A Cancer-Related microRNA Signature Shows Biomarker Utility in Multiple Myeloma.
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ABSTRACT:
SUBMITTER: Papanota AM
PROVIDER: S-EPMC8658678 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Multiple myeloma (MM) is a prevalent hematological malignancy that poses significant challenges for treatment. Dysregulated cholesterol metabolism has been linked to tumorigenesis, disease progression, and therapy resistance. However, the correlation between cholesterol metabolism-related genes (CMGs) and the prognosis of MM remains unclear. Univariate Cox regression analysis and LASSO Cox regression analysis were applied to construct an overall survival-related signature based on the Gene Expression Omnibus database. The signature was validated using three external datasets. Enrichment analysis and immune analysis were performed between two risk groups. Furthermore, an optimal nomogram was established for clinical application, and its performance was assessed by the calibration curve and C-index. A total of 6 CMGs were selected to establish the prognostic signature, including ANXA2, CHKA, NSDHL, PMVK, SCAP and SQLE. The prognostic signature demonstrated good prognostic performance and correlated with several important clinical parameters, including number of transplants, International Staging System, albumin, beta2-Microglobulin and lactate dehydrogenase levels. The function analysis and immune analysis revealed that the metabolic pathways and immunologic status were associated with risk score. The nomogram incorporating the signature along with other clinical characteristics was constructed and the discrimination was verified by the calibration curve and C-index. Our findings indicated the potential prognostic connotation of cholesterol metabolism in MM. The development and validation of the prognostic signature is expected to aid in predicting prognosis and guiding precision treatment for MM.
| S-EPMC10632470 | biostudies-literature
Project description:Multiple myeloma bone disease (MMBD) constitutes a common and severe complication of multiple myeloma (MM), impacting the quality of life and survival. We evaluated the clinical value of a panel of 19 miRNAs associated with osteoporosis in MMBD. miRNAs were isolated from the plasma of 62 newly diagnosed MM patients with or without MMBD. First-strand cDNA was synthesized, and relative quantification was performed using qPCR. Lastly, we carried out extensive biostatistical analysis. Circulating levels of let-7b-5p, miR-143-3p, miR-17-5p, miR-214-3p, and miR-335-5p were significantly higher in the blood plasma of MM patients with MMBD compared to those without. Receiver operating characteristic curve and logistic regression analyses showed that these miRNAs could accurately predict MMBD. Furthermore, a standalone multi-miRNA-based logistic regression model exhibited the best predictive potential regarding MMBD. Two of those miRNAs also have a prognostic role in MM since survival analysis indicated that lower circulating levels of both let-7b-5p and miR-335-5p were associated with significantly worse progression-free survival, independently of the established prognostic factors. Our study proposes a miRNA signature to facilitate MMBD diagnosis, especially in ambiguous cases. Moreover, we provide evidence of the prognostic role of let-7b-5p and miR-335-5p as non-invasive prognostic biomarkers in MM.
| S-EPMC8345491 | biostudies-literature
Project description:BackgroundMultiple myeloma (MM) is a highly malignant hematological tumor with a poor overall survival (OS). Due to the high heterogeneity of MM, it is necessary to explore novel markers for the prognosis prediction for MM patients. Ferroptosis is a form of regulated cell death, playing a critical role in tumorigenesis and cancer progression. However, the predictive role of ferroptosis-related genes (FRGs) in MM prognosis remains unknown.MethodsThis study collected 107 FRGs previously reported and utilized the least absolute shrinkage and selection operator (LASSO) cox regression model to construct a multi-genes risk signature model upon FRGs. The ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) were carried out to evaluate immune infiltration level. Drug sensitivity was assessed based on the Genomics of Drug Sensitivity in Cancer database (GDSC). Then the synergy effect was determined with Cell counting kit-8 (CCK-8) assay and SynergyFinder software.ResultsA 6-gene prognostic risk signature model was constructed, and MM patients were divided into high and low risk groups. Kaplan-Meier survival curves showed that patients in the high risk group had significantly reduced OS compared with patients in the low risk group. Besides, the risk score was an independent predictor for OS. Receiver operating characteristic (ROC) curve analysis confirmed the predictive capacity of the risk signature. Combination of risk score and ISS stage had better prediction performance. Enrichment analysis revealed immune response, MYC, mTOR, proteasome and oxidative phosphorylation were enriched in high risk MM patients. We found high risk MM patients had lower immune scores and immune infiltration levels. Moreover, further analysis found that MM patients in high risk group were sensitive to bortezomib and lenalidomide. At last, the results of the in vitro experiment showed that ferroptosis inducers (RSL3 and ML162) may synergistically enhance the cytotoxicity of bortezomib and lenalidomide against MM cell line RPMI-8226.ConclusionThis study provides novel insights into roles of ferroptosis in MM prognosis prediction, immune levels and drug sensitivity, which complements and improves current grading systems.
