Skip to main content

Advertisement

SpringerLink
Log in

Identification of LOXL3-associating immune infiltration landscape and prognostic value in hepatocellular carcinoma

  • Original Article
  • Published:
Virchows Archiv Aims and scope Submit manuscript

Abstract

In recent years, breakthroughs in the field of tumor immunotherapy with immune checkpoint inhibitors (ICIs) have made a therapeutic revolution, which has been shown to improve the prognosis of patients with hepatocellular carcinoma (HCC). Immune infiltrates represent a major component of tumor microenvironment (TME), and play an essential role in both tumor progression and therapeutic response. The major unmet challenge in tumor immunotherapy is exploring the intrinsic and extrinsic mechanisms of TME promoting the management of HCC. Lysyl oxidase like 3 (LOXL3) participates in the remodeling of extracellular matrix (ECM) and the cross-linking of collagen and elastic fibers. It has been reported that LOXL3 is associated with the development and tumorigenesis of multiple types of cancer. RNA sequencing data and corresponding clinical information were extracted from The Cancer Genome Atlas (TCGA) databases, then subjected to gene expression, tumor microenvironment, survival, enrichment analyses utilizing R packages. In this study, we first found that LOXL3 gene was upregulated in tumor tissues compared with the normal tissues. Furthermore, LOXL3 expression is positively correlated with the infiltration of multiple immune cells and the expression of immune checkpoint genes in HCC. Meanwhile, high LOXL3 expression predicted poor outcomes of the patients with HCC. Functional enrichment analysis suggested that LOXL3 was mainly linked to extracellular structure and matrix organization, cell−cell adhesion, and T cell activation. This is the first comprehensive study to indicate that LOXL3 is correlated with immune infiltrates and may serve as a novel biomarker predicting prognosis and immunotherapy in HCC.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Data availability

All data is available under reasonable request.

Abbreviations

CAMs:

cell adhesion molecules

CCLE:

cancer cell line encyclopedia

CI:

confidence interval

DEGs:

differentially expressed genes

DSS:

disease-specific survival

ECM:

extracellular matrix

FC:

fold change

FDR:

false discovery rate

GEPIA:

gene expression profiling interactive analysis

GO:

Gene Ontology

GTEx:

genotype-tissue expression

HBV:

hepatitis B virus

HCC:

hepatocellular carcinoma

HCV:

hepatitis C virus

HR:

hazard ratio

ICB:

immune checkpoint blockade

ICIs:

immune checkpoint inhibitors

IHC:

immunohistochemistry

KEGG:

Kyoto encyclopedia of genes and genomes

LOX:

lysyl oxidase

LOXL3:

lysyl oxidase like 3

MMRs:

mismatch repair system

NAFLD:

nonalcoholic fatty liver disease

NCCN:

national comprehensive cancer network

OS:

overall survival

PD-1:

programmed death receptor 1

PD-L1:

programmed death receptor ligand 1

PPI:

protein–protein interaction

TACE:

transarterial chemoembolization

TCGA:

the cancer genome atlas

THPA:

the human protein atlas

TIMER:

tumor immune estimation resource

TME:

tumor microenvironment

VEGF:

vascular endothelial growth factor

References

  1. Llovet JM, Kelley RK, Villanueva A, Singal AG, Pikarsky E, Roayaie S, Lencioni R, Koike K, Zucman-Rossi J, Finn RS (2021) Hepatocellular carcinoma. Nat Rev Dis Primers 7(1):6

    Article  Google Scholar 

  2. Villanueva A (2019) Hepatocellular Carcinoma. N Engl J Med 380(15):1450–1462

    CAS  Google Scholar 

  3. European Association for the Study of the Liver. Electronic address: easloffice@easloffice.eu, European Association for the Study of the Liver (2018) EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma. J Hepatol 69(1):182–236

    Article  Google Scholar 

  4. Schreiber RD, Old LJ, Smyth MJ (2011) Cancer immunoediting: integrating immunity’s roles in cancer suppression and promotion. Science. 331(6024):1565–1570

