REFERENCES

1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209-49.

2. Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012;487:330-7.

3. Ciardiello F, Ciardiello D, Martini G, Napolitano S, Tabernero J, Cervantes A. Clinical management of metastatic colorectal cancer in the era of precision medicine. CA Cancer J Clin. 2022;72:372-401.

4. Siegel RL, Wagle NS, Cercek A, Smith RA, Jemal A. Colorectal cancer statistics, 2023. CA Cancer J Clin. 2023;73:233-54.

5. Hou S, Guo T, Chang X, et al. Impact of aging on colon cancer burden: a cross-sectional analysis of nationally representative data. Medicine. 2025;104:e46650.

6. Gefen R, Emile SH, Horesh N, Garoufalia Z, Wexner SD. Age-related variations in colon and rectal cancer: an analysis of the national cancer database. Surgery. 2023;174:1315-22.

7. Llosa NJ, Cruise M, Tam A, et al. The vigorous immune microenvironment of microsatellite instable colon cancer is balanced by multiple counter-inhibitory checkpoints. Cancer Discov. 2015;5:43-51.

8. Shia J. Immunohistochemistry versus microsatellite instability testing for screening colorectal cancer patients at risk for hereditary nonpolyposis colorectal cancer syndrome. Part I. The utility of immunohistochemistry. J Mol Diagn. 2008;10:293-300.

9. McCarthy AJ, Capo-Chichi JM, Spence T, et al. Heterogenous loss of mismatch repair (MMR) protein expression: a challenge for immunohistochemical interpretation and microsatellite instability (MSI) evaluation. J Pathol Clin Res. 2019;5:115-29.

10. Guyot D'Asnières De Salins A, Tachon G, Cohen R, et al. Discordance between immunochemistry of mismatch repair proteins and molecular testing of microsatellite instability in colorectal cancer. ESMO Open. 2021;6:100120.

11. Deng H, Xu X, Zhang Y, Li Y. The complex role and molecular mechanism of family with sequence similarity genes in cancer: a comprehensive review. Discov Oncol. 2025;16:1443.

12. Huang X, Sun Y, Song J, et al. Prognostic value of fatty acid metabolism-related genes in colorectal cancer and their potential implications for immunotherapy. Front Immunol. 2023;14:1301452.

13. Liu W, Wang S, Qian K, Zhang J, Zhang Z, Liu H. Expression of family with sequence similarity 172 member A and nucleotide-binding protein 1 is associated with the poor prognosis of colorectal carcinoma. Oncol Lett. 2017;14:3587-93.

14. Huang X, Chi H, Gou S, et al. An Aggrephagy-Related LncRNA Signature for the Prognosis of Pancreatic Adenocarcinoma. Genes. 2023;14:124.

15. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Soft. 2010;33:1-22.

16. Zhang H, Li R, Cao Y, et al. Poor clinical outcomes and immunoevasive contexture in intratumoral IL-10-producing macrophages enriched gastric cancer patients. Ann Surg. 2022;275:e626-35.

17. Pei S, Zhang P, Chen H, et al. Integrating single-cell RNA-seq and bulk RNA-seq to construct prognostic signatures to explore the role of glutamine metabolism in breast cancer. Front Endocrinol. 2023;14:1135297.

18. Pei S, Zhang P, Yang L, et al. Exploring the role of sphingolipid-related genes in clinical outcomes of breast cancer. Front Immunol. 2023;14:1116839.

19. Liu J, Zhang P, Yang F, et al. Integrating single-cell analysis and machine learning to create glycosylation-based gene signature for prognostic prediction of uveal melanoma. Front Endocrinol. 2023;14:1163046.

20. Su C, Xue J, Liu N. Cox regression analysis of prognostic factors of intensity-modulated radiotherapy in patients with bladder cancer. Arch Esp Urol. 2023;76:411-7.

21. Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA. Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol Biol. 2018;1711:243-59.

22. Yoshihara K, Shahmoradgoli M, Martínez E, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.

23. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinf. 2013;14:7.

24. 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.

25. Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018;28:1747-56.

26. Chi H, Gao X, Xia Z, et al. FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC. Front Mol Biosci. 2023;10:1200335.

27. Geeleher P, Cox NJ, Huang RS. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. Genome Biol. 2014;15:R47.

28. Chan TA, Wolchok JD, Snyder A. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med. 2015;373:1984.

29. Schreiber RD, Old LJ, Smyth MJ. Cancer immunoediting: integrating immunity's roles in cancer suppression and promotion. Science. 2011;331:1565-70.

30. Kyrochristou I, Anagnostopoulos G, Giannakodimos I, Psalla K, Rogdakis A. Inflammation biomarkers as predictors of nodal metastases in colorectal cancer. Folia Med. 2025:67.

