Construction Of LncRNA-mRNA Prognosis Network Model Of Multiple Myeloma And New Positioning Of Potential Personalized Drugs

Backgrounds and aims.Multiple Myeloma(MM),a malignant plasma cell disease that occurs from B lymphocytes,is the second most common hematological tumor in the world.The data shows that the number of patients with MM in the world is about750,000,and the number of new cases is about 114,000,which is one of the rare malignant tumors among all cancers.The incidence of MM in China has surpassed that of acute leukemia,which is similar to the global incidence trend.MM is the second most common hematological malignancy after non-Hodgkin's lymphoma,and under the influence of many factors,the incidence rate of MM has been increased,and the age of onset is expected to be earlier in recent years.MM is characterized by abnormal amplification of malignant plasma cells in the bone marrow,resulting in excessive production of monoclonal immunoglobulins in blood and urine,impaired renal function,anemia,osteolytic bone lesions,and repeated infections in patients.In addition,MM patients may have immunodeficiency that seriously affects the patient's quality of life and longevity.In the past ten years,the research on the molecular mechanism of MM cells has made rapid progress,and corresponding new treatment strategies have been developed.Abnormal expression of several lnc RNAs in MM,their clinical significance,biological functions and potential molecular mechanisms have also been reported.However,although there are numerous members of the lnc RNAs family,only a few have been well-documented to be associated with MM prognosis.Many undiscovered lnc RNAs may also be associated with MM progression and patient outcomes.In addition,the predictive ability of a single indicator is limited,and a prognostic model consisting of multiple indicators is needed clinically for a comprehensive clinical evaluation of tumor prognosis.Prognostic models combining several prognostic indicators have been used in a variety of other tumors,but MM prognostic models containing lnc RNA and m RNA and networks between them have not been reported.Autophagy has been widely known as an intracellular "scavenger".Autophagy is a process of phagocytizing its own cytoplasmic proteins or organelles and coating them into vesicles,and fuses with lysosomes to form autophagosomes,which degrade the contents of their encapsulation.Autophagy is a process inherent in cells that degrades non-essential or misfolded cellular components.The word autophagy comes from Greek,auto=self,phagy=phagein= eat,which means eating yourself.However,autophagy seems to be unclear in the field of tumors.Sometimes it can initiate type II programmed death,which can play a vital role in killing tumor cells,and sometimes improve the tolerance of tumor cells to stress and improve the survival rate of tumor cells.Therefore,autophagy is a double-edged sword,which has a dual role in promoting and inhibiting tumors.This feature also provides two distinct ideas for tumor prevention: inhibition of autophagy-improving anti-cancer treatment,or activation of autophagy-inducing autophagic death in tumor cells.Autophagy plays an important role in the development of plasma cells and the pathological process of MM.Autophagy is generally thought to be involved in the pro-survival mechanisms of MM cells and interacts with the ubiquitin-proteasome system to maintain the homeostasis of MM cells through degraded and misfolded proteins for energy recovery.Therefore,inhibition of autophagy can effectively induce MM cell death and can act synergistically with proteasome inhibitors.However,excessive activation of autophagy may also result in excessive degradation of organelles that induce autophagic cell death.Thus,activation of autophagic cell death may also represent a promising approach to the treatment of MM.The process of autophagy is mediated by autophagy-related genes(ARGs).Previously,researchers have identified 234 ARGs to facilitate cancer research.Some studies using these ARGs have shown that these ARGs have significant clinical implications in various types of cancer,including glioma,liver cancer and thyroid cancer.However,the clinical significance of ARGs in MM,especially the prognostic value has not been reported.The mechanism of action of a drug in a disease may depend on molecular events and epigenetic processes of the disease.Currently,disease-related high-throughput data is widely stored in various public databases.Data from microarray and RNA-sequencing(RNA-seq)has been compiled into the Gene Expression Omnibus(GEO),Array Express or Sequence Read Archive(SRA) databases.Previous MM-related studies have not used prognostic-related genes for MM potential drug research,leaving some shortcomings.Hence,the current study was composed of three parts: The first part first screened the gene chip with MM prognostic data,and statistically analyzed the lnc RNA and m RNA closely related to prognosis.A network of the relationship between lnc RNA and m RNA was then performed and partial lnc RNA and m RNA were selected to construct a MM prognostic model.The MM prognosis model is expected to provide a new direction for the clinical application of MM.The second part continued to use computational biology