    Article  CAS  Google Scholar 

  5. Vinay DS, Ryan EP, Pawelec G, Talib WH, Stagg J, Elkord E, Lichtor T, Decker WK, Whelan RL, Kumara HMCS, Signori E, Honoki K, Georgakilas AG, Amin A, Helferich WG, Boosani CS, Guha G, Ciriolo MR, Chen S, Mohammed SI, Azmi AS, Keith WN, Bilsland A, Bhakta D, Halicka D, Fujii H, Aquilano K, Ashraf SS, Nowsheen S, Yang X, Choi BK, Kwon BS (2015) Immune evasion in cancer: mechanistic basis and therapeutic strategies. Semin Cancer Biol 35(Suppl):S185–S198

    Article  CAS  Google Scholar 

  6. Rabinovich GA, Gabrilovich D, Sotomayor EM (2007) Immunosuppressive strategies that are mediated by tumor cells. Annu Rev Immunol 25:267–296

    Article  CAS  Google Scholar 

  7. Sangro B, Sarobe P, Hervás-Stubbs S, Melero I (2021) Advances in immunotherapy for hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol:1–19

  8. Molnar J, Fong KS, He QP et al (2003) Structural and functional diversity of lysyl oxidase and the LOX-like proteins. Biochim Biophys Acta 1647(1-2):220–224

    Article  CAS  Google Scholar 

  9. Kagan HM, Li W (2003) Lysyl oxidase: properties, specificity, and biological roles inside and outside of the cell. J Cell Biochem 88(4):660–672

    Article  CAS  Google Scholar 

  10. Ye M, Song Y, Pan S, Chu M, Wang ZW, Zhu X (2020) Evolving roles of lysyl oxidase family in tumorigenesis and cancer therapy. Pharmacol Ther 215:107633

    Article  CAS  Google Scholar 

  11. Laurentino TS, Soares R, Marie S, Oba-Shinjo SM (2019) LOXL3 function beyond amino oxidase and role in pathologies, including cancer. Int J Mol Sci 20(14)

  12. Ribeiro AL, Kaid C, Silva P, Cortez BA, Okamoto OK (2017) Inhibition of lysyl oxidases impairs migration and angiogenic properties of tumor-associated pericytes. Stem Cells Int 2017:4972078

    Article  CAS  Google Scholar 

  13. Jeong YJ, Park SH, Mun SH, Kwak SG, Lee SJ, Oh HK (2018) Association between lysyl oxidase and fibrotic focus in relation with inflammation in breast cancer. Oncol Lett 15(2):2431–2440

    PubMed  Google Scholar 

  14. Kasashima H, Yashiro M, Okuno T, Miki Y, Kitayama K, Masuda G, Kinoshita H, Morisaki T, Fukuoka T, Hasegawa T, Sakurai K, Toyokawa T, Kubo N, Tanaka H, Muguruma K, Hirakawa K, Ohira M (2018) Significance of the lysyl oxidase members lysyl oxidase like 1, 3, and 4 in gastric cancer. Digestion. 98(4):238–248

    Article  CAS  Google Scholar 

  15. Ren J, Wang X, Wei G, Meng Y (2021) Exposure to desflurane anesthesia confers colorectal cancer cells metastatic capacity through deregulation of miR-34a/LOXL3. Eur J Cancer Prev 30(2):143–153

    Article  CAS  Google Scholar 

  16. Ye M, Zhou J, Gao Y, Pan S, Zhu X (2020) The prognostic value of the lysyl oxidase family in ovarian cancer. J Clin Lab Anal 34(12):e23538

    Article  CAS  Google Scholar 

  17. Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z (2017) GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 45(W1):W98–W102

    Article  CAS  Google Scholar 

  18. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS (2017) TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res 77(21):e108–e110

    Article  CAS  Google Scholar 

  19. Becht E, Giraldo NA, Lacroix L, Buttard B, Elarouci N, Petitprez F, Selves J, Laurent-Puig P, Sautès-Fridman C, Fridman WH, de Reyniès A (2016) Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol 17(1):218

