Skip to main content

Advertisement

Log in

Comprehensive pan-cancer analysis reveals CCDC58 as a carcinogenic factor related to immune infiltration

  • Published:
Apoptosis Aims and scope Submit manuscript

Abstract

CCDC58, a member of the CCDC protein family, has been primarily associated with the malignant progression of hepatocellular carcinoma (HCC) and breast cancer, with limited research conducted on its involvement in other tumor types. We aimed to assess the significance of CCDC58 in pan-cancer. We utilized the TCGA, GTEx, and UALCAN databases to perform the differential expression of CCDC58 at both mRNA and protein levels. Prognostic value was evaluated through univariate Cox regression and Kaplan–Meier methods. Mutation and methylation analyses were conducted using the cBioPortal and SMART databases. We identified genes interacting with and correlated to CCDC58 through STRING and GEPIA2, respectively. Subsequently, we performed GO and KEGG enrichment analyses. To gain insights into the functional status of CCDC58 at the single-cell level, we utilized CancerSEA. We explored the correlation between CCDC58 and immune infiltration as well as immunotherapy using the ESTIMATE package, TIMER2.0, TISIDB, TIDE, TIMSO, and TCIA. We examined the relationship between CCDC58 and tumor heterogeneity, stemness, DNA methyltransferases, and MMR genes. Lastly, we constructed a nomogram based on CCDC58 in HCC and investigated its association with drug sensitivity. CCDC58 expression was significantly upregulated and correlated with poor prognosis across various tumor types. The mutation frequency of CCDC58 was found to be increased in 25 tumors. We observed a negative correlation between CCDC58 expression and the methylation sites in the majority of tumors. CCDC58 showed negative correlations with immune and stromal scores, as well as with NK T cells, Tregs, CAFs, endothelial cells, and immunomodulators. Its value in immunotherapy was comparable to that of tumor mutational burden. CCDC58 exhibited positive correlations with tumor heterogeneity, stemness, DNA methyltransferase genes, and MMR genes. In HCC, CCDC58 was identified as an independent risk factor and demonstrated potential associations with multiple drugs. CCDC58 demonstrates significant clinical value as a prognostic marker and indicator of immune response across various tumor types. Its comprehensive analysis provides insights into its potential implications in pan-cancer research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

The data are available from the corresponding author for reasonable requests.

Abbreviations

ACC:

Adrenocortical carcinoma

AUC:

Area under the curve

BLCA:

Bladder urothelial carcinoma

BP:

Biological process

BRCA:

Breast invasive carcinoma

CAFs:

Cancer-associated fibroblasts

CC:

Cellular component

CCDC58:

Coiled-coil domain containing 58

CESC:

Cervical squamous cell carcinoma and endocervical adenocarcinoma

CHOL:

Cholangiocarcinoma

CNA:

Copy number alteration

CNV:

Copy number variants

COAD:

Colon adenocarcinoma

COADREAD:

Colon adenocarcinoma/rectum adenocarcinoma

DFI:

Disease-free interval

DLBC:

Lymphoid neoplasm diffuse large B-cell lymphoma

DSS:

Disease-specific survival

ESCA:

Esophageal carcinoma

GBM:

Glioblastoma multiforme

GBMLGG:

Glioma

GO:

Gene ontology

GSEA:

Gene set enrichment analysis

HCC:

Hepatocellular carcinoma

HNSC:

Head and neck squamous cell carcinoma

HRD:

Homologous recombination deficiency

IC50:

Half-maximal inhibitory concentration

ICB:

Immune checkpoint blockade

IPS:

Immune phenotype scores

KEGG:

Kyoto encyclopedia of genes and genomes

KICH:

Kidney chromophobe

KIPAN:

Pan-kidney cohort (KICH + KIRC + KIRP)

KIRC:

Kidney renal clear cell carcinoma

KIRP:

Kidney renal papillary cell carcinoma

KM:

Kaplan–Meier

LAML:

Acute myeloid leukemia

LGG:

Brain lower grade glioma

LIHC:

Liver hepatocellular carcinoma

LOH:

Loss of heterozygosity

LUAD:

Lung adenocarcinoma

LUSC:

