Ask about this productRelated genes to: SERINC1 antibody
- Gene:
- SERINC1 NIH gene
- Name:
- serine incorporator 1
- Previous symbol:
- TDE2
- Synonyms:
- TMS-2, TDE1L, KIAA1253
- Chromosome:
- 6q22.31
- Locus Type:
- gene with protein product
- Date approved:
- 2003-08-21
- Date modifiied:
- 2016-10-05
Related products to: SERINC1 antibody
Related articles to: SERINC1 antibody
- The metabolism of fatty acids is essential in the initiation and progression of cervical cancer. The objective of this research is to develop a prognostic model associated with fatty acid metabolism and to identify the protein within this framework, with the intention of investigating its function in cervical squamous cell carcinoma (CESC) and its correlation with patient prognosis. - Source: PubMed
Publication date: 2025/11/26
Yang QinliZhang JingShang XiujuanSun BohaoWang TongWu YichenZhu ChunhongXiang WenqingWang Hao - At present, although some studies have offered certain insights into the genetic factors related to unruptured intracranial aneurysms (uIAs), the potential genetic targets associated with uIAs remain largely unknown. Thus, this research adopted Mendelian randomization (MR) analysis to study two genome-wide association studies on uIAs, aiming to determine the reliable genetic susceptibility and potential therapeutic targets for uIAs. - Source: PubMed
Publication date: 2025/01/21
Liu ShumingGuan HuiyuanWang Feng - The primary objective of this study is to develop a prediction model for peritoneal metastasis (PM) in colorectal cancer by integrating the genomic features of primary colorectal cancer, along with clinicopathological features. Concurrently, we aim to identify potential target implicated in the peritoneal dissemination of colorectal cancer through bioinformatics exploration and experimental validation. By analyzing the genomic landscape of primary colorectal cancer and clinicopathological features from 363 metastatic colorectal cancer patients, we identified 22 differently distributed variables, which were used for subsequent LASSO regression to construct a PM prediction model. The integrated model established by LASSO regression, which incorporated two clinicopathological variables and seven genomic variables, precisely discriminated PM cases (AUC 0.899; 95% CI 0.860-0.937) with good calibration (Hosmer-Lemeshow test p = .147). Model validation yielded AUCs of 0.898 (95% CI 0.896-0.899) and 0.704 (95% CI 0.622-0.787) internally and externally, respectively. Additionally, the peritoneal metastasis-related genomic signature (PGS), which was composed of the seven genes in the integrated model, has prognostic stratification capability for colorectal cancer. The divergent genomic landscape drives the driver genes of PM. Bioinformatic analysis concerning these driver genes indicated SERINC1 may be associated with PM. Subsequent experiments indicate that knocking down of SERINC1 functionally suppresses peritoneal dissemination, emphasizing its importance in CRCPM. In summary, the genomic landscape of primary cancer in colorectal cancer defines peritoneal metastatic pattern and reveals the potential target of SERINC1 for PM in colorectal cancer. - Source: PubMed
Publication date: 2024/05/13
Hu MinhuiLuo RuiYang KeliYu YangPan QiwenYuan MingmingChen RongrongWang HuiQin QiyuanMa TenghuiWang Huaiming - The clinical characteristics of growth hormone (GH)-producing pituitary adenomas/somatotroph pituitary neuroendocrine tumors (GHomas/somatotroph PitNETs) vary across patients. In this study, we aimed to integrate the genetic alterations, protein expression profiles, transcriptomes, and clinical characteristics of GHomas/somatotroph PitNETs to identify molecules associated with acromegaly characteristics. Targeted capture sequencing and copy number analysis of 36 genes and nontargeted proteomics analysis were performed on fresh-frozen samples from 121 sporadic GHomas/somatotroph PitNETs. Targeted capture sequencing revealed GNAS as the only driver gene, as previously reported. Classification by consensus clustering using both RNA sequencing and proteomics revealed many similarities between the proteome and the transcriptome. Gene ontology analysis was performed for differentially expressed proteins between wild-type and mutant GNAS samples identified by nontargeted proteomics and involved in G protein-coupled receptor (GPCR) pathways. The results suggested that GNAS mutations impact endocrinological features in acromegaly through GPCR pathway induction. ATP2A2 and ARID5B correlated with the GH change rate in the octreotide loading test, and WWC3, SERINC1, and ZFAND3 correlated with the tumor volume change rate after somatostatin analog treatment. These results identified a biological connection between GNAS mutations and the clinical and biochemical characteristics of acromegaly, revealing molecules associated with acromegaly that may affect medical treatment efficacy. - Source: PubMed
Publication date: 2022/11/27
Yamato AzusaNagano HidekazuGao YueMatsuda TatsumaHashimoto NaokoNakayama AkitoshiYamagata KazuyukiYokoyama MasatakaGong YingboShi XiaoyanZhahara Siti NurulKono TakashiTaki YukiFuruki NaotoNishimura MotoiHoriguchi KentaroIwadate YasuoFukuyo MasakiRahmutulla BahityarKaneda AtsushiHasegawa YoshinoriKawashima YusukeOhara OsamuIshikawa TetsuoKawakami EiryoNakamura YasuhiroInoshita NaokoYamada ShozoFukuhara NoriakiNishioka HiroshiTanaka Tomoaki - This study aimed to explore underlying mechanisms by which sphingolipid-related genes play a role in kidney renal clear cell carcinoma (KIRC) and construct a new prognosis-related risk model. We used a variety of bioinformatics methods and databases to complete our exploration. Based on the TCGA database, we used multiple R-based extension packages for data transformation, processing, and statistical analyses. First, on analyzing the CNV, SNV, and mRNA expression of 29 sphingolipid-related genes in various types of cancers, we found that the vast majority were protective in KIRC. Subsequently, we performed cluster analysis of patients with KIRC using sphingolipid-related genes and successfully classified them into the following three clusters with significant prognostic differences: Cluster 1, Cluster 2, and Cluster 3. We performed differential analyses of transcription factor activity, drug sensitivity, immune cell infiltration, and classical oncogenes to elucidate the unique roles of sphingolipid-related genes in cancer, especially KIRC, and provide a reference for clinical treatment. After analyzing the risk rates of sphingolipid-related genes in KIRC, we successfully established a risk model composed of seven genes using LASSO regression analysis, including SPHK1, CERS5, PLPP1, SGMS1, SGMS2, SERINC1, and KDSR. Previous studies have suggested that these genes play important biological roles in sphingolipid metabolism. ROC curve analysis results showed that the risk model provided good prediction accuracy. Based on this risk model, we successfully classified patients with KIRC into high- and low-risk groups with significant prognostic differences. In addition, we performed correlation analyses combined with clinicopathological data and found a significant correlation between the risk model and patient's M, T, stage, grade, and fustat. Finally, we developed a nomogram that predicted the 5-, 7-, and 10-year survival in patients with KIRC. The model we constructed had strong predictive ability. In conclusion, we believe that this study provides valuable data and clues for future studies on sphingolipid-related genes in KIRC. - Source: PubMed
Publication date: 2022/06/29
Sun YonghaoXu YingkunChe XiangyuWu Guangzhen