Sorafenib, rapamycin, and venetoclax attenuate doxorubicin-induced senescence and promote apoptosis in HCT116 cells

Cellular senescence in cancer: molecular mechanisms and therapeutic targets

Aging exhibits several hallmarks in common with cancer, such as cellular senescence, dysbiosis, inflammation, genomic instability, and epigenetic changes. In recent decades, research into the role of cellular senescence on tumor progression has received widespread attention. While how senescence limits the course of cancer is well established, senescence has also been found to promote certain malignant phenotypes. The tumor-promoting effect of senescence is mainly elicited by a senescence-associated secretory phenotype, which facilitates the interaction of senescent tumor cells with their surroundings. Targeting senescent cells therefore offers a promising technique for cancer therapy. Drugs that pharmacologically restore the normal function of senescent cells or eliminate them would assist in reestablishing homeostasis of cell signaling. Here, we describe cell senescence, its occurrence, phenotype, and impact on tumor biology. A "one-two-punch" therapeutic strategy in which cancer cell senescence is first induced, followed by the use of senotherapeutics for eliminating the senescent cells is introduced. The advances in the application of senotherapeutics for targeting senescent cells to assist cancer treatment are outlined, with an emphasis on drug categories, and the strategies for their screening, design, and efficient targeting. This work will foster a thorough comprehension and encourage additional research within this field.

A Conversation with ChatGPT on Contentious Issues in Senescence and Cancer Research

Artificial intelligence (AI) platforms, such as Generative Pretrained Transformer (ChatGPT), have achieved a high degree of popularity within the scientific community due to their utility in providing evidence-based reviews of the literature. However, the accuracy and reliability of the information output and the ability to provide critical analysis of the literature, especially with respect to highly controversial issues, has generally not been evaluated. In this work, we arranged a question/answer session with ChatGPT regarding several unresolved questions in the field of cancer research relating to therapy-induced senescence (TIS), including the topics of senescence reversibility, its connection to tumor dormancy, and the pharmacology of the newly emerging drug class of senolytics. ChatGPT generally provided responses consistent with the available literature, although occasionally overlooking essential components of the current understanding of the role of TIS in cancer biology and treatment. Although ChatGPT, and similar AI platforms, have utility in providing an accurate evidence-based review of the literature, their outputs should still be considered carefully, especially with respect to unresolved issues in tumor biology. SIGNIFICANCE STATEMENT: Artificial Intelligence platforms have provided great utility for researchers to investigate biomedical literature in a prompt manner. However, several issues arise when it comes to certain unresolved biological questions, especially in the cancer field. This work provided a discussion with ChatGPT regarding some of the yet-to-be-fully-elucidated conundrums of the role of therapy-induced senescence in cancer treatment and highlights the strengths and weaknesses in utilizing such platforms for analyzing the scientific literature on this topic.

Researching New Drug Combinations with Senolytic Activity Using Senescent Human Lung Fibroblasts MRC-5 Cell Line

Therapeutically targeting senescent cells seems to be an interesting perspective in treating chronic lung diseases, which are often associated with human aging. The combination of the drug dasatinib and the polyphenol quercetin is used in clinical trials as a senolytic, and the first results point to the relief of physical dysfunction in patients with idiopathic pulmonary fibrosis. In this work, we tested new combinations of drugs and polyphenols, looking for senolytic activity using human lung fibroblasts (MRC-5 cell line) with induced senescence. We researched drugs, such as azithromycin, rapamycin, metformin, FK-506, aspirin, and dasatinib combined with nine natural polyphenols, namely caffeic acid, chlorogenic acid, ellagic acid, ferulic acid, gallic acid, epicatechin, hesperidin, quercetin, and resveratrol. We found new effective senolytic combinations with dasatinib and ellagic acid and dasatinib and resveratrol. Both drug combinations increased apoptosis, reduced BCL-2 expression, and increased caspase activity in senescent MRC-5 cells. Ellagic acid senolytic activity was more potent than quercetin, and resveratrol counteracted inflammatory cytokine release during senolysis in vitro. In conclusion, dasatinib and ellagic acid and dasatinib and resveratrol present in vitro senolytic potential like that observed for the combination in clinical trials of dasatinib and quercetin, and maybe they could be future alternatives in the senotherapeutic field.

Cellular senescence-related gene signature as a valuable predictor of prognosis in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a lethal tumor. Its prognosis prediction remains a challenge. Meanwhile, cellular senescence, one of the hallmarks of cancer, and its related prognostic genes signature can provide critical information for clinical decision-making.

METHOD

Using bulk RNA sequencing and microarray data of HCC samples, we established a senescence score model via multi-machine learning algorithms to predict the prognosis of HCC. Single-cell and pseudo-time trajectory analyses were used to explore the hub genes of the senescence score model in HCC sample differentiation.

RESULT

A machine learning model based on cellular senescence gene expression profiles was identified in predicting HCC prognosis. The feasibility and accuracy of the senescence score model were confirmed in external validation and comparison with other models. Moreover, we analyzed the immune response, immune checkpoints, and sensitivity to immunotherapy drugs of HCC patients in different prognostic risk groups. Pseudo-time analyses identified four hub genes in HCC progression, including CDCA8, CENPA, SPC25, and TTK, and indicated related cellular senescence.

CONCLUSIONS

This study identified a prognostic model of HCC by cellular senescence-related gene expression and insight into novel potential targeted therapies.