Unraveling senescence in cancer: mechanistic complexities and therapeutic opportunities

Area of Science:

• Cellular Biology
• Oncology
• Immunology
• Aging Research

Background:

• Senescence is a cellular state triggered by stressors, crucial for development, immunity, tissue repair, and aging.
• It acts as a tumor suppressor by arresting cell cycles, but prolonged senescence can paradoxically drive cancer progression.
• The senescence-associated secretory phenotype (SASP) in cancer cells promotes proliferation, invasion, and metastasis.

Purpose of the Study:

• To elucidate the molecular mechanisms of senescence in cancer suppression and progression.
• To review senescence-inducing conventional and emerging cancer therapies.
• To highlight senotherapeutic strategies and machine learning applications in oncology.

Main Methods:

• Comprehensive literature review of senescence in cancer.
• Analysis of molecular pathways regulating senescence.
• Examination of therapeutic strategies targeting senescence.
• Discussion of machine learning for biomarker discovery and patient stratification.

Main Results:

• Senescence can inhibit cancer by inducing growth arrest and enhancing immune surveillance.
• Prolonged senescence and SASP can promote tumorigenesis and metastasis.
• Conventional and novel therapies can induce senescence; senotherapeutics offer targeted approaches.
• Machine learning aids in identifying senescence biomarkers and stratifying cancer patients.

Conclusions:

• Senescence presents a dual role in cancer, acting as both a suppressor and promoter.
• Targeting senescence, particularly senescent cells, holds significant therapeutic promise.
• Integrating machine learning with senescence research can revolutionize cancer treatment efficacy.