysis (p = < 2e-16 in the training set, p = 3e-04 in the validation set, and p = 0.007 in the test set), which showed that the survival probability of different groups was significantly different, and the PI of the network played an influential role in the prediction of survival probability. In the residual verification of the PI, the fitting curve of the scatter plot was roughly parallel to the x-axis, and the p-value of the test was 0.11, proving that the PI and survival time were independent of each other and the survival prediction ability of the PI was less affected than survival time. The areas under the curve of the training set were 0.843, 0.871, 0.903, and 0.941; those of the validation set were 0.687, 0.895, 1.000, and 0.967; and those of the test set were 0.757, 0.852, 0.683, and 0.898.

Conclusion

The survival prediction model, which combines the DAE and the Cox proportional hazard regression model, can effectively predict the prognosis of patients with GBM.

The emerging potentials of lncRNA DRAIC in human cancers

Long non-coding RNA (lncRNA) is a subtype of noncoding RNA that has more than 200 nucleotides. Numerous studies have confirmed that lncRNA is relevant during multiple biological processes through the regulation of various genes, thus affecting disease progression. The lncRNA DRAIC, a newly discovered lncRNA, has been found to be abnormally expressed in a variety of diseases, particularly cancer. Indeed, the dysregulation of DRAIC expression is closely related to clinicopathological features. It was also reported that DRAIC is key to biological functions such as cell proliferation, autophagy, migration, and invasion. Furthermore, DRAIC is of great clinical significance in human disease. In this review, we discuss the expression signature, clinical characteristics, biological functions, relevant mechanisms, and potential clinical applications of DRAIC in several human diseases.

LncRNAs as Theragnostic Biomarkers for Predicting Radioresistance in Cancer: A Systematic Review and Meta-Analysis

Background

Radioresistance is the major obstacle after cancer radiotherapy. The dysregulation of long non-coding RNAs (lncRNAs) was closely related the radioresistance response. This meta-analysis was aimed to interpret the relationship between lncRNAs and radiotherapy responses in different cancers.

Method

The studies were selected from databases including PubMed, ISI Web of Science, Embase, Google Scholar, PMC, and CNKI (China National Knowledge Infrastructure). The publication time was limited to before March 20, 2021. The hazard ratios (HRs) and 95% confidence interval were calculated with random-effects models. Subgroup analyses, sensitivity analyses, and publication bias were also conducted.

Result

Twenty-seven lncRNAs in 14 cancer types were investigated, in which 23 lncRNAs were upregulated and four lncRNAs were downregulated. Dysregulation of these lncRNAs were found to be related to radioresistance response. The pooled HR and 95% confidence interval for the combined up-regulated lncRNAs was 1.73 (95% CI=1.50-2.00; P< 0.01) and down-regulated lncRNAs was 2.09 (95% CI= 1.60-2.72; P< 0.01). The HR values of the subgroup analysis for glioma (HR= 2.22, 95% CI= 1.79-2.74; p< 0.01), non-small cell lung cancer (HR=1.48, 95% CI=1.18-1.85; P<0.01), nasopharyngeal carcinoma (HR=4.26; 95% CI= 1.58-11.46; P< 0.01), and breast cancer (HR=1.29; 95% CI= 1.08-1.54; P< 0.01) were obtained. Moreover, the expression of lncRNAs was significantly related to overall survival of patients no matter if the sample size was >50 or not. In addition, the HR values of the subgroup analysis for lncRNA H19 (HR=2.68; 95% CI= 1.92-3.74; P <0.01), lncRNA FAM201A (HR=2.15; 95% CI= 1.15-3.99; P <0.01), and lncRNA HOTAIR (HR=1.22; 95% CI= 0.98-1.54; P =0.08) were also obtained.

Conclusion

LncRNAs can induce cancer radioresistance by regulating cell death-related signaling pathways. Results indicated that lncRNAs, especially lncRNA H19, FAM201A, and HOTAIR, could be considered as a predictive theragnostic biomarker to evaluate radiotherapy response.