Lung squamous cell carcinoma

MATH:

Mutational and clonal intratumoral heterogeneity

MDSCs:

Myeloid-derived suppressor cells

MESO:

Mesothelioma

MF:

Molecular function

MMR:

Mismatch repair

MSI:

Microsatellite instability

NEO:

Neoantigen load

OS:

Overall survival

OV:

Ovarian serous cystadenocarcinoma

PAAD:

Pancreatic adenocarcinoma

PCPG:

Pheochromocytoma and paraganglioma

PFI:

Progression-free interval

PPI:

Protein–protein interaction

PRAD:

Prostate adenocarcinoma

READ:

Rectum adenocarcinoma

ROC:

Receiver operating characteristic

SARC:

Sarcoma

SKCM:

Skin cutaneous melanoma

SNV:

Single nucleotide variants

STAD:

Stomach adenocarcinoma

STES:

Stomach and esophageal carcinoma

TGCT:

Testicular germ cell tumors

THCA:

Thyroid carcinoma

THYM:

Thymoma

TILs:

Tumor-infiltrating lymphocytes

TMB:

Tumor mutational burden

TME:

Tumor microenvironment

TNBC:

Triple-negative breast cancer

UCEC:

Uterine corpus endometrial carcinoma

UCS:

Uterine carcinosarcoma

UVM:

Uveal melanoma

References

  1. Zoller E, Laborenz J, Kramer L, Boos F, Raschle M, Alexander RT et al (2020) The intermembrane space protein Mix23 is a novel stress-induced mitochondrial import factor. J Biol Chem 295:14686–14697

    PubMed  PubMed Central  Google Scholar 

  2. Liu Z, Yan W, Liu S, Liu Z, Xu P, Fang W (2023) Regulatory network and targeted interventions for CCDC family in tumor pathogenesis. Cancer Lett 565:216225

    CAS  PubMed  Google Scholar 

  3. Pangeni RP, Channathodiyil P, Huen DS, Eagles LW, Johal BK, Pasha D et al (2015) The GALNT9, BNC1 and CCDC8 genes are frequently epigenetically dysregulated in breast tumours that metastasise to the brain. Clin Epigenet 7:57

    Google Scholar 

  4. Deng T, Shen P, Li A, Zhang Z, Yang H, Deng X et al (2021) CCDC65 as a new potential tumor suppressor induced by metformin inhibits activation of AKT1 via ubiquitination of ENO1 in gastric cancer. Theranostics 11:8112–8128

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Zhang Z, Xu P, Hu Z, Fu Z, Deng T, Deng X et al (2022) CCDC65, a gene knockout that leads to early death of mice, acts as a potentially novel tumor suppressor in lung adenocarcinoma. Int J Biol Sci 18:4171–4186

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Wang Z, Li Y, Yang J, Liang Y, Wang X, Zhang N et al (2022) Circ-TRIO promotes TNBC progression by regulating the miR-432-5p/CCDC58 axis. Cell Death Dis 13:776

    PubMed  PubMed Central  Google Scholar 

  7. Chen L, Zhang J, Yang Y, Shu J, Zheng J, Zhan X et al (2023) Coiled-coil domain-containing protein 58 (CCDC58) is a novel prognostic biomarker correlated with mitochondrial functions in hepatocellular carcinoma. Am J Transl Res 15:2568–2584

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Li X, Wang Y, Xu C, Reheman X, Wang Y, Xu R et al (2022) Analysis of competitive endogenous mechanism and survival prognosis of serum exosomes in ovarian cancer patients based on sequencing technology and bioinformatics. Front Genet 13:850089

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Kunitomi H, Kobayashi Y, Wu RC, Takeda T, Tominaga E, Banno K et al (2020) LAMC1 is a prognostic factor and a potential therapeutic target in endometrial cancer. J Gynecol Oncol 31:e11

    PubMed  Google Scholar 

  10. Consortium GT (2013) The genotype-tissue expression (GTEx) project. Nat Genet 45:580–585

    Google Scholar 

  11. Ghandi M, Huang FW, Jane-Valbuena J, Kryukov GV, Lo CC, McDonald ER et al (2019) Next-generation characterization of the cancer cell line encyclopedia. Nature 569:503–508