| S-EPMC9950937 | biostudies-literature
Project description:Background: Metabolic reprogramming is an important hallmark of cancer. Glycolysis provides the conditions on which multiple myeloma (MM) thrives. Due to MM's great heterogeneity and incurability, risk assessment and treatment choices are still difficult. Method: We constructed a glycolysis-related prognostic model by Least absolute shrinkage and selection operator (LASSO) Cox regression analysis. It was validated in two independent external cohorts, cell lines, and our clinical specimens. The model was also explored for its biological properties, immune microenvironment, and therapeutic response including immunotherapy. Finally, multiple metrics were combined to construct a nomogram to assist in personalized prediction of survival outcomes. Results: A wide range of variants and heterogeneous expression profiles of glycolysis-related genes were observed in MM. The prognostic model behaved well in differentiating between populations with various prognoses and proved to be an independent prognostic factor. This prognostic signature closely coordinated with multiple malignant features such as high-risk clinical features, immune dysfunction, stem cell-like features, cancer-related pathways, which was associated with the survival outcomes of MM. In terms of treatment, the high-risk group showed resistance to conventional drugs such as bortezomib, doxorubicin and immunotherapy. The joint scores generated by the nomogram showed higher clinical benefit than other clinical indicators. The in vitro experiments with cell lines and clinical subjects further provided convincing evidence for our study. Conclusion: We developed and validated the utility of the MM glycolysis-related prognostic model, which provides a new direction for prognosis assessment, treatment options for MM patients.
| S-EPMC10272536 | biostudies-literature
Project description:Background: Cuproptosis is a newly identified unique copper-triggered modality of mitochondrial cell death, distinct from known death mechanisms such as necroptosis, pyroptosis, and ferroptosis. Multiple myeloma (MM) is a hematologic neoplasm characterized by the malignant proliferation of plasma cells. In the development of MM, almost all patients undergo a relatively benign course from monoclonal gammopathy of undetermined significance (MGUS) to smoldering myeloma (SMM), which further progresses to active myeloma. However, the prognostic value of cuproptosis in MM remains unknown. Method: In this study, we systematically investigated the genetic variants, expression patterns, and prognostic value of cuproptosis-related genes (CRGs) in MM. CRG scores derived from the prognostic model were used to perform the risk stratification of MM patients. We then explored their differences in clinical characteristics and immune patterns and assessed their value in prognosis prediction and treatment response. Nomograms were also developed to improve predictive accuracy and clinical applicability. Finally, we collected MM cell lines and patient samples to validate marker gene expression by quantitative real-time PCR (qRT-PCR). Results: The evolution from MGUS and SMM to MM was also accompanied by differences in the CRG expression profile. Then, a well-performing cuproptosis-related risk model was developed to predict prognosis in MM and was validated in two external cohorts. The high-risk group exhibited higher clinical risk indicators. Cox regression analyses showed that the model was an independent prognostic predictor in MM. Patients in the high-risk group had significantly lower survival rates than those in the low-risk group (p < 0.001). Meanwhile, CRG scores were significantly correlated with immune infiltration, stemness index and immunotherapy sensitivity. We further revealed the close association between CRG scores and mitochondrial metabolism. Subsequently, the prediction nomogram showed good predictive power and calibration. Finally, the prognostic CRGs were further validated by qRT-PCR in vitro. Conclusion: CRGs were closely related to the immune pattern and self-renewal biology of cancer cells in MM. This prognostic model provided a new perspective for the risk stratification and treatment response prediction of MM patients.