Evolving Insights Into the Biological Function and Clinical Significance of Long Noncoding RNA in Glioblastoma

Glioblastoma (GBM) is one of the most prevalent and aggressive cancers worldwide. The overall survival period of GBM patients is only 15 months even with standard combination therapy. The absence of validated biomarkers for early diagnosis mainly accounts for worse clinical outcomes of GBM patients. Thus, there is an urgent requirement to characterize more biomarkers for the early diagnosis of GBM patients. In addition, the detailed molecular basis during GBM pathogenesis and oncogenesis is not fully understood, highlighting that it is of great significance to elucidate the molecular mechanisms of GBM initiation and development. Recently, accumulated pieces of evidence have revealed the central roles of long noncoding RNAs (lncRNAs) in the tumorigenesis and progression of GBM by binding with DNA, RNA, or protein. Targeting those oncogenic lncRNAs in GBM may be promising to develop more effective therapeutics. Furthermore, a better understanding of the biological function and underlying molecular basis of dysregulated lncRNAs in GBM initiation and development will offer new insights into GBM early diagnosis and develop novel treatments for GBM patients. Herein, this review builds on previous studies to summarize the dysregulated lncRNAs in GBM and their unique biological functions during GBM tumorigenesis and progression. In addition, new insights and challenges of lncRNA-based diagnostic and therapeutic potentials for GBM patients were also introduced.

A Radiosensitivity Prediction Model Developed Based on Weighted Correlation Network Analysis of Hypoxia Genes for Lower-Grade Glioma

Background and Purpose

Hypoxia is one of the basic characteristics of the physical microenvironment of solid tumors. The relationship between radiotherapy and hypoxia is complex. However, there is no radiosensitivity prediction model based on hypoxia genes. We attempted to construct a radiosensitivity prediction model developed based on hypoxia genes for lower-grade glioma (LGG) by using weighted correlation network analysis (WGCNA) and least absolute shrinkage and selection operator (Lasso).

Methods

In this research, radiotherapy-related module genes were selected after WGCNA. Then, Lasso was performed to select genes in patients who received radiotherapy. Finally, 12 genes (AGK, ETV4, PARD6A, PTP4A2, RIOK3, SIGMAR1, SLC34A2, SMURF1, STK33, TCEAL1, TFPI, and UROS) were included in the model. A radiosensitivity-related risk score model was established based on the overall rate of The Cancer Genome Atlas (TCGA) dataset in patients who received radiotherapy. The model was validated in TCGA dataset and two Chinese Glioma Genome Atlas (CGGA) datasets. A novel nomogram was developed to predict the overall survival of LGG patients.

Results

We developed and verified a radiosensitivity-related risk score model based on hypoxia genes. The radiosensitivity-related risk score served as an independent prognostic indicator. This radiosensitivity-related risk score model has prognostic prediction ability. Moreover, a nomogram integrating risk score with age and tumor grade was established to perform better for predicting 1-, 3-, and 5-year survival rates.

Conclusions

We developed and validated a radiosensitivity prediction model that can be used by clinicians and researchers to predict patient survival rates and achieve personalized treatment of LGG.

An 11-Gene Signature Based on Treatment Responsiveness Predicts Radiation Therapy Survival Benefit Among Breast Cancer Patients

Purpose

We developed a strategy of building prognosis gene signature based on clinical treatment responsiveness to predict radiotherapy survival benefit in breast cancer patients.

Methods and Materials

Analyzed data came from the public database. PFS was used as an indicator of clinical treatment responsiveness. WGCNA was used to identify the most relevant modules to radiotherapy response. Based on the module genes, Cox regression model was used to build survival prognosis signature to distinguish the benefit group of radiotherapy. An external validation was also performed.

Results

In the developed dataset, MEbrown module with 534 genes was identified by WGCNA, which was most correlated to the radiotherapy response of patients. A number of 11 hub genes were selected to build the survival prognosis signature. Patients that were divided into radio-sensitivity group and radio-resistant group based on the signature risk score had varied survival benefit. In developed dataset, the 3-, 5-, and 10-year AUC of the signature were 0.814 (CI95%: 0.742–0.905), 0.781 (CI95%: 0.682–0.880), and 0.762 (CI95%: 0.626–0.897), respectively. In validation dataset, the 3- and 5-year AUC of the signature were 0.706 (CI95%: 0.523–0.889) and 0.743 (CI95%: 0.595–0.891). The signature had higher predictive power than clinical factors alone and had more clinical prognosis efficiency. Functional enrichment analysis revealed that the identified genes were mainly enriched in immune-related processes. Further immune estimated analysis showed the difference in distribution of immune micro-environment between radio-sensitivity group and radio-resistant group.

Conclusions

The 11-gene signature may reflect differences in tumor immune micro-environment that underlie the differential response to radiation therapy and could guide clinical-decision making related to radiation in breast cancer patients.