31. Burdelski C, Borcherding L, Kluth M, et al. Family with sequence similarity 13C (FAM13C) overexpression is an independent prognostic marker in prostate cancer. Oncotarget. 2017;8:31494-508.

32. Hauge H, Patzke S, Aasheim HC. Characterization of the FAM110 gene family. Genomics. 2007;90:14-27.

33. Xie M, Cai L, Li J, et al. FAM110B inhibits non-small cell lung cancer cell proliferation and invasion through inactivating Wnt/β-catenin signaling. Onco Targets Ther. 2020;13:4373-84.

34. Xi T, Zhang G. Integrated analysis of tumor differentiation genes in pancreatic adenocarcinoma. PLoS ONE. 2018;13:e0193427.

35. Wang X, Duanmu J, Fu X, Li T, Jiang Q. Analyzing and validating the prognostic value and mechanism of colon cancer immune microenvironment. J Transl Med. 2020;18:324.

36. Pramanik S, Kutzner A, Heese K. Lead discovery and in silico 3D structure modeling of tumorigenic FAM72A (p17). Tumour Biol. 2015;36:239-49.

37. Zhang T, Nie Y, Gu J, et al. Identification of mitochondrial-related prognostic biomarkers associated with primary bile acid biosynthesis and tumor microenvironment of hepatocellular carcinoma. Front Oncol. 2021;11:587479.

38. Gao Y, Liu J, Zhao D, Diao G. A novel prognostic model for identifying the risk of hepatocellular carcinoma based on angiogenesis factors. Front Genet. 2022;13:857215.

39. Jorissen RN, Christie M, Mouradov D, et al. Wild-type APC predicts poor prognosis in microsatellite-stable proximal colon cancer. Br J Cancer. 2015;113:979-88.

40. Schumacher TN, Schreiber RD. Neoantigens in cancer immunotherapy. Science. 2015;348:69-74.

41. McGranahan N, Furness AJ, Rosenthal R, et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science. 2016;351:1463-9.

42. Zhang Z, Wu Y. Research progress on mechanisms of tumor immune microenvironment and gastrointestinal resistance to immunotherapy: mini review. Front Immunol. 2025;16:1641518.

43. Mantovani A, Sozzani S, Locati M, Allavena P, Sica A. Macrophage polarization: tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes. Trends Immunol. 2002;23:549-55.

44. Gajewski TF, Schreiber H, Fu YX. Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol. 2013;14:1014-22.

45. Meyiah A, Khan FI, Alfaki DA, Murshed K, Raza A, Elkord E. The colorectal cancer microenvironment: Preclinical progress in identifying targets for cancer therapy. Transl Oncol. 2025;53:102307.

46. Crotty S. Follicular helper CD4 T cells (TFH). Annu Rev Immunol. 2011;29:621-63.

47. Sharpe AH, Pauken KE. The diverse functions of the PD1 inhibitory pathway. Nat Rev Immunol. 2018;18:153-67.

48. Murray PJ, Allen JE, Biswas SK, et al. Macrophage activation and polarization: nomenclature and experimental guidelines. Immunity. 2014;41:14-20.

49. Sorino J, Della Mura M, Ingravallo G, et al. Fusobacterium nucleatum and gastric cancer: an emerging connection. Int J Mol Sci. 2025:26.

50. Nishikawa H, Sakaguchi S. Regulatory T cells in tumor immunity. Int J Cancer. 2010;127:759-67.

51. Fridman WH, Zitvogel L, Sautès-Fridman C, Kroemer G. The immune contexture in cancer prognosis and treatment. Nat Rev Clin Oncol. 2017;14:717-34.

52. Wei SC, Levine JH, Cogdill AP, et al. Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Cell. 2017;170:1120-33.e17.

53. DeNardo DG, Ruffell B. Macrophages as regulators of tumour immunity and immunotherapy. Nat Rev Immunol. 2019;19:369-82.

54. Sharma P, Allison JP. Immune checkpoint targeting in cancer therapy: toward combination strategies with curative potential. Cell. 2015;161:205-14.

55. Zhang L, Zhao L, Lin X, et al. Comparison of tumor non-specific and PD-L1 specific imaging by near-infrared fluorescence/cerenkov luminescence dual-modality in-situ imaging. Mol Imaging. 2024;23:15353508241261473.

56. Archibald SJ, Holland JP, Korde A, Martins AF, Shuhendler AJ, Scott PJH. Combining nuclear medicine with other modalities: future prospect for multimodality imaging. Mol Imaging. 2024;23:15353508241245265.

Journal of Cancer Metastasis and Treatment
ISSN 2454-2857 (Online) 2394-4722 (Print)

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/