Skip to main content

Advertisement

SpringerLink
Log in

Prediction of prognosis and immunotherapy response with a robust immune-related lncRNA pair signature in lung adenocarcinoma

  • Original Article
  • Published:
Cancer Immunology, Immunotherapy Aims and scope Submit manuscript

Abstract

The tumor immune microenvironment plays essential roles in regulating inflammation, angiogenesis, immune modulation, and sensitivity to therapies. Here, we developed a powerful prognostic signature with immune-related lncRNAs (irlncRNAs) in lung adenocarcinoma (LUAD). We obtained differentially expressed irlncRNAs by intersecting the transcriptome dataset for The Cancer Genome Atlas (TCGA)-LUAD cohort and the ImmLnc database. A rank-based algorithm was applied to select top-ranking altered irlncRNA pairs for the model construction. We built a prognostic signature of 33 irlncRNA pairs comprising 40 unique irlncRNAs in the TCGA-LUAD cohort (training set). The immune signature significantly dichotomized LUAD patients into high- and low-risk groups regarding overall survival, which is likewise independently predictive of prognosis (hazard ratio = 3.580, 95% confidence interval = 2.451–5.229, P < 0.001). A nomogram with a C-index of 0.79 demonstrates the superior prognostic accuracy of the signature. The prognostic accuracy of the signature of 33 irlncRNA pairs was validated using the GSE31210 dataset (validation set) from the Gene Expression Omnibus database. Immune cell infiltration was calculated using ESTIMATE, CIBERSORT, and MCP-count methodologies. The low-risk group exhibited high immune cell infiltration, high mutation burden, high expression of CTLA4 and human leukocyte antigen genes, and low expression of mismatch repair genes, which predicted response to immunotherapy. Interestingly, pRRophetic analysis demonstrated that the high-risk group possessed reverse characteristics was sensitive to chemotherapy. The established immune signature shows marked clinical and translational potential for predicting prognosis, tumor immunogenicity, and therapeutic response in LUAD.

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

Abbreviations

AUC:

Area under the curve

C-index:

Concordance index

CIBERSORT:

Cell type identification by estimating relative subsets of RNA transcripts

CTLA4:

Cytotoxic T lymphocyte-associated antigen 4

DCA:

Decision curve analysis

ESTIMATE:

Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data

GEO:

Gene Expression Omnibus

GO:

Gene ontology

GSEA:

Gene set enrichment analysis

HR:

Hazard ratio

HLA:

Human leukocyte antigen; MMR

KEGG:

Kyoto encyclopedia of genes and genomes

LASSO:

Least absolute shrinkage and selection operator

LUAD:

Lung adenocarcinoma

LUSC:

Lung squamous cell carcinoma

LAG3:

Lymphocyte activation gene 3

MCP-counter:

Microenvironment cell population count

NSCLC:

Non-small cell lung cancer

OS:

Overall survival

ROC:

Receiver operating characteristic

TCGA:

The Cancer Genome Atlas

TME:

Tumor microenvironment

TMB:

Tumor mutation burden

TIM3:

T cell membrane protein 3

95% CI:

95% Confidence interval

References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394–424. https://doi.org/10.3322/caac.21492

    Article  PubMed  Google Scholar 

  2. Miller KD, Nogueira L, Mariotto AB, Rowland JH, Yabroff KR, Alfano CM, Jemal A, Kramer JL, Siegel RL (2019) Cancer treatment and survivorship statistics, 2019. CA Cancer J Clin 69:363–385. https://doi.org/10.3322/caac.21565

    Article  PubMed  Google Scholar 

  3. Garon EB, Hellmann MD, Rizvi NA et al (2019) Five-year overall survival for patients with advanced nonsmall-cell lung cancer treated with pembrolizumab: results from the phase I KEYNOTE-001 study. J Clin Oncol 37:2518–2527. https://doi.org/10.1200/JCO.19.00934

