Wang Lab

1. Wu T#, Dai Y#, Xu Y, Zheng J, Chen S, Zhang Y, Tian P, Zheng X*, Wang H*. ExosomePurity: tumour purity deconvolution in serum exosomes based on miRNA signatures. Briefings in Bioinformatics. 2023 May 19;24(3):bbad119.

2. Chen S#, Zheng J#, Zhang B, Tang X, Cun Y, Wu T, Xu Y, Ma T, Cheng J, Yu Z*, Wang H*. Identification and characterization of virus-encoded circular RNAs in host cells. Microb Genom. 2022 Jun;8(6):mgen000848.

3. Huang X, Xu Y, Qian L, Zhao Q, Liu P, Lü J, Guo Y, Ma W, Wang G, Li S, Luo A, Yang X, Wang H*, Yu Z*. Evolution of gene expression signature in mammary gland stem cells from neonatal to old mice. Cell Death Dis. 2022 Apr 12;13(4):335.

4. Xu Y, Zheng J, Cai Z, Li W, Köhler J, Dai Y, Cheng X, Wu T, Zhang F, Wang H*. Therapeutic Response-Based Reclassification of Multiple Tumor Subtypes Reveals Intrinsic Molecular Concordance of Therapy Across Histologically Disparate Cancers. Front Cell Dev Biol. 2021 Nov 12;9:773101.

5. Zheng J, Wang H*. Pharmaco-omics data sheds light on therapy-oriented prospects of precision medicine. EBioMedicine. 2021 Aug;70:103493.

6. Dai Y, Cao Y, Köhler J, Lu A, Xu S*, Wang H*. Unbiased RNA-Seq-driven identification and validation of reference genes for quantitative RT-PCR analyses of pooled cancer exosomes. BMC Genomics. 2021 Jan 6;22(1):27.

7. Cai Z#, Xue H#, Xu Y, Köhler J, Cheng X, Dai Y, Zheng J, Wang H*. Fcirc: A comprehensive pipeline for the exploration of fusion linear and circular RNAs. Gigascience. 2020 Jun 1;9(6):giaa054.

8. Esteban-Burgos L, Wang H, Nieto P, Zheng J, Blanco-Aparicio C, Varela C, Gómez-López G, Fernández-García F, Sanclemente M, Guerra C, Drosten M, Galán J, Caleiras E, Martínez-Torrecuadrada J, Fajas L, Peng SB, Santamaría D, Musteanu M*, Barbacid M*. Tumor regression and resistance mechanisms upon CDK4 and RAF1 inactivation in KRAS/P53 mutant lung adenocarcinomas. Proc Natl Acad Sci U S A. 2020 Sep 29;117(39):24415-24426.

9. Luo A#, Xu Y#, Li S#, Bao J, Lü J, Ding N, Zhao Q, Fu Y, Liu F, Cho WC, Wei X*, Wang H*, Yu Z*. Cancer stem cell property and gene signature in bone-metastatic Breast Cancer cells. Int J Biol Sci. 2020 Jul 19;16(14):2580-2594.

10. Kim K*, Lane EA, Saftien A, Wang H, Xu Y, Wirtz-Peitz F, Perrimon N*. Drosophila as a model for studying cystic fibrosis pathophysiology of the gastrointestinal system. Proc Natl Acad Sci U S A. 2020 May 12;117(19):10357-10367.

11. Wang H*, Lv Q, Xu Y, Cai Z, Zheng J, Cheng X, Dai Y, Jänne P, Ambrogio C*, Köhler J*. An integrative pharmacogenomics analysis identifies therapeutic targets in KRAS-mutant lung cancer. EbioMedicine. 2019 Nov;49:106-117.

12. Chen W, Wang J, Shi J, Yang X, Yang P, Wang N, Yang S, Xie T, Yang H, Zhang M, Wang H*, Fei J*. Longevity Effect of Liuwei Dihuang in Both Caenorhabditis Elegans and Aged Mice. Aging and Disease. 2019 Jun 1;10(3):578-591.

13. Liu R#, Wang J#, Ukai M, Sewon K, Chen P, Suzuki Y, Wang H*, Aihara K*, Okada-Hatakeyama M*, Chen L*. Hunt for the tipping point during endocrine resistance process in breast cancer by dynamic network biomarkers. JMol Cell Biol. 2018 Nov 1.

14. Ambrogio C*, Köhler J, Zhou ZW, Wang H, Paranal R, Li J, Capelletti M, Caffarra C, Li S, Lv Q, Gondi S, Hunter JC, Lu J, Chiarle R, Santamaría D, Westover KD*, Jänne PA*. KRAS Dimerization Impacts MEK Inhibitor Sensitivity and Oncogenic Activity of Mutant KRAS. Cell. 2018 Feb 8;172(4):857-868.e15.

15. Wang L*, Wang H, Song D, Xu M, Liebmen M*. New strategies for targeting drug combinations to overcome mutation-driven drug resistance. Seminars in Cancer Biology. 2017 Feb;42:44-51.

