CoChat
Sign Up
Cited by 76Telomeres, Telomerase, and SenescenceSingle-cell and spatial transcriptomicsGenetics, Aging, and Longevity in Model Organisms

Comprehensive assessment of cellular senescence in the tumor microenvironment

Open access
Wang, Xiaoman, Ma, Lifei, Pei, Xiaoya, Wang, Heping, Tang, Xiaoqiang, Pei, Jian-Fei, Ding, Yang-Nan, Qu, Siyao +6 more·2022·Briefings in Bioinformatics
DOI 10.1093/bib/bbac118PMC PMC9116224
Open this paper in CoChat — free accountOpen PDF

Abstract

Cellular senescence (CS), a state of permanent growth arrest, is intertwined with tumorigenesis. Due to the absence of specific markers, characterizing senescence levels and senescence-related phenotypes across cancer types remain unexplored. Here, we defined computational metrics of senescence levels as CS scores to delineate CS landscape across 33 cancer types and 29 normal tissues and explored CS-associated phenotypes by integrating multiplatform data from ~20 000 patients and ~212 000 single-cell profiles. CS scores showed cancer type-specific associations with genomic and immune characteristics and significantly predicted immunotherapy responses and patient prognosis in multiple cancers. Single-cell CS quantification revealed intra-tumor heterogeneity and activated immune microenvironment in senescent prostate cancer. Using machine learning algorithms, we identified three CS genes as potential prognostic predictors in prostate cancer and verified them by immunohistochemical assays in 72 patients. Our study provides a comprehensive framework for evaluating senescence levels and clinical relevance, gaining insights into CS roles in cancer- and senescence-related biomarker discovery.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/bib/bbac118

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • These crude estimations are limited in applications during normal aging and tissue degeneration.

    “However, these crude estimations are still limited in applications during normal aging and tissue degeneration, as quiescent or differentiated cells dominate in such cases instead of proliferating cancer cells.”

    From Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators (2024) · cites this paper as [26]

    The Introduction says that senescence measurements lack universal, specific markers and that existing detection approaches have practical limitations. It does not say that crude estimations are particularly limited during normal aging or tissue degeneration, or attribute such a limitation to the predominance of quiescent or differentiated cells. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • At transcriptome level, using databases with annotated SRGs to estimate senescence provides some crude estimation of senescent versus dedifferentiated proliferating cells.

    “At the transcriptome level, using databases with annotated senescence-related genes (SRGs)7,22,23,24 to estimate the level of cellular senescence by either tally the normalized value25 or the ranking of these genes26 provides some crude estimation of senescent versus dedifferentiated proliferating cells”

    From Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators (2024) · cites this paper as [26]

    The Results section (‘Delineate senescence landscape at the pan-cancer level’; page unavailable) describes a transcriptome-based score combining curated senescence-related genes and reports that it distinguished known senescent from proliferative cells. The paper does not establish that the score distinguishes senescent cells specifically from dedifferentiated proliferating cells. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

No citation lists for this paper

No citation lists for this paper yet — checked again daily.

