Systematic transcriptional analysis of human cell lines for gene expression landscape and tumor representation

Jin, Han; Zhang, Cheng; Zwahlen, Martin; von Feilitzen, Kalle; Karlsson, Max; Shi, Mengnan; Yuan, Meng; Song, Xiya; Li, Xiangyu; Yang, Hong; Turkez, Hasan; Fagerberg, Linn; Uhlén, Mathias; Mardinoglu, Adil

doi:10.1038/s41467-023-41132-w

Cited by 28 publications

(6 citation statements)

References 59 publications

(76 reference statements)

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“…To avoid any harmful effects on HEK-293 cells by the suppression of RORB, we validated the potential target gene as not being expressed in HEK-293 cells, according to the open access cell line section of the Human Protein Atlas. 32 In addition, we did not observe any anomalies during AAV production in the cells’ growth behavior or phenotype upon miR-F_04 expression.…”

Section: Discussionmentioning

confidence: 58%

Suppression of toxic transgene expression by optimized artificial miRNAs increases AAV vector yields in HEK-293 cells

Blahetek,

Mayer,

Zuber

et al. 2024

Molecular Therapy - Methods & Clinical Development

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Section: Discussionmentioning

confidence: 58%

Suppression of toxic transgene expression by optimized artificial miRNAs increases AAV vector yields in HEK-293 cells

Blahetek,

Mayer,

Zuber

et al. 2024

Molecular Therapy - Methods & Clinical Development

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“…Jin et al integrated 45 The Human Protein Atlas (HPA) cancer cell lines with 973 The Cancer Cell Line Encyclopedia (CCLE) cancer cell lines, resulting in a total of 985 cancer cell lines for research purposes [ 26 , 27 ]. The expression of POLD1 in prostate cancer cell lines was analyzed using tools provided by Jin et al, revealing that POLD1 was highly expressed in PC3 and DU145 cells, and least expressed in MDA-PCa-2b cells (Fig.…”

Section: Resultsmentioning

confidence: 99%

Identification of metastasis-related genes for predicting prostate cancer diagnosis, metastasis and immunotherapy drug candidates using machine learning approaches

Wang,

Ji,

Zhang

et al. 2024

Biol Direct

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Background Prostate cancer (PCa) is the second leading cause of tumor-related mortality in men. Metastasis from advanced tumors is the primary cause of death among patients. Identifying novel and effective biomarkers is essential for understanding the mechanisms of metastasis in PCa patients and developing successful interventions. Methods Using the GSE8511 and GSE27616 data sets, 21 metastasis-related genes were identified through the weighted gene co-expression network analysis (WGCNA) method. Subsequent functional analysis of these genes was conducted on the gene set cancer analysis (GSCA) website. Cluster analysis was utilized to explore the relationship between these genes, immune infiltration in PCa, and the efficacy of targeted drug IC50 scores. Machine learning algorithms were then employed to construct diagnostic and prognostic models, assessing their predictive accuracy. Additionally, multivariate COX regression analysis highlighted the significant role of POLD1 and examined its association with DNA methylation. Finally, molecular docking and immunohistochemistry experiments were carried out to assess the binding affinity of POLD1 to PCa drugs and its impact on PCa prognosis. Results The study identified 21 metastasis-related genes using the WGCNA method, which were found to be associated with DNA damage, hormone AR activation, and inhibition of the RTK pathway. Cluster analysis confirmed a significant correlation between these genes and PCa metastasis, particularly in the context of immunotherapy and targeted therapy drugs. A diagnostic model combining multiple machine learning algorithms showed strong predictive capabilities for PCa diagnosis, while a transfer model using the LASSO algorithm also yielded promising results. POLD1 emerged as a key prognostic gene among the metastatic genes, showing associations with DNA methylation. Molecular docking experiments supported its high affinity with PCa-targeted drugs. Immunohistochemistry experiments further validated that increased POLD1 expression is linked to poor prognosis in PCa patients. Conclusions The developed diagnostic and metastasis models provide substantial value for patients with prostate cancer. The discovery of POLD1 as a novel biomarker related to prostate cancer metastasis offers a promising avenue for enhancing treatment of prostate cancer metastasis.

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“…A previous report (Wu et al, 2002) has shown that MAML3 activates transcription of Notch responsive promotors in U2OS cells less efficiently in combination with NICD1‐3 than with NICD4, although MAML2 shows similar levels of activation for all four paralogs. Though all three MAML paralogs are expressed in HeLa cells, MAML3 is expressed at the lowest level compared to MAML1 and 2 (by 4.5‐ and 2.2‐fold); thus, these differences in activation may be muted (Jin et al, 2023). The approach we described for measuring the energetics of binding of MAML1 and ANK to CSL should help to determine whether variations in activation can be attributed to differences in MAML affinities among the four Notch paralogs.…”

Section: Discussionmentioning

confidence: 99%

Unraveling paralog‐specific Notch signaling through thermodynamics of ternary complex formation and transcriptional activation of chimeric receptors

Ramsey,

Barrick

2024

Protein Science

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Notch signaling in humans is mediated by four paralogous receptors that share conserved architectures and possess overlapping, yet non‐redundant functions. The receptors share a canonical activation pathway wherein upon extracellular ligand binding, the Notch intracellular domain (NICD) is cleaved from the membrane and translocates to the nucleus where its N‐terminal RBP‐j‐associated molecule (RAM) region and ankyrin repeat (ANK) domain bind transcription factor CSL and recruit co‐activator Mastermind‐like‐1 (MAML1) to activate transcription. However, different paralogs can lead to distinct outcomes. To better understand paralog‐specific differences in Notch signaling, we performed a thermodynamic analysis of the Notch transcriptional activation complexes for all four Notch paralogs using isothermal titration calorimetry. Using chimeric constructs, we find that the RAM region is the primary determinant of stability of binary RAMANK:CSL complexes, and that the ANK regions are largely the determinants of MAML1 binding to pre‐formed RAMANK:CSL complexes. Free energies of these binding reactions (ΔGRA and ΔGMAML) vary among the four Notch paralogs, although variations for Notch2, 3, and 4 offset in the free energy of the ternary complex (ΔGTC, where ΔGTC = ΔGRA + ΔGMAML). To probe how these affinity differences affect Notch signaling, we performed transcriptional activation assays with the paralogous and chimeric NICDs, and analyzed the results with an independent multiplicative model that quantifies contributions of the paralogous RAM, ANK, and C‐terminal regions (CTR) to activation. This analysis shows that transcription activation correlates with ΔGTC, but that activation is further modified by CTR identity in a paralog‐specific way.

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Systematic transcriptional analysis of human cell lines for gene expression landscape and tumor representation

Cited by 28 publications

References 59 publications

Suppression of toxic transgene expression by optimized artificial miRNAs increases AAV vector yields in HEK-293 cells

Suppression of toxic transgene expression by optimized artificial miRNAs increases AAV vector yields in HEK-293 cells

Identification of metastasis-related genes for predicting prostate cancer diagnosis, metastasis and immunotherapy drug candidates using machine learning approaches

Unraveling paralog‐specific Notch signaling through thermodynamics of ternary complex formation and transcriptional activation of chimeric receptors

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