scholarly journals Predicting Overall Survival Time in Glioblastoma Patients Using Gradient Boosting Machines Algorithm and Recursive Feature Elimination Technique

Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 4976
Author(s):  
Golestan Karami ◽  
Marco Giuseppe Orlando ◽  
Andrea Delli Pizzi ◽  
Massimo Caulo ◽  
Cosimo Del Gratta
Keyword(s):  
Overall Survival ◽  
Survival Time ◽  
Cox Regression ◽  
Feature Selection Method ◽  
Rate Of Change ◽  
Supervised Machine Learning ◽  
Recursive Feature Elimination ◽  
Gradient Boosting ◽  
Survival Prediction ◽  
Classifier Performance

Despite advances in tumor treatment, the inconsistent response is a major challenge among glioblastoma multiform (GBM) that lead to different survival time. Our aim was to integrate multimodal MRI with non-supervised and supervised machine learning methods to predict GBM patients’ survival time. To this end, we identified different compartments of the tumor and extracted their features. Next, we applied Random Forest-Recursive Feature Elimination (RF-RFE) to identify the most relevant features to feed into a GBoost machine. This study included 29 GBM patients with known survival time. RF-RFE GBoost model was evaluated to assess the survival prediction performance using optimal features. Furthermore, overall survival (OS) was analyzed using univariate and multivariate Cox regression analyses, to evaluate the effect of ROIs and their features on survival. The results showed that a RF-RFE Gboost machine was able to predict survival time with 75% accuracy. The results also revealed that the rCBV in the low perfusion area was significantly different between groups and had the greatest effect size in terms of the rate of change of the response variable (survival time). In conclusion, not only integration of multi-modality MRI but also feature selection method can enhance the classifier performance.

scholarly journals A Nomogram Based on a Three-Gene Signature Derived from AATF Coexpressed Genes Predicts Overall Survival of Hepatocellular Carcinoma Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Jun Liu ◽  
Jianjun Lu ◽  
Zhanzhong Ma ◽  
Wenli Li
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Clinical Practice ◽  
Correlation Coefficient ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Gene Signature ◽  
Cancer Genome ◽  
Survival Prediction

Background. Hepatocellular carcinoma (HCC) is a common cancer with an extremely high mortality rate. Therefore, there is an urgent need in screening key biomarkers of HCC to predict the prognosis and develop more individual treatments. Recently, AATF is reported to be an important factor contributing to HCC. Methods. We aimed to establish a gene signature to predict overall survival of HCC patients. Firstly, we examined the expression level of AATF in the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA), and the International Union of Cancer Genome (ICGC) databases. Genes coexpressed with AATF were identified in the TCGA dataset by the Poisson correlation coefficient and used to establish a gene signature for survival prediction. The prognostic significance of this gene signature was then validated in the ICGC dataset and used to build a combined prognostic model for clinical practice. Results. Gene expression data and clinical information of 2521 HCC patients were downloaded from three public databases. AATF expression in HCC tissue was higher than that in matched normal liver tissues. 644 genes coexpressed with AATF were identified by the Poisson correlation coefficient and used to establish a three-gene signature (KIF20A, UCK2, and SLC41A3) by the univariate and multivariate least absolute shrinkage and selection operator Cox regression analyses. This three-gene signature was then used to build a combined nomogram for clinical practice. Conclusion. This integrated nomogram based on the three-gene signature can predict overall survival for HCC patients well. The three-gene signature may be a potential therapeutic target in HCC.

Correlation of neutrophil lymphocyte ratio, platelet lymphocyte ratio and rate of change of CA 19.9 in predicting outcome for metastatic pancreatic cancer.

2018 ◽  
Vol 36 (4_suppl) ◽  
pp. 326-326
Author(s):  
Andrew Peter Dean ◽  
Dom Higgs ◽  
Adarsh Das ◽  
Madeline Rogers-Seeley ◽  
Sean Fennessy ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Cox Regression ◽  
Univariate Analysis ◽  
Rate Of Change ◽  
Cox Proportional Hazards Model ◽  
Metastatic Pancreatic Cancer ◽  
Lymphocyte Ratio ◽  
Daily Rate ◽  
Ca 19.9

