An integrated mRNA-lncRNA signature for relapse prediction in laryngeal cancer

Construction of a 10-gene prognostic score model of predicting recurrence for laryngeal cancer

We constructed a prognostic score (PS) model to predict the recurrence risk in patients previously diagnosed with laryngeal cancer (LC). Here the training dataset, consisting of 82 LC samples, was downloaded from The Cancer Genome Atlas (TCGA). The PS model then divided the LC samples into high- and low-risk groups, which predicted well the survival time of LC in three datasets (TCGA dataset: AUC = 0.899; GSE27020: AUC = 0.719; and GSE25727: AUC = 0.662). Therefore, the PS model based on the 10 genes and its nomogram is proposed to help predict the recurrence risk in patients with LC.

A tumor immune microenvironment-related integrated signature can predict the pathological response and prognosis of esophageal squamous cell carcinoma following neoadjuvant chemoradiotherapy: A multicenter study in China

BACKGROUND

Currently, there are insufficient indicators for the reliable assessment of treatment response following neoadjuvant chemoradiotherapy (nCRT) in patients with esophageal squamous cell carcinoma (ESCC). Considering the essential role of protein-coding and non-coding RNAs in gene regulation and cellular processes, we systematically explored the molecular features and clinical significance of mRNA and lncRNA in 249 pretreatment biopsies from four hospitals in three districts with a high incidence of ESCC patients in China.

METHODS

During the discovery phrase, 13 differentially expressed genes were identified and validated between samples with a complete pathological response (pCR) and those with an incomplete pathological response (<pCR). Subsequently, we constructed a predictive mRNA and lncRNA signature (SERPINE1, LINC00592, and PRKAG2-AS1) using Fisher's linear discriminant analysis (FLDA) with stepwise variant-selection, followed by validation of its predictive ability in both internal and external cohorts.

RESULTS

Our signature showed great value in predicting the response to nCRT among ESCC samples and acted as an independent predictive indicator, in addition to demonstrating great potential in estimating patient prognosis. Interestingly, we found that patients with a high signature score had lower PD-L1 and IDO1 expression levels but higher CD8⁺ T cells infiltration, suggesting that PD-L1 and IDO1 are negatively correlated with a high signature score and further associated with pCR and a better prognosis.

CONCLUSION

The present study identified a promising three-gene-based predictive signature that has powerful clinical implications for the identification of pCR and a good prognosis among patients with ESCC. Further immune-related exploration may provide an opportunity for future therapeutic combination.

Prediction of lncRNA-Disease Associations via Closest Node Weight Graphs of the Spatial Neighborhood Based on the Edge Attention Graph Convolutional Network

Accumulated evidence of biological clinical trials has shown that long non-coding RNAs (lncRNAs) are closely related to the occurrence and development of various complex human diseases. Research works on lncRNA–disease relations will benefit to further understand the pathogenesis of human complex diseases at the molecular level, but only a small proportion of lncRNA–disease associations has been confirmed. Considering the high cost of biological experiments, exploring potential lncRNA–disease associations with computational approaches has become very urgent. In this study, a model based on closest node weight graph of the spatial neighborhood (CNWGSN) and edge attention graph convolutional network (EAGCN), LDA-EAGCN, was developed to uncover potential lncRNA–disease associations by integrating disease semantic similarity, lncRNA functional similarity, and known lncRNA–disease associations. Inspired by the great success of the EAGCN method on the chemical molecule property recognition problem, the prediction of lncRNA–disease associations could be regarded as a component recognition problem of lncRNA–disease characteristic graphs. The CNWGSN features of lncRNA–disease associations combined with known lncRNA–disease associations were introduced to train EAGCN, and correlation scores of input data were predicted with EAGCN for judging whether the input lncRNAs would be associated with the input diseases. LDA-EAGCN achieved a reliable AUC value of 0.9853 in the ten-fold cross-over experiments, which was the highest among five state-of-the-art models. Furthermore, case studies of renal cancer, laryngeal carcinoma, and liver cancer were implemented, and most of the top-ranking lncRNA–disease associations have been proven by recently published experimental literature works. It can be seen that LDA-EAGCN is an effective model for predicting potential lncRNA–disease associations. Its source code and experimental data are available at https://github.com/HGDKMF/LDA-EAGCN.

