About Us

In the age of rapid technological advancements, the field of genome research has entered a transformative "big data" era. This shift, propelled by breakthroughs in high-throughput technologies, makes it possible to analyze biological data across various molecular levels, including the genome, transcriptome, proteome, and metabolome. The successful completion of large cancer projects has further opened doors to exploring and constructing extensive, intricate genomic datasets across multiple omics areas.

At the BioinfOMICS Lab, we specialize in employing bioinformatics strategies to dissect and understand cancer omics data. Our team has developed a range of databases and web tools designed to facilitate this analysis. Moreover, we actively collaborate with experimental biologists and medical scientists, engaging in research on pivotal topics such as cancer immunology, the discovery of cancer biomarkers, the study of cancer stem cells, and the development of novel bioinformatics tools.


Research Fields

  1. Bioinforamtics
  2. Cancer Genomics
  3. Next Generation Sequencing Analysis
  4. Lipidomics
  5. Omics Analysis







Group Leader


Prof. Wei-Chung Cheng 鄭維中
Program for Cancer Molecular Biology and Drug Discovery, China Medical University
Education:
  • Ph.D. National Tsing Hua University
Work Experience:
  • 2022~ China Medical University Program for Cancer Biology and Drug Discovery Professor
  • 2020-2022 China Medical University Associate Professor
  • 2019-2020 China Medical University Graduate Institute of Biomedical Sciences Associate Professor
  • 2015-2019 China Medical University Graduate Institute of Cancer Biology Assistant Professor
  • 2014-2015 China Medical University Research Center for Tumor Medical Science Researcher
Research Interest:
  • Genomics, Lipidomics, Bioinformatics, Next Generation Sequencing

Collaborative Physician


Dr. Yo-Liang Lai 賴宥良
Attending Physician
Department of Radiation Oncology, China Medical University Hospital, Taiwan
Education: School of medicine, Kaohsiung Medical University, Taiwan
Field of Study
  • Radiation oncology
  • Radiomics
  • Bioinformatics

Postdoctoral



Chia-Hsin Liu 劉佳鑫
National Yang-Ming University

Field of Study
  • Bioinformatics
  • Carcinobiology
Wen-Jen Lin 林文仁
School of medicine, China Medical University, Taiwan

Field of Study
  • Lipidomic analysis
  • Lipoprotein composition and function
  • Cancer metabolism and drug resistance
  • Metabolic syndrome dyslipidemia


Yi-Hong Liu 劉奕宏
National Yang-Ming University

Field of Study
  • Molecular biology
  • Bioinformatics
  • Carcinobiology

Research Assistants



Pei-Chun Shen 沈培鈞
M.S., Institute of Bioinformatics and Systems Biology,
National Chiao Tung University, Taiwan

Field of Study
  • Molecular Biology
  • Bioinformatics
Hsiu-Cheng Liu 劉修誠
Graduate Institute of Biomedical Sciences, China Medical University, Taiwan

Field of Study
  • Statistics


Meng-Hsin Tsai 蔡孟欣
M.S., Department of Management Information Systems, National Chung Hsing University, Taiwan

Field of Study
  • Data mining
Tzu-Ya Hung 黃姿雅
M.S., Section of Epidemiology, Graduate institute of Public Health, National Defense Medical Center, Taiwan

Field of Study
  • Biostatistic
  • Bioinformatics

Ph.D. Students



Dr. Yu-De Wang 王又德
Ph.D. student, Attending Physician
Department of Urology, China Medical University Hospital, Taiwan
School of medicine, National Taiwan University, Taiwan

Field of Study
  • Urology
  • Bioinformatics
Michael Anekson Widjaya 安順
China Medical University , Taiwan

Field of Study
  • Large Language Models (LLMs)
  • Molecular biology
  • Bioinformatics

M.S. Students



I-Chen Lin 林依真
School of Chinese Medicine, China Medical University, Taiwan

Field of Study
  • Diagnosis of Traditional Chinese Medicine
  • Bioinformatics
  • Cancer genomics





