Unraveling Aging-Associated Molecular Drivers in Tumour Progression and Therapy Response

Abstract:

Objective:

One potential aging-associated gene or biomarker will be selected from transcriptomic and epigenomic datasets derived from clinical tumour samples of elderly cancer patients and age-matched healthy controls. We aim to investigate the role of the candidate in modulating tumour progression and therapy response, with a focus on age-related changes in the tumour microenvironment. The ultimate goal is to understand how aging-associated molecular pathways contribute to cancer development and treatment resistance.

Background:

Aging is a major risk factor for cancer, with more than 60% of newly diagnosed cancer cases occurring in individuals aged 65 and above [1]. The aging process is accompanied by systemic changes such as chronic inflammation (“inflammaging”), immune senescence, and alterations in DNA repair mechanisms [2]. These changes may create a tumour-permissive environment and influence therapeutic efficacy. Despite growing evidence linking aging biology to cancer progression, the specific molecular factors that bridge these processes remain poorly defined. Identifying aging-associated genes or biomarkers that actively shape the tumour microenvironment could help develop precision oncology strategies tailored to elderly patients.

[1]. Siegel, R.L., et al., Cancer statistics, 2023. CA Cancer J Clin, 2023. 73(1): p. 17–48.
[2]. Fulop, T., et al., Immunosenescence and inflamm-aging as two sides of the same coin: friends or foes? Front Immunol, 2018. 8: p. 1960.
[3]. Campisi, J., Aging, cellular senescence, and cancer. Annu Rev Physiol, 2013. 75: p. 685–705.

Skills and experience required for the project:

Preferred discipline(s):

Year 2 or Year 3 undergraduate students in biomedical sciences, molecular biology, genetics, bioinformatics, or related disciplines are preferred.

Learning skills:

Students should be motivated to acquire theoretical knowledge on aging biology, tumour immunology, and bioinformatics, and develop practical laboratory and computational skills under mentor guidance.

Basic experimental skills:

Prior exposure to molecular/cellular/animal experiments is preferred; students will have opportunities to work with cell culture models and basic molecular assays.

Troubleshooting skills:

Students should be able to critically analyse experimental or computational challenges, maintain clear communication with mentors, and propose alternative strategies when obstacles arise.