Prediction of Tumor Microenvironment Characteristics and Treatment Response in Lung Squamous Cell Carcinoma by Pseudogene OR7E47P-related Immune Genes

Ya-qi Zhao , Hao-han Zhang , Jie Wu , Lan Li , Jing Li , Hao Zhong , Yan Jin , Tian-yu Lei , Xin-yi Zhao , Bin Xu , Qi-bin Song , Jie He

Current Medical Science ›› 2023, Vol. 43 ›› Issue (6) : 1133 -1150.

PDF
Current Medical Science ›› 2023, Vol. 43 ›› Issue (6) : 1133 -1150. DOI: 10.1007/s11596-023-2798-2
Original Articles

Prediction of Tumor Microenvironment Characteristics and Treatment Response in Lung Squamous Cell Carcinoma by Pseudogene OR7E47P-related Immune Genes

Author information +
History +
PDF

Abstract

Objective

Pseudogenes are initially regarded as nonfunctional genomic sequences, but some pseudogenes regulate tumor initiation and progression by interacting with other genes to modulate their transcriptional activities. Olfactory receptor family 7 subfamily E member 47 pseudogene (OR7E47P) is expressed broadly in lung tissues and has been identified as a positive regulator in the tumor microenvironment (TME) of lung adenocarcinoma (LUAD). This study aimed to elucidate the correlation between OR7E47P and tumor immunity in lung squamous cell carcinoma (LUSC).

Methods

Clinical and molecular information from The Cancer Genome Atlas (TCGA) LUSC cohort was used to identify OR7E47P-related immune genes (ORIGs) by weighted gene correlation network analysis (WGCNA). Based on the ORIGs, 2 OR7E47P clusters were identified using non-negative matrix factorization (NMF) clustering, and the stability of the clustering was tested by an extreme gradient boosting classifier (XGBoost). LASSO-Cox and stepwise regressions were applied to further select prognostic ORIGs and to construct a predictive model (ORPScore) for immunotherapy. The Botling cohorts and 8 immunotherapy cohorts (the Samstein, Braun, Jung, Gide, IMvigor210, Lauss, Van Allen, and Cho cohorts) were included as independent validation cohorts.

Results

OR7E47P expression was positively correlated with immune cell infiltration and enrichment of immune-related pathways in LUSC. A total of 57 ORIGs were identified to classify the patients into 2 OR7E47P clusters (Cluster 1 and Cluster 2) with distinct immune, mutation, and stromal programs. Compared to Cluster 1, Cluster 2 had more infiltration by immune and stromal cells, lower mutation rates of driver genes, and higher expression of immune-related proteins. The clustering performed well in the internal and 5 external validation cohorts. Based on the 7 ORIGs (HOPX, STX2, WFS, DUSP22, SLFN13, GGCT, and CCSER2), the ORPScore was constructed to predict the prognosis and the treatment response. In addition, the ORPScore was a better prognostic factor and correlated positively with the immunotherapeutic response in cancer patients. The area under the curve values ranged from 0.584 to 0.805 in the 6 independent immunotherapy cohorts.

Conclusion

Our study suggests a significant correlation between OR7E47P and TME modulation in LUSC. ORIGs can be applied to molecularly stratify patients, and the ORPScore may serve as a biomarker for clinical decision-making regarding individualized prognostication and immunotherapy.

Keywords

pseudogene / olfactory receptor family 7 subfamily E member 47 pseudogene-related immune gene / tumor microenvironment / immunotherapy / lung squamous cell carcinoma

Cite this article

Download citation ▾
Ya-qi Zhao, Hao-han Zhang, Jie Wu, Lan Li, Jing Li, Hao Zhong, Yan Jin, Tian-yu Lei, Xin-yi Zhao, Bin Xu, Qi-bin Song, Jie He. Prediction of Tumor Microenvironment Characteristics and Treatment Response in Lung Squamous Cell Carcinoma by Pseudogene OR7E47P-related Immune Genes. Current Medical Science, 2023, 43(6): 1133-1150 DOI:10.1007/s11596-023-2798-2

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

ThaiAA, SolomonBJ, SequistLV, et al.. Lung cancer. Lancet, 2021, 398(10299): 535-554

