A three-lncRNA signature of pretreatment biopsies predicts pathological response and outcome in esophageal squamous cell carcinoma with neoadjuvant chemoradiotherapy

Multi-omics approaches for biomarker discovery in predicting the response of esophageal cancer to neoadjuvant therapy: A multidimensional perspective

Neoadjuvant chemoradiotherapy (NCRT) followed by surgery has been established as the standard treatment strategy for operable locally advanced esophageal cancer (EC). However, achieving pathologic complete response (pCR) or near pCR to NCRT is significantly associated with a considerable improvement in survival outcomes, while pCR patients may help organ preservation for patients by active surveillance to avoid planned surgery. Thus, there is an urgent need for improved biomarkers to predict EC chemoradiation response in research and clinical settings. Advances in multiple high-throughput technologies such as next-generation sequencing have facilitated the discovery of novel predictive biomarkers, specifically based on multi-omics data, including genomic/transcriptomic sequencings and proteomic/metabolomic mass spectra. The application of multi-omics data has shown the benefits in improving the understanding of underlying mechanisms of NCRT sensitivity/resistance in EC. Particularly, the prominent development of artificial intelligence (AI) has introduced a new direction in cancer research. The integration of multi-omics data has significantly advanced our knowledge of the disease and enabled the identification of valuable biomarkers for predicting treatment response from diverse dimension levels, especially with rapid advances in biotechnological and AI methodologies. Herein, we summarize the current status of research on the use of multi-omics technologies in predicting NCRT response for EC patients. Current limitations, challenges, and future perspectives of these multi-omics platforms will be addressed to assist in experimental designs and clinical use for further integrated analysis.

Noncoding RNAs in esophageal cancer: A glimpse into implications for therapy resistance

Esophageal cancer (EC) is one of the most common malignancies of the digestive system and has a high morbidity and mortality worldwide. Chemotherapy in combination with radiotherapy is one of the most important treatment modalities for EC. Chemoradiotherapy is currently acknowledged worldwide as being the standard treatment for locally advanced or unresectable disease. Unfortunately, due to the existence of therapy resistance, a number of EC patients fail to benefit from drug or irradiation treatment, which ultimately leads to poor outcomes. Considerable efforts have been made to explore the mechanisms underlying the therapy resistance of EC. Notably, noncoding RNAs (ncRNAs), including microRNAs (miRNAs), long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs), are current research areas for the modulation of therapy responses and may serve as new targets to overcome treatment resistance in EC. Herein, we summarized the mechanisms by which ncRNAs are involved in drug and radiation resistance in EC and highlighted their role in promoting or repressing treatment resistance. Additionally, we discussed the clinical relevance of ncRNAs, which may serve as potential therapeutic targets and predictive biomarkers for EC.

Comprehensive analyses of N6 -methyladenosine-related long noncoding RNA profiles with prognosis, chemotherapy response, and immune landscape in small cell lung cancer

Small cell lung cancer (SCLC) is the most devastating subtype of lung cancer with no clinically available prognostic biomarkers. N⁶ -methyladenosine (m⁶ A) and noncoding RNAs play critical roles in cancer development and treatment response. However, little is known about m⁶ A-related long noncoding RNAs (lncRNAs) in SCLC. We used 206 limited-stage SCLC (LS-SCLC) samples from two cohorts to undertake the first and most comprehensive exploration of the m⁶ A-related lncRNA profile in SCLC and constructed a relevant prognostic signature. In total, 289 m⁶ A-related lncRNAs were screened out. We then built a seven-lncRNA-based signature in the training cohort with 48 RNA sequencing data using univariate and multivariate Cox regression models. The signature was well validated in an independent cohort containing 158 cases with quantitative PCR data. In both cohorts, the signature divided patients into high- and low-risk groups with significantly different survival rates (both p < 0.001). Our signature predicted chemotherapy survival benefit in patients with LS-SCLC. Receiver operating characteristic and C-index analyses indicated that the signature was better at predicting prognosis and chemotherapy benefit than other clinicopathologic features. Moreover, the signature was identified as an independent predictor of prognosis and chemotherapy response in different cohorts. Furthermore, functional analysis showed that multiple activated immune-related pathways were enriched in the low-risk group. Additionally, the signature was also closely related to various immune checkpoints and inflammatory responses. We generated the first clinically available m⁶ A-related lncRNA signature to predict prognosis and chemotherapy benefit in patients with LS-SCLC. Our findings could help optimize the clinical management of patients with LS-SCLC and inform future therapeutic targets for SCLC.