| S-EPMC10157051 | biostudies-literature
Project description:BACKGROUND:The aim was to identify a novel prognostic miRNA signature for colon cancer (CC) in silico. METHODS:Data on the expression of miRNAs and relevant clinical information for 407 patients were obtained from The Cancer Genome Atlas (TCGA), and the samples were randomly split into a validation set (n = 203) and training set (n = 204). The differential expression of miRNAs between normal tissues and patients with CC was analyzed. We detected a miRNA expression signature in the training dataset by using a Cox proportional hazard regression model. Then, we verified the signature in the validation set. Association of the miRNA signature with overall survival was assessed in the validation cohort and combined cohort by log-rank test and based on Kaplan-Meier curves. The receiver operating characteristic and disease-free survival analyses were performed to evaluate the miRNA signature of CC in the combined cohort. Multivariate and univariate Cox analyses related to survival for the miRNA signature were performed, and a nomogram was built as a prognostic model for CC. To explore the function of target genes of the miRNA signature, Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were used. RESULTS:Between the matched normal tissues and colon cancer tissues, 267 differentially expressed miRNAs were detected, and a single-factor CoxPH model showed that 13 miRNAs were related to overall survival in the training cohort. Then, a five-miRNA signature was identified using a CoxPH regression model with multiple factors. The five-miRNA signature had significant prognostic value in the training cohort and was validated in the validation cohort and combined cohort. A total of 193 target genes of the miRNA signature were identified. According to the results of functional analysis of the target genes, the signaling pathways MAPK, AMPK and PI3K-Akt, focal adhesion, and microRNAs in cancer were remarkably enriched. CONCLUSION:A five-miRNA signature had increased prognostic value for CC, which may provide important biological insights for the discovery and development of molecular predictors to improve the prognosis of patients with CC.
| S-EPMC7015317 | biostudies-literature
Project description:Multiple myeloma (MM), a hematologic malignancy, is characterized by malignant plasma cells clonal proliferation. Many evidences indicated the indirect interaction between hypoxic environment and immune state in MM tumorigenesis, but the underlying mechanism remains unclear. MM-related datasets were downloaded from the Gene Expression Omnibus (GEO) database. The R packages were applied for screening protective differentially expressed genes (DEGs) and risk DEGs. The signature was constructed based the most prognostic gene signature in the training and assessed in the validation cohorts. The immune cell infiltration, the expression of the HLA family and immune checkpoint genes inside the low- and high-risk groups were compared to determine the differences in immune infiltration and immunotherapy responses. Moreover, the expression of HLA families and immune checkpoints inside the low- and high-risk groups was markedly disordered. The results indicated hypoxia- and immune-related genes, including CHRDL1, DDIT4, DNTT, FAM133A, MYB, PRR15, QTRT1, and ZNF275, were identified and used to construct a prognostic signature. Role of DDIT4 in multiple myeloma was confirmed in vivo and in vitro. DDIT4 knockdown inhibited MM cell viability, migration and invasion potential as well as promoted myeloma cells apoptosis under hypoxia. Taken together, our study may contribute to the treatment and prognosis prediction of MM.
| S-EPMC9468480 | biostudies-literature
Project description:BackgroundHypoxia plays a significant role in the pathogenesis of pancreatic cancer, but the effect of hypoxia-related genes in pancreatic cancer remains to be elucidated. This study aimed to identify hypoxia-related genes related to pancreatic cancer and construct a prognostic signature.MethodsPancreatic cancer datasets were retrieved from TCGA database. Cox regression analyses were used to identify hypoxia-related genes and construct a prognostic signature. Datasets from International Cancer Genome Consortium and GEO databases were used as validated cohorts. The CIBERSORT method was applied to estimate the fractions of immune cell types. DNA methylation and protein levels of the genes in pancreatic cancer were examined.ResultsThree hypoxia-related genes (TES, LDHA, and ANXA2) were identified as associated with patient survival and selected to construct a prognostic signature. Patients were divided into high- and low-risk groups based on the signature. Those in the high-risk group showed worse survival than those in the low-risk group. The signature was shown to be involved in the HIF-1 signaling pathway. The time-dependent ROC analyses of three independent validated cohorts further revealed that this signature had a better prognostic value in the prediction of the survival of pancreatic cancer patients. Immune cells analysis for three datasets demonstrated that high-risk signature was significantly associated with macrophages and T cells. DNA methylation and protein levels of the three genes validated their aberrant expression in pancreatic cancer.ConclusionsOur research provided a novel and reliable prognostic signature that composes of three hypoxia-related genes to estimate the prognosis of pancreatic cancer.