[2]

HirschFR, ScagliottiGV, MulshineJL, et al.. Lung cancer: current therapies and new targeted treatments. Lancet, 2017, 389(10066): 299-311

[3]

BorghaeiH, GettingerS, VokesEE, et al.. Five-Year Outcomes From the Randomized, Phase III Trials CheckMate 017 and 057: Nivolumab Versus Docetaxel in Previously Treated Non-Small-Cell Lung Cancer. J Clin Oncol, 2021, 39(7): 723-733

[4]

Paz-AresL, VicenteD, TafreshiA, et al.. A Randomized, Placebo-Controlled Trial of Pembrolizumab Plus Chemotherapy in Patients With Metastatic Squamous NSCLC: Protocol-Specified Final Analysis of KEYNOTE-407. J Thorac Oncol, 2020, 15(10): 1657-1669

[5]

PanY, ZhanL, ChenL, et al.. POU5F1B promotes hepatocellular carcinoma proliferation by activating AKT. Biomed Pharmacother, 2018, 100: 374-380

[6]

HayashiH, AraoT, TogashiY, et al.. The OCT4 pseudogene POU5F1B is amplified and promotes an aggressive phenotype in gastric cancer. Oncogene, 2015, 34(2): 199-208

[7]

YuJ, ZhangJ, ZhouL, et al.. The Octamer-Binding Transcription Factor 4 (OCT4) Pseudogene, POU Domain Class 5 Transcription Factor 1B (POU5F1B), is Upregulated in Cervical Cancer and Down-Regulation Inhibits Cell Proliferation and Migration and Induces Apoptosis in Cervical Cancer Cell Lines. Med Sci Monit, 2019, 25: 1204-1213

[8]

HuangJL, CaoSW, OuQS, et al.. The long non-coding RNA PTTG3P promotes cell growth and metastasis via up-regulating PTTG1 and activating PI3K/AKT signaling in hepatocellular carcinoma. Mol Cancer, 2018, 17(1): 93

[9]

ZhangN, ZhangH, WuW, et al.. Machine learning-based identification of tumor-infiltrating immune cell-associated lncRNAs for improving outcomes and immunotherapy responses in patients with low-grade glioma. Theranostics, 2022, 12(13): 5931-5948

[10]

ZhangH, ZhangN, WuW, et al.. Machine learning-based tumor-infiltrating immune cell-associated lncRNAs for predicting prognosis and immunotherapy response in patients with glioblastoma. Brief Bioinform, 2022, 23(6): bbac386

[11]

SunJ, ZhangZ, BaoS, et al.. Identification of tumor immune infiltration-associated lncRNAs for improving prognosis and immunotherapy response of patients with non-small cell lung cancer. J Immunother Cancer, 2020, 8(1): e000110

[12]

LiY, JiangT, ZhouW, et al.. Pan-cancer characterization of immune-related lncRNAs identifies potential oncogenic biomarkers. Nat Commun, 2020, 11(1): 1000

[13]

GlusmanG, BaharA, SharonD, et al.. The olfactory receptor gene superfamily: data mining, classification, and nomenclature. Mamm Genome, 2000, 11(11): 1016-1023

[14]

VadevooSMP, GunassekaranGR, LeeC, et al.. The macrophage odorant receptor Olfr78 mediates the lactate-induced M2 phenotype of tumor-associated macrophages. Proc Natl Acad Sci USA, 2021, 118(37): e2102434118

[15]

MartinAL, AnadonCM, BiswasS, et al.. Olfactory Receptor OR2H1 Is an Effective Target for CAR T Cells in Human Epithelial Tumors. Mol Cancer Ther, 2022, 21(7): 1184-1194

[16]

ChenZ, HuangZ, ChenLX. The Olfactory Receptor Pseudo-pseudogene: A Potential Therapeutic Target in Human Diseases. Biomed Environ Sci, 2018, 31(2): 168-170

[17]

YoungerRM, AmadouC, BethelG, et al.. Characterization of clustered MHC-linked olfactory receptor genes in human and mouse. Genome Res, 2001, 11(4): 519-530

[18]

WuJ, LiL, ZhangH, et al.. A risk model developed based on tumor microenvironment predicts overall survival and associates with tumor immunity of patients with lung adenocarcinoma. Oncogene, 2021, 40(26): 4413-4424