Selected Publications

  • Liu CH, Lai YL, Shen PC, Liu HC, Tsai MH, Wang YD, Lin WJ, Chen FH, Li CY, Wang SC, Hung MC, Cheng WC* . DriverDBv4: a multi-omics integration database for cancer driver gene research Nucleic Acids Res. 2023 Nov 13;52(D1):D1246–D1252.
  • Peng PH, Chen JL, Wu HH, Yang WH, Lin LJ, Lai JC, Chang JS, Syu JL, Wu HT, Hsu FT, Cheng WC* Hsu KW*. Interplay between lncRNA RP11-367G18.1 variant 2 and YY1 plays a vital role in hypoxia-mediated gene expression and tumorigenesis. Cancer Cell Int. 2023 Nov 8;23(1):266.
  • Widjaya MA, Liu CH, Lee SD, Cheng WC . Transcriptomics Meta-Analysis Reveals Phagosome and Innate Immune System Dysfunction as Potential Mechanisms in the Cortex of Alzheimer's Disease Mouse Strains. J Mol Neurosci. 2023 Sep 21(Online ahead of print).
  • Widjaya MA, Cheng YJ, Kuo YM, Liu CH, Cheng WC* Lee SD*. Transcriptomic Analyses of Exercise Training in Alzheimer's Disease Cerebral Cortex. J Alzheimers Dis. 2023 Mar 23 (Online ahead of print)
  • Hsu KW, Liu SH, Lai YL, Chen FH, Lin LJ, Peng PH, Li CY, Wang SC, Chen JL, Wu HH, Wu MZ, Sher YP, Cheng WC* . Using bioinformatics approaches to identify survival-related oncomiRs as potential miRNA-based treatments for lung adenocarcinoma. Comput Struct Biotechnol J. 2022 Aug 22;20:4626-4635.
  • Lai YL, Liu CH, Wang SC, Huang SP, Cho YC, Bao BY, Su CC, Yeh HC, Lee CH, Teng PC, Chuu CP, Chen DN, Li CY*, Cheng WC* . Identification of a Steroid Hormone-Associated Gene Signature Predicting the Prognosis of Prostate Cancer through an Integrative Bioinformatics Analysis. Cancers (Basel) . 2022 Mar 19;14(6):1565.
  • Lin YZ, Liu SH, Wu WR, Shen YC, Wang YL, Liao CC, Lin PL, Chang H, Liu LC, Cheng WC* . miR-4759 suppresses breast cancer through immune checkpoint blockade. Comput Struct Biotechnol J. 2022 Jan;20:241-251.
  • Teng PC, Huang SP, Liu CH, Lin TY, Cho YC, Lai YL, Wang SC, Yeh HC, Chuu CP, Chen DN, Cheng WC* . Identification of DNA Damage Repair-Associated Prognostic Biomarkers for Prostate Cancer Using Transcriptomic Data Analysis. Int J Mol Sci. 2021 Oct 29;22(21):11771.
  • Liu SH, Hsu KW, Lai YL, Lin YF, Chen FH, Peng PH, Lin LJ, Wu HH, Li Chia Yang, Wang SC, Wu MZ, Sher YP*, Cheng WC* . Systematic identification of clinically relevant miRNAs for potential miRNA-based therapy in lung adenocarcinoma. Molecular Therapy-Nucleic Acids. 2021 Sep 3; 25: 1-10
  • Chao YC, Chang HC, Jiang JK, Yang CY, Chen FH, Lai YL, Lin WJ, Li CY, Wang SC, Yang MH,Lin YF* Cheng WC* . Using bioinformatics approaches to investigate driver genes and identify BCL7A as a prognostic gene in colorectal cancer. Comput Struct Biotechnol J. 2021 Jul 1;19:3922-3929
  • Lin WJ, Chen PC, Liu HC, Cho YC, Hsu MK, Lin IC, Chen FH, Yang JC, Ma WL, Cheng WC* . LipidSig: a web-based tool for lipidomic data analysis. Nucleic Acids Res. 2021 Jul 2;49(W1):W336-W345.
  • Laio TS☨, Cheng WC☨ , Yang CY, Chen YQ, Su SH, Yeh TY, Lan HY, Lee CC, Lin HH, Lin CC, Lu RH, Chiou ET, Jiang JK*, Hwang WL*. The microRNA-210-Stathmin1 Axis Decreases Cell Stiffness to Facilitate the Invasiveness of Colorectal Cancer Stem Cells. Cancers 2021 Apr 12;13(8):1833