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  12. Goldman MJ, Craft B, Hastie M, Repecka K, McDade F, Kamath A et al (2020) Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol 38:675–678

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Chandrashekar DS, Karthikeyan SK, Korla PK, Patel H, Shovon AR, Athar M et al (2022) UALCAN: an update to the integrated cancer data analysis platform. Neoplasia 25:18–27

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Uhlen M, Fagerberg L, Hallstrom BM, Lindskog C, Oksvold P, Mardinoglu A et al (2015) Proteomics: tissue-based map of the human proteome. Science 347:1260419

    PubMed  Google Scholar 

  15. Liu J, Lichtenberg T, Hoadley KA, Poisson LM, Lazar AJ, Cherniack AD et al (2018) An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell 173:400-416e11

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA et al (2012) The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2:401–404

    PubMed  Google Scholar 

  17. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H et al (2000) The protein data bank. Nucleic Acids Res 28:235–242

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Li Y, Ge D, Lu C (2019) The SMART app: an interactive web application for comprehensive DNA methylation analysis and visualization. Epigenet Chromatin 12:71

    CAS  Google Scholar 

  19. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J et al (2019) STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47:D607–D613

    CAS  PubMed  Google Scholar 

  20. Tang Z, Kang B, Li C, Chen T, Zhang Z (2019) GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res 47:W556–W560

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q et al (2020) TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res 48:W509–W514

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z et al (2021) clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2:100141

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Yuan H, Yan M, Zhang G, Liu W, Deng C, Liao G et al (2019) CancerSEA: a cancer single-cell state atlas. Nucleic Acids Res 47:D900–D908

    CAS  PubMed  Google Scholar 

  24. Ru B, Wong CN, Tong Y, Zhong JY, Zhong SSW, Wu WC et al (2019) TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics 35:4200–4202

    CAS  PubMed  Google Scholar 

  25. Xu L, Deng C, Pang B, Zhang X, Liu W, Liao G et al (2018) TIP: a web server for resolving tumor immunophenotype profiling. Cancer Res 78:6575–6580

    CAS  PubMed  Google Scholar 

  26. Bonneville R, Krook MA, Kautto EA, Miya J, Wing MR, Chen HZ et al (2017) Landscape of microsatellite instability across 39 cancer types. JCO Precis Oncol 1:1–15

    Google Scholar 

  27. Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH et al (2018) The immune landscape of cancer. Immunity 48:812–830

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Malta TM, Sokolov A, Gentles AJ, Burzykowski T, Poisson L, Weinstein JN et al (2018) Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell 173:338-354e15

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Fu J, Li K, Zhang W, Wan C, Zhang J, Jiang P et al (2020) Large-scale public data reuse to model immunotherapy response and resistance. Genome Med 12:21

    PubMed  PubMed Central  Google Scholar 

  30. Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X et al (2018) Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med 24:1550–1558

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Zeng Z, Wong CJ, Yang L, Ouardaoui N, Li D, Zhang W et al (2022) TISMO: syngeneic mouse tumor database to model tumor immunity and immunotherapy response. Nucleic Acids Res 50:D1391–D1397

    CAS  PubMed  Google Scholar 

  32. Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D et al (2017) Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep 18:248–262

    CAS  PubMed  Google Scholar 

  33. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S et al (2013) Genomics of drug sensitivity in cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res 41:D955–D961

    CAS  PubMed  Google Scholar 

  34. Chi C, Ye Y, Chen B, Huang H (2021) Bipartite graph-based approach for clustering of cell lines by gene expression-drug response associations. Bioinformatics 37:2617–2626

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Tsherniak A, Vazquez F, Montgomery PG, Weir BA, Kryukov G, Cowley GS et al (2017) Defining a cancer dependency map. Cell 170:564–576

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Yang L, Liu Q, Zhang X, Liu X, Zhou B, Chen J et al (2020) DNA of neutrophil extracellular traps promotes cancer metastasis via CCDC25. Nature 583:133–138