326 Background: CA19.9, NLR and PLR have all been proposed as prognostic in pancreatic cancer. We analysed correlation between NLR, PLR and rate of change of CA19.9. Methods: A total of 63 metastatic pancreatic cancer patients were identified from our database and evaluated retrospectively for blood count, NLR, PLR and serial CA19.9 levels during treatment. Daily Rate of Change of CA19.9 levels were calculated for the first 90 days (DRC90) of the patient’s treatment. Kaplan-Meir curves, univariate and multivariate Cox-regression analyses were calculated to assess the effects of these 3 markers on overall survival. Results: In a univariate analysis, PLR > 240, NLR > 5 and DRC90 > 0.4% were all significantly associated with deceased overall survival. The Cox proportional hazards model showed that NLR < 5 (HR 0.475, 95% CI 0.259 to 0.873, P = 0.017), PLR < 240 (HR 0.444, 95% CI 0.229 to 0.861, P = 0.016), and a DRC90 < 0.4% (HR 0.294, 95% CI 0.102 to 0.851, P = 0.024) were independent predictors of good prognosis (22.6 months vs. 9.6 months, 22.3 months vs 12.4 months and 23.9 months vs. 9.3 months respectively). In multivariate analysis, only a DRC90 < 0.4% was independently associated with a longer survival (HR 0.239, 95% CI 0.076 to 0.752, P = 0.014). The formula (F) {PLR + (NLRxNLR) + (DRC90 x 100)} was predictive for survival, as patients with F > 190 (HR 3.295, 95% CI 1.232 to 8.807, P = 0.017), having a significantly lower survival rate than patients with F < 190 (25.1 months vs. 10.6 months, log-rank P = 0.009). Conclusions: These findings indicate the prognostic utility of the rate of CA 19.9 decline - measured as a standardised daily percentage change in value over 90 days. Our data validates daily rate of change of CA 19.9 over 90 days as an independent variable that correlates with prognosis, independent of PLR and NLR. We also identified a novel formula - PLR + (NLRxNLR) + (DRC90 x 100) - as being predictive for survival. We would like to increase the sample size to further validate our initial findings and investigate possible relationships in combining these variables for better prognostication in metastatic pancreatic cancer.

scholarly journals Integrated Analysis of lncRNA-Associated ceRNA Network Identifies Two lncRNA Signatures as a Prognostic Biomarker in Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Shuyan Zhang ◽  
Shanshan Li ◽  
Jian-Lin Guo ◽  
Ningyi Li ◽  
Cai-Ning Zhang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Cox Proportional Hazards ◽  
Integrated Analysis ◽  
Survival Prediction ◽  
Survival Prognosis ◽  
Cox Regression Analysis ◽  
Cerna Network

Background. Gastric cancer (GC) is a malignant tumour that originates in the gastric mucosal epithelium and is associated with high mortality rates worldwide. Long noncoding RNAs (lncRNAs) have been identified to play an important role in the development of various tumours, including GC. Yet, lncRNA biomarkers in a competing endogenous RNA network (ceRNA network) that are used to predict survival prognosis remain lacking. The aim of this study was to construct a ceRNA network and identify the lncRNA signature as prognostic factors for survival prediction. Methods. The lncRNAs with overall survival significance were used to construct the ceRNA network. Function enrichment, protein-protein interaction, and cluster analysis were performed for dysregulated mRNAs. Multivariate Cox proportional hazards regression was performed to screen the potential prognostic lncRNAs. RT-qPCR was used to measure the relative expression levels of lncRNAs in cell lines. CCK8 assay was used to assess the proliferation of GC cells transfected with sh-lncRNAs. Results. Differentially expressed genes were identified including 585 lncRNAs, 144 miRNAs, and 2794 mRNAs. The ceRNA network was constructed using 35 DElncRNAs associated with overall survival of GC patients. Functional analysis revealed that these dysregulated mRNAs were enriched in cancer-related pathways, including TGF-beta, Rap 1, calcium, and the cGMP-PKG signalling pathway. A multivariate Cox regression analysis and cumulative risk score suggested that two of those lncRNAs (LINC01644 and LINC01697) had significant prognostic value. Furthermore, the results indicate that LINC01644 and LINC01697 were upregulated in GC cells. Knockdown of LINC01644 or LINC01697 suppressed the proliferation of GC cells. Conclusions. The authors identified 2-lncRNA signature in ceRNA regulatory network as prognostic biomarkers for the prediction of GC patient survival and revealed that silencing LINC01644 or LINC01697 inhibited the proliferation of GC cells.

scholarly journals A non-lab nomogram of survival prediction in home hospice care patients with gastrointestinal cancer

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Muqing Wang ◽  
Xubin Jing ◽  
Weihua Cao ◽  
Yicheng Zeng ◽  
Chaofen Wu ◽  
...  
Keyword(s):  
Survival Time ◽  
Gastrointestinal Cancer ◽  
Hospice Care ◽  
Cox Regression ◽  
Karnofsky Performance Status ◽  
Training Dataset ◽  
Survival Prediction ◽  
Testing Dataset ◽  
Home Based ◽  
Home Hospice Care

