Recombinant Human POLD2
- Known as:
- Recombinant Human POLD2
- Catalog number:
- CF68
- Product Quantity:
- 10ug
- Category:
- -
- Supplier:
- Novoprotein
- Gene target:
- Recombinant Human POLD2
Related genes to: Recombinant Human POLD2
- Gene:
- POLD2 NIH gene
- Name:
- DNA polymerase delta 2, accessory subunit
- Previous symbol:
- -
- Synonyms:
- -
- Chromosome:
- 7p13
- Locus Type:
- gene with protein product
- Date approved:
- 1995-05-30
- Date modifiied:
- 2016-07-26
Related products to: Recombinant Human POLD2
Related articles to: Recombinant Human POLD2
- The development of resistance to trastuzumab in HER2-positive breast cancer is a serious clinical problem that limits the effectiveness of targeted therapy. In a significant proportion of patients, the mechanisms in the development of resistance remain poorly understood. The BT-474 cell line was selected as an optimal model for study because it represents a HER2-positive luminal B subtype breast cancer cell line. To identify the molecular mechanisms of resistance, a comprehensive transcriptomic analysis based on RNA-seq data comparison of three independent datasets including both sensitive and trastuzumab-resistant variants was applied. The methodological approach included multistep bioinformatics analysis followed by identification of regulatory interactions. The study identified genes with increased expression (FUCA2, HSPE1, SHLD1, NMD3) and genes with decreased expression (GPC5, FSTL1, ATG16L2, POLD2) in resistant cells. Key transcription factors (E2F1, MYC, YBX1, HEY1, NFIC, TFAP2A, AP-1/JUN, NCOA1) regulating the expression of the detected genes during the development of resistance were identified. The changes identified indicate a complex reprogramming of transcriptional activity affecting cell cycle processes, DNA repair, metabolism, and the epithelial-mesenchymal transition. The findings expand our understanding of the molecular mechanisms of trastuzumab resistance and open prospects for the development of novel therapeutic strategies to overcome drug resistance in HER2-positive breast cancer. - Source: PubMed
Shifon S AKarpets I OChesnokova A SKaritskaya P EUkladov E OEvgenov I VSidorov S VGulyaeva L F - Cancer drug resistance remains a major barrier to durable treatment success, often leading to relapse despite advances in precision oncology. While combination therapies are being increasingly investigated, such as chemotherapy with small molecule inhibitors, predicting drug response and identifying rational drug combinations based on resistance mechanisms remain major challenges. Therefore, a proteome-wide, single-gene overexpression screening platform is essential for guiding rational therapy selection. Here, we present (xb1-landing pad human RFeome-integrated system for a proteome-wide ene verexpression), a robust, scalable, and reproducible screening platform that enables single-copy, site-specific integration and overexpression of ~19,000 human open across cancer cell models. Using BOGO, we identified drug-specific response drivers for 16 chemotherapeutic agents and integrated clinical datasets to uncover proliferation and resistance-associated genes with prognostic potential. Drug response similarity networks revealed both shared and unique mechanisms, highlighting key pathways such as autophagy, apoptosis, and Wnt signaling, and notable resistance-associated genes including BCL2, POLD2, and TRADD. In particular, we proposed a synergistic combination of the BCL2 family inhibitor ABT-263 (Navitoclax) and the DNA analog TAS-102 (Lonsurf), which revealed that lysosomal modulation is a key mechanism driving DNA analog resistance. This combination therapy selectively enhanced cytotoxicity in colorectal and pancreatic cancer cells , and demonstrated therapeutic benefit in both cell line-derived xenograft (CDX) and patient-derived xenograft (PDX) models. Together, these findings establish BOGO as a powerful gene overexpression perturbation platform for systematically identifying chemoresistance and chemosensitization drivers, and for discovering rational combination therapies. Its scalability and reproducibility position BOGO as a broadly applicable tool for functional genomics and therapeutic discovery beyond cancer resistance. - Source: PubMed
Publication date: 2025/11/01
Jo Kyeong BeomAlruwaili Mohammed MKim Da-EunKoh YongjunKim HyeyeonYou KwontaeKim Ji-SunSane SabaGuo YanqiWright Jacob PNaranjo Maricris NCote Atina GRoth Frederick PHill David EChoi Jung-HyunLee HunsangMatreyek Kenneth AFarh Kyle K-HPark Jong-EunKim HyunKyungBakin Andrei VKim Dae-Kyum - DNA metabolism genes play pivotal roles in the regulation of cellular processes that contribute to cancer progression, immune modulation, and therapeutic response in prostate cancer (PC). Understanding the mechanisms by which these genes influence the tumor microenvironment and immune evasion is crucial for identifying prognostic biomarkers and developing targeted therapies. We performed an integrative analysis using transcriptomic data from the TCGA cohort and external validation datasets. Differentially expressed genes (DEGs) were identified using the edgeR algorithm with an FDR < 0.01 and a minimum fold change of 1.5. Gene enrichment analysis was conducted through GO and KEGG pathways to explore the biological significance of DNA metabolism genes in PC. In addition, clustering analyses, machine learning models, and single-cell RNA sequencing (scRNA-seq) were employed to investigate the immune characteristics, prognostic value, and therapeutic relevance of these genes. A total of 536 DEGs were identified across six subtypes of prostate cancer, with key DNA metabolism genes such as POLD2, RAD9A, REV3L, MSH6, and WRNIP1 highlighted as critical players. Gene enrichment analyses revealed that these DEGs were significantly associated with pathways involved in DNA repair, cellular aging, and telomere maintenance. Clustering analysis identified two distinct subgroups (C1 and C2) based on DNA metabolism gene expression, with C1 exhibiting a more aggressive phenotype, higher immune infiltration, and poorer prognosis. Machine learning models, particularly the CoxBoost algorithm, identified 21 key genes contributing to an effective prognostic model. Furthermore, scRNA-seq analysis confirmed the upregulation of DNA metabolism genes in PC cells compared to normal cells. Our findings highlight the importance of DNA metabolism genes in the progression and immune dynamics of PC. These genes not only serve as potential biomarkers for prognosis but also offer promising targets for personalized therapies. The integration of multi-omics data and advanced computational models provides new insights into the molecular underpinnings of PC and holds potential for improving treatment strategies. - Source: PubMed
Publication date: 2025/07/18
Abroudi Ali ShakeriDjamali MelikaAzizi Hossein - Glioma is a prevalent and aggressive form of brain neoplasm, characterized by a 5-year survival rate of less than 10%. Despite the encouraging outcomes demonstrated by numerous prognostic models for gliomas in preliminary research, these models frequently do not meet anticipated results when subjected to external validation. Our goal is to uncover potential prognostic biomarkers and therapeutic targets by concentrating on mismatch repair-related genes (MRRGs) that are significantly linked to glioma. - Source: PubMed
Publication date: 2025/05/09
Wang TongSun BohaoYu RuiZhang JingWu YichenWang DelinNi XiaoyingWang Hao - Lung adenocarcinoma (LUAD) is the most aggressive lung cancer phenotype, and patients' clinical response is often limited by primary or acquired mechanisms of resistance to oncological therapy. One of the current clinical needs is to define clinical predictors for the prognosis of LUAD, aiming at offering patients a persistent treatment likely to delay disease progression as much as possible. This study relies on data from The Cancer Genome Atlas (TCGA) to define the functional roles and prognostic implications of DNA replication-related genes in LUAD. - Source: PubMed
Publication date: 2025/03/27
Wu XiujuanXu QiangAiyiti PaerhatiMeng DefengZhang JingangCitarella FabrizioChristopoulos PetrosLiu TongDuan Wei