  • Cheng WC Chang CY, Lo CC, Hsieh CY, Kuo TT, Tseng GC, Wong SC, Chiang SF, Huang KC, Lai LC, Lu TP, Chao KSC* Identification of theranostic factors for patients developing metastasis after surgery for early-stage lung adenocarcinoma. Theranostics. 2021 Jan 26;11(8):3661-3675
  • Qi Y, Lai YL, Shen PC, Chen FH, Lin LJ, Wu HH, Peng PH,Hsu KW*, Cheng WC* . Identification and validation of a miRNA-based prognostic signature for cervical cancer through an integrated bioinformatics approach. Sci Rep. 2020 Dec 17; 10(1):22270.
  • Chen L☨, Ma WL☨, Cheng WC☨ , Yang JC, Wang HC, Su YT, Ahmad A, Hung YC, Chang WC*. Targeting lipid droplet lysophosphatidylcholine for cisplatin chemotherapy. J Cell Mol Med. 2020 Jun 16;24(13):7187-7200.
  • Liu SH, Shen PC, Chen CY, Hsu AN, Cho YC, Lai YL, Chen FH, Li CY, Wang SC, Chen M, Chung IF, Cheng WC* . DriverDBv3: a multi-omics database for cancer driver gene research. Nucleic Acids Res. 2020 Jan 8;48(D1):D863-D870.
  • Cheng WC , Liao TT, Lin CC, Yuan LE, Lan HY, Lin HH, Teng HW, Chang HC, Lin CH, Yang CY, Huang SC, Jiang JK, Yang SH, Yang MH*, . RAB27B-activated secretion of stem-like tumor exosomes delivers the biomarker microRNA-146a-5p, which promotes tumorigenesis and associates with an immunosuppressive tumor microenvironment in colorectal cancer. Int J Cancer. 2019 Oct 15; 145(8):2209-2224
  • Gong L, Wong CH, Cheng WC , Tjong H, Menghi F, Ngan CY, Liu ET, Wei CL*. Picky comprehensively detects high-resolution structural variants in nanopore long reads. Nat Methods. 2018 Jun;15(6):455-460
  • Wu MZ, Cheng WC , Chen SF, Nieh S, Connor CO, Liu CL, Tsai WW, Wu CJ, Martin L, Lin YS, Wu KJ, Lu LF, and Belmonte JC. miR-25/93 mediates hypoxia-induced immunosuppression by repressing cGAS. Nat Cell Biol. 2017 Oct;19(10):1286-1296.
  • Chung IF, Chang SH, Chen CY, Liu SH, Li CY, Chan CH, Shih CC, Cheng WC* . YM500v3: a database for small RNA sequencing in human cancer research. Nucleic Acids Res. 2017 Jan 4; 45(D1):D925-D931.
  • Chung IF, Chen CY, Su SC, Li CY, Wu KJ, Wang HW*, Cheng WC* . DriverDBv2: a database for human cancer driver gene research. Nucleic Acids Res. 2016 Jan 4;44(D1):D975-979.
  • Cheng WC , Chung IF, Tsai CF, Huang TS, Cheng CY, Wang SC, Chang TY, Sun HJ, Chao YC, Cheng CC, Wu CW, Wang HW*. YM500v2: A small RNA sequencing (smRNA-seq) database for human cancer miRNome research. Nucleic Acids Res. , 2015 Jan 28;43(D1):D862-D867 (SCI, IF: 11.561; 10/292 (3.4%) in BIOCHEMISTRY & MOLECULAR BIOLOGY)
  • Cheng WC , Chung IF,Chen CY, Sun HJ, Fen JJ, Teng WC, Chang TY, Wong TT, Wang HW*. DriverDB: an exome sequencing database for cancer driver gene identification. Nucleic Acids Res. , 2014 Jan;42(D1):D1048-1054
  • Cheng WC , Chung IF, Huang TS, Chang ST, Sun HJ, Wong TT, Wang HW*. YM500: a small RNA sequencing (smRNA-Seq) database for microRNA research. Nucleic Acids Res. , 2013 Jan;41(D1): D285-D294.