Abstract Background Patients suffering from gastrointestinal cancer comprise a large group receiving home hospice care in China, however, little is known about the prediction of their survival time. This study aimed to develop a gastrointestinal cancer-specific non-lab nomogram predicting survival time in home-based hospice. Methods We retrospectively studied the patients with gastrointestinal cancer from a home-based hospice between 2008 and 2018. General baseline characteristics, disease-related characteristics, and related assessment scale scores were collected from the case records. The data were randomly split into a training set (75%) for developing a predictive nomogram and a testing set (25%) for validation. A non-lab nomogram predicting the 30-day and 60-day survival probability was created using the least absolute shrinkage and selection operator (LASSO) Cox regression. We evaluated the performance of our predictive model by means of the area under receiver operating characteristic curve (AUC) and calibration curve. Results A total of 1618 patients were included and divided into two sets: 1214 patients (110 censored) as training dataset and 404 patients (33 censored) as testing dataset. The median survival time for overall included patients was 35 days (IQR, 17–66). The 5 most significant prognostic variables were identified to construct the nomogram among all 28 initial variables, including Karnofsky Performance Status (KPS), abdominal distention, edema, quality of life (QOL), and duration of pain. In training dataset validation, the AUC at 30 days and 60 days were 0.723 (95% CI, 0.694–0.753) and 0.733 (95% CI, 0.702–0.763), respectively. Similarly, the AUC value was 0.724 (0.673–0.774) at 30 days and 0.725 (0.672–0.778) at 60 days in the testing dataset validation. Further, the calibration curves revealed good agreement between the nomogram predictions and actual observations in both the training and testing dataset. Conclusion This non-lab nomogram may be a useful clinical tool. It needs prospective multicenter validation as well as testing with Chinese clinicians in charge of hospice patients with gastrointestinal cancer to assess acceptability and usability.

scholarly journals Tumor microenvironment related novel signature predict lung adenocarcinoma survival

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10628
Author(s):  
Juan Chen ◽  
Rui Zhou
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Prognostic Model ◽  
Cox Regression ◽  
Survival Prediction ◽  
Clinical Parameters ◽  
Ppi Network ◽  
Cox Regression Analysis

Background Lung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer‐related mortality worldwide. Growing evidence has suggested that tumor microenvironment (TME) plays a pivotal role in tumorigenesis and progression. Hence, we investigate the correlation of TME related genes with LUAD prognosis. Method The information of LUAD gene expression data was obtained from The Cancer Genome Atlas (TCGA). According to their immune/stromal scores calculated by the ESTIMATE algorithm, differentially expressed genes (DEGs) were identified. Then, we performed univariate Cox regression analysis on DEGs to obtain genes that are apparently bound up with LUAD survival (SurGenes). Functional annotation and protein-protein interaction (PPI) was also conducted on SurGenes. By validating the SurGenes with data sets of lung cancer from the Gene Expression Omnibus (GEO), 106 TME related SurGenes were generated. Further, intersection analysis was executed between the 106 TME related SurGenes and hub genes from PPI network, PTPRC and CD19 were obtained. Gene Set Enrichment Analysis and CIBERSORT analysis were performed on PTPRC and CD19. Based on the TCGA LUAD dataset, we conducted factor analysis and Step-wise multivariate Cox regression analysis for 106 TME related SurGenes to construct the prognostic model for LUAD survival prediction. The LUAD dataset in GEO (GSE68465) was used as the testing dataset to confirm the prognostic model. Multivariate Cox regression analysis was used between risk score from the prognostic model and clinical parameters. Result A total of 106 TME related genes were collected in our research totally, which were markedly correlated with the overall survival (OS) of LUAD patient. Bioinformatics analysis suggest them mainly concentrated on immune response, cell adhesion, and extracellular matrix. More importantly, among 106 TME related SurGenes, PTPRC and CD19 were highly interconnected nodes among PPI network and correlated with immune activity, exhibiting significant prognostic potential. The prognostic model was a weighted linear combination of the 106 genes, by which the low-OS LUAD samples could be separated from the high-OS samples with success. This model was also able to rebustly predict the situation of survival (training set: p-value < 0.0001, area under the curve (AUC) = 0.649; testing set: p-value = 0.0009, AUC = 0.617). By combining with clinical parameters, the prognostic model was optimized. The AUC achieved 0.716 for 3 year and 0.699 for 5 year. Conclusion A series of TME-related prognostic genes were acquired in this research, which could reflect immune disorders within TME, and PTPRC and CD19 show the potential to be an indicator for LUAD prognosis and tumor microenvironment modulation. The prognostic model constructed base on those prognostic genes presented a high predictive ability, and may have clinical implications in the overall survival prediction of LUAD.

