CoChat
Sign Up
Cited by 14Lung Cancer Research StudiesFerroptosis and cancer prognosisCancer Immunotherapy and Biomarkers

Construction of a prognostic model with CAFs for predicting the prognosis and immunotherapeutic response of lung squamous cell carcinoma

Open access
Zhang, Xiang, Xiao, Qingqing, Zhang, Cong, Zhou, Qinghua, Xu, Tao·2024·Journal of Cellular and Molecular Medicine
DOI 10.1111/jcmm.18262PMC PMC10960179
Open this paper in CoChat — free account

Abstract

Lung squamous cell carcinoma (LUSC) is one of the subtypes of lung cancer (LC) that contributes to approximately 25%–30% of its prevalence. Cancer‐associated fibroblasts (CAFs) are key cellular components of the TME, and the large number of CAFs in tumour tissues creates a favourable environment for tumour development. However, the function of CAFs in the LUSC is complex and uncertain. First, we processed the scRNA‐seq data and classified distinct types of CAFs. We also identified prognostic CAFRGs using univariate Cox analysis and conducted survival analysis. Additionally, we assessed immune cell infiltration in CAF clusters using ssGSEA. We developed a model with a significant prognostic correlation and verified the prognostic model. Furthermore, we explored the immune landscape of LUSC and further investigated the correlation between malignant features and LUSC. We identified CAFs and classified them into three categories: iCAFs, mCAFs and apCAFs. The survival analysis showed a significant correlation between apCAFs and iCAFs and LUSC patient prognosis. Kaplan–Meier analysis showed that patients in CAF cluster C showed a better survival probability compared to clusters A and B. In addition, we identified nine significant prognostic CAFRGs (CLDN1, TMX4, ALPL, PTX3, BHLHE40, TNFRSF12A, VKORC1, CST3 and ADD3) and subsequently employed multivariate Cox analysis to develop a signature and validate the model. Lastly, the correlation between CAFRG and malignant features indicates the potential role of CAFRG in promoting tumour angiogenesis, EMT and cell cycle alterations. We constructed a CAF prognostic signature for identifying potential prognostic CAFRGs and predicting the prognosis and immunotherapeutic response for LUSC. Our study may provide a more accurate prognostic assessment and immunotherapy targeting strategies for LUSC.

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 · 10.1111/jcmm.18262

Cites

1 of 1 references verified0 citation errors · 0 not checkable

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?

  • In lung squamous cell carcinoma, elevated expression of the CST3 gene in tumor-associated fibroblasts is associated with reduced responsiveness to immunotherapy and worse clinical outcomes.

    “In lung squamous cell carcinoma, elevated expression of the CST3 gene in tumor-associated fibroblasts, an alternative source of cystatin C, is also associated with reduced responsiveness to immunotherapy and worse clinical outcomes39.”

    From Oligomeric cystatin C supports the immunosuppressive activity of myeloid cells through interaction with inhibitory receptors (2025) · cites this paper as [39]

    The Results (Construction and validation of prognostic model; no page given) report CST3 overexpression in LUSC tumour tissue, and the Discussion identifies CST3 as one of nine genes in a prognostic CAF-related signature. The Results (Exploration of the immune landscape; no page given) associate immunotherapy responsiveness with the composite risk groups, not with CST3 expression in tumour-associated fibroblasts specifically. The paper therefore does not establish that elevated fibroblast CST3 itself predicts reduced responsiveness or worse outcomes. (LLM, conf=0.86)

    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.