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Wang KC, Chang HY (2011) Molecular mechanisms of long non-coding RNAs. Mol Cell 43:904–914. https://doi.org/10.1016/j.molcel.2011.08.018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Goodall GJ, Wickramasinghe VO (2020) RNA in cancer. Nat Rev Cancer. https://doi.org/10.1038/s41568-020-00306-0

    Article  PubMed  Google Scholar 

  6. Chen YG, Satpathy AT, Chang HY (2017) Gene regulation in the immune system by long non-coding RNAs. Nat Immunol 18:962–972. https://doi.org/10.1038/ni.3771

    Article  CAS  PubMed  Google Scholar 

  7. Krawczyk M, Emerson BM (2014) p50-associated COX-2 extragenic RNA (PACER) activates COX-2 gene expression by occluding repressive NF-kappaB complexes. Elife 3:e01776. https://doi.org/10.7554/eLife.01776

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Ranzani V, Rossetti G, Panzeri I et al (2015) The long intergenic non-coding RNA landscape of human lymphocytes highlights the regulation of T cell differentiation by linc-MAF-4. Nat Immunol 16:318–325. https://doi.org/10.1038/ni.3093

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Li Y, Jiang T, Zhou W et al (2020) Pan-cancer characterization of immune-related lncRNAs identifies potential oncogenic biomarkers. Nat Commun 11:1000. https://doi.org/10.1038/s41467-020-14802-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kim S, Lin CW, Tseng GC (2016) MetaKTSP: a meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis. Bioinformatics 32:1966–1973. https://doi.org/10.1093/bioinformatics/btw115

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Hong W, Liang L, Gu Y, Qi Z, Qiu H, Yang X, Zeng W, Ma L, Xie J (2020) Immune-related lncRNA to construct novel signature and predict the immune landscape of human hepatocellular carcinoma. Mol Ther Nucleic Acids 22:937–947. https://doi.org/10.1016/j.omtn.2020.10.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zhu J, Liu Y, Zhao M, Cao K, Ma J, Peng S (2021) Identification of downstream signaling cascades of ACK1 and prognostic classifiers in non-small cell lung cancer. Aging (Albany NY). https://doi.org/10.18632/aging.202408

    Article  PubMed Central  Google Scholar 

  13. Liu Y, Wu L, Ao H, Zhao M, Leng X, Liu M, Ma J, Zhu J (2019) Prognostic implications of autophagy-associated gene signatures in non-small cell lung cancer. Aging (Albany NY) 11:11440–11462. https://doi.org/10.18632/aging.102544

    Article  CAS  Google Scholar 

  14. Karakiewicz PI, Briganti A, Chun FK et al (2007) Multi-institutional validation of a new renal cancer-specific survival nomogram. J Clin Oncol 25:1316–1322. https://doi.org/10.1200/JCO.2006.06.1218

    Article  PubMed  Google Scholar 

  15. Okayama H, Kohno T, Ishii Y et al (2012) Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res 72:100–111. https://doi.org/10.1158/0008-5472.CAN-11-1403

    Article  CAS  PubMed  Google Scholar 

  16. Yoshihara K, Shahmoradgoli M, Martínez E et al (2013) Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 4:2612. https://doi.org/10.1038/ncomms3612

    Article  CAS  PubMed  Google Scholar 

  17. Becht E, Giraldo NA, Lacroix L et al (2016) Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol 17:218. https://doi.org/10.1186/s13059-016-1070-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA (2018) Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol Biol 1711:243–259. https://doi.org/10.1007/978-1-4939-7493-1_12

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA (2015) Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 12:453–457. https://doi.org/10.1038/nmeth.3337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Geeleher P, Cox N, Huang RS (2014) pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS ONE 9:e107468. https://doi.org/10.1371/journal.pone.0107468

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Geeleher P, Cox NJ, Huang RS (2014) Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. Genome Biol 15:R47. https://doi.org/10.1186/gb-2014-15-3-r47

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Heinaniemi M, Nykter M, Kramer R et al (2013) Gene-pair expression signatures reveal lineage control. Nat Methods 10:577–583. https://doi.org/10.1038/nmeth.2445