A Novel Glycolysis-Related Long Noncoding RNA Signature for Predicting Overall Survival in Gastric Cancer

Background: The aim of this study was to construct a glycolysis-related long noncoding RNA (lncRNA) signature to predict the prognosis of patients with gastric cancer (GC). Methods: Glycolysis-related genes were obtained from the Molecular Signatures Database (MSigDB), lncRNA expression profiles and clinical data of GC patients were obtained from The Cancer Genome Atlas database (TCGA). Furthermore, univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analysis were used to construct prognostic glycolysis-related lncRNA signature. The specificity and sensitivity of the signature was verified by receiver operating characteristic (ROC) curves. We constructed a nomogram to predict the 1-year, 3-year, and 5-year survival rates of GC patients. Besides, the relationship between immune infiltration and the risk score was analyzed in the high and low risk groups. Multi Experiment Matrix (MEM) was used to analyze glycolysis-related lncRNA target genes. R "limma" package was used to analyze the mRNA expression levels of the glycolysis-related lncRNA target genes in TCGA. Gene set enrichment analysis (GSEA) was employed to further explore the biological pathways in the high-risk group and the glycolysis-related lncRNA target gene. Results: A prognostic signature was conducted based on nine glycolysis-related lncRNAs, which are AL391152.1, AL590705.3, RHOXF1-AS1, CFAP61-AS1, LINC00412, AC005165.1, AC110995.1, AL355574.1 and SCAT1. The area under the ROC curve (AUC) values at 1-year, 3-year, and 5-year were 0.765, 0.828 and 0.707 in the training set, and 0.669, 740 and 0.807 in the testing set, respectively. In addition, the nomogram could efficaciously predict the 1-year, 3-year, and 5-year survival rates of the GC patients. Then, we discovered that GC patients with high-risk scores were more likely to respond to immunotherapy. GSEA revealed that the signature was mainly associated with the calcium signaling pathway, extracellular matrix (ECM) receptor interaction, and focal adhesion in high-risk group, also indicated that SBSPON is related to aminoacyl-tRNA biosynthesis, citrate cycle, fructose and mannose metabolism, pentose phosphate pathway and pyrimidine metabolism. Conclusion: Our study shows that the signature can predict the prognosis of GC and may provide new insights into immunotherapeutic strategies.

Identification and Validation of a Novel Necroptosis-Related Long Noncoding RNA Prognostic Signature for Lung Adenocarcinoma

Background

Several cancers, including lung adenocarcinoma (LUAD), are caused by genes related to necroptosis. However, it is still unknown how necroptosis-related long noncoding RNAs (lncRNAs) may be involved in LUAD. In order to predict the prognosis of LUAD patients and personalize immunotherapy, this study set out to construct a necroptosis-related lncRNA prognostic signature (NLPS).

Methods

The Cancer Genome Atlas (TCGA) database was used to download the LUAD transcriptome data and the associated clinical data. lncRNAs associated with necroptosis were screened using coexpression analysis. An NLPS was built using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. The Gene Expression Omnibus (GEO) database's GSE30219 was used to validate the NLPS. The prognostic value of the risk score was assessed using Kaplan-Meier survival, receiver operating characteristic (ROC) Cox regression, multivariate Cox regression, and nomogram analyses. Then, we looked into the differences between the low- and high-risk groups in the tumor immune microenvironment, immunotherapy response, and half-maximal inhibitory concentration (IC50).

Results

The 14 lncRNAs in a novel NLPS were created. With further validation in the GSE30219 dataset, the risk score according to the NLPS was an independent prognostic indicator for LUAD patients. Patients with better overall survival (OS) in the low-risk group, who were characterized by increased immune cell infiltration, tumor mutational burden (TMB), and immunophenoscore (IPS), may have hot tumors and higher immunotherapy response rates. In addition, the risk score was also closely linked to sensitivity to various anticancer medications.

Conclusions

We constructed a novel NLPS that could predict OS and sensitivity to immunotherapy in LUAD patients.