Cites

Flagged first, then by citations

Research works printed but not linked: 77

  1. [1]Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. CA Cancer J Clin. 2021; 71(1): 7‐ 33. 33433946
  2. [2]Herbst RS, Morgensztern D, Boshoff C. The biology and management of non‐small cell lung cancer. Nature. 2018; 553(7689): 446‐ 454. 29364287
  3. [3]Denisov EV, Schegoleva AA, Gervas PA, et al. Premalignant lesions of squamous cell carcinoma of the lung: the molecular make‐up and factors affecting their progression. Lung Cancer. 2019; 135: 21‐ 28. 31446997
  4. [4]Perez‐Moreno P, Brambilla E, Thomas R, Soria JC. Squamous cell carcinoma of the lung: molecular subtypes and therapeutic opportunities. Clin Cancer Res. 2012; 18(9): 2443‐ 2451. 22407829
  5. [5]Bertaglia V, Vallone S, Pacchiana MV, Novello S. Advanced squamous lung cancer: therapeutic options, future directions, unmet needs and results of a monocentric survey. Lung Cancer Manag. 2017; 6(3): 93‐ 107. 30643575 PMC6310352
  6. [6]Lau S, Pan Y, Velcheti V, et al. Squamous cell lung cancer: current landscape and future therapeutic options. Cancer Cell. 2022; 40(11): 1279‐ 1293. 36270277
  7. [7]Khozin S, Miksad RA, Adami J, et al. Real‐world progression, treatment, and survival outcomes during rapid adoption of immunotherapy for advanced non‐small cell lung cancer. Cancer. 2019; 125(22): 4019‐ 4032. 31381142 PMC6899461
  8. [8]Sosa IV, Giuranno L, Dubois LJ, et al. Drug resistance in non‐small cell lung cancer: a potential for NOTCH targeting? Front Oncol. 2018; 8: 267. 30087852 PMC6066509
  9. [9]Bajbouj K, Al‐Ali A, Ramakrishnan RK, et al. Histone modification in NSCLC: molecular mechanisms and therapeutic targets. Int J Mol Sci. 2021; 22(21): 11701. 34769131 PMC8584007
  10. [10]Li Y, Li X, Deng M, Ye C, Peng Y, Lu Y. Cancer‐associated fibroblasts hinder lung squamous cell carcinoma oxidative stress‐induced apoptosis via METTL3 mediated m(6)a methylation of COL10A1. Oxidative Med Cell Longev. 2022; 2022: 4320809. PMC9560815 36246404
  11. [11]Saw PE, Chen J, Song E. Targeting CAFs to overcome anticancer therapeutic resistance. Trends Cancer. 2022; 8(7): 527‐ 555. 35331673
  12. [12]Kubo N, Araki K, Kuwano H, Shirabe K. Cancer‐associated fibroblasts in hepatocellular carcinoma. World J Gastroenterol. 2016; 22(30): 6841‐ 6850. 27570421 PMC4974583
  13. [13]Irvine AF, Waise S, Green EW, Stuart B, Thomas GJ. Characterising cancer‐associated fibroblast heterogeneity in non‐small cell lung cancer: a systematic review and meta‐analysis. Sci Rep. 2021; 11(1): 3727. 33580106 PMC7881148
  14. [14]Du Y, Jiang X, Wang B, et al. The cancer‐associated fibroblasts related gene CALD1 is a prognostic biomarker and correlated with immune infiltration in bladder cancer. Cancer Cell Int. 2021; 21(1): 283. 34051818 PMC8164301
  15. [15]Zhang L, Xu C, Wang SH, et al. Cancer‐associated fibroblast‐related gene signatures predict survival and drug response in patients with colorectal cancer. Front Genet. 2022; 13: 1054152. 36506313 PMC9732269
  16. [16]Herrera M, Berral‐Gonzalez A, Lopez‐Cade I, et al. Cancer‐associated fibroblast‐derived gene signatures determine prognosis in colon cancer patients. Mol Cancer. 2021; 20(1): 73. 33926453 PMC8082938
  17. [17]Zhang C, Fei Y, Wang H, et al. CAFs orchestrates tumor immune microenvironment‐a new target in cancer therapy? Front Pharmacol. 2023; 14: 1113378. 37007004 PMC10064291
  18. [18]Hwang B, Lee JH, Bang D. Single‐cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med. 2018; 50(8): 1‐ 14. PMC6082860 30089861
  19. [19]Sun G, Li Z, Rong D, et al. Single‐cell RNA sequencing in cancer: applications, advances, and emerging challenges. Mol Ther Oncolytics. 2021; 21: 183‐ 206. 34027052 PMC8131398
  20. [20]Chen Z, Zhao M, Liang J, et al. Dissecting the single‐cell transcriptome network underlying esophagus non‐malignant tissues and esophageal squamous cell carcinoma. EBioMedicine. 2021; 69: 103459. 34192657 PMC8253912
  21. [21]Yang Z, Li C, Fan Z, et al. Single‐cell sequencing reveals variants in ARID1A, GPRC5A and MLL2 driving self‐renewal of human bladder cancer stem cells. Eur Urol. 2017; 71(1): 8‐ 12. 27387124
  22. [22]Guo X, Zhang Y, Zheng L, et al. Global characterization of T cells in non‐small‐cell lung cancer by single‐cell sequencing. Nat Med. 2018; 24(7): 978‐ 985. 29942094
  23. [23]Noro R, Ishigame T, Walsh N, et al. A two‐gene prognostic classifier for early‐stage lung squamous cell carcinoma in multiple large‐scale and geographically diverse cohorts. J Thorac Oncol. 2017; 12(1): 65‐ 76. 27613525 PMC6503970
  24. [24]Bueno R, Richards WG, Harpole DH, et al. Multi‐institutional prospective validation of prognostic mRNA signatures in early stage squamous lung cancer (Alliance). J Thorac Oncol. 2020; 15(11): 1748‐ 1757. 32717408 PMC8278226
