STK32B

Last update 08 May 2025

Basic Info

Synonyms

HSA250839, serine/threonine kinase 32B, Serine/threonine-protein kinase 32B

+ [5]

Introduction-

100 Clinical Results associated with STK32B

100 Translational Medicine associated with STK32B

0 Patents (Medical) associated with STK32B

Literatures (Medical) associated with STK32B

01 May 2023·Journal of molecular neuroscience : MN

Association Analysis of 27 Single Nucleotide Polymorphisms in a Chinese Population with Essential Tremor.

Article

Author: Tian, Jun ; Lv, Dayao ; Pu, Jiali ; Zhao, Gaohua ; Gu, Luyan ; Zhao, Guohua ; Lu, Jinyu ; Yan, Yaping ; Song, Zhe ; Yin, Xinzhen ; Gao, Ting ; Zhang, Baorong ; Cao, Lanxiao

Genetic factors play a major role in essential tremor (ET) pathogenesis. This study aimed to assess variant burden in ET-associated genes in a relatively large Chinese population cohort. We genotyped 27 single nucleotide polymorphisms (SNPs) previously reported to be associated with ET by multiplex PCR amplicon sequencing assay in 488 familial and sporadic ET patients and 514 healthy controls (HCs). Then, we performed allelic and genotypic association test by Pearson chi-square test or Fisher's exact test. A total of 1002 samples were included in our analysis, consisting of 488 ET patients and 514 sex and age-matched HCs. For rs10937625, the C allele was linked to increased risk of ET (P = 0.019, OR = 1.503, 95% CI = 1.172-1.928). The carriers of the C/C homozygote and C/T heterozygote showed a significantly higher risk of ET, compared with the T/T homozygote under the dominant model (P = 0.019, OR = 1.628, 95% CI = 1.221-2.170). There were no statistically significant differences in the frequency of other SNPs between ET patients and healthy controls. Rs10937625 (STK32B) may increase the risk of ET in eastern Chinese population.

01 Jan 2022·Transfusion Medicine and Hemotherapy

Preliminary Study of Genome-Wide Association Identified Novel Susceptibility Genes for Hemorheological Indexes in a Chinese Population

Article

Author: Xie, Ruigang ; Sun, Yuxiao ; Cui, Mingxia ; Gao, Chuanyu ; Wang, Xianqing ; Cheng, Zhaoyun ; Dai, Guoyou ; Chen, Yan ; Guo, Zhiping ; Li, Yongqiang ; Lu, Guoqing ; Wang, Aifeng

Background: Genome-wide association studies for various hemorheological characteristics have not been reported. We aimed to identify genetic loci associated with hemorheological indexes in a cohort of healthy Chinese Han individuals. Methods: Genotyping was performed using Applied Biosystems Axiom™ Precision Medicine Diversity Array in 838 individuals, and 6,423,076 single nucleotide polymorphisms were available for genotyping. The relations were examined in an additive genetic model using mixed linear regression and combined with identical by descent matrix. Results: We identified 38 genetic loci (p < 5 × 10−6) related to hemorheological traits. In which, LOC102724502-OLIG2 rs28371438 was related to the levels of nd30 (p = 8.58 × 10−07), nd300 (p = 1.89 × 10−06), erythrocyte rigidity (p = 1.29 × 10−06), assigned viscosity (p = 6.20 × 10−08) and whole blood high cut relative (p = 7.30 × 10−08). The association of STK32B rs4689231 for nd30 (p = 3.85 × 10−06) and nd300 (p = 2.94 × 10−06) and GTSCR1-LINC01541 rs11661911 for erythrocyte rigidity (p = 9.93 × 10−09) and whole blood high cut relative (p = 2.09 × 10−07) was found. USP25-MIR99AHG rs1297329 was associated with erythrocyte rigidity (p = 1.81 × 10−06) and erythrocyte deformation (p = 1.14 × 10−06). Moreover, the association of TMEM232-SLC25A46 rs3985087 and LINC00470-METTL4 rs9966987 for fibrinogen (p = 1.31 × 10−06 and p = 4.29 × 10−07) and plasma viscosity (p = 1.01 × 10−06 and p = 4.59 × 10−07) was found. Conclusion: These findings may represent biological candidates for hemorheological indexes and contribute to hemorheological study.

01 Jan 2022·Contrast Media & Molecular Imaging

[Retracted] Prognostic Value of Stem Cell Index‐Related Characteristics in Primary Hepatocellular Carcinoma

Article

Author: Sheng, Xia ; Liu, Jin ; Pan, Huifen ; Rao, Guihua

The objective of this study is to form a cancer stem cell index‐based model to stratify HCC risk and predict survival. After screening the Tumor Genome Atlas (TCGA) of liver and normal liver tissue samples, we obtained differentially expressed genes (DEGs). We employed a weighted correlation network analysis (WGCNA) and differentially expressed genes were studied in HCC to find the modules most associated with cancer stem cells (mRNAsi). At the same time, gene ontology and Kyoto Genome Encyclopedia (KEGG) were used for functional annotation and combined with LASSO, univariate, and multivariate COX regression analyses, a prediction model of key module genes of cancer stem cells was developed. The model’s clinical efficacy was measured using the C index, calibration curve, multiindex ROC curve, and clinical decision curve. WGCNA found that black modules were most correlated with tumour stem cell index. Seven genes (CSDC2, GNA14, LGI2, MMRN1, PDE2A, SELP, and STK32B) were filtered by univariate, LASSO, and multivariate Cox regression analyses to establish the primary HCC model. The survival analysis and ROC curve in the TCGA training and validation cohort showed good performance. The independent prognostic factor of primary HCC was risk score, according to univariate and multivariate Cox regression analyses. It is found that the stem cell index model of 7 genes could predict factors independently, indicating that signatures of the stem cell will play a significant role in liver cancer survival prediction and risk stratification.

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