TECHNICAL RESOURCES · 应用与案例

Applications & Cases

Multi-omics technologies are widely applied across the core fields of life-science and medical research

Methodology note · content is citable as-is

01Application Areas

Multi-omics technologies are widely applied across the core fields of life-science and medical research

Cancer Research

metabolic reprogramming, tumor biomarkers, drug-resistance mechanisms

Plant Science

stress responses, metabolic pathways, quality improvement

Microbiome

gut microbiota, environmental microbes, host–microbiota interactions

Clinical Medicine

precision diagnosis, disease subtyping, drug-response prediction

Drug R&D

target discovery, efficacy evaluation, toxicity mechanisms

02Cancer-Research Case Studies

Breakthrough applications of integrative multi-omics in tumor biology

Metabolic Reprogramming in Liver Cancer

Integrative proteomics and genomics revealed metabolic-reprogramming mechanisms in HBV-related hepatocellular carcinoma, identifying PYCR2 and ADH1A as potential prognostic biomarkers.

Technical Approach

paired tumor–adjacent tissues from 159 patients; integrated proteome + genome + transcriptome analysis

Representative Publications

Gao Q, Zhu H, Dong L, et al. (2019). Integrated Proteogenomic Characterization of HBV-Related Hepatocellular Carcinoma. Cell, 179(2): 561-577. DOI: 10.1016/j.cell.2019.08.052

Gut-Microbiome Biomarkers for Colorectal Cancer

Fecal metagenomic sequencing identified non-invasive diagnostic biomarkers for colorectal cancer; the predictive model achievedan AUC of 0.84, significantly outperforming conventional methods.

Technical Approach

multi-cohort fecal metagenomic discovery + independent qPCR validation

Representative Publications

Yu J, Feng Q, Wong SH, et al. (2017). Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut, 66(1): 70-78. DOI: 10.1136/gutjnl-2015-309800

03Plant-Science Case Studies

Omics technologies advancing plant stress biology and metabolic-regulation research

Salt Tolerance in Rice

Transcriptomics salt tolerance comparative transcriptomics

Comparative transcriptome analysis under salt stress revealed differential responses between salt-tolerant and salt-sensitive rice cultivars in proline accumulation, starch–sucrose metabolism and flavonoid biosynthesis, providing molecular targets for breeding salt-tolerant rice.

Technical Approach

salt-tolerant vs salt-sensitive cultivars, 4 time points (0/6/24/48 h), RNA-seq transcriptome analysis

Representative Publications

Fang X, Mo J, Zhou H, et al. (2023). Comparative transcriptome analysis of gene responses of salt-tolerant and salt-sensitive rice cultivars to salt stress. Scientific Reports, 13: 19089. DOI: 10.1038/s41598-023-46389-1

Active Ingredients in Medicinal Plants

transcription factor artemisinin glandular-trichome development

The AP2 transcription factor TAR1 was found to regulate glandular-trichome development and artemisinin biosynthesis in Artemisia annua by directly targeting the key enzyme genes ADS and CYP71AV1, providing targets for genetic engineering of high-yield artemisinin production.

Research Significance

key regulatory genes for artemisinin metabolic engineering and synthetic biology

Representative Publications

Tan H, Xiao L, Gao S, et al. (2015). TRICHOME AND ARTEMISININ REGULATOR 1 Is Required for Trichome Development and Artemisinin Biosynthesis in Artemisia annua. Molecular Plant, 8(9): 1396-1411. DOI: 10.1016/j.molp.2015.04.002

04Microbiome Case Studies

Combined metagenomics and metabolomics resolving microbial-community functions

Gut Microbiota & Metabolic Health

metagenomics gut microbiota probiotic intervention

A metagenome-wide association study of gut microbiota in 345 Chinese subjects found significantly reduced butyrate-producing bacteria and increased opportunistic pathogens in type-2 diabetes patients, revealing the key role of gut dysbiosis in metabolic disease.