    CAS  PubMed  ADS  Google Scholar 

  37. Siriphak S, Chanakankun R, Proungvitaya T, Roytrakul S, Tummanatsakun D, Seubwai W et al (2021) Kallikrein-11, in association with coiled-coil domain containing 25, as a potential prognostic marker for cholangiocarcinoma with lymph node metastasis. Molecules 26:3105

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Chanakankun R, Proungvitaya T, Chua-On D, Limpaiboon T, Roytrakul S, Jusakul A et al (2020) Serum coiled-coil domain containing 25 protein as a potential screening/diagnostic biomarker for cholangiocarcinoma. Oncol Lett 19:930–942

    CAS  PubMed  Google Scholar 

  39. Dickson I (2020) NETs promote liver metastasis via CCDC25. Nat Rev Gastroenterol Hepatol 17:451

    PubMed  Google Scholar 

  40. Gong Y, Qiu W, Ning X, Yang X, Liu L, Wang Z et al (2015) CCDC34 is up-regulated in bladder cancer and regulates bladder cancer cell proliferation, apoptosis and migration. Oncotarget 6:25856–25867

    PubMed  PubMed Central  Google Scholar 

  41. Geng W, Liang W, Fan Y, Ye Z, Zhang L (2018) Overexpression of CCDC34 in colorectal cancer and its involvement in tumor growth, apoptosis and invasion. Mol Med Rep 17:465–473

    CAS  PubMed  Google Scholar 

  42. Zhou M, Chen X, Bai H, Sun Y, Zhang Z, Li S et al (2021) RABL2A-CCDC34 axis promotes sorafenib resistance in hepatocellular carcinoma. DNA Cell Biol 40:1418–1427

    CAS  PubMed  Google Scholar 

  43. Liu LB, Huang J, Zhong JP, Ye GL, Xue L, Zhou MH et al (2018) High expression of CCDC34 is associated with poor survival in cervical cancer patients. Med Sci Monit 24:8383–8390

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Wang J, Wu X, Dai W, Li J, Xiang L, Tang W et al (2020) The CCDC43-ADRM1 axis regulated by YY1, promotes proliferation and metastasis of gastric cancer. Cancer Lett 482:90–101

    CAS  PubMed  Google Scholar 

  45. Chen Y, Pei M, Li J, Wang Z, Liu S, Xiang L et al (2022) Disruption of the CCDC43-FHL1 interaction triggers apoptosis in gastric cancer cells. Exp Cell Res 415:113107

    CAS  PubMed  Google Scholar 

  46. Wang J, Liu G, Liu M, Xiang L, Xiao Y, Zhu H et al (2018) The FOXK1-CCDC43 axis promotes the invasion and metastasis of colorectal cancer cells. Cell Physiol Biochem 51:2547–2563

    CAS  PubMed  Google Scholar 

  47. Lin H, Gao Y, Sun K, Zhang Q, Li Y, Chen M et al (2022) COA3 overexpression promotes non-small cell lung cancer metastasis by reprogramming glucose metabolism. Am J Cancer Res 12:3662–3678

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Xu X, Cao W, Sun W, Wang Z, Chen H, Zheng Z et al (2019) Knockdown of CCDC132 attenuates gastric cancer cells proliferation and tumorigenesis by facilitating DNA damage signaling. Cancer Manag Res 11:9585–9597

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Chen PS, Hsu HP, Phan NN, Yen MC, Chen FW, Liu YW et al (2021) CCDC167 as a potential therapeutic target and regulator of cell cycle-related networks in breast cancer. Aging 13:4157–4181

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Hu X, Zhao Y, Wei L, Zhu B, Song D, Wang J et al (2017) CCDC178 promotes hepatocellular carcinoma metastasis through modulation of anoikis. Oncogene 36:4047–4059

    CAS  PubMed  Google Scholar 

  51. Bagci O, Kurtgoz S (2015) Amplification of Cellular oncogenes in solid tumors. N Am J Med Sci 7:341–346

    PubMed  PubMed Central  Google Scholar 

  52. Zucman-Rossi J, Villanueva A, Nault JC, Llovet JM (2015) Genetic landscape and biomarkers of hepatocellular carcinoma. Gastroenterology 149:1226-1239e4