scholarly journals A signature of seven immune‐related genes predicts overall survival in male gastric cancer patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xin Xu ◽  
Yida Lu ◽  
Youliang Wu ◽  
Mingliang Wang ◽  
Xiaodong Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Overall Survival ◽  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Survival Prediction ◽  
Immune Related Genes

Abstract Background Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC. Methods RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT. Results A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment. Conclusions Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

scholarly journals A Novel Survival Model Based on Ferroptosis-related Gene Signature for Overall Survival Prediction in Bladder Cancer

2021 ◽  
Author(s):  
Yingchun Liang ◽  
Fangdie Ye ◽  
Chenyang Xu ◽  
Lujia Zou ◽  
Yun Hu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Bladder Cancer ◽  
Prognostic Value ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Prognosis Prediction

Abstract Background: The effective treatment and prognosis prediction of bladder cancer(BLCA) remains a medical problem. Ferroptosis is an iron-dependent form of programmed cell death. Ferroptosis are closely related to tumor occurrence and progression, but the prognostic value of ferroptosis-related genes (FRGs) in BLCA remains to be further clarified. In this study, we identified a FRGs signature with potential prognostic value for patients with BLCA. Methods: The corresponding clinical data and the mRNA expression profile of BLCA patients were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to extract FRGs related to survival time, Cox regression model was applied to construct a multigene signature. Both principal component analysis (PCA) and single-sample gene set enrichment analysis (ssGSEA) were performed for functional annotation. Results: Clinical traits were combined with FRGs, so that 15 prognostic-related FRGs were identified by Cox regression. High expression of CISD1, GCLM, CRYAB, SLC7A11, TFRC, ACACA, ZEB1, SQLE, FADS2, ABCC1, G6PD and PGD are related to poor survival rates of BLCA patients. Multivariate Cox regression constructed a prognostic model with 7 FRGs and divided patients into two risk groups. Compared with the low-risk group, the overall survival(OS) of patients in the high-risk group was significantly lower (P <0.001). In multivariate regression analysis, the risk score was shown to be an independent predictor of OS (HR> 1, P <0.01). ROC curve analysis verified the predictive ability of the model. In addition, the two risk groups displayed different immune statuses in the ssGSEA and different distributed patterns in PCA. Conclusion: Our research suggests that a new gene model related to ferroptosis can be applied for the prognosis prediction of BLCA. Targeting FRGs may be a treatment option for BLCA.

scholarly journals A Novel Glucose Metabolism-Related Gene Signature for Overall Survival Prediction in Patients with Glioblastoma

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Chaocai Zhang ◽  
Minjie Wang ◽  
Fenghu Ji ◽  
Yizhong Peng ◽  
Bo Wang ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Glucose Metabolism ◽  
Cox Regression ◽  
Gene Signature ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Survival Prediction ◽  
Related Gene ◽  
Cox Regression Analysis

Introduction. Glioblastoma (GBM) is one of the most frequent primary intracranial malignancies, with limited treatment options and poor overall survival rates. Alternated glucose metabolism is a key metabolic feature of tumour cells, including GBM cells. However, due to high cellular heterogeneity, accurately predicting the prognosis of GBM patients using a single biomarker is difficult. Therefore, identifying a novel glucose metabolism-related biomarker signature is important and may contribute to accurate prognosis prediction for GBM patients. Methods. In this research, we performed gene set enrichment analysis and profiled four glucose metabolism-related gene sets containing 327 genes related to biological processes. Univariate and multivariate Cox regression analyses were specifically completed to identify genes to build a specific risk signature, and we identified ten mRNAs (B4GALT7, CHST12, G6PC2, GALE, IL13RA1, LDHB, SPAG4, STC1, TGFBI, and TPBG) within the Cox proportional hazards regression model for GBM. Results. Depending on this glucose metabolism-related gene signature, we divided patients into high-risk (with poor outcomes) and low-risk (with satisfactory outcomes) subgroups. The results of the multivariate Cox regression analysis demonstrated that the prognostic potential of this ten-gene signature is independent of clinical variables. Furthermore, we used two other GBM databases (Chinese Glioma Genome Atlas (CGGA) and REMBRANDT) to validate this model. In the functional analysis results, the risk signature was associated with almost every step of cancer progression, such as adhesion, proliferation, angiogenesis, drug resistance, and even an immune-suppressed microenvironment. Moreover, we found that IL31RA expression was significantly different between the high-risk and low-risk subgroups. Conclusion. The 10 glucose metabolism-related gene risk signatures could serve as an independent prognostic factor for GBM patients and might be valuable for the clinical management of GBM patients. The differential gene IL31RA may be a potential treatment target in GBM.