Identification of a Seven-lncRNA-mRNA Signature for Recurrence and Prognostic Prediction in Relapsed Acute Lymphoblastic Leukemia Based on WGCNA and LASSO Analyses

Abnormal expressions of long noncoding RNAs (lncRNAs) and protein-encoding messenger RNAs (mRNAs) are important for the development of childhood acute lymphoblastic leukemia (ALL). This study developed an lncRNA-mRNA integrated classifier for the prediction of recurrence and prognosis in relapsed childhood ALL by using several transcriptome data. Weighted gene coexpression network analysis revealed that green, turquoise, yellow, and brown modules were preserved across the TARGET, GSE60926, GSE28460, and GSE17703 datasets, and they were associated with clinical relapse and death status. A total of 184 genes in these four modules were differentially expressed between recurrence and nonrecurrence samples. Least absolute shrinkage and selection operator analysis showed that seven genes constructed a prognostic signature (including one lncRNA: LINC00652 and six mRNAs: INSL3, NIPAL2, REN, RIMS2, RPRM, and SNAP91). Kaplan-Meier curve analysis observed that patients in the high-risk group had a significantly shorter overall survival than those of the low-risk group. Receiver operating characteristic curve analysis demonstrated that this signature had high accuracy in predicting the 5-year overall survival and recurrence outcomes, respectively. LINC00652 may function by coexpressing with the above prognostic genes (INSL3, SNAP91, and REN) and lipid metabolism-related genes (MIA2, APOA1). Accordingly, this lncRNA-mRNA-based classifier may be clinically useful to predict the recurrence and prognosis for childhood ALL. These genes represent new targets to explain the mechanisms for ALL.

An autophagic gene-based signature to predict the survival of patients with low-grade gliomas

Background

Since autophagy remains an important topic of investigation, the RNA‐sequence profiles of autophagy‐related genes (ARGs) can provide insights into predicting low‐grade gliomas (LGG) prognosis.

Methods

The RNA‐seq profiles of autophagic genes and prognosis data of LGG were integrated from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression model were carried out to identify the differentially expressed prognostic autophagy‐related genes. Then, the autophagic‐gene signature was formed and verified in TCGA test set and external CGGA cohorts. Time‐dependent receiver operating characteristic (ROC) was examined to test the accuracy of this signature feature. A nomogram was conducted to meet the needs of clinicians. Sankey diagrams were performed to visualize the relationship between the multigene signatures and clinic‐pathological features.

Results

Twenty‐four ARGs were finally identified most relevant to LGG prognosis. According to the specific prediction index formula, the patients were classified into low‐risk or high‐risk groups. Prognostic accuracy was proved by time‐dependent ROC analysis, with AUC 0.9, 0.93, and 0.876 at the survival time of 2‐, 3‐, and 5‐year, respectively, which was superior to the AUC of the isocitrate dehydrogenase (IDH) mutation. The result was confirmed while validated in the TCGA test set and external validation CGGA cohort. A nomogram was constructed to meet individual needs. With a visualization approach, Sankey diagrams show the relationship of the histological type, IDH status, and predict index. In TCGA and CGGA cohorts, both low‐risk groups displayed better survival rate in LGG while histological type and IDH status did not show consistency results.

Conclusions

24‐ARGs may play crucial roles in the progression of LGG, and LGG patients were effectively divided into low‐risk and high‐risk groups according to prognostic prediction. Overall, our study will provide novel strategies for clinical applications.