    CAS  PubMed  Google Scholar 

  53. Long J, Wang A, Bai Y, Lin J, Yang X, Wang D et al (2019) Development and validation of a TP53-associated immune prognostic model for hepatocellular carcinoma. EBioMedicine 42:363–374

    PubMed  PubMed Central  Google Scholar 

  54. Cancer Genome Atlas Research Network (2017) Electronic address wbe, cancer genome atlas research N. comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell 169:1327–1341

    Google Scholar 

  55. Ramalho-Carvalho J, Henrique R, Jeronimo C (2018) Methylation-specific PCR. Methods Mol Biol 1708:447–472

    CAS  PubMed  Google Scholar 

  56. Kaaij LJ, Mokry M, Zhou M, Musheev M, Geeven G, Melquiond AS et al (2016) Enhancers reside in a unique epigenetic environment during early zebrafish development. Genome Biol 17:146

    PubMed  PubMed Central  Google Scholar 

  57. Shapovalov Y, Hoffman D, Zuch D, de Mesy Bentley KL, Eliseev RA (2011) Mitochondrial dysfunction in cancer cells due to aberrant mitochondrial replication. J Biol Chem 286:22331–22338

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Yang Y, Pan C, Yu L, Ruan H, Chang L, Yang J et al (2019) SSBP1 upregulation in colorectal cancer regulates mitochondrial mass. Cancer Manag Res 11:10093–10106

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Jiang HL, Sun HF, Gao SP, Li LD, Huang S, Hu X et al (2016) SSBP1 suppresses TGFbeta-driven epithelial-to-mesenchymal transition and metastasis in triple-negative breast cancer by regulating mitochondrial retrograde signaling. Cancer Res 76:952–964

    CAS  PubMed  Google Scholar 

  60. Su J, Li Y, Liu Q, Peng G, Qin C, Li Y (2022) Identification of SSBP1 as a ferroptosis-related biomarker of glioblastoma based on a novel mitochondria-related gene risk model and in vitro experiments. J Transl Med 20:440

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Vesely MD, Kershaw MH, Schreiber RD, Smyth MJ (2011) Natural innate and adaptive immunity to cancer. Annu Rev Immunol 29:235–271

    CAS  PubMed  Google Scholar 

  62. Hallett WH, Murphy WJ (2004) Natural killer cells: biology and clinical use in cancer therapy. Cell Mol Immunol 1:12–21

    CAS  PubMed  Google Scholar 

  63. Wang K, Yu M, Zhang Z, Yin R, Chen Q, Zhao X et al (2023) Integrated analysis of single-cell and bulk transcriptome identifies a signature based on NK cell marker genes to predict prognosis and therapeutic response in clear cell renal cell carcinoma. Transl Cancer Res 12:1270–1289

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Na HY, Park Y, Nam SK, Koh J, Kwak Y, Ahn SH et al (2021) Prognostic significance of natural killer cell-associated markers in gastric cancer: quantitative analysis using multiplex immunohistochemistry. J Transl Med 19:529

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Wang F, Lau JKC, Yu J (2021) The role of natural killer cell in gastrointestinal cancer: killer or helper. Oncogene 40:717–730

    PubMed  Google Scholar 

  66. Reid FSW, Egoroff N, Pockney PG, Smith SR (2021) A systematic scoping review on natural killer cell function in colorectal cancer. Cancer Immunol Immunother 70:597–606

    PubMed  Google Scholar 

  67. Diaz-Montero CM, Rini BI, Finke JH (2020) The immunology of renal cell carcinoma. Nat Rev Nephrol 16:721–735

    PubMed  Google Scholar 

  68. Ping G, Tian Y, Zhou Z (2022) Constructing a tregs-associated signature to predict the prognosis of colorectal cancer patients: a STROBE-compliant retrospective study. Medicine 101:e31382

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Zimmer N, Trzeciak ER, Graefen B, Satoh K, Tuettenberg A (2022) GARP as a therapeutic target for the modulation of regulatory T cells in cancer and autoimmunity. Front Immunol 13:928450