  25. [25]Zhang Y, Guo L, Dai Q, et al. A signature for pan‐cancer prognosis based on neutrophil extracellular traps. J Immunother Cancer. 2022; 10(6): e004210. 35688556 PMC9189842
  26. [26]Hao Y, Hao S, Andersen‐Nissen E, et al. Integrated analysis of multimodal single‐cell data. Cell. 2021; 184(13): 3573‐ 3587. 34062119 PMC8238499
  27. [27]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(1): 87‐ 98. 33420488
  28. [29]Qiu X, Hill A, Packer J, Lin D, Ma YA, Trapnell C. Single‐cell mRNA quantification and differential analysis with census. Nat Methods. 2017; 14(3): 309‐ 315. 28114287 PMC5330805
  29. [30]Van de Sande B, Flerin C, Davie K, et al. A scalable SCENIC workflow for single‐cell gene regulatory network analysis. Nat Protoc. 2020; 15(7): 2247‐ 2276. 32561888
  30. [31]Jin S, Guerrero‐Juarez CF, Zhang L, et al. Inference and analysis of cell‐cell communication using CellChat. Nat Commun. 2021; 12(1): 1088. 33597522 PMC7889871
  31. [32]Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics. 2010; 26(12): 1572‐ 1573. 20427518 PMC2881355
  32. [33]Van Der Maaten L. Accelerating t‐SNE using tree‐based algorithms. J Mach Learn Res. 2014; 15(1): 3221‐ 3245.
  33. [34]Zhang Z. Reshaping and aggregating data: an introduction to reshape package. Ann Transl Med. 2016; 4(4): 78. 27004225 PMC4779770
  34. [35]Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA‐seq data. BMC Bioinformatics. 2013; 14: 7. 23323831 PMC3618321
  35. [36]Blanche P, Dartigues JF, Jacqmin‐Gadda H. Estimating and comparing time‐dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med. 2013; 32(30): 5381‐ 5397. 24027076
  36. [37]Zhang H, Meltzer P, Davis S. RCircos: an R package for Circos 2D track plots. BMC Bioinformatics. 2013; 14: 244. 23937229 PMC3765848
  37. [39]Wickham H. Reshaping data with the reshape package. J Stat Softw. 2007; 21(12): 1‐ 20.
  38. [41]Wu T, Hu E, Xu S, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb). 2021; 2(3): 100141. 34557778 PMC8454663
  39. [42]Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018; 28(11): 1747‐ 1756. 30341162 PMC6211645
  40. [43]Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015; 12(5): 453‐ 457. 25822800 PMC4739640
  41. [44]Li T, Fan J, Wang B, et al. TIMER: a web server for comprehensive analysis of tumor‐infiltrating immune cells. Cancer Res. 2017; 77(21): e108‐ e110. 29092952 PMC6042652
  42. [45]Dienstmann R, Villacampa G, Sveen A, et al. Relative contribution of clinicopathological variables, genomic markers, transcriptomic subtyping and microenvironment features for outcome prediction in stage II/III colorectal cancer. Ann Oncol. 2019; 30(10): 1622‐ 1629. 31504112 PMC6857614
  43. [46]Aran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017; 18(1): 220. 29141660 PMC5688663
  44. [47]Finotello F, Mayer C, Plattner C, et al. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA‐seq data. Genome Med. 2019; 11(1): 34. 31126321 PMC6534875
  45. [48]Tamminga M, Hiltermann T, Schuuring E, et al. Immune microenvironment composition in non‐small cell lung cancer and its association with survival. Clin Transl Immunol. 2020; 9(6): e1142. PMC7291326 32547744
  46. [49]Racle J, de Jonge K, Baumgaertner P, Speiser DE, Gfeller D. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. elife. 2017; 6: 6. PMC5718706 29130882
  47. [50]Zhang H, Chen R, Yang S, et al. Lobaplatin for the treatment of SK‐MES‐1 lung squamous cell line in vitro and in vivo. Onco Targets Ther. 2016; 9: 4215‐ 4224. 27471396 PMC4948715
  48. [51]Lichterman JN, Reddy SM. Mast cells: a new frontier for cancer immunotherapy. Cells‐Basel. 2021; 10(6): 1270. PMC8223777 34063789
  49. [52]Fu J, Li K, Zhang W, et al. Large‐scale public data reuse to model immunotherapy response and resistance. Genome Med. 2020; 12(1): 21. 32102694 PMC7045518
  50. [53]Maeser D, Gruener RF, Huang RS. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform. 2021; 22(6): bbab260. 34260682 PMC8574972
  51. [54]Lee E, Chuang HY, Kim JW, Ideker T, Lee D. Inferring pathway activity toward precise disease classification. PLoS Comput Biol. 2008; 4(11): e1000217. 18989396 PMC2563693
  52. [55]Tirosh I, Izar B, Prakadan SM, et al. Dissecting the multicellular ecosystem of metastatic melanoma by single‐cell RNA‐seq. Science. 2016; 352(6282): 189‐ 196. 27124452 PMC4944528
  53. [56]Senbabaoglu Y, Gejman RS, Winer AG, et al. Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures. Genome Biol. 2016; 17(1): 231. 27855702 PMC5114739
  54. [57]Socinski MA, Obasaju C, Gandara D, et al. Clinicopathologic features of advanced squamous NSCLC. J Thorac Oncol. 2016; 11(9): 1411‐ 1422. 27296106
