RGS1 expression is associated with poor prognosis in multiple myeloma

Roh, Jin; Shin, Su‐Jin; Lee, A-Neum; Yoon, Dok Hyun; Suh, Cheolwon; Park, Chan‐Jeoung; Huh, Jooryung; Park, Chan-Sik

doi:10.1136/jclinpath-2016-203713

Cited by 32 publications

(32 citation statements)

References 36 publications

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“…To date, several molecular markers have been adopted as a standard staging system (DS/ISS); they are still inadequate in prognostic prediction and providing treatment choices [5]. The appropriate biomarkers that can reduce the probability of recurrence and progression are a clinicopathological priority for MM risk stratification [6,7].…”

Section: Introductionmentioning

confidence: 99%

Abnormal PTBP1 Expression Sustains the Disease Progression of Multiple Myeloma

Bai

Chen

2020

Disease Markers

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Multiple myeloma (MM) is a hematopoietic malignancy characterized by heterogeneity, which corresponds to alternative splicing (AS) profiles and disadjust gene expression. Bioinformatics analysis of AS factors possibly related to MM progression identified the polypyrimidine tract binding protein (PTBP1) as candidate. The purpose of this study was to confirm the incidence and prognostic value of PTBP1 in MM patients. Several cohorts of 2971 patients presenting newly diagnosed and relapsed MM were enrolled. Correlations between PTBP1 expression and clinicopathological characteristics, proliferative activity, and response to therapy of myeloma cells were analyzed. Moreover, the effect of PTBP1 on the AS pattern of specific aerobic glycolysis-related genes was explored in MM patients. Clinically, PTBP1 expression was present at all stages; it increased with disease progression and poor prognosis, which was even stronger elevated in patients with high tumor burden and drug resistance. Mechanistically, PTBP1 modulated AS of PKM2 and aerobic glycolysis-related genes in MM patients, which play synergistic or additive effects in clinical outcome. PTBP1 may be a novel marker for prognostic prediction and a promising therapeutic target for the development of anti-MM treatments.

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Section: Introductionmentioning

confidence: 99%

Abnormal PTBP1 Expression Sustains the Disease Progression of Multiple Myeloma

Bai

Chen

2020

Disease Markers

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“…For example, loss of nuclear CDKN1B expression is correlated with a worse 5-year survival of primary melanoma patients in Kaplan-Meier analysis, but gain of cytoplasmic CDKN1B was associated with a poor 5-year survival of metastatic melanoma patients. KIF20A and RGS1 genes have been reported to play critical roles in the development and progression of cancer, and promote the proliferation, migration and invasion of cancer cells [58,59]. In OSskcm, KIF20A and RGS1 were found to be strongly associated with cutaneous melanoma prognosis.…”

Section: Discussionmentioning

confidence: 96%

OSskcm: an online survival analysis webserver for skin cutaneous melanoma based on 1085 transcriptomic profiles

et al. 2020

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Background: Cutaneous melanoma is one of the most aggressive and lethal skin cancers. It is greatly important to identify prognostic biomarkers to guide the clinical management. However, it is technically challenging for untrained researchers to process high dimensional profiling data and identify potential prognostic genes in profiling datasets. Methods: In this study, we developed a webserver to analyze the prognostic values of genes in cutaneous melanoma using data from TCGA and GEO databases. The webserver is named Online consensus Survival webserver for Skin Cutaneous Melanoma (OSskcm) which includes 1085 clinical melanoma samples. The OSskcm is hosted in a windows tomcat server. Server-side scripts were developed in Java script. The database system is managed by a SQL Server, which integrates gene expression data and clinical data. The Kaplan-Meier (KM) survival curves, Hazard ratio (HR) and 95% confidence interval (95%CI) were calculated in a univariate Cox regression analysis. Results: In OSskcm, by inputting official gene symbol and selecting proper options, users could obtain KM survival plot with log-rank P value and HR on the output web page. In addition, clinical characters including race, stage, gender, age and type of therapy could also be included in the prognosis analysis as confounding factors to constrain the analysis in a subgroup of melanoma patients. Conclusion: The OSskcm is highly valuable for biologists and clinicians to perform the assessment and validation of new or interested prognostic biomarkers for melanoma. OSskcm can be accessed online at: http://bioin fo.henu.edu. cn/Melan oma/Melan omaLi st.jsp.

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“…Very recent evidence suggests that overproduction in PCM of a regulator of G-protein signalling coded for by the RGS1 gene and demonstrated by an immunohistochemical technique on BM trephine biopsy samples, correlated with shorter OS [59]. If confirmed, this would be a readily accessible procedure for incorporation in routine investigations in PCD.…”

Section: Risk Stratificationmentioning

confidence: 87%

Towards Stratified Medicine in Plasma Cell Myeloma

Egan¹,

Drain²,

Conway³

et al. 2016

IJMS

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Plasma cell myeloma is a clinically heterogeneous malignancy accounting for approximately one to 2% of newly diagnosed cases of cancer worldwide. Treatment options, in addition to long-established cytotoxic drugs, include autologous stem cell transplant, immune modulators, proteasome inhibitors and monoclonal antibodies, plus further targeted therapies currently in clinical trials. Whilst treatment decisions are mostly based on a patient’s age, fitness, including the presence of co-morbidities, and tumour burden, significant scope exists for better risk stratification, sub-classification of disease, and predictors of response to specific therapies. Clinical staging, recurring acquired cytogenetic aberrations, and serum biomarkers such as β-2 microglobulin, and free light chains are in widespread use but often fail to predict the disease progression or inform treatment decision making. Recent scientific advances have provided considerable insight into the biology of myeloma. For example, gene expression profiling is already making a contribution to enhanced understanding of the biology of the disease whilst Next Generation Sequencing has revealed great genomic complexity and heterogeneity. Pathways involved in the oncogenesis, proliferation of the tumour and its resistance to apoptosis are being unravelled. Furthermore, knowledge of the tumour cell surface and its interactions with bystander cells and the bone marrow stroma enhance this understanding and provide novel targets for cell and antibody-based therapies. This review will discuss the development in understanding of the biology of the tumour cell and its environment in the bone marrow, the implementation of new therapeutic options contributing to significantly improved outcomes, and the progression towards more personalised medicine in this disorder.

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RGS1 expression is associated with poor prognosis in multiple myeloma

Abstract: RGS1 expression may be a prognostic marker for risk stratification and a promising target for the development of a new MM therapy.

Cited by 32 publications

References 36 publications

Abnormal PTBP1 Expression Sustains the Disease Progression of Multiple Myeloma

Abnormal PTBP1 Expression Sustains the Disease Progression of Multiple Myeloma

OSskcm: an online survival analysis webserver for skin cutaneous melanoma based on 1085 transcriptomic profiles

Towards Stratified Medicine in Plasma Cell Myeloma

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