Other Publications

    2023
  1. Hong X, Hsieh MT, Tseng TY, Lin HY, Chang HC, Yau ST, Cheng WC , Ke B, Liao HH, Wu CY, Liu AA, Wu MM, Huang KY, Yang PC, Kuo SC, Hung MC* . Diarylheptanoid 35d overcomes EGFR TKI resistance by inducing hsp70-mediated lysosomal degradation of EGFR in EGFR-mutant lung adenocarcinoma. J Biol Chem. 2023 Jun;299(6):104814
  2. Wu HE, Su CC, Wang SC, Liu PL, Cheng WC , Yeh HC, Chuu CP, Chen JK, Bao BY, Lee CH, Ke CC, Chen YR, Yu YH, Huang SP,Li CY. Anticancer Effects of Morusin in Prostate Cancer via Inhibition of Akt/mTOR Signaling Pathway. Am J Chin Med. 2023;51(4):1019-1039.
  3. Lee HC, Cheng WC , Ma WL, Lin YH, Shin SJ, Lin YH. Cite Share Association of lipid composition and unsaturated fatty acids of VLDL with atrial remodeling in metabolic syndrome. Sci Rep. 2023 Apr 21;13(1):6575.
  4. 2022
  5. Chen YR, Wang SC, Huang SP, Su CC, Liu PL, Cheng WC , Chuu CP, Chen JK, Bao BY, Lee CH, Ke CC, Wu HE, Chang HH, Yeh HC, Li CY. Protodioscin inhibits bladder cancer cell migration and growth, and promotes apoptosis through activating JNK and p38 signaling pathways. Biomedicine & Pharmacotherapy. 2022 Dec;156,113929.
  6. Yu YC, Ahmed A, Lai HC, Cheng WC , Yang JC, Chang WC, Chen LM, Shan YS, Ma WL*. Review of the endocrine organ-like tumor hypothesis of cancer cachexia in pancreatic ductal adenocarcinoma. Front Oncol. 2022 Nov 17;12:1057930.
  7. Chang CK, Chang KH, Cheng WC , Chen PK, Chiang EI, Chang SH, Li YC, Chen CH, Chen DY*. Lipid metabolomic signature might predict subclinical atherosclerosis in patients with active rheumatoid arthritis. Clin Exp Rheumatol. 2022 Oct 4; 41(5):1120-1128.
  8. Yeh HC, Su CC, Wu YH, Lee CH, Bao BY, Cheng WC , Wang SC, Liu PL, Chiu CC, Chuu CP, Ke CC, Wu HE, Chen YR, Chung WJ, Huang SP*, Li CY*. Novel insights into the anti-cancer effects of 3-bromopyruvic acid against castration-resistant prostate cancer. Eur J Pharmacol. 2022 May 15;923:174929.
  9. Lu TL, Sher YP, Chen HC, Cheng WC , Hsu LH, Lee CC*. Articulatin B chain induced dendritic cells maturation and driven type I T helper cells and cytotoxic T cells activation. Life Sci. 2022 May 13;302:120635.
  10. Ma WL, Chang N, Yu Y, Su YT, Chen GY, Cheng WC , Wu YC, Li CC, Chang WC, Yang JC*. Ursolic acid silences CYP19A1/aromatase to suppress gastric cancer growth. Cancer Med. 2022 May 11;11(14):2824-2835.
  11. Li CY, Huang SP, Chen YT, Wu HE, Cheng WC , Huang CY, Yu CC, Lin VC, Geng JH, Lu TL, Bao BY*. TNFRSF13B is a potential contributor to prostate cancer. Cancer Cell Int. 2022 May 6;22(1):180.
  12. Wu FL, Chu PY, Chen GY, Wang K, Hsu WY, Ahmed A, Ma WL, Cheng WC , Wu YC*, Yang JC*. Natural anthraquinone compound emodin as a novel inhibitor of aurora A kinase: A pilot study. Chem Biol Drug Des. 2022 Jan;99(1):126-135.
  13. 2021
  14. Su CC, Wang SC, Chen IC, Chiu FY, Liu PL, Huang CH, Huang KH, Fang SH, Cheng WC , Huang SP, Yeh HC, Liu CC, Lee PY, Huang MY, Li CY. Zerumbone suppresses the LPS-induced inflammatory response and represses activation of the NLRP3 inflammasome in macrophages. Front. Pharmacol . 2021 May ;12, 1008.
  15. Lin YA, Chu PY, Ma WL, Cheng WC , Chan ST, Yang JC, Wu YC. Enzyme-Digested Peptides Derived from Lates calcarifer Enhance Wound Healing after Surgical Incision in a Murine Model. Mar Drugs . 2021 Mar 16;19(3):154.
  16. Liu WL, Li CY, Cheng WC , Chang CY, Chen YH, Lu CY, Wang SC, Liu YR, Cheng MH, Chong IW, Liu PL.High Mobility Group Box 1 Promotes Lung Cancer Cell Migration and Motility via Regulation of Dynamin-Related Protein 1. Int J Mol Sci . 2021 Mar 31;22(7):3628.
  17. 2020
  18. Qi Y, Lai YL, Shen PC, Chen FH, Lin LJ, Wu HH, Peng PH, Hsu KW, Cheng WC *. Identification and validation of a miRNA-based prognostic signature for cervical cancer through an integrated bioinformatics approach. Sci Rep . 2020 Dec; 10(1):22270
  19. Tsai LH, Hsu KW, Chiang CM, Yang HJ, Liu YH, Yang SF, Peng PH, Cheng WC , Wu HH. Cite Share Targeting interleukin-17 receptor B enhances gemcitabine sensitivity through downregulation of mucins in pancreatic cancer. Sci Rep . 2020 Oct 20;10(1):17817.
  20. Lin CC, Huang YK, Cho CF, Lin YS, Lo CC, Kuo TT, Tseng GC, Cheng WC , Chang WC, Hsiao TH, Lai LC, Shih JY, Liu YH, Chao KSC, Hsu JL, Lee PC, Sun X, Hung MC, Sher YP. Targeting positive feedback between BASP1 and EGFR as a therapeutic strategy for lung cancer progression. Theranostics . 2020 Aug 29;10(24):10925-10939.