  55. [58]Uehara Y, Hakozaki T, Kitadai R, et al. Association between the baseline tumor size and outcomes of patients with non‐small cell lung cancer treated with first‐line immune checkpoint inhibitor monotherapy or in combination with chemotherapy. Transl Lung Cancer res. 2022; 11(2): 135‐ 149. 35280320 PMC8902087
  56. [59]Avagliano A, Fiume G, Bellevicine C, et al. Thyroid cancer and fibroblasts. Cancers (Basel). 2022; 14(17): 4172. 36077709 PMC9455043
  57. [60]Zhong Z, Sanchez‐Lopez E, Karin M. Autophagy, inflammation, and immunity: a troika governing cancer and its treatment. Cell. 2016; 166(2): 288‐ 298. 27419869 PMC4947210
  58. [61]Liu Y, Li C, Lu Y, Liu C, Yang W. Tumor microenvironment‐mediated immune tolerance in development and treatment of gastric cancer. Front Immunol. 2022; 13: 1016817. 36341377 PMC9630479
  59. [62]Cheng YQ, Wang SB, Liu JH, et al. Modifying the tumour microenvironment and reverting tumour cells: new strategies for treating malignant tumours. Cell Prolif. 2020; 53(8): e12865. 32588948 PMC7445401
  60. [63]Feng B, Wu J, Shen B, Jiang F, Feng J. Cancer‐associated fibroblasts and resistance to anticancer therapies: status, mechanisms, and countermeasures. Cancer Cell Int. 2022; 22(1): 166. 35488263 PMC9052457
  61. [64]Remsing RL, Sumi NJ, Hu Q, et al. IGF‐binding proteins secreted by cancer‐associated fibroblasts induce context‐dependent drug sensitization of lung cancer cells. Sci Signal. 2022; 15(747): j5879. PMC9528501 35973030
  62. [65]Landau DA, Clement K, Ziller MJ, et al. Locally disordered methylation forms the basis of intratumor methylome variation in chronic lymphocytic leukemia. Cancer Cell. 2014; 26(6): 813‐ 825. 25490447 PMC4302418
  63. [66]Ohlund D, Handly‐Santana A, Biffi G, et al. Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer. J Exp Med. 2017; 214(3): 579‐ 596. 28232471 PMC5339682
  64. [67]Geng X, Chen H, Zhao L, et al. Cancer‐associated fibroblast (CAF) heterogeneity and targeting therapy of CAFs in pancreatic cancer. Front Cell Dev Biol. 2021; 9: 655152. 34336821 PMC8319605
  65. [68]Elyada E, Bolisetty M, Laise P, et al. Cross‐species single‐cell analysis of pancreatic ductal adenocarcinoma reveals antigen‐presenting cancer‐associated fibroblasts. Cancer Discov. 2019; 9(8): 1102‐ 1123. 31197017 PMC6727976
  66. [69]Dominguez CX, Muller S, Keerthivasan S, et al. Single‐cell RNA sequencing reveals stromal evolution into LRRC15(+) myofibroblasts as a determinant of patient response to cancer immunotherapy. Cancer Discov. 2020; 10(2): 232‐ 253. 31699795
  67. [70]Downs‐Canner SM, Meier J, Vincent BG, Serody JS. B cell function in the tumor microenvironment. Annu Rev Immunol. 2022; 40: 169‐ 193. 35044794
  68. [71]Myers JA, Miller JS. Exploring the NK cell platform for cancer immunotherapy. Nat Rev Clin Oncol. 2021; 18(2): 85‐ 100. 32934330 PMC8316981
  69. [72]Shi L, Li K, Guo Y, et al. Modulation of NKG2D, NKp46, and Ly49C/I facilitates natural killer cell‐mediated control of lung cancer. Proc Natl Acad Sci USA. 2018; 115(46): 11808‐ 11813. 30381460 PMC6243255
  70. [73]Furuse M, Hata M, Furuse K, et al. Claudin‐based tight junctions are crucial for the mammalian epidermal barrier: a lesson from claudin‐1‐deficient mice. J Cell Biol. 2002; 156(6): 1099‐ 1111. 11889141 PMC2173463
  71. [74]Millan JL, Whyte MP. Alkaline phosphatase and hypophosphatasia. Calcif Tissue Int. 2016; 98(4): 398‐ 416. 26590809 PMC4824800
  72. [75]Mensali N, Koksal H, Joaquina S, et al. ALPL‐1 is a target for chimeric antigen receptor therapy in osteosarcoma. Nat Commun. 2023; 14(1): 3375. 37291203 PMC10250459
  73. [76]Pavanello S, Stendardo M, Mastrangelo G, et al. Inflammatory long pentraxin 3 is associated with leukocyte telomere length in night‐shift workers. Front Immunol. 2017; 8: 516. 28536575 PMC5422482
  74. [77]Goulart MR, Watt J, Siddiqui I, et al. Pentraxin 3 is a stromally‐derived biomarker for detection of pancreatic ductal adenocarcinoma. NPJ Precis Oncol. 2021; 5(1): 61. 34188166 PMC8242009
  75. [78]Keller C, Katz R, Cushman M, Fried LF, Shlipak M. Association of kidney function with inflammatory and procoagulant markers in a diverse cohort: a cross‐sectional analysis from the multi‐ethnic study of atherosclerosis (MESA). BMC Nephrol. 2008; 9: 9. 18681974 PMC2533297
  76. [79]Xu K, Wang R, Xie H, et al. Single‐cell RNA sequencing reveals cell heterogeneity and transcriptome profile of breast cancer lymph node metastasis. Oncogene. 2021; 10(10): 66. PMC8492772 34611125
  77. [80]Kiang KM, Zhang P, Li N, et al. Loss of cytoskeleton protein ADD3 promotes tumor growth and angiogenesis in glioblastoma multiforme. Cancer Lett. 2020; 474: 118‐ 126. 31958485

