One-lincRNA and five-mRNA based signature for prognosis of multiple myeloma patients undergoing proteasome inhibitors therapy

Liu, Yunhe; Yang, Ning; Peng, Xueqing; Liu, Gang; Zhong, Hua; Liu, Lei

doi:10.1016/j.biopha.2019.109254

Cited by 9 publications

(5 citation statements)

References 52 publications

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“…rfSRC resulted in 58 GPM genes, 52 BPM genes and 129 combined genes ( Supplementary S1 Table E). This feature selection method has been extensively used in similar survival-based studies in the past [ 47 , 50 , 51 , 52 ]. First, MLR models were built and predicted OS was used to stratify high and low risk patients, similar to the pathway-specific analysis before.…”

Section: Resultsmentioning

confidence: 99%

Risk prediction in cutaneous melanoma patients from their clinico-pathological features: superiority of clinical data over gene expression data

Arora

Kaur

Lathwal

et al. 2020

Heliyon

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Risk assessment in cutaneous melanoma (CM) patients is one of the major challenges in the effective treatment of CM patients. Traditionally, clinico-pathological features such as Breslow thickness, American Joint Committee on Cancer (AJCC) tumor staging, etc. are utilized for this purpose. However, due to advancements in technology, most of the upcoming risk prediction methods are gene-expression profile (GEP) based. In this study, we have tried to develop new GEP and clinico-pathological features-based biomarkers and assessed their prognostic strength in contrast to existing prognostic methods. We developed risk prediction models using the expression of the genes associated with different cancer-related pathways and got a maximum hazard ratio (HR) of 2.52 with p-value~10 À8 for the apoptotic pathway. Another model, based on combination of apoptotic and notch pathway genes boosted the HR to 2.57. Next, we developed models based on individual clinical features and got a maximum HR of 2.45 with p-value~10 À6 for Breslow thickness. We also developed models using the best features of clinical as well as gene-expression data and obtained a maximum HR of 3.19 with p-value~10 À9. Finally, we developed a new ensemble method using clinical variables only and got a maximum HR of 6.40 with p-valuẽ 10 À15. Based on this method, a web-based service and an android application named 'CMcrpred' is available at (https://webs.iiitd.edu.in/raghava/cmcrpred/) and Google Play Store respectively to facilitate scientific community. This study reveals that our new ensemble method based on only clinico-pathological features overperforms methods based on GEP based profiles as well as currently used AJCC staging. It also highlights the need to explore the full potential of clinical variables for prognostication of cancer patients.

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Section: Resultsmentioning

confidence: 99%

Risk prediction in cutaneous melanoma patients from their clinico-pathological features: superiority of clinical data over gene expression data

Arora

Kaur

Lathwal

et al. 2020

Heliyon

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“…A 16-gene (ATIC, BNIP3L, CALCOCO2, DNAJB1, DNAJB9, EIF4EBP1, EVA1A, FKBP1B, FOXO1, FOXO3, GABARAP, HIF1A, NCKAP1, PRKAR1A, TM9SF1, and SUPT20H) prognostic model related to autophagy was also established by Lasso (Zhu et al, 2019). Moreover, a six-gene risk score model (ZNF486, EPHA5, RP11.326C3.15, DUSP6, DUSP10, and TRIAP1) for the prognostic prediction of PItreated myeloma patients was developed by the random survival forest variable hunting (RSF-VH) algorithm (Liu et al, 2019). Interestingly, Chen Sun et al developed the current state-of-theart prognostic model for MM via a complete hazard-ranking algorithm called GuanRank with Gaussian process regression (GPR), which seemed to be more accurate than Cox model and random survival forests, despite several limitations (Sun et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…The International Staging System (ISS) and the revised International Staging System (R-ISS) were established to create a unified prognostic index, but incorporating other important prognostic factors into the current risk stratification systems is challenging (Ooi et al, 2016). Recently, prognostic models based on gene expression signature make it possible to predict risk stratification in newly diagnosed MM patients, and more importantly, it is even better in predicting OS than R-ISS (Liu et al, 2019;.…”

Section: Introductionmentioning

confidence: 99%

Super-Enhancer Associated Five-Gene Risk Score Model Predicts Overall Survival in Multiple Myeloma Patients

et al. 2020

Front. Cell Dev. Biol.

