AKNAD1

Last update 23 Jan 2025

Basic Info

Synonyms

AKNA domain containing 1, AKNAD1, C1orf62

+ [2]

Introduction-

100 Clinical Results associated with AKNAD1

100 Translational Medicine associated with AKNAD1

0 Patents (Medical) associated with AKNAD1

Literatures (Medical) associated with AKNAD1

01 Nov 2022·PROTEOMICS – Clinical Applications

Evaluation of autoantibody signatures in pituitary adenoma patients using human proteome arrays

Article

Author: Duttagupta, Siddhartha P. ; Banerjee, Arghya ; Barpanda, Abhilash ; Ray, Arka ; Shah, Abhidha ; Zhu, Heng ; Goel, Atul ; Nissa, Mehar Un ; Dash, Ankita ; Gupta, Ishika ; Srivastava, Sanjeeva

AbstractPurposeTo identify the specific diagnostic biomarkers related to pituitary adenomas (PAs), we performed serological antibody profiles for three types of PAs, namely Acromegaly, Cushing's and Nonfunctional Pituitary Adenomas (NFPAs), using the human proteome (HuProt) microarray. This is the first study describing the serum autoantibody profile of PAs.Experimental DesignWe performed serological autoantibody profiling of four healthy controls, four Acromegaly, three Cushing's and three NFPAs patient samples to obtain their autoantibody profiles, which were used for studying expression, interaction and altered biological pathways. Further, significant autoantibodies of PAs were compared with data available for glioma, meningioma and AAgAtlas for their specificity.ResultsAutoantibody profile of PAs led to the identification of differentially expressed significant proteins such as AKNAD1 (AT‐Hook Transcription Factor [AKNA] Domain Containing 1), NINJ1 (Nerve injury‐induced protein 1), L3HYPDH (Trans‐3‐hydroxy‐L‐proline dehydratase), RHOG (Rho‐related GTP‐binding protein) and PTP4A1 (Protein Tyrosine Phosphatase Type IVA 1) in Acromegaly. Protein ABR (Active breakpoint cluster region‐related protein), ST6GALNAC6 (ST6 N‐acetylgalactosaminide alpha‐2, 6‐sialyltransferase 6), NOL3 (Nucleolar protein 3), ANXA8 (Annexin A8) and POLR2H (RNA polymerase II, I and III subunit H) showed an antigenic response in Cushing's patient's serum samples. Protein dipeptidyl peptidase 3 (DPP3) and reticulon‐4 (RTN4) exhibited a very high antigenic response in NFPA patients. These proteins hold promise as potential autoantibody biomarkers in PAs.

01 Mar 2022·Current Medicinal Chemistry

Identification of WHO II/III Gliomas by 16 Prognostic-related Gene Signatures using Machine Learning Methods

Article

Author: Zhang, Ning ; Li, Qi Feng ; Guo, Yu ; Wu, Ya Meng ; Sa, Yu

Background:It is found that the prognosis of gliomas of the same grade has large differences among World Health Organization (WHO) grade II and III in clinical observation. Therefore, a better understanding of the genetics and molecular mechanisms underlying WHO grade II and III gliomas is required, with the aim of developing a classification scheme at the molecular level rather than the conventional pathological morphology level.Method:We performed survival analysis combined with machine learning methods of Least Absolute Shrinkage and Selection Operator using expression datasets downloaded from the Chinese Glioma Genome Atlas as well as The Cancer Genome Atlas. Risk scores were calculated by the product of expression level of overall survival-related genes and their multivariate Cox proportional hazards regression coefficients. WHO grade II and III gliomas were categorized into the low-risk subgroup, medium-risk subgroup, and high-risk subgroup. We used the 16 prognostic-related genes as input features to build a classification model based on prognosis using a fully connected neural network. Gene function annotations were also performed.Results:The 16 genes (AKNAD1, C7orf13, CDK20, CHRFAM7A, CHRNA1, EFNB1, GAS1, HIST2H2BE, KCNK3, KLHL4, LRRK2, NXPH3, PIGZ, SAMD5, ERINC2, and SIX6) related to the glioma prognosis were screened. The 16 selected genes were associated with the development of gliomas and carcinogenesis. The accuracy of an external validation data set of the fully connected neural network model from the two cohorts reached 95.5%. Our method has good potential capability in classifying WHO grade II and III gliomas into low-risk, medium-risk, and high-risk subgroups. The subgroups showed significant (P<0.01) differences in overall survival.Conclusion:This resulted in the identification of 16 genes that were related to the prognosis of gliomas. Here we developed a computational method to discriminate WHO grade II and III gliomas into three subgroups with distinct prognoses. The gene expressionbased method provides a reliable alternative to determine the prognosis of gliomas.

01 Nov 2021·GenomicsQ3 · BIOLOGY

Identification of a risk prediction model for clinical prognosis in HER2 positive breast cancer patients

Q3 · BIOLOGY

Article

Author: Zhou, Danyang ; Wang, Shusen ; Jiang, Kuikui ; Xu, Fei ; Wu, Ying ; Hong, Ruoxi

Background New biomarkers are needed to identify different clinical outcomes for HER2+ breast cancer (BC). Methods Differential genes of HER2+ BC were screened based on TCGA database. We used WGCNA to identify the genes related to the survival. Genetic Algorithm was used to structure risk prediction model. The prognostic model was validated in GSE data. Results We constructed a risk prediction model of 6 genes to identify prognosis of HER2+ BC, including CLEC9A, PLD4, PIM1, PTK2B, AKNAD1 and C15orf27. Kaplan-Meier curve showed that the model effectively distinguished the survival of HER2+ BC patients. The multivariate Cox regression suggested that the risk model was an independent predictor for HER2+ BC. Analysis related to immune showed that significant differences in immune infiltration between high- and low-risk groups classified by the prognostic model. Conclusions Our study identified a risk prediction model of 6 genes that could distinguish the prognosis of HER2+ BC.

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