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Shang B, Liu Y, Jiang SJ, Liu Y (2015) Prognostic value of tumor-infiltrating FoxP3 + regulatory T cells in cancers: a systematic review and meta-analysis. Sci Rep 5:15179

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  71. Winerdal ME, Marits P, Winerdal M, Hasan M, Rosenblatt R, Tolf A et al (2011) FOXP3 and survival in urinary bladder cancer. BJU Int 108:1672–1678

    CAS  PubMed  Google Scholar 

  72. Sahai E, Astsaturov I, Cukierman E, DeNardo DG, Egeblad M, Evans RM et al (2020) A framework for advancing our understanding of cancer-associated fibroblasts. Nat Rev Cancer 20:174–186

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Kalluri R (2016) The biology and function of fibroblasts in cancer. Nat Rev Cancer 16:582–598

    CAS  PubMed  Google Scholar 

  74. Kalluri R, Zeisberg M (2006) Fibroblasts in cancer. Nat Rev Cancer 6:392–401

    CAS  PubMed  Google Scholar 

  75. Maishi N, Hida K (2017) Tumor endothelial cells accelerate tumor metastasis. Cancer Sci 108:1921–1926

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Sobierajska K, Ciszewski WM, Sacewicz-Hofman I, Niewiarowska J (2020) Endothelial cells in the tumor microenvironment. Adv Exp Med Biol 1234:71–86

    CAS  PubMed  Google Scholar 

  77. Phuengkham H, Ren L, Shin IW, Lim YT (2019) Nanoengineered immune niches for reprogramming the immunosuppressive tumor microenvironment and enhancing cancer immunotherapy. Adv Mater 31:e1803322

    PubMed  Google Scholar 

  78. Chan TA, Yarchoan M, Jaffee E, Swanton C, Quezada SA, Stenzinger A et al (2019) Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol 30:44–56

    CAS  PubMed  Google Scholar 

  79. Yamamoto H, Imai K (2015) Microsatellite instability: an update. Arch Toxicol 89:899–921

    CAS  PubMed  Google Scholar 

  80. Toh M, Ngeow J (2021) Homologous recombination deficiency: cancer predispositions and treatment implications. Oncologist 26:e1526–e1537

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Miranda A, Hamilton PT, Zhang AW, Pattnaik S, Becht E, Mezheyeuski A et al (2019) Cancer stemness, intratumoral heterogeneity, and immune response across cancers. Proc Natl Acad Sci USA 116:9020–9029

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  82. Saygin C, Matei D, Majeti R, Reizes O, Lathia JD (2019) Targeting cancer stemness in the clinic: from hype to hope. Cell Stem Cell 24:25–40

    CAS  PubMed  Google Scholar 

  83. Merlos-Suarez A, Barriga FM, Jung P, Iglesias M, Cespedes MV, Rossell D et al (2011) The intestinal stem cell signature identifies colorectal cancer stem cells and predicts disease relapse. Cell Stem Cell 8:511–524

    CAS  PubMed  Google Scholar 

  84. Siu MK, Wong ES, Kong DS, Chan HY, Jiang L, Wong OG et al (2013) Stem cell transcription factor NANOG controls cell migration and invasion via dysregulation of E-cadherin and FoxJ1 and contributes to adverse clinical outcome in ovarian cancers. Oncogene 32:3500–3509

    CAS  PubMed  Google Scholar 

  85. Yin ZQ, Liu JJ, Xu YC, Yu J, Ding GH, Yang F et al (2014) A 41-gene signature derived from breast cancer stem cells as a predictor of survival. J Exp Clin Cancer Res 33:49

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

Authors

Contributions

HW, QG, WS, and CQ participated in the conception, design, acquisition of data, analysis, and interpretation. All authors participated in drafting the article and gave final approval.

Corresponding authors

Correspondence to Wenxiang Shi or Chenjie Qiu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethical approval

Not applicable.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 9150.6 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, H., Geng, Q., Shi, W. et al. Comprehensive pan-cancer analysis reveals CCDC58 as a carcinogenic factor related to immune infiltration. Apoptosis 29, 536–555 (2024). https://doi.org/10.1007/s10495-023-01919-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10495-023-01919-0

Keywords

Navigation