The human microbiome can significantly impact health outcomes and may hold a vital role in disease diagnostics; if fulfilled, microbiome diagnostics may lead to a more effective, personalized approach that is better targeted, with early intervention and successful treatment.1
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How Microbiomes Reflect Systemic Health
The human microbiota can be described as a set of organisms that inhabit and interact with the human body. The human microbiome refers to the microbiota that inhabits a specific site in the body, such as the skin, mucosa, respiratory tract, gastrointestinal tract, urogenital tract, and the mammary gland.2 It consists of trillions of microbial cells and thousands of microbial species, mainly including bacteria but also viruses, fungi, protozoa, and helminths.3
These microorganisms colonize the various anatomical regions, forming complex and distinct ecosystems that adapt to the unique environmental conditions in each site.2
From birth, a continuous interaction between the body and the microbiota takes place, which play vital roles in maintaining health and wellbeing. The microbiome is continuously evolving in response to factors such as age, lifestyle, nutrition and diet, hormonal changes, genes, and even underlying disease. These factors significantly influence the human microbiome, with an alteration in microbiota, or dysbiosis, having the potential to cause serious conditions.2
Conditions ranging from cancer to inflammatory bowel disease, to cardiovascular disease and antibiotic-resistant infections, have all been associated with dysbiosis.2,4 As a result, maintaining a balanced microbiome plays a significant role in a healthy life.2
Current Limitations of Conventional Diagnostics
Conventional diagnostic methods range from traditional microbiology methods to molecular genetic methods.5
Microbiology methods are fundamental for diagnosing a range of infections; however, they do carry some limitations including the lengthy time for obtaining results, possibilities of false-negative results due to antibiotic use, and limited sensitivity to detecting microorganisms.5
Molecular approaches for diagnostics are more accurate and specific for accelerated detection and diagnosis of pathogens related to infections. However, in many countries, the use of molecular approaches for microbiological diagnostics, may not be feasible because of limited resources and high cost.5
Quantitative polymerase chain reaction (qPCR) is the standard of care for pathogen detection in several infectious diseases and is available in many clinical settings, with advantages such as increased sensitivity and detection compared to culturing. However, while qPCR can detect taxonomy-specific and antibiotic resistance-determining targets, it is limited to targets that require pre-specification before amplifying.6
Another molecular method includes ribosomal RNA (rRNA) through next-generation sequencing (NGS). The RNA sequence that encodes the 16S rRNA subunit in bacteria has been studied in taxonomy for several years due to its highly conserved regions with hypervariable areas across different species.6
NGS enables accelerated sequencing of these targets, with clinical 16S sequencing being used for pathogen detection in culture-negative infections, such as endocarditis, however, this method does not capture any additional genomic information. As a result, it is predominantly useful in species- and genus-level identification, but not for identifying other genetic components, such as antibiotic resistance genes.6
Microbiome Markers for Early Disease Detection
The human microbiota may hold potential for discovering effective disease diagnostic biomarkers. Commonly used biomarkers are derived from biological materials or imaging data, however, with the rise in artificial intelligence and machine learning, microbiome-based datasets can now also be mined to identify predictive, disease-specific markers.3
Disturbances in the microflora have been associated with several human diseases, including gastrointestinal tract diseases, cardiovascular disease, inflammation, neurological disorders, allergies, resistant bacterial infections, and cancer.3,7
Recent research has suggested that microbiome signatures may be used as disease diagnostic biomarkers.3 Microbial signatures such as Bacteroides fragilis and Fusobacterium nucleatum, have been associated with cancer development and progression, providing vital information on the processes behind cancer growth.1
With dysbiosis being recognized as a hallmark of many diseases, the microbiota is known to influence the development, progression, and treatment response of a range of malignancies, including cancers such as breast, lung, pancreatic and prostate.1,8
Lung cancer has been associated with an altered microbiota, especially via the gut-lung axis. The lung microbiome composition differs extensively between healthy individuals and those who have lung cancer, with increased levels of Streptococcus and Veillonella being associated with poor cancer prognosis. These bacteria have been detected in the sputum and bronchoalveolar lavage fluid and can be used as microbial markers for early disease detection.1