Identification and validation of a novel cellular senescence-related lncRNA prognostic signature for predicting immunotherapy response in stomach adenocarcinoma

Background: Cellular senescence is a novel hallmark of cancer associated with patient outcomes and tumor immunotherapy. However, the value of cellular senescence-related long non-coding RNAs (lncRNAs) in predicting prognosis and immunotherapy response for stomach adenocarcinoma (STAD) patients needs further investigation.

Methods: The transcriptome and corresponding clinical information of STAD and cellular senescence-related genes were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and CellAge databases. Differential expression analysis and coexpression analysis were performed to obtain cellular senescence-related lncRNAs. Univariate regression analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were conducted to establish the cellular senescence-related lncRNA prognostic signature (CSLPS). Next, the survival curve, ROC curve, and nomogram were developed to assess the capacity of predictive models. Moreover, principal component analysis (PCA), gene set enrichment analysis (GSEA), tumor microenvironment (TME), tumor mutation burden (TMB), microsatellite instability (MSI), and tumor immune dysfunction and exclusion (TIDE) score analysis were performed between high- and low-risk groups.

Results: A novel CSLPS involving fifteen lncRNAs (REPIN1-AS1, AL355574.1, AC104695.3, AL033527.2, AC083902.1, TYMSOS, LINC00460, AC005165.1, AL136115.1, AC007405.2, AL391152.1, SCAT1, AC129507.1, AL121748.1, and ADAMTS9-AS1) was developed. According to the nomogram, the risk model based on the CSLPS was an independent prognostic factor and could predict 1-, 3-, and 5-year overall survival for STAD patients. GSEA suggested that the high-risk group was mainly associated with Toll-like receptor, JAK/STAT, NOD-like receptor, and chemokine signaling pathways. Further analysis revealed that STAD patients in the low-risk group with better clinical outcomes had a higher TMB, higher proportion of high microsatellite instability (MSI-H), better immune infiltration, and lower TIDE scores.

Conclusion: A fifteen-CSlncRNA prognostic signature could predict survival outcomes, and patients in the low-risk group may be more sensitive to immunotherapy.

The Key Clinical Questions of Neoadjuvant Chemoradiotherapy for Resectable Esophageal Cancer—A Review

Over 50% of individuals with esophageal cancer (EC) present with advanced stages of the disease; therefore, their outcome following surgery alone is poor, with only 25%–36% being alive 5 years post-surgery. Based on the evidence that the CROSS and NEOCRTEC5010 trials provided, neoadjuvant chemoradiotherapy (nCRT) is now the standard therapy for patients with locally advanced EC. However, there are still many concerning clinical questions that remain controversial such as radiation dose, appropriate patient selection, the design of the radiation field, the time interval between chemoradiotherapy (CRT) and surgery, and esophageal retention. With immune checkpoint inhibitors (ICIs) rapidly becoming a mainstay of cancer therapy, along with radiation, chemotherapy, and surgery, the combination mode of immunotherapy is also becoming a hot topic of discussion. Here, we try to provide constructive suggestions to answer the perplexing problems and clinical concerns for the progress of nCRT for EC in the future.

Oxidative Stress-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Responses in Patients With LUAD

Lung adenocarcinoma is increasingly harmful to society and individuals as cancer with an inferior prognosis and insensitive to chemotherapy. Previous studies have demonstrated that oxidative stress and lncRNAs play a vital role in many biological processes. Therefore, we explored the role of lncRNAs associated with oxidative stress in the prognosis and survival of LUAD patients. We examined the expression profiles of lncRNAs and oxidative stress genes in this study. A prognosis prediction model and a nomogram were built based on oxidative stress-related lncRNAs. Functional and drug sensitivity analyses were also performed depending on oxidative stress-related lncRNA signature. Moreover, we investigated the relationship between immune response and immunotherapy. The results showed that a risk scoring model based on 16 critical oxidative stress lncRNAs was able to distinguish the clinical status of LUAD and better predict the prognosis and survival. Additionally, the model demonstrated a close correlation with the tumor immune system, and these key lncRNAs also revealed the relationship between LUAD and chemotherapeutic drug sensitivity. Our work aims to provide new perspectives and new ideas for the treatment and management of LUAD.