Cited by

Sorted by citations

Authors
Zhang, X., Xiao, Q., Zhang, C., Zhou, Q., & Xu, T.
Affiliations
  • Sichuan University (CN)
  • Chengdu Medical College (CN)
  • Southwest Medical University (CN)
Corresponding author
Xu, Tao
ORCID
Venue
Journal of Cellular and Molecular Medicine
Publisher
Wiley
Published
2024-03-23
Volume
28
Issue
8
Pages
e18262-e18262
ISSN
  • 1582-1838 (ISSN-L)
  • 1582-4934
DOI
10.1111/jcmm.18262
PMC
PMC10960179
OpenAlex
W4393115521
Cited by
14
References
1
Citations by year
2026: 6 · 2025: 6 · 2024: 2
FWCI
2.40
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Cellular and Molecular Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Oncology › Lung Cancer Research Studies
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Ferroptosis and cancer prognosis
  • Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
Keywords
lung squamous cell carcinoma, cancer-associated fibroblasts, prognostic signature, immunotherapy response prediction, tumor microenvironment, CAF subtypes, immune cell infiltration, prognostic gene signature, tumor angiogenesis, epithelial-mesenchymal transition, cell cycle alterations
MeSH terms
Cancer-Associated Fibroblasts, Adducins, Calmodulin-Binding Proteins, Carcinoma, Non-Small-Cell Lung, Carcinoma, Squamous Cell, Humans, Immunotherapy, Lung, Lung Neoplasms, Prognosis, Vitamin K Epoxide Reductases

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