| S-EPMC9250441 | biostudies-literature
Project description:Mounting evidence has demonstrated that a lot of miRNAs are overexpressed or downregulated in colorectal cancer (CRC) tissues and play a crucial role in tumorigenesis, invasion, and migration. The aim of our study was to screen new biomarkers related to CRC prognosis by bioinformatics analysis. By using the R language edgeR package for the differential analysis and standardization of miRNA expression profiles from The Cancer Genome Atlas (TCGA), 502 differentially expressed miRNAs (343 up-regulated, 159 down-regulated) were screened based on the cut-off criteria of p < 0.05 and |log2FC|>1, then all the patients (421) with differentially expressed miRNAs and complete survival time, status were then randomly divided into train group (212) and the test group (209). Eight miRNAs with p < 0.005 were revealed in univariate cox regression analysis of train group, then stepwise multivariate cox regression was applied for constituting a five-miRNA (hsa-miR-5091, hsa-miR-10b-3p, hsa-miR-9-5p, hsa-miR-187-3p, hsa-miR-32-5p) signature prognostic biomarkers with obviously different overall survival. Test group and entire group shown the same results utilizing the same prescient miRNA signature. The area under curve (AUC) of receiver operating characteristic (ROC) curve for predicting 5 years survival in train group, test group, and whole cohort were 0.79, 0.679, and 0.744, respectively, which demonstrated better predictive power of prognostic model. Furthermore, Univariate cox regression and multivariate cox regression considering other clinical factors displayed that the five-miRNA signature could serve as an independent prognostic factor. In order to predict the potential biological functions of five-miRNA signature, target genes of these five miRNAs were analyzed by Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway and Gene Ontology (GO) enrichment analysis. The top 10 hub genes (ESR1, ADCY9, MEF2C, NRXN1, ADCY5, FGF2, KITLG, GATA1, GRIA1, KAT2B) of target genes in protein protein interaction (PPI) network were screened by string database and Cytoscape 3.6.1 (plug-in cytoHubba). In addition, 19 of target genes were associated with survival prognosis. Taken together, the current study showed the model of five-miRNA signature could efficiently function as a novel and independent prognosis biomarker and therapeutic target for CRC patients.
| S-EPMC6863365 | biostudies-literature
Project description:PurposePyroptosis is an inflammation-based programmed cell death that holds great potential as a novel cancer therapeutic target in patients with multiple myeloma (MM). However, thus far, the function of pyroptosis-related genes (PRGs) in MM and their prognostic relevance remains undetermined.MethodsThe model was established by the LASSO analysis, based on the Gene Expression Omnibus (GEO) dabatase, and its efficacy was verified using two external datasets. The model's predictive ability was assessed by the Kaplan-Meier survival and time-dependent receiver operating characteristic (ROC) curves. Finally, a nomogram was established for clinical application. We also confirmed the validity of our model using specimens and in vitro experiments.ResultsWe established an 11-PRG signature profile, and verified its efficacy using two validation cohorts (VCs). In both cohorts, patients were separated into two subpopulations, according to their median risk scores (RS). Our analysis revealed that high-risk (HR) patients experienced considerably lower overall survival (OS), compared to the low-risk (LR) patients. Using functional enrichment and immune infiltration analyses, we demonstrated that the immunologic status was strongly related to RS. Furthermore, using a pyroptosis inhibitor Q-VD-OPh, we revealed that MM cell proliferation and progression was drastically suppressed and the doxorubicin (DOX)-induced apoptosis was reversed.ConclusionBased on our analysis, pyroptosis not only serves as a measure of MM treatment efficiency and patient prognosis, but is also a possible target for anti-MM therapy.
| S-EPMC8918477 | biostudies-literature