Identification of immune-related lncRNA panel for predicting immune checkpoint blockade and prognosis in head and neck squamous cell carcinoma

Purpose

Immunotherapy is changing head and neck squamous cell carcinoma (HNSCC) treatment pattern. According to the Chinese Society of Clinical Oncology (CSCO) guidelines, immunotherapy has been deemed as first‐line recommendation for recurrent/metastatic HNSCC, marking that advanced HNSCC has officially entered the era of immunotherapy. Long non‐coding RNAs (lncRNAs) impact every step of cancer immunity. Therefore, reliable immune‐lncRNAs able to accurately predict the immune landscape and survival of HNSCC are crucial to clinical management.

Methods

In the current study, we downloaded the transcriptomic and clinical data of HNSCC from The Cancer Genome Atlas and identified differentially expressed immune‐related lncRNAs (DEir‐lncRNAs). Further then, Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to identify proper DEir‐lncRNAs to construct optimal risk model. Low‐risk and high‐risk groups were classified based on the optimal cut‐off value generated by the areas under curve for receiver operating characteristic curves (AUC), and Kaplan–Meier survival curves were utilized to validate the prediction model. We then evaluated the model based on the clinical factors, immune cell infiltration, and chemotherapeutic and immunotherapeutic efficacy between two groups.

Results

In our study, we identified 256 Deir‐lncRNAs in HNSCC. A total of 18 Deir‐lncRNA pairs (consisting of 35 Deir‐lncRNAs) were used to construct a risk model significantly associated with survival of HNSCC. Cox proportional hazard regression analysis confirmed that our risk model could be served as an independent prognostic indicator. Besides, HNSCC patients with low‐risk score significantly enriched of CD8⁺ T cell, and corelated with high chemosensitivity and immunotherapeutic sensitivity.

Conclusion

Our risk model could be served as a promising clinical prediction indicator, effective discoursing of the immune cell infiltration of HNSCC patients, and distinguishing patients who could benefit from chemotherapy and immunotherapy.

Pyroptosis-Related lncRNAs Predict the Prognosis and Immune Response in Patients With Breast Cancer

Background: Breast cancer (BC) is the most common malignant tumor and the leading cause of cancer-related death in women worldwide. Pyroptosis and long noncoding RNAs (lncRNAs) have been demonstrated to play vital roles in the tumorigenesis and development of BC. However, the clinical significance of pyroptosis-related lncRNAs in BC remains unclear.

Methods: Using the mRNA and lncRNA profiles of BC obtained from TCGA dataset, a risk model based on the pyroptosis-related lncRNAs for prognosis was constructed using univariate and multivariate Cox regression model, and least absolute shrinkage and selection operator. Patients were divided into high- and low-risk groups based on the risk model, and the prognosis value and immune response in different risk groups were analyzed. Furthermore, functional enrichment annotation, therapeutic signature, and tumor mutation burden were performed to evaluate the risk model we established. Moreover, the expression level and clinical significance of the selected pyroptosis-related lncRNAs were further validated in BC samples.

Results: 3,364 pyroptosis-related lncRNAs were identified using Pearson’s correlation analysis. The risk model we constructed comprised 10 pyroptosis-related lncRNAs, which was identified as an independent predictor of overall survival (OS) in BC. The nomogram we constructed based on the clinicopathologic features and risk model yielded favorable performance for prognosis prediction in BC. In terms of immune response and mutation status, patients in the low-risk group had a higher expression of immune checkpoint markers and exhibited higher fractions of activated immune cells, while the high-risk group had a highly percentage of TMB. Further analyses in our cohort BC samples found that RP11-459E5.1 was significantly upregulated, while RP11-1070N10.3 and RP11-817J15.3 were downregulated and significantly associated with worse OS.

Conclusion: The risk model based on the pyroptosis-related lncRNAs we established may be a promising tool for predicting the prognosis and personalized therapeutic response in BC patients.

Identification of molecular patterns and diagnostic biomarkers in juvenile idiopathic arthritis based on the gene expression of m6A regulators

The role of N ⁶-methyladenosine modification in immunity is increasingly being appreciated. However, the landscape of m⁶A regulators in juvenile idiopathic arthritis (JIA) is poorly understood. Thus, this study explored the impact of m⁶A modification and related lncRNAs in JIA immune microenvironment. Fourteen m⁶A regulators and eight lncRNAs were identified as potential diagnostic biomarkers for JIA. Two diagnostic models for JIA were also constructed. The putative molecular regulatory mechanism of FTO-mediated m⁶A modification in JIA was hypothesized. Three distinct m⁶A patterns mediated by 26 m⁶A regulators and three diverse lncRNA clusters mediated by 405 lncRNAs were thoroughly investigated. They exhibited dramatically diverse immune microenvironments and expression of HLA genes. The identification of two separate subtypes of enthesitis-related arthritis implies that our work may aid in the establishment of a more precise categorization system for JIA. m⁶A modification-related genes were obtained, and their underlying biological functions were explored. The m⁶Ascore system developed for individual JIA patients may be utilized to evaluate the immunological state or molecular pattern, thereby offering therapy recommendations. In short, through the investigation of the m⁶A regulators in JIA, the current work may contribute to our knowledge of the pathophysiology of JIA.