Application Value

precision probiotic formulation and personalized dietary intervention

Representative Publications

Qin J, Li Y, Cai Z, et al. (2012). A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature, 490: 55-60. DOI: 10.1038/nature11450

Soil Microbial-Community Function

metagenomics petroleum degradation environmental remediation

Metagenomic analysis of petroleum-degradation functional genes in microbial communities across three oilfield soils identified Proteobacteria and Actinobacteria as the dominant degraders, revealed the impact of oil contamination on nitrogen-cycle genes, and provided key microbial resources for bioremediation strategies.

Application Value

development of microbial degrader agents for oil-contaminated soils and ecological restoration

Representative Publications

Liu Q, He W, Zhang W, et al. (2024). Metagenomic analysis reveals the microbial response to petroleum contamination in oilfield soils. Science of The Total Environment, 912: 168972. DOI: 10.1016/j.scitotenv.2023.168972

05Clinical-Medicine Applications

Multi-omics has demonstrated enormous clinical potential in precision medicine

Application	Omics strategy	Sample type	Clinical value
Early cancer screening	plasma metabolome + ctDNA methylation	plasma	non-invasive early detection
Disease subtyping	transcriptome + proteome	tissue / blood	precise subtype classification
Drug-response prediction	genome + metabolome	blood / tissue	personalized medication plans
Prognosis assessment	multi-omics integrative scoring	tissue	recurrence-risk prediction
Rare-disease diagnosis	whole genome + transcriptome	blood	definitive molecular etiology

06Representative Publications in Clinical Medicine

Multi-omics technologies are moving precision medicine from concept to clinic

Early cancer screening

Nakatsu G, et al. (2015). Gut mucosal microbiome across stages of colorectal carcinogenesis. Nature Communications, 6: 8727. DOI: 10.1038/ncomms9727

Disease subtyping

TCGA (2012). Comprehensive molecular portraits of human breast tumours. Nature, 490: 61. DOI: 10.1038/nature11412

Drug-response prediction

Haslam A, Prasad V. (2019). Estimation of the Percentage of US Patients With Cancer Who Are Eligible for and Respond to Checkpoint Inhibitor Immunotherapy Drugs. JAMA Network Open, 2(5): e192535. DOI: 10.1001/jamanetworkopen.2019.2535

Prognosis assessment

Shen R, Mo Q, Schultz N, et al. (2012). Integrative subtype discovery in glioblastoma using iCluster. PLoS ONE, 7(4): e35236. DOI: 10.1371/journal.pone.0035236

Rare-disease diagnosis

Wojcik MH, et al. (2024). Genome Sequencing for Diagnosing Rare Diseases. New England Journal of Medicine, 390(21): 1985-1997. DOI: 10.1056/NEJMoa2314761

Q&AFAQ

Has multi-omics been used to find prognostic markers in liver cancer?

Yes. In HBV-related hepatocellular carcinoma, integrated proteomics and genomics revealed metabolic-reprogramming mechanisms and identified PYCR2 and ADH1A as potential prognostic biomarkers.

Could a stool test replace invasive screening for colorectal cancer?

Fecal metagenomic sequencing identified non-invasive diagnostic biomarkers for colorectal cancer, and the predictive model reached an AUC of 0.84, significantly outperforming conventional methods.

How would I design a salt-stress transcriptome study in rice?

Compare salt-tolerant and salt-sensitive cultivars at 4 time points (0/6/24/48 h) using RNA-seq; the comparative analysis revealed differential responses in proline accumulation, starch–sucrose metabolism and flavonoid biosynthesis.

What has gut-microbiome sequencing shown in people with type 2 diabetes?

A metagenome-wide association study of 345 Chinese subjects found significantly reduced butyrate-producing bacteria and increased opportunistic pathogens in type-2 diabetes patients, revealing the key role of gut dysbiosis in metabolic disease.

For early cancer screening, is a blood draw enough instead of a tissue sample?

Yes. In early cancer screening the omics strategy is plasma metabolome plus ctDNA methylation, the sample type is plasma, and the clinical value is non-invasive early detection.