A Novel Ferroptosis-related Gene Signature for Overall Survival Prediction in Patients with Bladder cancer

2021 ◽  
Author(s):  
Liyuan Wu ◽  
Feiya Yang ◽  
Nianzeng Xing
Keyword(s):  
Overall Survival ◽  
Bladder Cancer ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Gene Signature ◽  
Survival Prediction ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Prognostic Prediction

Abstract Background Bladder cancer (BC) is a highly heterogeneous disease, which makes the prognostic prediction challenging. Ferroptosis is related to a variety of biological pathways, including those involved in the metabolism of amino acids, lipids, and iron. However, the prognostic value of ferroptosis-related genes in BC remains to be further elucidated. Methods In this study, the mRNA expression profiles and corresponding clinical data of BC patients were downloaded from public databases. The least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized to construct a multigene signature and validated it. Results Our results showed 12 differentially expressed genes (DEGs) were correlated with overall survival (OS) in the univariate Cox regression analysis (all adjusted P< 0.05). A 9-gene signature was constructed to stratify patients into two risk groups. Patients in the high-risk group showed significantly reduced OS compared with patients in the low-risk group (P < 0.001). The risk score was an independent predictor for OS in multivariate Cox regression analyses (HR> 1, P< 0.01). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Functional analysis revealed that immune-related pathways were enriched, and immune status were different between two risk groups, especially in humoral immune response process. Conclusion In conclusion, a novel ferroptosis-related gene signature can be used for prognostic prediction in BC. Targeting ferroptosis may be a therapeutic alternative for BC.

scholarly journals The Significance of Inflammatory Markers in the Prognosis of Newly Diagnosed Multiple Myeloma Patients

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 15-15
Author(s):  
Lin Gui ◽  
Fei Wang ◽  
Jinning Shi ◽  
Baoan Chen
Keyword(s):  
Risk Factors ◽  
Multiple Myeloma ◽  
Overall Survival ◽  
Multivariate Analysis ◽  
Survival Time ◽  
Cox Regression ◽  
Conflicts Of Interest ◽  
Univariate Analysis ◽  
Rank Test ◽  
Newly Diagnosed

Objective: To explore the significance of the ratio of neutrophils to lymphocytes (NLR), monocytes to lymphocytes (MLR), and platelets to lymphocytes (PLR) in the prognosis of patients with newly diagnosed multiple myeloma. Methods: We retrospectively reviewed the data for 60 multiple myeloma patients who were diagnosed in Jiangning Hospital Affiliated to Nanjing Medical University from August 2011 to March 2020. According to NLR、MLR、PLR, the patients were divided into the low NLR group (NLR&lt;3.61) or high NLR group (NLR≥3.61), low MLR group (MLR&lt;0.33) or high MLR group (MLR ≥0.33), low PLR group (PLR&lt; 129.78) and high PLR group (PLR ≥129.78). Overall survival time (OS) was used as the prognostic evaluation criteria, and Kaplan-Meier survival curve, Log-rank test and Cox regression model were used to carry out univariate and multivariate analysis on clinical and laboratory parameters. Results: Among the 60 patients, 33 were male and 27 were female, the median age of onset was 65 years old, 19 were in the high NLR group, 41 were in the low NLR group, 24 were in the high MLR group, 36 were in the low MLR group, 26 were in the high PLR group, and 34 were in the low PLR group. The univariate analysis showed the prognosis was influenced by factors including NLR, PLR, age, ISS stages, hemoglobin (HGB), albumin (ALT). MLR, type of immunoglobulin, white globulin ratio (A/G), gender, β2-microglobulin, lactate dehydrogenase (LDH) and creatinine were not correlated with the total survival time of patients. The multivariate analysis showed that ISS III stages, PLR≥129.78、HGB&lt;100g/L were independent risk factors influencing the prognosis of MM patients. Conclusion: ISS III stages, PLR≥129.78、HGB&lt;100g/L are independent prognostic risk factors in newly diagnosed multiple myeloma patients, which can be used as an economical and effective method for early evaluation of patient prognosis. Key Wordsmultiple myeloma; overall survival; NLR; PLR; MLR Disclosures No relevant conflicts of interest to declare.

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