Long non-coding RNA-associated competing endogenous RNA axes in the olfactory epithelium in schizophrenia: a bioinformatics analysis

The etiology of schizophrenia (SCZ), as a serious mental illness, is unknown. The significance of genetics in SCZ pathophysiology is yet unknown, and newly identified mechanisms involved in the regulation of gene transcription may be helpful in determining how these changes affect SCZ development and progression. In the current work, we used a bioinformatics approach to describe the role of long non-coding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs) in the olfactory epithelium (OE) samples in order to better understand the molecular regulatory processes implicated in SCZ disorders in living individuals. The Gene Expression Omnibus database was used to obtain the OE microarray dataset (GSE73129) from SCZ sufferers and control subjects, which contained information about both lncRNAs and mRNAs. The limma package of R software was used to identify the differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs). RNA interaction pairs were discovered using the Human MicroRNA Disease Database, DIANA-LncBase, and miRTarBase databases. In this study, the Pearson correlation coefficient was utilized to find positive correlations between DEmRNAs and DElncRNAs in the ceRNA network. Eventually, lncRNA-associated ceRNA axes were developed based on co-expression relations and DElncRNA-miRNA-DEmRNA interactions. This work found six potential DElncRNA-miRNA-DEmRNA loops in SCZ pathogenesis, including, SNTG2-AS1/hsa-miR-7-5p/SLC7A5, FLG-AS1/hsa-miR-34a-5p/FOSL1, LINC00960/hsa-miR-34a-5p/FOSL1, AQP4-AS1/hsa-miR-335-5p/FMN2, SOX2-OT/hsa-miR-24-3p/NOS3, and CASC2/hsa-miR-24-3p/NOS3. According to the findings, ceRNAs in OE might be promising research targets for studying SCZ molecular mechanisms. This could be a great opportunity to examine different aspects of neurodevelopment that may have been hampered early in SCZ patients.

Biomarkers for the prediction of esophageal cancer neoadjuvant chemoradiotherapy response: A systemic review

Neoadjuvant chemoradiotherapy followed by surgery has been established as the standard treatment for locally advanced esophageal cancer. For patients with complete regression after neoadjuvant chemotherapy, active surveillance rather than planned surgery has been proposed as an organ preservation strategy. Reliable biomarkers to predict chemoradiation response is needed. We first summarized the previous reports of biomarkers with the potential to predict the treatment response of esophageal cancer neoadjuvant chemoradiotherapy. These traditional biomarkers are classified into three groups: genetic biomarkers, RNA biomarkers, and protein biomarkers. We then summarized some special types of biomarkers, including metabolites biomarkers, immune and tumor microenvironment biomarkers, and microbiome biomarkers.

(Neo)adjuvant Chemoradiotherapy is Beneficial to the Long-term Survival of Locally Advanced Esophageal Squamous Cell Carcinoma: A Network Meta-analysis

PURPOSE

To determine the most effective and safest treatment mode for locally advanced resectable esophageal squamous cell carcinoma through a network meta-analysis.

METHOD

A Bayesian model was used for a network meta-analysis comparing the efficacy and safety of surgery alone, neoadjuvant therapy, and adjuvant therapy.