Long Non-Coding RNA SNHG6 Supports Glioma Progression Through Upregulation of Notch1, Sox2, and EMT

Gliomas, particularly the advanced grade glioblastomas, have poor 5-year survival rates and worse outcomes. lncRNAs and EMT have been extensively studied in gliomas but the disease progression remains poorly understood. SNHG6 has been shown to affect glioma cell proliferation but its effect on EMT of glioma cells along with its effect on disease progression is not known. We screened four glioma cell lines; H4, A172, U87MG, and SW088 and grouped them based on high vs. low SNHG6 expression. Transfections with SNHG6 specific siRNA resulted in induction of apoptosis of high SNHG6 expressing A172 and U87MG cells. This was accompanied by inhibition of EMT and downregulation of EMT-modulating factor Notch1, β-catenin activity and the cancer stem cell marker Sox2. The regulation was not found to be reciprocal as silencing of Notch1 and Sox2 failed to affect SNHG6 levels. The levels of SNHG6 and Notch1 were also found elevated in Grade IV glioma patients (n = 4) relative to Grade II glioma patients (n = 5). These results identify SNHG6 and Notch1 as valid targets for glioma therapy.

Long Non-Coding RNAs in Diagnosis, Treatment, Prognosis, and Progression of Glioma: A State-of-the-Art Review

Glioma is the most common malignant central nervous system tumor with significant mortality and morbidity. Despite considerable advances, the exact molecular pathways involved in tumor progression are not fully elucidated, and patients commonly face a poor prognosis. Long non-coding RNAs (lncRNAs) have recently drawn extra attention for their potential roles in different types of cancer as well as non-malignant diseases. More than 200 lncRNAs have been reported to be associated with glioma. We aimed to assess the roles of the most investigated lncRNAs in different stages of tumor progression and the mediating molecular pathways in addition to their clinical applications. lncRNAs are involved in different stages of tumor formation, invasion, and progression, including regulating the cell cycle, apoptosis, autophagy, epithelial-to-mesenchymal transition, tumor stemness, angiogenesis, the integrity of the blood-tumor-brain barrier, tumor metabolism, and immunological responses. The well-known oncogenic lncRNAs, which are upregulated in glioma, are H19, HOTAIR, PVT1, UCA1, XIST, CRNDE, FOXD2-AS1, ANRIL, HOXA11-AS, TP73-AS1, and DANCR. On the other hand, MEG3, GAS5, CCASC2, and TUSC7 are tumor suppressor lncRNAs, which are downregulated. While most studies reported oncogenic effects for MALAT1, TUG1, and NEAT1, there are some controversies regarding these lncRNAs. Expression levels of lncRNAs can be associated with tumor grade, survival, treatment response (chemotherapy drugs or radiotherapy), and overall prognosis. Moreover, circulatory levels of lncRNAs, such as MALAT1, H19, HOTAIR, NEAT1, TUG1, GAS5, LINK-A, and TUSC7, can provide non-invasive diagnostic and prognostic tools. Modulation of expression of lncRNAs using antisense oligonucleotides can lead to novel therapeutics. Notably, a profound understanding of the underlying molecular pathways involved in the function of lncRNAs is required to develop novel therapeutic targets. More investigations with large sample sizes and increased focus on in-vivo models are required to expand our understanding of the potential roles and application of lncRNAs in glioma.

More evidence for prediction model of radiosensitivity

With the development of precision medicine, searching for potential biomarkers plays a major role in personalized medicine. Therefore, how to predict radiosensitivity to improve radiotherapy is a burning question. The definition of radiosensitivity is complex. Radiosensitive gene/biomarker can be useful for predicting which patients would benefit from radiotherapy. The discovery of radiosensitivity biomarkers require multiple pieces of evidence. A prediction model of breast cancer radiosensitivity based on six genes was established. We had put forward some supplements on the basis of the present study. We found that there were no differences between high- and low-risk scores in the non-radiotherapy group. Patients who received radiotherapy had a significantly better overall survival than non-radiotherapy patients in the predicted low-risk score patients. Furthermore, there was no difference between radiotherapy group and non-radiotherapy group in the high-risk score group. Those results firmly supported the prediction model of radiosensitivity. In addition, building a radiosensitivity prediction model was systematically discussed. Genes of model could be screened by different methods, such as Cox regression analysis, Lasso Cox regression method, random forest algorithm and other methods. In the future, precision radiotherapy might depend on the combination of multi-omics data and high dimensional image data.