Additionally, immune checkpoint inhibitors such as anti-PD-(L)1 therapies can also be affected by the gut microbiota composition, with bacteria including Akkermansia muciniphila and Bifidobacterium being associated with better immunotherapy responses. Conversely, loss of these microbes can contribute to resistance, suggesting their potential as predictive markers to guide treatment selection.1
Role of Metagenomics/Multi-omics for Enhanced Sensitivity
Metagenomics consists of the study of genomes of a mixed community of organisms from environmental and human samples; these studies can be performed using metagenomics sequencing, or PCR that is based on 16S rRNA gene amplicon sequencing analysis, to study microbial rRNA.3
As previously mentioned, sequence-based methods that depend on 16S rRNA amplicon sequencing provide limited data on functional relationships within microbial communities, or on the relationship between the microbiota and the human host. Due to this, researchers have started to combine this analysis method with shotgun metagenomics to comprehensively sample the entire genome in all the organisms present in a sample.3
Multi-omics can also be used to predict diagnosis, prognosis, and efficient treatment plans for diseases. With a multi-omics approach, genes, DNA, proteins, metabolites, microbes, as well as both pathological and medical imaging information, can be integrated and analyzed in a comprehensive manner for a more unified and accurate hypothesis about the disease.3
Some clinical trials have recently used diverse methods for defining characteristics of patients who develop primary or acquired resistance to immunotherapy, with these trials aiming to develop an integrated approach for predicting drug response that depend on multimodal data including epigenetics, radiomics, genomics, transcriptomics, immunophenotypic data, and fecal microbiome data.3,9
Personalized Health and Preventative Care
With the human microbiome playing a significant role in how individuals respond to treatment, influence drug metabolism, or even immune system behavior, the integration of these insights can be used to create personalized care, including personalized cancer therapies for improved efficacy and reduced side effects.1
Additionally, with everyone having a unique microbial ecosystem, personalized care and treatment may be more effective than a ‘one-size-fits-all’ approach, as gut bacteria can impact absorption and breakdown of drugs, and mapping an individual’s microbial composition may enable tailored treatment plans and more effective preventative health strategies.1
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Future Outlook
While some microbial markers have been explored for their use in various diseases, there are also significant challenges in this area that require addressing, including the lack of standardized procedures, the need for complete verification of biomarkers collected from the microbiota, and the inherent variability of microbiota composition between individuals and populations.1
However, deciphering the intricacy of the microbiome and diseases may aid the diagnostic field in providing a less invasive, more personalized and successful approach for diagnostics and therapeutics.1
References
- Eslami, M., Naderian, R., Bahar, A. et al. (2025) ‘Microbiota as diagnostic biomarkers: advancing early cancer detection and personalized therapeutic approaches through microbiome profiling’, Frontiers in Immunology, 16. Available at: https://doi.org/10.3389/fimmu.2025.1559480.
- Ogunrinola, G.A., Oyewale, J.O., Oshamika, O.O. and Olasehinde, G.I. (2020) ‘The human microbiome and its impacts on health’, International Journal of Microbiology, 2020, pp. 1–7. Available at: https://doi.org/10.1155/2020/8045646.
- Hajjo, R., Sabbah, D.A. and Al Bawab, A.Q. (2022) ‘Unlocking the potential of the human microbiome for identifying disease diagnostic biomarkers’, Diagnostics, 12(7), p.1742. Available at: https://doi.org/10.3390/diagnostics12071742.
- Carding, C.R., Davis, N. and Hoyles, L. (2015) ‘The human intestinal microbiome: disease and drug metabolism’, Pharmacological Reviews, 67(3), pp. 861–896. Available at: https://doi.org/10.1124/pr.115.010769.
- Ivashko, M., Burmei, S., Yusko, L., Chaikovska, T. and Boyko, N. (2023) ‘Microbiological diagnostics: from traditional to molecular genetic methods: a literature review’, Bulletin of Medical and Biological Research, 5(4), pp. 34–41. Available at: https://doi.org/10.61751/bmbr/4.2023.34.
- Damhorst, G.L., Adelman, M.W., Woodworth, M.H. and Kraft, C.S. (2020) ‘Current capabilities of gut microbiome–based diagnostics and the promise of clinical application’, The Journal of Infectious Diseases, 223(Supplement 3). Available at: https://doi.org/10.1093/infdis/jiaa689.
- Cryan, J.F., O’Riordan, K.J., Cowan, C.S.M. et al. (2019) ‘The microbiota–gut–brain axis’, Physiological Reviews, 99(4), pp. 1877–2013. Available at: https://doi.org/10.1152/physrev.00018.2018.
- Garrett, W.S. (2015) ‘Cancer and the microbiota’, Science, 348(6230), pp. 80–86. Available at: https://doi.org/10.1126/science.aaa4972.
- Immune Resistance Interrogation Study (IRIS) (2025) ClinicalTrials.gov. Available at: https://clinicaltrials.gov/study/NCT04243720 (Accessed: 25 August 2025).
Last Updated: Sep 1, 2025