16. Liu Y, Fei T, Zheng X, Brown M, Zhang P*, Liu X*, Wang H*. An integrative pharmacogenomic approach identifies two-drug combination therapies for personalized cancer medicine. Sci Rep. 2016, 26:6.

17. Wang J, Lv Q, Li X, Liu Y, Liu K, Wang H*. Post-transcriptional and translational regulation modulates gene co-expression behavior in more synchronized pace to carry out molecular function in the cell. Gene. 2016, 587(2).

18. Zhang N#, Wang H#, Fang Y, Wang J*, Zheng X*, Liu X*. Predicting anticancer drug responses using a dual-layer integrated cell line-drug network model. Plos Computational Biology. 2015 Sep 29;11(9).

19. Wang H*#, Zheng X#, Fei T, Wang J, Li X, Liu Y, Zhang F. Towards pathway-centric cancer therapies via pharmacogenomic profiling analysis of ERK signalling pathway. Clin Transl Med. 2015 Dec;4(1):66.

20. Wang H*, Meyer C, Fei T, Wang G, Zhang F*, Liu X. A systematic approach identifies FOXA1 as a key factor in the loss of epithelial traits during the epithelial-to-mesenchymal transition in lung cancer. BMC Genomics. 2013, 14: 680.

21. Wang H*, Wang Q, Shen B and Li X*. Systematic investigation of global coordination among mRNA and protein in cellular society. BMC Genomics. 2010, 11:364.

22. Wang H*#, Wang Q*#, Li X, Shen B, Ding M and Shen Z. Towards patterns tree of gene coexpression in eukaryotic species. Bioinformatics. 2008,24(11)：1367-1373.

Fcirc	In cancer cells, fusion genes can produce linear and chimeric fusion-circular RNAs (f-circRNAs), which play a role in the regulation of gene expression and have been linked to malignant transformation, cancer progression, treatment resistance, and have even been identified as innovative therapeutic targets (e.g., EML4-ALK). Fcirc is a powerful and comprehensive Python-based pipeline for identifying linear and circular RNA transcripts from known fusion events in RNA-Seq datasets with higher accuracy and shorter computation times than previously released algorithms.
ExosomePurity	Exosomes, biomacromolecules secreted by cancer cells, play pivotal roles in tumor initiation and progression, holding promise for non-invasive cancer monitoring. However, accurate measurement of tumor-derived exosome purity is crucial as exosomes from both cancerous and healthy cells often coexist in liquid biopsies, facilitating early detection and unbiased biomarker identification. ExosomePurity, a deconvolution model leveraging miRNA-Seq data, estimates tumor purity in cancer patient serum exosomes. Demonstrating robustness and accuracy in both simulated and real-world datasets, ExosomePurity also extends to correcting bias in differential gene expression analysis. This tool enables the research community to more effectively explore non-invasive cancer diagnosis and progression tracking.
scPharm	Tumor heterogeneity presents a major challenge in cancer therapy, limiting its effectiveness. Single-cell RNA sequencing (scRNA-seq) provides a method to capture gene expression profiles at single-cell resolution, while drug perturbation experiments generate valuable pharmacological data at the cellular level. scPharm is a computational framework that integrates large-scale drug genomics maps with scRNA-seq data to identify pharmacological subtypes within tumors and prioritize personalized drugs. scPharm has demonstrated robust efficacy in samples from cancer patients, tumor cell lines, and mouse tumor models, exhibiting outstanding predictive performance and computational efficiency compared to other tools. Additionally, scPharm has the capability to predict combination therapy strategies and assess drug side effects. In summary, scPharm offers a novel approach to reveal therapeutic heterogeneity within tumors at single-cell resolution, advancing precision medicine in cancer.
vcRNAdb	Viral infections contribute to the development of various human malignancies, including hepatocellular carcinoma, cervical cancer, and gastric cancer. Recent studies have revealed that viral infections lead to the expression of a specific type of non-coding RNA called circular RNA (circRNA), which plays crucial regulatory roles in viral biology and related malignancies. Therefore, this study employs bioinformatics approaches to systematically identify viral circRNAs from RNA-Seq samples of virus-infected individuals, primarily sourced from cancer samples. A viral circRNA database is established to collect and annotate the biological characteristics of these circRNAs. The database aims to provide data resources to advance research in viral biology, novel biomarkers, and precision medicine for cancer.
DTDB	In cancer treatment, similar types of cancer often require different treatments, while different types of cancer may require similar treatments. However, leveraging comprehensive analyses of drug response data and molecular changes, especially in revealing the consistency of treatment mechanisms among histological subtypes of cancer, has not been fully exploited. This study integrated pharmacological, genomic, and transcriptomic analysis data provided by the Cancer Genome Project to systematically investigate the pharmacological subtypes and molecular mechanisms of cancer through computer-based research, uncovering similarities in treatment responses among different tumor subtypes. We also introduced a new method to redefine intercellular and interdrug similarity, providing a new perspective for studying cancer heterogeneity. This research demonstrates how cancer can be classified using pharmacological and genomic data and provides resources for the reclassification of tumors based on treatment response.

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