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      Palbociclib Enhances Migration and Invasion of Cancer Cells via Senescence-Associated Secretory Phenotype-Related CCL5 in Non-Small-Cell Lung Cancer


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          Palbociclib is the first CDK4/6 inhibitor approved by FDA and has been studied in many types of cancer. However, some studies showed that it could induce epithelial-mesenchymal transition (EMT) of cancer cells. To test the effect of palbociclib on non-small-cell lung cancer (NSCLC) cells, we treated NSCLC cells with different concentrations of palbociclib and detected its effects via MTT, migration and invasion assays, and apoptosis test. Further RNA sequencing was performed in the cells treated with 2  μM palbociclib or control. And Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and protein-protein interaction network (PPI) were analyzed to explore the mechanism of palbociclib. The results showed that palbociclib significantly inhibited the growth of NSCLC cells and promoted apoptosis of cells, however, enhanced the migration and invasion abilities of cancer cells. RNA sequencing showed that cell cycle, inflammation-/immunity-related signaling, cytokine-cytokine receptor interaction, and cell senescence pathways were involved in the process, and CCL5 was one of the significantly differential genes affected by palbociclib. Further experiments showed that blocking CCL5-related pathways could reverse the malignant phenotype induced by palbociclib. Our results revealed that palbociclib-induced invasion and migration might be due to senescence-associated secretory phenotype (SASP) rather than EMT and suggested that SASP could act as a potential target to potentiate the antitumor effects of palbociclib in cancer treatment.

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          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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            The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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                Author and article information

                J Oncol
                J Oncol
                Journal of Oncology
                27 September 2022
                : 2022
                : 2260625
                1Key Laboratory of Cellular Physiology of the Ministry of Education, & Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
                2Department of Respiratory and Critical Care Medicine, The First Hospital, Shanxi Medical University, Taiyuan, Shanxi, China
                3Department of Etiology, Shanxi Cancer Hospital, Taiyuan, Shanxi, China
                4Cancer Center, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Shanxi, China
                Author notes

                Academic Editor: Magesh Muthu

                Author information
                Copyright © 2022 Pengzhou Kong et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 2 June 2022
                : 31 August 2022
                : 2 September 2022
                Funded by: Scientific Research Foundation for Advanced Talents of Shanxi Bethune Hospital
                Award ID: 2021RC018
                Funded by: Hospital Fund of Shanxi Bethune Hospital
                Award ID: 2020ZL02
                Funded by: Key Research and Development Program of Shanxi Province
                Award ID: 201903D321136
                Funded by: Fund for Shanxi “136 Project”
                Funded by: National Natural Science Foundation of China
                Award ID: 82072746
                Research Article

                Oncology & Radiotherapy
                Oncology & Radiotherapy


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