Construction of a prognosis‑associated long noncoding RNA‑mRNA network for multiple myeloma based on microarray and bioinformatics analysis

The Landscape of lncRNAs in Multiple Myeloma: Implications in the "Hallmarks of Cancer", Clinical Perspectives and Therapeutic Opportunities

Simple Summary

Multiple myeloma (MM) is an aggressive hematological neoplasia caused by the uncontrolled proliferation of aberrant plasmacells. Neoplastic transformation and progression are driven by a number of biological processes, called ‘hallmarks of cancer’, which are regulated by different molecules, including long non-coding RNAs. A deeper understanding of the mechanisms that regulate MM development and progression will help to improve patients stratification and management, and promote the identification of new therapeutic targets.

Abstract

Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nucleotides that are not translated into proteins. Nowadays, lncRNAs are gaining importance as key regulators of gene expression and, consequently, of several biological functions in physiological and pathological conditions, including cancer. Here, we point out the role of lncRNAs in the pathogenesis of multiple myeloma (MM). We focus on their ability to regulate the biological processes identified as “hallmarks of cancer” that enable malignant cell transformation, early tumor onset and progression. The aberrant expression of lncRNAs in MM suggests their potential use as clinical biomarkers for diagnosis, patient stratification, and clinical management. Moreover, they represent ideal candidates for therapeutic targeting.

Identification of Immune-Related lncRNA Pairs and Construction and Validation of a New Prognostic Signature of Colon Cancer

BACKGROUND

More and more evidence has shown that immune-related long noncoding ribonucleic acid (irlncRNAs) is a potential prognostic factor for colon cancer. The relevant gene pair pattern can improve the sensitivity of the prognostic model. Therefore, our present study aimed to identify irlncRNA Pairs and construct and validate a new prognostic signature in colon cancer.

METHODS

We downloaded the expression matrix of mRNA and lncRNA of patients with colon cancer and their clinical information from the public TCGA database. We obtained immune genes from the ImmPort database. Coexpression analysis was performed to identify irlncRNAs. We built an irlncRNA pair matrix by comparing the expression levels of each lncRNA pair in a cycle. Univariate Cox regression analysis, LASSO penalized regression analysis, and multivariate Cox regression analysis were performed to determine the final variables to construct the prognostic risk score model (a new signature). We draw the receiver operating characteristic (ROC) curves of the signature and clinical characteristics and determine the optimal cutoff value by the optimal Akaike Information Criterion (AIC) value. Based on the optimal cutoff value of the ROC curve of the signature, colon cancer patients were divided into the high- and low-risk groups. Then, the signature was evaluated by clinicopathological features, tumor-infiltrating immune cells, checkpoint-related biomarkers, targeted therapy, and chemotherapy.

RESULTS

We identified 8 lncRNA pairs including AC103740.1|LEF1-AS1, LINC02391|AC053503.5, WWC2-AS2|AL355916.2, AC104090.1|NEURL1-AS1, AC099524.1|AL161908.1, AC074011.1|AL078601.2, AL355916.2|LINC01723, and AP003392.4|LINC00598 from 71 differently expressed irlncRNAs. We constructed a prognostic risk score model (a new signature) using these optimal eight irlncRNA pairs. ROC curve analysis revealed that the highest AUC value of the signature was 0.776 at 1 year, with the optimal cutoff value of 1.283. Our present study also showed that the constructed signature could accurately identify adverse survival outcomes, prognostic clinicopathological features, and specify tumor invasion status. The expression of immune checkpoint-related genes and chemical drug sensitivity were related to different risk groups.

CONCLUSION

In our present study, we constructed a new irlncRNA signature of colon cancer based on the irlncRNA pairs instead of the special expression level of lncRNA. We found this signature had not only good prognostic value but also certain clinical value, which might provide a new insight into the treatment and prognosis of colon cancer.

An interactive nomogram based on clinical and molecular signatures to predict prognosis in multiple myeloma patients

Although novel drugs and treatments have been developed and improved, multiple myeloma (MM) is still recurrent and difficult to cure. In the present study, the magenta module containing 400 hub genes was determined from the training dataset of GSE24080 through weighted gene co-expression network analysis (WGCNA). Then, using the least absolute shrinkage and selection operator (Lasso) analysis, a fifteen-gene signature was firstly selected and the predictive performance for overall survival (OS) was favorable, which was identified by Receiver Operating Characteristic (ROC) curves. The risk score model was constructed based on survival-associated fifteen genes from the Lasso model, which classified MM patients into high-risk and low-risk groups. Areas under the curve (AUC) of ROC curve and log-rank test showed that the high-risk group was correlated to the dismal survival outcome of MM patients, which was also identified in testing dataset of GSE9782. The calibration plot, the AUC value of the ROC curve and Concordance-index showed that the interactive nomogram with risk score could favorably predict the probability of multi-year OS of MM patients. Therefore, it may help clinicians make a precise therapeutic decision based on the easy-to-use tool of the nomogram.

Integrated Analysis of a Risk Score System Predicting Prognosis and a ceRNA Network for Differentially Expressed lncRNAs in Multiple Myeloma

Long non-coding RNAs (lncRNAs) are non-protein-coding RNAs longer than 200 nucleotides. Accumulating evidence demonstrates that lncRNA is a potential biomarker for cancer diagnosis and prognosis. However, there are no prognostic biomarkers and lncRNA models for multiple myeloma (MM). Hence, it is necessary to screen novel lncRNA that can potentially participate in the initiation and progression of MM and consequently construct a risk score system for the disease. Raw microarray datasets were obtained from the Gene Expression Omnibus website. Weighted gene co-expression network analysis and principal component analysis identified 12 lncRNAs of interest. Then, univariate, least absolute shrinkage and selection operator Cox regression and multivariate Cox hazard regression analysis identified two lncRNAs (LINC00996 and LINC00525) that were formulated to construct a risk score system to predict survival. Receiver operating characteristic analysis certificated the superior performance in predicting 3-year overall survival (area under the curve = 0.829). The similar prognostic values of the two-lncRNA signature were also observed in the tested The Cancer Genome Atlas dataset. Furthermore, two other lncRNAs (LINC00324 and LINC01128) were differentially expressed between CD138+ plasma cells from normal donors and MM patients and were verified to be associated with cancer stage in the Gene Expression Omnibus dataset. A lncRNA-mediated competing endogenous RNA network, including 2 lncRNAs, 12 mitochondrial RNAs, and 103 target messenger RNAs, was constructed. In conclusion, we developed a two-lncRNA expression signature to predict the prognosis of MM and constructed a key lncRNA-based competing endogenous RNA network in MM. These lncRNAs were associated with survival and are probably involved in the occurrence and progression of MM.