    Article  CAS  Google Scholar 

  20. Helmink BA, Reddy SM, Gao J, Zhang S, Basar R, Thakur R, Yizhak K, Sade-Feldman M, Blando J, Han G, Gopalakrishnan V, Xi Y, Zhao H, Amaria RN, Tawbi HA, Cogdill AP, Liu W, LeBleu VS, Kugeratski FG, Patel S, Davies MA, Hwu P, Lee JE, Gershenwald JE, Lucci A, Arora R, Woodman S, Keung EZ, Gaudreau PO, Reuben A, Spencer CN, Burton EM, Haydu LE, Lazar AJ, Zapassodi R, Hudgens CW, Ledesma DA, Ong SF, Bailey M, Warren S, Rao D, Krijgsman O, Rozeman EA, Peeper D, Blank CU, Schumacher TN, Butterfield LH, Zelazowska MA, McBride KM, Kalluri R, Allison J, Petitprez F, Fridman WH, Sautès-Fridman C, Hacohen N, Rezvani K, Sharma P, Tetzlaff MT, Wang L, Wargo JA (2020) B cells and tertiary lymphoid structures promote immunotherapy response. Nature. 577(7791):549–555

    Article  CAS  Google Scholar 

  21. Aran D, Hu Z, Butte AJ (2017) xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol 18(1):220

    Article  CAS  Google Scholar 

  22. Ru B, Wong CN, Tong Y, Zhong JY, Zhong SSW, Wu WC, Chu KC, Wong CY, Lau CY, Chen I, Chan NW, Zhang J (2019) TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics. 35(20):4200–4202

    Article  CAS  Google Scholar 

  23. Cerretelli G, Ager A, Arends MJ, Frayling IM (2020) Molecular pathology of Lynch syndrome. J Pathol 250(5):518–531

    Article  Google Scholar 

  24. Ortiz-Barahona V, Joshi RS, Esteller M (2020) Use of DNA methylation profiling in translational oncology. Semin Cancer Biol S1044-579X(20):30271–30276

    Google Scholar 

  25. Fridman WH, Zitvogel L, Sautès-Fridman C, Kroemer G (2017) The immune contexture in cancer prognosis and treatment. Nat Rev Clin Oncol 14(12):717–734

    Article  CAS  Google Scholar 

  26. Azimi F, Scolyer RA, Rumcheva P, Moncrieff M, Murali R, McCarthy SW, Saw RP, Thompson JF (2012) Tumor-infiltrating lymphocyte grade is an independent predictor of sentinel lymph node status and survival in patients with cutaneous melanoma. J Clin Oncol 30(21):2678–2683

    Article  Google Scholar 

  27. Ingold Heppner B, Untch M, Denkert C, Pfitzner BM, Lederer B, Schmitt W, Eidtmann H, Fasching PA, Tesch H, Solbach C, Rezai M, Zahm DM, Holms F, Glados M, Krabisch P, Heck E, Ober A, Lorenz P, Diebold K, Habeck JO, Loibl S (2016) Tumor-infiltrating lymphocytes: a predictive and prognostic biomarker in neoadjuvant-treated HER2-positive breast cancer. Clin Cancer Res 22(23):5747–5754

    Article  CAS  Google Scholar 

  28. Faivre S, Rimassa L, Finn RS (2020) Molecular therapies for HCC: looking outside the box. J Hepatol 72(2):342–352

    Article  CAS  Google Scholar 

  29. Llovet JM, Montal R, Sia D, Finn RS (2018) Molecular therapies and precision medicine for hepatocellular carcinoma. Nat Rev Clin Oncol 15(10):599–616

    Google Scholar 

  30. Greten TF, Wang XW, Korangy F (2015) Current concepts of immune based treatments for patients with HCC: from basic science to novel treatment approaches. Gut. 64(5):842–848

    Article  CAS  Google Scholar 

  31. Pinter M, Scheiner B, Peck-Radosavljevic M (2021) Immunotherapy for advanced hepatocellular carcinoma: a focus on special subgroups. Gut. 70(1):204–214