References 97 · Cited by 76

Research works printed but not linked: 78

  1. [1]Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin 2020; 70: 7– 30. 31912902
  2. [2]Mathon NF, Lloyd AC. Cell senescence and cancer. Nat Rev Cancer 2001; 1: 203– 13. 11902575
  3. [3]Zhang R, Chen HZ, Liu DP. The four layers of aging. Cell Syst 2015; 1: 180– 6. 27135911
  4. [5]Chen Z, Trotman LC, Shaffer D, et al. Crucial role of p53-dependent cellular senescence in suppression of Pten-deficient tumorigenesis. Nature 2005; 436: 725– 30. 16079851 PMC1939938
  5. [6]Campisi J. Cellular senescence as a tumor-suppressor mechanism. Trends Cell Biol 2001; 11: S27– 31. 11684439
  6. [7]Braig M, Lee S, Loddenkemper C, et al. Oncogene-induced senescence as an initial barrier in lymphoma development. Nature 2005; 436: 660– 5. 16079837
  7. [8]Partridge AH, Hughes ME, Warner ET, et al. Subtype-dependent relationship between young age at diagnosis and breast cancer survival. J Clin Oncol 2016; 34: 3308– 14. 27480155
  8. [10]Berian JR, Benson AB, III, Nelson H. Young age and aggressive treatment in colon cancer. JAMA 2015; 314: 613– 4. 26262799
  9. [11]Prieto LI, Baker DJ. Cellular senescence and the immune system in cancer. Gerontology 2019; 65: 505– 12. 31212284 PMC6703936
  10. [13]Calcinotto A, Kohli J, Zagato E, et al. Cellular senescence: aging, cancer, and injury. Physiol Rev 2019; 99: 1047– 78. 30648461
  11. [15]Hernandez-Segura A, Nehme J, Demaria M. Hallmarks of cellular senescence. Trends Cell Biol 2018; 28: 436– 53. 29477613
  12. [16]Ou HL, Hoffmann R, Gonzalez-Lopez C, et al. Cellular senescence in cancer: from mechanisms to detection. Mol Oncol 2021; 15: 2634– 71. 32981205 PMC8486596
  13. [18]Avelar RA, Ortega JG, Tacutu R, et al. A multidimensional systems biology analysis of cellular senescence in aging and disease. Genome Biol 2020; 21: 91. 32264951 PMC7333371
  14. [20]Kang TW, Yevsa T, Woller N, et al. Senescence surveillance of pre-malignant hepatocytes limits liver cancer development. Nature 2011; 479: 547– 51. 22080947
  15. [21]Biran A, Zada L, Abou Karam P, et al. Quantitative identification of senescent cells in aging and disease. Aging Cell 2017; 16: 661– 71. 28455874 PMC5506427
  16. [24]Ding YN, Wang HY, Chen HZ, et al. Targeting senescent cells for vascular aging and related diseases. J Mol Cell Cardiol 2021; 162: 43– 52. 34437878
  17. [25]Cheng LQ, Zhang ZQ, Chen HZ, et al. Epigenetic regulation in cell senescence. J Mol Med (Berl) 2017; 95: 1257– 68. 28887637
  18. [28]He S, Sharpless NE. Senescence in health and disease. Cell 2017; 169: 1000– 11. 28575665 PMC5643029
  19. [29]Collado M, Blasco MA, Serrano M. Cellular senescence in cancer and aging. Cell 2007; 130: 223– 33. 17662938
  20. [30]Medema JP. Escape from senescence boosts tumour growth. Nature 2018; 553: 37– 8. 32086500
  21. [31]Krtolica A, Campisi J. Integrating epithelial cancer, aging stroma and cellular senescence. Adv Gerontol 2003; 11: 109– 16. 12820530
  22. [32]Aunan JR, Cho WC, Soreide K. The biology of aging and cancer: a brief overview of shared and divergent molecular hallmarks. Aging Dis 2017; 8: 628– 42. 28966806 PMC5614326
  23. [33]Mermel CH, Schumacher SE, Hill B, et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol 2011; 12: R41. 21527027 PMC3218867
  24. [34]Taylor AM, Shih J, Ha G, et al. Genomic and functional approaches to understanding cancer aneuploidy. Cancer Cell 2018; 33: 676– 89.E3. 29622463 PMC6028190
  25. [35]El Gammal AT, Bruchmann M, Zustin J, et al. Chromosome 8p deletions and 8q gains are associated with tumor progression and poor prognosis in prostate cancer. Clin Cancer Res 2010; 16: 56– 64. 20028754
  26. [36]Hause RJ, Pritchard CC, Shendure J, et al. Classification and characterization of microsatellite instability across 18 cancer types. Nat Med 2016; 22: 1342– 50. 27694933
  27. [37]Colaprico A, Silva TC, Olsen C, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res 2016; 44: e71. 26704973 PMC4856967
  28. [38]Voeller HJ, Truica CI, Gelmann EP. Beta-catenin mutations in human prostate cancer. Cancer Res 1998; 58: 2520– 3. 9635571
  29. [39]Salmon H, Remark R, Gnjatic S, et al. Host tissue determinants of tumour immunity. Nat Rev Cancer 2019; 19: 215– 27. 30867580 PMC7787168