  21. Chen L☨, Ma WL☨, Cheng WC☨ , Yang JC, Wang HC, Su YT, Ahmad A, Hung YC, Chang WC. Targeting lipid droplet lysophosphatidylcholine for cisplatin chemotherapy. J Cell Mol Med . 2020 Jun 16;24(13):7187-7200
  22. Ke CC, Chen LC, Yu CC, Cheng WC , Huang CY, Lin VC, Lu TL, Huang SP, Bao BY. Genetic Analysis Reveals a Significant Contribution of CES1 to Prostate Cancer Progression in Taiwanese Men. Cancers (Basel) . 2020 May 25;12(5):1346.
  23. Chang WC, Wang HC, Cheng WC , Yang JC, Chung WM, Ho YP, Chen L, Hung YC, Ma WL*. LDLR-mediated lipidome-transcriptome reprogramming in cisplatin insensitivity. Endocr Relat Cancer . 2020 Feb;27(2):81-95.
  24. 2019
  25. Yang JC, Chang N, Wu DC, Cheng WC , Chung WM, Chang WC, Lei FJ, Liu CJ, Wu IC, Lai HC, Ma WL*. Preclinical evaluation of exemestane as a novel chemotherapy for gastric cancer. J Cell Mol Med . 2019 Nov;23(11):7417-7426.
  26. Liu LC, Wang YL, Lin PL, Zhang X, Cheng WC , Liu SH, Chen CJ, Hung Y, Jan CI, Chang LC, Qi X, Hsieh-Wilson LC, Wang SC*. Long non-coding RNA HOTAIR promotes invasion of breast cancer cells through chondroitin sulfotransferase CHST15. Int J Cancer . 2019 Nov 1;145(9):2478-2487.
  27. Yu CC, Chen LC, Lin VC, Huang CY, Cheng WC , Hsieh AR, Chang TY, Lu TL, Lee CH, Huang SP, Bao BY. Effect of genetic variants in cell adhesion pathways on the biochemical recurrence in prostate cancer patients with radical prostatectomy. Cancer Med . 2019 Jun;8(6):2777-2783.
  28. Hung YL, Wang SC, Suzuki K, Fang SH, Chen CS, Cheng WC , Su CC, Yeh HC, Tu HP, Liu PL, Huang MY, Li CY. Bavachin attenuates LPS-induced inflammatory response and inhibits the activation of NLRP3 inflammasome in macrophages. Phytomedicine . 2019 Jun;59:152785.
  29. Kao SH, Cheng WC , Wang YT, Wu HT, Yeh HY, Chen YJ, Tsai MH, Wu KJ*. Regulation of miRNA biogenesis and histone modification by K63-polyubiquitinated DDX17 controls cancer stem-like features. Cancer Res . 2019 May 15;79(10):2549-2563.
  30. Han Y, Yang J, Qian X, Cheng WC , Liu SH, Hua X, Zhou L, Yang Y, Wu Q, Liu P, Lu Y*. DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies. Nucleic Acids Res . 2019 May 7;47(8):e45.
  31. Su CC, Hsieh KL, Liu PL, Yeh HC, Huang SP, Fang SH, Cheng WC , Huang KH, Chiu FY, Lin IL, Huang MY, Li CY*. AICAR Induces Apoptosis and Inhibits Migration and Invasion in Prostate Cancer Cells Through an AMPK/mTOR-Dependent Pathway. Int J Mol Sci . 2019 Apr 3;20(7).
  32. Hsu RH, Hwu WL, Chen M, Chung IF, Peng SS, Chen CY, Cheng WC , Chien YH, Lee NC*. Next-generation sequencing identifies TRPV4-related skeletal dysplasia in a boy with progressive bowlegs. Pediatr Neonatol . 2019 Feb;60(1):102-104.
  33. Hwang WL, Lan HY, Cheng WC , Huang SC, Yang MH*. Tumor stem-like cell-derived exosomal RNAs prime neutrophils for facilitating tumorigenesis of colon cancer. J Hematol Oncol . 2019 Jan;12(1):10.
  34. 2018
  35. Huang CY, Hsueh YM, Chen LC, Cheng WC , Yu CC, Chen WJ, Lu TL, Lan KJ, Lee CH, Huang SP, Bao BY*. Clinical significance of glutamate metabotropic receptors in renal cell carcinoma risk and survival. Cancer Med . 2018 Dec;7(12):6104-6111.
  36. Hung YL, Wang SC, Suzuki K, Fang SH, Chen CS, Cheng WC , Su CC, Yeh HC, Tu HP, Liu PL, Huang MY, Li CY. Bavachin attenuates LPS-induced inflammatory response and inhibits the activation of NLRP3 inflammasome in macrophages. Phytomedicine . 2018 Dec 10;59:152785
  37. Huang MY, Tu CE, Wang SC, Hung YL, Su CC, Fang SH, Chen CS, Liu PL, Cheng WC , Huang YW, Li CY*. Corylin inhibits LPS-induced inflammatory response and attenuates the activation of NLRP3 inflammasome in microglia. BMC Complement Altern Med . 2018 Aug;18(1):221.
  38. Shih JC, Ma GC, Cheng WC , Chen CY, Wu WJ, Chen M. SMAD2 as a risk locus for human left atrial isomerism detected by mother-fetus-pair exome sequencing and image studies including fetal ultrasound. Ultrasound Obstet Gynecol . 2019 May;53(5):702-705
  39. Kao TL, Kuan YP, Cheng WC , Chang WC, Jeng LB, Yeh S, Ma WL. Estrogen receptors orchestrate cell growth and differentiation to facilitate liver regeneration. Theranostics . 2018 Apr 9;8(10):2672-2682.
  40. Chang WC, Cheng WC , Cheng BH, Chen L, Ju LJ, Ou YJ, Jeng LB, Yang MD, Hung YC, Ma WL. Mitochondrial Acetyl-CoA Synthetase 3 is Biosignature of Gastric Cancer Progression. Cancer Med . 2018 Mar ; 7(4):1240-1252
  41. Lin KH, Huang MY, Cheng WC , Wang SC, Fang SH, Tu HP, Su CC, Hung YL, Liu PL, Chen CS, Wang YT, Li CY. RNA-seq transcriptome analysis of breast cancer cell lines under shikonin treatment. Sci Rep . 2018 Feb 8;8(1):2672.