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Li B, Cui Y, Diehn M, Li R (2017) Development and validation of an individualized immune prognostic signature in early-stage nonsquamous non-small cell lung cancer. JAMA Oncol 3:1529–1537. https://doi.org/10.1001/jamaoncol.2017.1609

    Article  PubMed  PubMed Central  Google Scholar 

  24. Luchini C, Bibeau F, Ligtenberg MJL et al (2019) ESMO recommendations on microsatellite instability testing for immunotherapy in cancer, and its relationship with PD-1/PD-L1 expression and tumour mutational burden: a systematic review-based approach. Ann Oncol Off J Eur Soc Med Oncol 30:1232–1243. https://doi.org/10.1093/annonc/mdz116

    Article  CAS  Google Scholar 

  25. Altorki NK, Markowitz GJ, Gao D, Port JL, Saxena A, Stiles B, McGraw T, Mittal V (2018) The lung microenvironment: an important regulator of tumour growth and metastasis. Nat Rev Cancer 19:9–31. https://doi.org/10.1038/s41568-018-0081-9

    Article  CAS  Google Scholar 

  26. Dudley JC, Lin MT, Le DT, Eshleman JR (2016) Microsatellite Instability as a Biomarker for PD-1 Blockade. Clin Cancer Res 22:813–820. https://doi.org/10.1158/1078-0432.CCR-15-1678

    Article  CAS  PubMed  Google Scholar 

  27. Le DT, Uram JN, Wang H et al (2015) PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med 372:2509–2520. https://doi.org/10.1056/NEJMoa1500596

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Sha D, Jin Z, Budczies J, Kluck K, Stenzinger A, Sinicrope FA (2020) Tumor mutational burden as a predictive biomarker in solid tumors. Cancer Discov 10:1808–1825. https://doi.org/10.1158/2159-8290.CD-20-0522

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Jardim DL, Goodman A, de Melo GD, Kurzrock R (2020) The challenges of tumor mutational burden as an immunotherapy biomarker. Cancer Cell. https://doi.org/10.1016/j.ccell.2020.10.001

    Article  PubMed  PubMed Central  Google Scholar 

  30. McGranahan N, Rosenthal R, Hiley CT et al (2017) Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution. Cell 171:1259–71.e11. https://doi.org/10.1016/j.cell.2017.10.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Beer DG, Kardia SL, Huang CC et al (2002) Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med 8:816–824. https://doi.org/10.1038/nm733

    Article  CAS  PubMed  Google Scholar 

  32. Director’s Challenge Consortium for the Molecular Classification of Lung A, Shedden K, Taylor JM et al (2008) Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med 14:822–827. https://doi.org/10.1038/nm.1790

    Article  CAS  Google Scholar 

  33. Wistuba II, Behrens C, Lombardi F et al (2013) Validation of a proliferation-based expression signature as prognostic marker in early stage lung adenocarcinoma. Clin Cancer Res 19:6261–6271. https://doi.org/10.1158/1078-0432.CCR-13-0596

    Article  CAS  PubMed  Google Scholar 

  34. Chen HY, Yu SL, Chen CH et al (2007) A five-gene signature and clinical outcome in non-small-cell lung cancer. N Engl J Med 356:11–20. https://doi.org/10.1056/NEJMoa060096

    Article  CAS  PubMed  Google Scholar 

  35. Kratz JR, He J, Van Den Eeden SK et al (2012) A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: development and international validation studies. The Lancet 379:823–832. https://doi.org/10.1016/s0140-6736(11)61941-7

    Article  Google Scholar 

  36. Slack FJ, Chinnaiyan AM (2019) The role of non-coding RNAs in oncology. Cell 179:1033–1055. https://doi.org/10.1016/j.cell.2019.10.017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Li LQ, Zhang LH, Zhang Y et al (2020) Construction of immune-related gene pairs signature to predict the overall survival of osteosarcoma patients. Aging (Albany NY) 12:22906–22926. https://doi.org/10.18632/aging.104017

    Article  CAS  Google Scholar 

  38. Zhao E, Zhou C, Chen S (2020) A signature of 14 immune-related gene pairs predicts overall survival in gastric cancer. Clin Transl Oncol. https://doi.org/10.1007/s12094-020-02414-7