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Multiple myeloma (MM) is a malignant plasma cell tumor with high heterogeneity, characterized by anemia, hypercalcemia, renal failure, and lytic bone lesions. Although various powerful prognostic factors and models have been exploited, the development of more accurate prognosis and treatment for MM patients is still facing many challenges. Given the essential roles of super-enhancer (SE) associated genes in the tumorigenesis of MM, we tried to initially screen and identify the significant prognostic factors from SE associated genes in MM by the least absolute shrinkage and selection operator (Lasso) penalized Cox regression, univariate and multivariate Cox regression analysis using GSE24080 and GSE9782 datasets. Risk score model of five genes including CSGALNACT1, FAM53B, TAPBPL, REPIN1, and DDX11, was further constructed and the Kaplan-Meier (K-M) curves showed that the low-risk group seems to have better clinical outcome of survival compared to the high-risk group. Time-dependent receiver operating characteristic (ROC) curves presented the favorable performance of the model. An interactive nomogram consisting of the five-gene risk group and eleven clinical traits was established and identified by calibration curves. Therefore, the risk score model of SE associated five genes developed here could be used to predict the prognosis of MM patients, which may assist the clinical treatment of MM patients in the future.

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“…Gene expression profiling (GEP) is a useful tool to estimate the aggressiveness of MM and will help to make individualized therapeutic decisions (3). Many different gene expression-based prognostic signatures have been reported for MM in the last decade (4)(5)(6)(7)(8). In 2011, the Multiple Myeloma Research Foundation (MMRF) CoMMpass Study was initiated, which gathered information on close to 1200 patients aged 27 to 93 years and followed up on a biannual basis for at least 8 years (9).…”

Section: Introductionmentioning

confidence: 99%

Developing mRNA signatures as a novel prognostic biomarker predicting high risk multiple myeloma

Wang

Guo²,

Lv³

et al. 2023

Front. Oncol.

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BackgroundMultiple myeloma (MM) remains an essentially incurable disease. This study aimed to establish a predictive model for estimating prognosis in newly diagnosed MM based on gene expression profiles.MethodsRNA-seq data were downloaded from the Multiple Myeloma Research Foundation (MMRF) CoMMpass Study and the Genotype-Tissue Expression (GTEx) databases. Weighted gene coexpression network analysis (WGCNA) and protein-protein interaction network analysis were performed to identify hub genes. Enrichment analysis was also conducted. Patients were randomly split into training (70%) and validation (30%) datasets to build a prognostic scoring model based on the least absolute shrinkage and selection operator (LASSO). CIBERSORT was applied to estimate the proportion of 22 immune cells in the microenvironment. Drug sensitivity was analyzed using the OncoPredict algorithm.ResultsA total of 860 newly diagnosed MM samples and 444 normal counterparts were screened as the datasets. WGCNA was applied to analyze the RNA-seq data of 1589 intersecting genes between differentially expressed genes and prognostic genes. The blue module in the PPI networks was analyzed with Cytoscape, and 10 hub genes were identified using the MCODE plug-in. A three-gene (TTK, GINS1, and NCAPG) prognostic model was constructed. This risk model showed remarkable prognostic value. CIBERSORT assessment revealed the risk model to be correlated with activated memory CD4 T cells, M0 macrophages, M1 macrophages, eosinophils, activated dendritic cells, and activated mast cells. Furthermore, based on OncoPredict, high-risk MM patients were sensitive to eight drugs.ConclusionsWe identified and constructed a three-gene-based prognostic model, which may provide new and in-depth insights into the treatment of MM patients.

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One-lincRNA and five-mRNA based signature for prognosis of multiple myeloma patients undergoing proteasome inhibitors therapy

Cited by 9 publications

References 52 publications

Risk prediction in cutaneous melanoma patients from their clinico-pathological features: superiority of clinical data over gene expression data

Risk prediction in cutaneous melanoma patients from their clinico-pathological features: superiority of clinical data over gene expression data

Super-Enhancer Associated Five-Gene Risk Score Model Predicts Overall Survival in Multiple Myeloma Patients

Developing mRNA signatures as a novel prognostic biomarker predicting high risk multiple myeloma

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