  30. [40]Yoshihara K, Shahmoradgoli M, Martinez E, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 2013; 4: 2612. 24113773 PMC3826632
  31. [41]Wang B, Kohli J, Demaria M. Senescent cells in cancer therapy: Friends or foes? Trends Cancer 2020; 6: 838– 57. 32482536
  32. [42]Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 2015; 12: 453– 7. 25822800 PMC4739640
  33. [44]Lauss M, Donia M, Harbst K, et al. Mutational and putative neoantigen load predict clinical benefit of adoptive T cell therapy in melanoma. Nat Commun 2017; 8: 1738. 29170503 PMC5701046
  34. [45]Wang C, Sun D, Huang X, et al. Integrative analyses of single-cell transcriptome and regulome using MAESTRO. Genome Biol 2020; 21: 198. 32767996 PMC7412809
  35. [46]Chen S, Zhu G, Yang Y, et al. Single-cell analysis reveals transcriptomic remodellings in distinct cell types that contribute to human prostate cancer progression. Nat Cell Biol 2021; 23: 87– 98. 33420488
  36. [47]van Tuyn J, Jaber-Hijazi F, MacKenzie D, et al. Oncogene-expressing senescent melanocytes up-regulate MHC class II, a candidate melanoma suppressor function. J Invest Dermatol 2017; 137: 2197– 207. 28647344 PMC5613751
  37. [48]Efremova M, Vento-Tormo M, Teichmann SA, et al. CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat Protoc 2020; 15: 1484– 506. 32103204
  38. [49]Yuan M, Zhu H, Xu J, et al. Tumor-derived CXCL1 promotes lung cancer growth via recruitment of tumor-associated neutrophils. J Immunol Res 2016; 2016: 6530410. 27446967 PMC4942661
  39. [50]Xiu MX, Liu YM, Kuang BH. The oncogenic role of Jagged1/Notch signaling in cancer. Biomed Pharmacother 2020; 129: 110416. 32593969
  40. [51]Dhawan P, Richmond A. Role of CXCL1 in tumorigenesis of melanoma. J Leukoc Biol 2002; 72: 9– 18. 12101257 PMC2668262
  41. [52]Kotsiou E, Okosun J, Besley C, et al. TNFRSF14 aberrations in follicular lymphoma increase clinically significant allogeneic T-cell responses. Blood 2016; 128: 72– 81. 27103745 PMC4956614
  42. [53]Carter SL, Cibulskis K, Helman E, et al. Absolute quantification of somatic DNA alterations in human cancer. Nat Biotechnol 2012; 30: 413– 21. 22544022 PMC4383288
  43. [54]Klein-Hessling S, Muhammad K, Klein M, et al. NFATc1 controls the cytotoxicity of CD8(+) T cells. Nat Commun 2017; 8: 511. 28894104 PMC5593830
  44. [55]Frazzi R. BIRC3 and BIRC5: multi-faceted inhibitors in cancer. Cell Biosci 2021; 11: 8. 33413657 PMC7792207
  45. [56]Du B, Liu M, Li C, et al. The potential role of TNFAIP3 in malignant transformation of gastric carcinoma. Pathol Res Pract 2019; 215: 152471. 31153693
  46. [57]Wang X, Chai Z, Li Y, et al. Identification of potential biomarkers for anti-PD-1 therapy in melanoma by weighted correlation network analysis. Genes (Basel) 2020; 11(4):435. PMC7230292 32316408
  47. [58]Qi X, Qi C, Wu T, et al. CSF1R and HCST: novel candidate biomarkers predicting the response to immunotherapy in non-small cell lung cancer. Technol Cancer Res Treat 2020; 19: 1533033820970663. 33153411 PMC7658512
  48. [59]Mariathasan S, Turley SJ, Nickles D, et al. TGFbeta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 2018; 554: 544– 8. 29443960 PMC6028240
  49. [60]Kim ST, Cristescu R, Bass AJ, et al. Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer. Nat Med 2018; 24: 1449– 58. 30013197
  50. [61]Auslander N, Zhang G, Lee JS, et al. Publisher correction: robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nat Med 2018; 24: 1942. PMC9639231 30333558
  51. [64]Jiang P, Gu S, Pan D, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med 2018; 24: 1550– 8. 30127393 PMC6487502
  52. [65]Pages F, Galon J, Dieu-Nosjean MC, et al. Immune infiltration in human tumors: a prognostic factor that should not be ignored. Oncogene 2010; 29: 1093– 102. 19946335
  53. [66]Taylor BS, Schultz N, Hieronymus H, et al. Integrative genomic profiling of human prostate cancer. Cancer Cell 2010; 18: 11– 22. 20579941 PMC3198787
  54. [67]Setlur SR, Royce TE, Sboner A, et al. Integrative microarray analysis of pathways dysregulated in metastatic prostate cancer. Cancer Res 2007; 67: 10296– 303. 17974971