    Article  PubMed  Google Scholar 

  39. Jiang P, Li Y, Xu Z, He S (2021) A signature of 17 immune-related gene pairs predicts prognosis and immune status in HNSCC patients. Transl Oncol 14:100924. https://doi.org/10.1016/j.tranon.2020.100924

    Article  PubMed  Google Scholar 

  40. Fridman WH, Zitvogel L, Sautes-Fridman C, Kroemer G (2017) The immune contexture in cancer prognosis and treatment. Nat Rev Clin Oncol 14:717–734. https://doi.org/10.1038/nrclinonc.2017.101

    Article  CAS  PubMed  Google Scholar 

  41. Brambilla E, Le Teuff G, Marguet S et al (2016) Prognostic effect of tumor lymphocytic infiltration in resectable non-small-cell lung cancer. J Clin Oncol 34:1223–1230. https://doi.org/10.1200/JCO.2015.63.0970

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Reynders K, De Ruysscher D (2016) Tumor infiltrating lymphocytes in lung cancer: a new prognostic parameter. J Thorac Dis 8:E833–E835. https://doi.org/10.21037/jtd.2016.07.75

    Article  PubMed  PubMed Central  Google Scholar 

  43. Shimizu K, Nakata M, Hirami Y, Yukawa T, Maeda A, Tanemoto K (2010) Tumor-infiltrating Foxp3+ regulatory T cells are correlated with cyclooxygenase-2 expression and are associated with recurrence in resected non-small cell lung cancer. J Thorac Oncol 5:585–590. https://doi.org/10.1097/JTO.0b013e3181d60fd7

    Article  PubMed  Google Scholar 

  44. Chan TA, Yarchoan M, Jaffee E, Swanton C, Quezada SA, Stenzinger A, Peters S (2019) Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol Off J Eur Soc Med Oncol 30:44–56. https://doi.org/10.1093/annonc/mdy495

    Article  CAS  Google Scholar 

  45. Rizvi H, Sanchez-Vega F, La K et al (2018) Molecular determinants of response to anti-programmed cell death (PD)-1 and anti-programmed death-ligand 1 (PD-L1) blockade in patients with non-small-cell lung cancer profiled with targeted next-generation sequencing. J Clin Oncol 36:633–641. https://doi.org/10.1200/jco.2017.75.3384

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Le DT, Durham JN, Smith KN et al (2017) Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357:409–413. https://doi.org/10.1126/science.aan6733

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by the Haiyan Foundation of Harbin Medical University Cancer Hospital under Grants [JJZD2020-01 and JJZD2018-01], the Chunhui Project Foundation of Education Department of China under Grant [2019020], and the Natural Science Foundation of China (82172786).

Author information

Authors and Affiliations

  1. Department of Clinical Laboratory, Biobank, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China

    Kui Cao & Jinhong Zhu

  2. Department of Clinical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China

    Kui Cao & Mingdong Liu

  3. Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China

    Keru Ma, Xiangyu Jiang & Jianqun Ma

Authors
  1. Kui Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingdong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Keru Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiangyu Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jianqun Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jinhong Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JZ and JM drafted the manuscript. KC and JM performed the analyses and interpreted all the data. KC, KM, and ML prepared the figures and tables. KC and XJ reviewed and revised the manuscript. All authors approved the final manuscript.

Corresponding authors

Correspondence to Jianqun Ma or Jinhong Zhu.

Ethics declarations

Consent for publication

Not applicable.

Availability of data and materials

Not applicable.

Competing interests

There was no competing interest to disclose.

Ethics approval and consent to participate

Not applicable.

Additional information

Publisher's Note

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

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cao, K., Liu, M., Ma, K. et al. Prediction of prognosis and immunotherapy response with a robust immune-related lncRNA pair signature in lung adenocarcinoma. Cancer Immunol Immunother 71, 1295–1311 (2022). https://doi.org/10.1007/s00262-021-03069-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00262-021-03069-1

Keywords

Search

Navigation