Integrated analysis identifies a long non-coding RNAs-messenger RNAs signature for prediction of prognosis in hepatitis B virus-hepatocellular carcinoma patients

Hepatitis B virus (HBV) infection is a leading cause of hepatocellular carcinoma (HCC), but HBV-HCC related prognosis signature remains rarely investigated. This study was to identify an integrated long non-coding RNAs-messenger RNAs (lncRNA-mRNA) signature for prediction of overall survival (OS) and explore their underlying functions.One RNA-sequencing dataset (training set, n = 95) and one microarray dataset E-TABM-36 (validation set, n = 44) were collected. Least absolute shrinkage and selection operator analysis was performed to identify an lncRNA-mRNA prognosis signature. The OS difference of patients in the high-risk and low-risk risk groups was evaluated by Kaplan-Meier curve. Area under the receiver operating characteristic curve (AUC), Harrell concordance index (C-index) calculation, and multivariate analyses with clinical characteristics were used to determine the prognostic ability. Furthermore, a coexpression network was constructed to interpret the functions.Nine signature genes (3 lncRNAs and 6 mRNAs) were selected to generate the risk score model. Patients belonging to the high-risk group showed a significantly shorter survival than those of the low-risk group. The prediction accuracy of the risk score for 5-year OS was 0.936 and 0.905 for the training set and validation set, respectively. Also, this risk score was independent of various clinical variables for the prognosis prediction. Incorporation of the risk score remarkably increased the predictive power of the routine clinical prognostic factors (vascular invasion status, tumor recurrence status) (AUC = 0.942 vs 0.628; C-index = 0.7997 vs 0.6908). Furthermore, LncRNA insulin-like growth factor 2 antisense RNA (IGF2-AS) and long intergenic non-protein coding RNA 342 (LINC00342) were predicted to exert tumor suppression effects by regulating homeobox D1 (HOXD1) and secreted frizzled related protein 5 (SFRP5), respectively; while lncRNA rhophilin Rho GTPase binding protein 1 antisense RNA 1 (RHPN1-AS1) may possess carcinogenic potential by promoting the transcription of chromobox 2 (CBX2), cell division cycle 20 (CDC20), matrix metallopeptidase 12 (MMP12), stratifin (SFN), tripartite motif containing 16 (TRIM16), and uroplakin 3A (UPK3A). These mRNAs may be associated with cell proliferation or apoptosis related pathways.This study may provide a novel, effective prognostic biomarker, and some therapeutic targets for HBV-HCC patients.

A 14-Methylation-Driven Differentially Expressed RNA as a Signature for Overall Survival Prediction in Patients with Uterine Corpus Endometrial Carcinoma

DNA methylation has been implicated as an important mechanism for the development of uterine corpus endometrial carcinoma (UCEC), indicating that methylation-driven genes may be potential biomarkers for survival prediction. In this study, we aimed to identify a new prognostic methylation signature for UCEC based on differentially expressed genes (DEGs) and long noncoding RNAs (lncRNAs) (DELs). Sample-matched RNA-sequencing and methylation-array data were downloaded from The Cancer Genome Atlas database, by analysis of which a total of 269 DEGs and 4 DELs were identified to be methylation driven. Least absolute shrinkage and selection operator analysis screened that 14 methylation-driven genes were significantly associated with overall survival (OS) and thus were used as a signature to establish a prognostic risk model. Based on the median threshold, the patients were divided into the low-risk and the high-risk groups, which showed significantly different survival periods under the Kaplan-Meier curve. The area under receiver operating characteristic curve (AUC) was 0.934, 0.919, and 0.952 for the training, validation, and entire cohort, respectively. Stratification analysis showed that the established risk model may add prognostic values to conventional clinical factors (age, neoplasm histologic grade, and clinical stage). A nomogram was constructed based on the risk model and clinical parameters, with the AUC of 0.978 and c-index of 0.8079. Database for Annotation, Visualization, and Integrated Discovery (DAVID) function enrichment and Human Protein Atlas (HPA) protein expression validation showed 5 of these 14 genes may be especially important for UCEC (hypermethylated lowly expressed: CCBE1, FOXL2, PHLDB2, and DTNA; hypomethylated highly expressed: CCNE1). Comparison with breast cancer in the methylation level indicated ABCA12, CCNE1, and CLRN3 may be specific methylation-driven genes for UCEC. LncRNA HCG11 may function by coexpressing with DTNA. In conclusion, this 14-DNA methylation signature combined with clinical factors may a potentially effective biomarker in predicting OS for UCEC patients.