methods to extract prognostic ARGs and construct another prognostic model,further revealing the relationship between autophagy and MM prognosis,and also provide a new perspective for clinical prognosis prediction method of MM.In the third part,we first extended the prognostic value of mRNA expression profiles,comprehensively analyzed the gene ontology(GO)pathway and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway of these gene populations,and used Connecting Mapping(CMAP)to predict the drugs related to MM prognosis.The drug and the targets were molecularly docked to verify the relationship between them,and the bioinformation basis is proposed for the“new use of old drugs”.Materials and methods:1 The lnc RNA and m RNA data from 558 MM patients in Genome Expression Omnibus dataset(SE24080)were acquired.2 The prognostic values were calculated using the survival package of the R software.Multivariate Cox analysis was used on the top 20 most significant prognosis-related m RNAs and lnc RNAs to develop prognostic signatures.The performances of these prognostic signatures were tested using the survival ROC package in the R software,which allows for time dependent receiver-operator characteristic(ROC)curve estimation.Weighted correlation network analysis(WGCNA)was conducted to explore the relationships between lnc RNAs and m RNAs and an lnc RNA-m RNA network was constructed using the Cytoscape software.3 Altogether,234 ARGs expression profiling was obtained from the gene expression omnibus(GEO)database GSE24080 containing 558 samples of MM patients with 54,675 probes.Those ARGs significantly associated with the EFS of MM were identified through univariate COX analysis.We processed the survival analysis via “survival” package of R software.To reveal the molecular functional characteristics besides autophagy of these prognostic ARGs,we conducted functional enrichment analysis,including GO and KEGG,by using R package cluster Profiler.To develop a prognostic signature based on independent factor with ARGs,multivariate COX regression was performed.Then,the prognostic signature for each patient was derived by multiplying the expression level of a prognosis-related ARGs by its corresponding coefficient.To explore the influenced pathways with respect to high-risk or low-risk group,gene set enrichment analysis(GSEA)was performed.By using GSEA,we tested whether the activated/repressed gene signatures were enriched in high-risk versus low-risk cases.4 GO and KEGG were determined with all the prognostic genes in MM.Protective and risky genes were uploaded to CMAP to identify the potentially unknown effects of existing drugs.An example was selected to be docked on the known molecules.Results:1 A univariate Cox regression analysis identified 39 lnc RNAs and 1445 m RNAs that were significantly related to the EFS of MM patients.2 The top 20 most significant survival-associated lnc RNAs and m RNAs were chosen as candidates for analyzing independent MM prognostic factors.Both signatures could be used to separate patients into two groups with distinct outcomes.The AUCs of ROC were 0.739 for the lnc RNA signature and 0.732 for the m RNA signature.In the lnc RNAs-m RNAs network,a total of 143 m RNAs were positively or negatively related to 23 prognosis-related lnc RNAs.NCRNA00201,LOC115110 and RP5-968J1.1 were the most dominant drivers.3 Univariate Cox regression analysis identified 55 ARGs that were significantly associated with event-free survival of MM.Furthermore,a prognostic signature with 16 survival-related ARGs revealed by multivariate Cox regression analysis was constructed,including ATIC,BNIP3 L,CALCOCO2,DNAJB1,DNAJB9,EIF4EBP1,EVA1 A,FKBP1B,FOXO1,FOXO3,GABARAP,HIF1 A,NCKAP1,PRKAR1 A,SUPT20H,and TM9SF1.Using this prognostic signature,MM patients could be separated into high-and low-risk groups with distinct clinical outcomes.AUC values for the ROC curve were 0.740,0.741,and 0.712 for 3 years,5 years and 10 years,respectively.4 A total of 1,445 genes significantly correlated with the EFS of MM patients were identified and included 676 protective and 769 risky indicators.KEGG pathway analysis revealed that these prognosis-associated genes were enriched in the “cell cycle,” “DNA replication,” and “P53 signaling pathway”.The top three most significant potential molecules were vorinostat,trifluoperazine,and thioridazine.CDK1 ranked as the core in the class of prognosis-related genes in MM based on PPI network analysis.With Sybyl-X 2.0,the majority of the top10 molecules mentioned above displayed high binding forces with CDK1.Among these molecules,trichostatin A had the greatest ability in combining with CDK1.Conclusions:1 This study constructs a model that is capable of predicting MM prognosis and formed a network with the corresponding prognosis-related m RNAs,which offer a new perspective for the clinical diagnosis and treatment of MM and suggest a new direction for interpreting the mechanisms of MM development.2 ARGs may play vital parts in the progression of MM and the ARGs-based prognostic model will provide newer ideas for clinical application of MM.3 Genes that mainly accumulate in the cell cycle pathway play an essential role in the prognosis of MM,and these prognosis-related genes also have great value in drug development.