RESULTS

Thirty clinical studies, including thirty-one articles, 4866 patients, were analyzed. Overall survival rate: Adjuvant chemoradiotherapy and neoadjuvant chemoradiotherapy were significantly advantageous over surgery alone [hazard ratio (HR) 0.73, 95% confidence interval (CI) 0.57-0.93; HR 0.75, 95%CI 0.65-0.86]. There was no statistically significant difference between adjuvant chemoradiotherapy and neoadjuvant chemoradiotherapy [HR 0.97, 95%CI 0.75-1.28]. Disease-free survival rate: Compared with surgery alone, neoadjuvant chemoradiotherapy had significant benefits [HR 0.65, 95%CI 0.53-0.78]; adjuvant chemoradiotherapy had similar, but not significant benefits [HR 0.7, 0.95%CI 0.45-1.06]. The difference between neoadjuvant chemoradiotherapy and adjuvant chemoradiotherapy was also not statistically significant [HR 0.94, 0.95%CI 0.61-1.43]. Surgery after neoadjuvant chemoradiotherapy: The R0 resection rate was significantly improved [relative risk (RR) 0.25, 95%CI 0.07-0.86], but the overall postoperative morbidity rate and 30-day postoperative mortality rate tended to increase [RR 1.27, 95%CI 0.8-2.01; RR 1.59, 95%CI 0.7-3.22]. Neither neoadjuvant chemotherapy nor neoadjuvant radiotherapy significantly altered the surgical safety or R0 resection rate.

CONCLUSION

Both neoadjuvant chemoradiotherapy and adjuvant chemoradiotherapy appear to be the best supplements to surgery for locally advanced resectable esophageal squamous cell carcinoma.

A novel immune-related long non-coding RNA signature improves the prognosis prediction in the context of head and neck squamous cell carcinoma

The tumor immune microenvironment plays an important role in head and neck squamous cell carcinoma (HNSCC). Reliable prognostic signatures able to accurately predict the immune landscape and survival rate of HNSCC patients are crucial to ensure an individualized/effective treatment. Here, we used HNSCC transcriptomic and clinical data retrieved from The Cancer Genome Atlas and identified differentially expressed immune-related long non-coding RNAs (DEirlncRNAs). DEirlncRNA pairs were recognized using univariate analysis. Cox and Lasso regression analyses were used to determine the association between DEirlncRNA pairs and the patients’ overall survival and build the prediction model. Receiver operating characteristic curves and Kaplan–Meier survival curves were used to validate the prediction model. We then reevaluated the model based on the clinical factors, tumor-infiltrating immune cells, chemotherapeutic efficacy, and immunosuppression biomarkers. We built a risk score model based on 18 DEirlncRNA pairs, closely related to the overall survival of patients (hazard ratio: 1.376; 95% confidence interval: 1.302–1.453; P < 0.0001). Compared with two recently published lncRNA signatures, our DEirlncRNA pair signature had a higher area under the curve, indicating better prognostic performance. Additionally, the signature score positively correlated with aggressive HNSCC outcomes (low immunity score, significantly reduced CD8 + T cell infiltration, and low expression of immunosuppression biomarkers). However, high-risk patients might have high chemosensitivity. Overall, the lncRNAs signature established here shows promising clinical prediction and the effective disclosure of the tumor immune microenvironment in HNSCC patients; therefore, such signature might help distinguish patients that could benefit from immunotherapy.

Identification and validation of a novel eight mutant-derived long non-coding RNAs signature as a prognostic biomarker for genome instability in low-grade glioma

Long non-coding RNAs (lncRNAs) comprise an integral part of the eukaryotic transcriptome. Alongside proteins, lncRNAs modulate lncRNA-based gene signatures of unstable transcripts, play a crucial role as antisense lncRNAs to control intracellular homeostasis and are implicated in tumorigenesis. However, the role of genomic instability-associated lncRNAs in low-grade gliomas (LGG) has not been fully explored. In this study, lncRNAs expression and somatic mutation profiles in low-grade glioma genome were used to identify eight novel mutant-derived genomic instability-associated lncRNAs including H19, FLG-AS1, AC091932.1, AC064875.1, AL138767.3, AC010273.2, AC131097.4 and ISX-AS1. Patients from the LGG gene mutagenome atlas were grouped into training and validation sets to test the performance of the signature. The genomic instability-associated lncRNAs signature (GILncSig) was then validated using multiple external cohorts. A total of 59 novel genomic instability-associated lncRNAs in LGG were used for least absolute shrinkage and selection operator (Lasso), single and multifactor Cox regression analysis using the training set. Furthermore, the independent predictive role of risk features in the training and validation sets were evaluated through survival analysis, receiver operating feature analysis and construction of a nomogram. Patients with IDH1 mutation status were grouped into two different risk groups based on the GILncSig score. The low-risk group showed a relatively higher rate of IDH1 mutations compared with patients in the high-risk group. Furthermore, patients in the low-risk group had better prognosis compared with patients in the high-risk group. In summary, this study reports a reliable prognostic prediction signature and provides a basis for further investigation of the role of lncRNAs on genomic instability. In addition, lncRNAs in the signature can be used as new targets for treatment of LGG.