  42. 2017
  43. Fan CC, Cheng WC , Huang YC, Sher YP, Liou NJ, Chien YC, Lin PS, Lin PS, Chen CH, Chang WC. EFHD2 promotes epithelial-to-mesenchymal transition and correlates with postsurgical recurrence of stage I lung adenocarcinoma. Sci Rep . 2017 Nov 6;7(1):14617.
  44. Chen L, Bao BY, Chang WC, Ho JY, Cheng BH, Wang CL, Tang Q, Cheng WC , Chang HW, Hung YC, Ma WL. Short androgen receptor poly-glutamine-promoted endometrial cancer is associated with benzo[a]pyrene-mediated aryl hydrocarbon receptor activation. J Cell Mol Med . 2017 Aug; 22(1):46-56
  45. Chiu KL, Lin YS, Kuo TT, Lo CC, Huang YK, Chang HF, Chuang EY, Lin CC, Cheng WC , Liu YN, Lai LC, and Sher YP. ADAM9 Enhances CDCP1 by Inhibiting miR-1 through EGFR Signaling Activation in Lung Cancer Metastasis. Oncotarget . 2017 May;8(29):47365-47378
  46. Hung YL, Fang SH, Wang SC, Cheng WC , Su CC, Chen CS, Huang MY, Hua KF, Shen KH, Wang YT, Suzuki K*, and Li CY*. Corylin protects LPS-induced sepsis and attenuates LPS-induced inflammatory response. Sci Rep . 2017 Apr; 7:46299
  47. 2016
  48. Wu HT, Kuo YC, Hung JJ, Huang CH, Chen WY, Chou TY, Chen Y, Chen YJ, Chen YJ, Cheng WC , Teng SC, Wu KJ. K63-polyubiquitinated HAUSP deubiquitinates HIF-1α and dictates H3K56 acetylation promoting hypoxia-induced tumour progression. Nat Commun . 2016 Dec; 7:13644.
  49. Chang WC, Huang SF, Lee YM, Lai HC, Cheng BH, Cheng WC , Ho YP, Jeng LB, Ma WL*. Cholesterol import and steroidogenesis are biosignatures for gastric cancer patient survival. Oncotarget 2016 Nov; 9:692-704
  50. Lai HC, Yeh CC, Jeng LB, Huang SF, Liao PY, Lei FJ, Cheng WC , Hsu CL, Cai X, Chang C*, Ma WL*. Androgen receptor mitigates postoperative disease progression of hepatocellular carcinoma by suppressing CD90+ populations and cell migration and by promoting anoikis in circulating tumor cells. Oncotarget 2016 Jun;
  51. Before 2016
  52. Wang HW, Sun HJ, Chang TY, Lo HH, Cheng WC , Tseng GC, Lin CT, Chang SJ, Pal N, and Chung IF. Discovering monotonic stemness marker genes from time-series stem cell microarray data. BMC Genomics , 2015 JAN:16 Suppl 2:S2
  53. Tsai YP, Chen HF, Chen SY, Cheng WC , Wang HW, Shen ZJ, Song C, Teng SC, He C, Wu KJ. TET1 regulates hypoxia-induced epithelial-mesenchymal transition by acting as a co-activator. Genome Biol . 2014 DEC:15(12):513.
  54. Huang HN, Chen SY, Hwang SM, Yu CC, Su MW, Mai W, Wang HW, Cheng WC , Schuyler SC, Ma N, Lu FL, Lu J. miR-200c and GATA binding protein 4 regulate human embryonic stem cell renewal and differentiation. Stem cell research . 2014 MAR:12(2):338-353
  55. Chen KH, Wang KJ, Tsai ML, Wang KM, Adrian AM, Cheng WC , Yang TS, Teng NC, Tan KP, Chang KS. Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm. BMC Bioinformatics . 2014, FEB:15(1):49.
  56. Li CY, Chiang CS, Cheng WC , Chen CR, Shu WY, Tsai ML, Huang CL, Chang HZ, Hseu RS, Chang CW, Fang SH, Hsu IC. Gene expression profiling of dendritic cells in different physiological stages under Cordyceps sinensis treatment. PLoS One 2012, JUL:7(7): e40824.
  57. Cheng WC , Shu WY, Li CY, Tsai ML, Chang CW, Chen CR, Wang TH, Hsu IC. Inter- and intra-individual variance of gene expression in clinical studies. PLoS One 2012, JUN:7(6):e38650.
  58. Chen CR, Shu WY, Tsai ML, Cheng WC , Hsu IC. THEME: a web tool for loop-design microarray data analysis. Computers in Biology and Medicine , 2012, FEB:42(2): 228-234.
  59. Chang CW, Cheng WC , Chen CR, Shu WY, Tsai ML, Huang CL, Hsu IC. Identification of Human Housekeeping Genes and Tissue-Selective Genes by Microarray Meta-Analysis. PLoS One 2011, JUL:6(7): e22859.
  60. Cheng WC , Chang CW, Chen CR, Tsai ML, Shu WY, Li CY, Hsu IC. Identification of Reference Genes across Physiological States for qRT-PCR through Microarray. PLoS One 2011, FEB:6(2): e17347.
  61. Li CY, Chao LP, Wang SC, Tsai ML, Chang HZ, Fang SH, Liao PC, Ho CL, Chen ST, Cheng WC , Chiang CS, Hua KF, Hsu IC. Honokiol Inhibits LPS-induced Maturation and Inflammatory Response on Human Monocyte-derived Dendritic Cells. Journal of Cellular Physiology . 2010, SEP:226(9): 2338-2349.
  62. Cheng WC , Tsai ML, Chang CW, Huang CL, Chen CR, Shu WY, Lee YS, Wang TH, Hong JH, Li CY, Hsu IC. Microarray Meta-analysis Database (M2DB): A Uniformly Pre-processed, Quality Controlled, and Manually Curated Human Clinical Microarray Database. BMC Bioinformatics . 2010, AUG:10;11(1):421.