    Article  CAS  Google Scholar 

  32. Benson AB, Angelica MI et al (2021) Hepatobiliary Cancers, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Cancer Netw 19(5):541–565

    Article  Google Scholar 

  33. Feng GS, Hanley KL, Liang Y, Lin X (2021) Improving the efficacy of liver cancer immunotherapy: the power of combined preclinical and clinical studies. Hepatology 73(Suppl 1):104–114

    Article  Google Scholar 

  34. Bagaev A, Kotlov N, Nomie K et al (2021) Conserved pan-cancer microenvironment subtypes predict response to immunotherapy. Cancer Cell 39(6):845–865

    Article  CAS  Google Scholar 

  35. Madden MZ, Rathmell JC (2021) The complex integration of T-cell metabolism and immunotherapy. Cancer Discov 11(7):1636–1643

    Article  CAS  Google Scholar 

  36. Sanmamed MF, Nie X, Desai SS, Villaroel-Espindola F, Badri T, Zhao D, Kim AW, Ji L, Zhang T, Quinlan E, Cheng X, Han X, Vesely MD, Nassar AF, Sun J, Zhang Y, Kim TK, Wang J, Melero I, Herbst RS, Schalper KA, Chen L (2021) A burned-out CD8+ T-cell subset expands in the tumor microenvironment and curbs cancer immunotherapy. Cancer Discov 11:1700–1715

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We acknowledge the TCGA, GTEx, CCLE, THPA, TIMER, TISIDB, GEPIA, and STRING databases for free use. Additionally, Ning Wang would like to thank his parents and wife for their encouragement and support in the scientific research work.

Author information

Author notes

  1. Ning Wang and Xue Zhou contributed equally to this work.

Authors and Affiliations

  1. Tianjin Third Central Hospital, 83 Jintang Road, Hedong District, Tianjin, 300170, China

    Ning Wang & Xiaowei Zhu

  2. Department of Nephrology, Tianjin Haihe Hospital, Haihe Clinical College of Tianjin Medical University, Tianjin, 300350, China

    Xue Zhou

  3. Department of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin, 300170, China

    Fei Tang

  4. Department of Respiratory Medicine, Tianjin Third Central Hospital, Tianjin, 300170, China

    Xue Wang

Authors
  1. Ning Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xue Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fei Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xue Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaowei Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Ning Wang and Xue Zhou contributed to the study conception, design, and visualization. Fei Tang and XiaoWei Zhu performed data analysis and figure generation. Xue Wang contributed to collection and integration of data. Ning Wang wrote the manuscript.

Corresponding author

Correspondence to Ning Wang.

Ethics declarations

Ethics approval

This article is not involved in any studies with human participants or animals performed by any of the authors.

Consent to participate

Not applicable.

Consent for publication

All authors consent to the publication of this research.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

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

Supplementary Information

Fig. S1

Identification of LOXL3 expression is in pan-cancer. a The expression of LOXL3 in 31 normal tissues based on GTEx database. b The expression of LOXL3 in 21 tumor cells based on CCLE database (PNG 613 kb)

High resolution image (TIF 4831 kb)

Fig. S2

Correlation analysis of LOXL3 expression with the mutation levels of five MMR genes involving in MLH1, MSH2, MSH6, PMS2, and EPCAM in pan-cancer. *P < 0.05, **P < 0.01, and ***P < 0.001 (PNG 201 kb)

High resolution image (TIF 1082 kb)

Fig. S3

Correlation analysis of LOXL3 expression with four DNA methyltransferase genes (DNMT1, red; DNMT2, blue; DNMT3A, green; DNMT3B, purple) in pan-cancer (PNG 1159 kb)

High resolution image (TIF 3809 kb)

ESM 1

(XLSX 19 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, N., Zhou, X., Tang, F. et al. Identification of LOXL3-associating immune infiltration landscape and prognostic value in hepatocellular carcinoma. Virchows Arch 479, 1153–1165 (2021). https://doi.org/10.1007/s00428-021-03193-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00428-021-03193-4

Keywords

Search

Navigation