  55. [68]Ross-Adams H, Lamb AD, Dunning MJ, et al. Integration of copy number and transcriptomics provides risk stratification in prostate cancer: a discovery and validation cohort study. EBioMedicine 2015; 2: 1133– 44. 26501111 PMC4588396
  56. [69]Li J, Xu C, Lee HJ, et al. A genomic and epigenomic atlas of prostate cancer in Asian populations. Nature 2020; 580: 93– 9. 32238934
  57. [70]Burton DGA, Stolzing A. Cellular senescence: immunosurveillance and future immunotherapy. Ageing Res Rev 2018; 43: 17– 25. 29427795
  58. [72]Hurez V, Daniel BJ, Sun L, et al. Mitigating age-related immune dysfunction heightens the efficacy of tumor immunotherapy in aged mice. Cancer Res 2012; 72: 2089– 99. 22496463 PMC3328641
  59. [73]Zhang H, Li S, Yang X, et al. miR-539 inhibits prostate cancer progression by directly targeting SPAG5. J Exp Clin Cancer Res 2016; 35: 60. 27037000 PMC4818461
  60. [74]Qie Y, Wang L, Du E, et al. TACC3 promotes prostate cancer cell proliferation and restrains primary cilium formation. Exp Cell Res 2020; 390: 111952. 32156598
  61. [75]Ye J, Chu C, Chen M, et al. TROAP regulates prostate cancer progression via the WNT3/survivin signalling pathways. Oncol Rep 2019; 41: 1169– 79. 30431120
  62. [76]McGranahan N, Favero F, de Bruin EC, et al. Clonal status of actionable driver events and the timing of mutational processes in cancer evolution. Sci Transl Med 2015; 7: 283ra254. PMC4636056 25877892
  63. [77]Davoli T, Uno H, Wooten EC, et al. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science 2017; 355(6322):eaaf8399. PMC5592794 28104840
  64. [79]Chan TA, Yarchoan M, Jaffee E, et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol 2019; 30: 44– 56. 30395155 PMC6336005
  65. [80]Chan TA, Wolchok JD, Snyder A. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 2015; 373: 1984. 26559592
  66. [81]Li PH, Zhang R, Cheng LQ, et al. Metabolic regulation of immune cells in proinflammatory microenvironments and diseases during ageing. Ageing Res Rev 2020; 64: 101165. 32898718
  67. [82]Ruscetti M, JPT M, Mezzadra R, et al. Senescence-induced vascular remodeling creates therapeutic vulnerabilities in pancreas cancer. Cell 2020; 181: 424, e421– 41. 32234521 PMC7278897
  68. [83]Zhang Y, Ma Y, Huang Y, et al. Benchmarking algorithms for pathway activity transformation of single-cell RNA-seq data. Comput Struct Biotechnol J 2020; 18: 2953– 61. 33209207 PMC7642725
  69. [84]Sun D, Wang J, Han Y, et al. TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment. Nucleic Acids Res 2021; 49: D1420– 30. 33179754 PMC7778907
  70. [85]Prakadan SM, Alvarez-Breckenridge CA, Markson SC, et al. Genomic and transcriptomic correlates of immunotherapy response within the tumor microenvironment of leptomeningeal metastases. Nat Commun 2021; 12(1):5955. PMC8511044 34642316
  71. [85]Chatsirisupachai K, Palmer D, Ferreira S, et al. A human tissue-specific transcriptomic analysis reveals a complex relationship between aging, cancer, and cellular senescence. Aging Cell 2019; 18: e13041. 31560156 PMC6826163
  72. [86]Tubbs A, Nussenzweig A. Endogenous DNA damage as a source of genomic instability in cancer. Cell 2017; 168: 644– 56. 28187286 PMC6591730
  73. [87]McCabe N, Turner NC, Lord CJ, et al. Deficiency in the repair of DNA damage by homologous recombination and sensitivity to poly(ADP-ribose) polymerase inhibition. Cancer Res 2006; 66: 8109– 15. 16912188
  74. [88]Frey MK, Pothuri B. Homologous recombination deficiency (HRD) testing in ovarian cancer clinical practice: a review of the literature. Gynecol Oncol Res Pract 2017; 4: 4. 28250960 PMC5322589
  75. [89]Kohli J, Wang B, Brandenburg SM, et al. Algorithmic assessment of cellular senescence in experimental and clinical specimens. Nat Protoc 2021; 16: 2471– 98. 33911261 PMC8710232
  76. [90]Coppe JP, Desprez PY, Krtolica A, et al. The senescence-associated secretory phenotype: the dark side of tumor suppression. Annu Rev Pathol 2010; 5: 99– 118. 20078217 PMC4166495
  77. [91]Yuan J, Hu Z, Mahal BA, et al. Integrated analysis of genetic ancestry and genomic alterations across cancers. Cancer Cell 2018; 34: 549, e549– 60. 30300578 PMC6348897
  78. [92]Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 2010; 26: 1572– 3. 20427518 PMC2881355