Targeting Antisense lncRNA PRKAG2-AS1, as a Therapeutic Target, Suppresses Malignant Behaviors of Hepatocellular Carcinoma Cells

Objective: Increasing evidence highlights antisense long non-coding RNAs (lncRNAs) as promising therapeutic targets for cancers. Herein, this study focused on the clinical implications and functions of a novel antisense lncRNA PRKAG2-AS1 in hepatocellular carcinoma (HCC).

Methods: PRKAG2-AS1 expression was examined in a cohort of 138 HCC patients by RT-qPCR. Overall survival (OS) and disease-free survival (DFS) analyses were presented based on PRKAG2-AS1 expression, followed by ROCs. After silencing PRKAG2-AS1, cell proliferation was assessed via CCK-8, colony formation and EdU staining assays. Migrated and invasive capacities were assessed by wound healing and transwell assays. The relationships between PRKAG2-AS1, miR-502-3p and BICD2 were validated by luciferase reporter, RIP and RNA pull-down assays. The expression and prognostic value of BICD2 were analyzed in TCGA database.

Results: PRKAG2-AS1 was up-regulated in HCC than normal tissue specimens. High PRKAG2-AS1 expression was indicative of poorer OS and DFS time. Area under the curves (AUCs) for OS and DFS were 0.8653 and 0.7891, suggesting the well predictive efficacy of PRKAG2-AS1 expression. Targeting PRKAG2-AS1 distinctly inhibited proliferation, migration, and invasion in HCC cells. PRKAG2-AS1 was mainly expressed in cytoplasm of HCC cells. PRKAG2-AS1 may directly bind to the sites of miR-502-3p. Up-regulation of BICD2 was found in HCC tissues and associated with unfavorable prognosis. BICD2 was confirmed to be a downstream target of miR-502-3p. PRKAG2-AS1 could regulate miR-502-3p/BICD2 axis.

Conclusion: Our findings identified a novel lncRNA PRKAG2-AS1 that was associated with clinical implications and malignant behaviors. Thus, PRKAG2-AS1 could become a promising therapeutic target.

Distribution of Peripheral Blood Cells in Esophageal Cancer Patients During Concurrent Chemoradiotherapy Predicts Long-Term Locoregional Progression Hazard After Treatment (GASTO1072)

PURPOSE

Predicting the response to chemoradiotherapy is critical for the optimal management of esophageal cancer; however, it remains an unmet clinical need. This study aimed to evaluate the predictive potential of peri-treatment peripheral blood cells (PBC) in disease progression hazard in esophageal cancer following chemoradiotherapy.

PATIENTS AND METHODS

A total of 87 patients with primary esophageal squamous cell carcinoma were subjected to definitive concurrent chemoradiotherapy in a Phase II trial. PBC parameters (hemoglobin, neutrophils, platelets, lymphocytes, and monocytes) were collected at seven time points throughout the course of radiotherapy. The potential of peri-treatment PBC parameters to predict the 3-year cumulative hazard of tumor progression was evaluated.

RESULTS

Patients with disease progression displayed distinct distribution patterns of peri-treatment PBC compared to that in patients without disease progression. Greater prediction capabilities for risk of locoregional disease progression were found in PBC collected after the start of radiotherapy compared to those in their pretreatment counterparts, and in individual parameters rather than cell-to-cell ratios. The most predictive PBC parameters were integrated by summation and designated as a PBC score (PBCS), which further augmented their predictive power. Patients classified according to their PBCS (high vs medium v. low) had significantly different 3-year cumulative hazards of locoregional progression (58% vs 29% vs 7%, P = 0.0017). Multivariate analysis confirmed that high PBCS (HR, 12.2; 95% CI, 2.0-76.3; P = 0.007) and medium (HR, 5.8; 95% CI 1.2-27.7; P = 0.028) were independent indicators of locoregional progression.

CONCLUSION

Systematic analysis of PBC distribution in esophageal cancer patients undergoing definitive chemoradiotherapy could help predict long-term locoregional progression hazard after treatment.