Book Chapter

  1. Liu SH, Cheng WC . Identification of Cancer Driver Genes from a Custom Set of Next Generation Sequencing Data. In “Cancer Driver Genes” Methods Mol Biol . 2019;1907:19-36


Tool & Database

By combining bioinformatics approaches to analyze cancer omics data, we have developed:

DriverDB : A database for cancer driver gene research.

YM500 : A database for cancer miRNA research.

LipidSig : A web tool for lipidomics data analysis.

SurvOmics : A multi-omics database for cancer prognostic biomarkers.








DriverDB http://driverdb.bioinfomics.org/

DriverDB is designed to illuminate the complex interplay between genetic mutations and cancer progression. This database serves as a bridge, translating vast arrays of oncogenomic data into actionable insights that have the potential to inform both clinical care and basic research. At its core, DriverDB integrates and analyzes data from thousands of exome and RNA sequencing datasets, leveraging a multitude of annotation databases and bioinformatics algorithms to pinpoint driver genes and mutations implicated in cancer.

From its initial iteration, DriverDB has been meticulously updated to expand its data coverage, incorporating not only exome sequencing data but also RNA-seq datasets, CNV analyses, methylation patterns, survival data, and most recently, proteomics. This evolution reflects the growing need for a holistic view of cancer, acknowledging that the disease's complexity cannot be fully understood through the lens of single-omics analyses. By integrating multi-omics data, DriverDB v4 now encompasses a broad spectrum of biological information, from genomic mutations to gene expression levels and beyond, across a wide array of cancer types.

DriverDB distinguishes itself with its user-friendly interface, offering two primary perspectives: 'Cancer'and 'Gene.' These views allow users to explore the intricate relationships between specific cancers and the genetic alterations driving them. Additional features such as 'Meta-Analysis,' 'Expression,' 'Hotspot,' and 'Gene Set' enable detailed investigations into how mutations, expression levels, and clinical data intersect, offering insights into potential therapeutic targets and biomarkers.

The latest version, DriverDBv4, introduces proteomics into the mix, providing a more nuanced understanding of the proteome's role in cancer. With new multi-omics algorithms for identifying cancer drivers and innovative visualization tools, DriverDBv4 aims to enrich our comprehension of cancer heterogeneity. This resource is invaluable for researchers seeking to uncover the molecular underpinnings of cancer, aiming to propel the field towards more personalized and effective clinical strategies.

In essence, DriverDB embodies a comprehensive and integrative approach to cancer genomics and multi-omics, offering researchers and clinicians a powerful tool to decode the complexity of cancer, identify driver genes, and ultimately, advance the quest for cures.

Reference

  1. Liu CH, Lai YL, Shen PC, Liu HC, Tsai MH, Wang YD, Lin WJ, Chen FH, Li CY, Wang SC, Hung MC, Cheng WC* . DriverDBv4: a multi-omics integration database for cancer driver gene research Nucleic Acids Res. 2023 Nov 13;52(D1):D1246–D1252.
  2. Liu SH, Shen PC, Chen CY, Hsu AN, Cho YC, Lai YL, Chen FH, Li CY, Wang SC, Chen M, Chung IF, Cheng WC* . DriverDBv3: a multi-omics database for cancer driver gene research. Nucleic Acids Res . 2020 Jan 8;48(D1):D863-D870.
  3. Chung IF, Chen CY, Su SC, Li CY, Wu KJ, Wang HW*, Cheng WC* . DriverDBv2: a database for human cancer driver gene research. Nucleic Acids Res . 2016 Jan;44(D1):D975-9.
  4. Cheng WC , Chung IF,Chen CY, Sun HJ, Fen JJ, Teng WC, Chang TY, Wong TT*, Wang HW*. DriverDB: A exome sequencing (exome-seq) database for cancer driver gene identification. Nucleic Acids Res , 2014, JAN:42(D1):D1048-1054







YM500 http://ym500.bioinfomics.org/

YM500 addresses the burgeoning interest and complex landscape of small non-coding RNAs (sncRNAs) in the realms of both fundamental research and biotechnological applications, particularly within the context of cancer. Recognizing the pivotal role of microRNAs (miRNAs) and other sncRNAs, such as PIWI-interacting RNAs (piRNAs), tRNA-derived fragments (tRFs), small nuclear RNAs (snRNAs), and small nucleolar RNAs (snoRNAs), in gene regulation and tumorigenesis, YM500 has been meticulously developed to serve as a nexus of high-throughput small RNA sequencing (smRNA-seq) data analysis. The database facilitates the exploration of miRNA quantification, isomiR identification—including aspects like RNA editing and arm switching events—and novel miRNA predictions.

YM500 extends beyond miRNAs in its latest version, encompassing other functional sncRNAs to provide a broader understanding of their involvement in gene regulation and cancer. New sections introduced, such as 'Survival' and 'Cancer', offer survival analysis results and insights into differential expression analyses, miRNA–gene interactions, and cancer miRNA-related pathways across various cancer types. This expansion not only facilitates a deeper understanding of sncRNAs' roles in tumorigenesis but also enhances the utility of YM500 for both basic research and biotechnological applications.