Authors
Wang, X., Ma, L., Pei, X., Wang, H., Tang, X., Pei, J.-F., Ding, Y.-N., Qu, S., Wei, Z.-Y., Wang, H.-Y., Wang, X., Wei, G.-H., Liu, D.-P., & Chen, H.-Z.
Affiliations
  • Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
  • State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
  • Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, China
  • Shanghai Medical College of Fudan University (CN)
Corresponding author
  • Wang, Xiaoman
  • Liu, De-Pei
  • Chen, Hou-Zao
ORCID
Venue
Briefings in Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2022-04-13
Volume
23
Issue
3
ISSN
  • 1467-5463 (ISSN-L)
  • 1477-4054
DOI
10.1093/bib/bbac118
PMC
PMC9116224
OpenAlex
W4223912833
Cited by
76
References
97
Citations by year
2026: 5 · 2025: 17 · 2024: 29 · 2023: 20 · 2022: 5
FWCI
10.98
Type
journal article
Language
en
Open access
Licence
Locations
  • Briefings in Bioinformatics (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
  • National Key Research and Development Project of China (2020YFC2008003, 2019YFA0801500)
  • Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (CIFMS2021-I2M-1-016, 2021-I2M-1-001, 2021-I2M-1-050, 2019-RCHL-006)
  • National Natural Science Foundation of China (82030017, 92149305, 91849207)
Topics
  • Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
Keywords
cellular senescence, tumor microenvironment, cancer types, immunotherapy response prediction, patient prognosis, prostate cancer, intratumoral heterogeneity, immune microenvironment, cross-platform data integration
MeSH terms
Humans, Immunotherapy, Male, Prostatic Neoplasms, Cellular Senescence, Genomics, Tumor Microenvironment

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review