Designed with an intuitive, user-friendly interface, YM500 allows researchers to navigate through complex datasets and analyses with ease, supporting a wide range of investigations from the identification of differentially expressed miRNAs and arm-switching events to meta-analyses based on custom-defined sample groups and clinical criteria. This integration of rich datasets, coupled with sophisticated analysis tools and a focus on user-defined explorations, positions YM500 as a pivotal resource in the advancement of sncRNA research and its implications in oncology and beyond.

Reference

  1. Chung IF, Chang SH, Chen CY, Liu SH, Li CY, Chan CH, Shih CC, and Cheng WC* . YM500v3: a database for small RNA sequencing in human cancer research Nucleic Acids Res . 2017 Jan; 45(D1):D925-D931.
  2. Cheng WC , Chung IF, Tsai CF, Huang TS, Cheng CY, Wang SC, Chang TY, Sun HJ, Chao YC, Cheng CC, Wu CW and Wang HW. YM500v2: A small RNA sequencing (smRNA-seq) database for human cancer miRNome research. Nucleic Acids Res . 2015 JAN:43(D1):D862-867 (SCI, IF: 11.561; 10/292 (3.4%) in BIOCHEMISTRY & MOLECULAR BIOLOGY)
  3. Cheng WC , Chung IF, Huang TS, Chang ST, Sun HJ, Wong TT*, Wang HW*. YM500: A small RNA Sequencing (smRNA-Seq) database for miRNA research. Nucleic Acids Res , JAN:41(D1): D285-D294.







LipidSig https://lipidsig.bioinfomics.org/

LipidSig is a pioneering web tool designed to address the growing complexities and analytical challenges in the field of lipidomics. Recognizing the unique and diverse characteristics of lipids, such as their classes, double bonds, and chain lengths, which significantly influence their biological functions, LipidSig emerges as a comprehensive solution for researchers and scientists engaged in exploring the intricate world of lipid biology.

This user-friendly platform streamlines the process of lipidomic data analysis by offering a suite of integrated features that cater to a wide range of research needs. From profiling and differential expression analysis to correlation, network analysis, and machine learning, LipidSig facilitates a deeper understanding of lipid effects on cellular and disease phenotypes. One of the tool's standout features is its ability to convert lipid species into specific characteristics based on a user-defined table, enhancing the efficiency of data mining for both individual lipids and groups based on their characteristics.

LipidSig is not just a data analysis tool; it is an enabler of scientific discovery. It empowers researchers to autonomously identify lipid species and assign them comprehensive characteristics upon data entry, streamlining the exploration of lipid functions and their biological implications. With features like the "Network" function, which provides a systems biology perspective on lipid interactions, and "Multiple Group" analysis for complex experimental designs, LipidSig offers an unmatched depth of analysis.

Available in its enhanced version, LipidSig 2.0, the platform now supports an expanded array of data processing methods and analytical processes, including data preprocessing, lipid ID annotation, differential expression, enrichment analysis, and network analysis. This evolution marks a significant step forward in the automation and sophistication of lipidomic research, making LipidSig an indispensable tool for advancing our understanding of the roles lipids play in cellular processes and disease development.

By leveraging LipidSig, researchers can navigate the complexities of lipidomic datasets with unprecedented ease and precision, paving the way for novel insights and advancements in the field of lipid biology. Whether for basic research or biotechnological applications, LipidSig stands as a testament to the power of innovative tools in transforming scientific exploration and discovery.

Reference

  1. Liu CH, Shen PC, Lin WJ, Liu HC, Tsai MH, Huang TY, Chen IC, Lai YL, Wang YD, Hung MC, Cheng WC* . LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis. Nucleic Acids Res . 2024 May 6.
  2. Lin WJ, Shen PC, Liu HC, Cho YC, Hsu MK, Lin IC, Chen FH, Yang JC, Ma WL, Cheng WC* . LipidSig: a web-based tool for lipidomic data analysis. Nucleic Acids Res . 2021 July 2 52(W1):W336–W345.







SurvOmics https://survomics.bioinfomics.org/

SurvOmics is developed to advance cancer research by focusing on prognostic biomarkers by integrating multi-omics data. This innovative tool addresses the pressing need for precise methodologies to analyze complex biological information across various cancer types, aiding in identifying key biomarkers that could predict disease progression and response to treatment.

At its core, SurvOmics enables researchers to uncover the intricate relationships between genes, cancers, and patient outcomes. It offers dedicated sections for exploring these relationships, alongside customizable analysis tools that allow for the construction of unique multi-omics signatures and the examination of biomarkers in relation to clinical factors for tailored patient group studies.

Designed to empower researchers in their quest to understand cancer's complexities, SurvOmics provides a comprehensive, user-friendly platform for the exploration of prognostic biomarkers. It stands as a vital resource in the pursuit of precision medicine, offering new insights into cancer prognosis and contributing to the development of targeted, effective therapies.



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We currently have openings for graduate students, research assistants, and postdoctoral fellows in computational systems biology. In particular, we are looking for researchers interested in studying human disease and omics data analysis. If you are interested, please email us at wccheng@mail.cmu.edu.tw.



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