[19]

SamsteinRM, LeeC-H, ShoushtariAN, et al.. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet, 2019, 51(2): 202-206

[20]

BraunDA, HouY, BakounyZ, et al.. Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell renal cell carcinoma. Nat Med, 2020, 26(6): 909-918

[21]

JungH, KimHS, KimJY, et al.. DNA methylation loss promotes immune evasion of tumours with high mutation and copy number load. Nat Commun, 2019, 10(1): 4278

[22]

GideTN, QuekC, MenziesAM, et al.. Distinct Immune Cell Populations Define Response to Anti-PD-1 Monotherapy and Anti-PD-1/Anti-CTLA-4 Combined Therapy. Cancer Cell, 2019, 35(2): 238-255

[23]

MariathasanS, TurleySJ, NicklesD, et al.. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature, 2018, 554(7693): 544-548

[24]

LaussM, DoniaM, HarbstK, et al.. Mutational and putative neoantigen load predict clinical benefit of adoptive T cell therapy in melanoma. Nat Commun, 2017, 8(1): 1738

[25]

Van AllenEM, MiaoD, SchillingB, et al.. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science, 2015, 350(6257): 207-211

[26]

ChoJW, HongMH, HaSJ, et al.. Genome-wide identification of differentially methylated promoters and enhancers associated with response to anti-PD-1 therapy in non-small cell lung cancer. Exp Mol Med, 2020, 52(9): 1550-1563

[27]

LiberzonA, BirgerC, ThorvaldsdóttirH, et al.. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst, 2015, 1(6): 417-425

[28]

HänzelmannS, CasteloR, GuinneyJ. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Biomformatics, 2013, 14: 7

[29]

SubramanianA, TamayoP, MoothaVK, et al.. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA, 2005, 102(43): 15545-15550

[30]

AranD, HuZ, ButteAJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol, 2017, 18(1): 220

[31]

FinotelloF, MayerC, PlattnerC, et al.. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Med, 2019, 11(1): 34

[32]

LiT, FanJ, WangB, et al.. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res, 2017, 77(21): e108-e110

[33]

YoshiharaK, ShahmoradgoliM, MartinezE, et al.. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun, 2013, 4: 2612

[34]

ThorssonV, GibbsDL, BrownSD, et al.. The Immune Landscape of Cancer. Immunity, 2018, 48(4): 812-830

[35]

LangfelderP, HorvathS. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 2008, 9: 559

[36]

BrunetJP, TamayoP, GolubTR, et al.. Metagenes and molecular pattern discovery using matrix factorization. Proc Natl Acad Sci USA, 2004, 101(12): 4164-4169

[37]

Chen TQ, Guestrin C, Assoc Comp M. XGBoost: A Scalable Tree Boosting System. In: 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). San Francisco, CA, 2016:785–794

[38]

WuCC, WangYA, LivingstonJA, et al.. Prediction of biomarkers and therapeutic combinations for anti-PD-1 immunotherapy using the global gene network association. Nat Commun, 2022, 13(1): 42

[39]

CristescuR, MoggR, AyersM, et al.. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science, 2018, 362(6411): eaar3593

[40]

TrujilloJA, SweisRF, BaoR, et al.. T Cell-Inflamed versus Non-T Cell-Inflamed Tumors: A Conceptual Framework for Cancer Immunotherapy Drug Development and Combination Therapy Selection. Cancer Immunol Res, 2018, 6(9): 990-1000

[41]

BenciJL, JohnsonLR, ChoaR, et al.. Opposing Functions of Interferon Coordinate Adaptive and Innate Immune Responses to Cancer Immune Checkpoint Blockade. Cell, 2019, 178(4): 933-948.e14

[42]

AuslanderN, ZhangG, LeeJS, et al.. Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nat Med, 2018, 24(10): 1545-1549

[43]

CharoentongP, FinotelloF, AngelovaM, et al.. Pancancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep, 2017, 18(1): 248-262

[44]

JiangP, GuS, PanD, et al.. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med, 2018, 24(10): 1550-1558

[45]

GeeleherP, CoxN, HuangRS. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One, 2014, 9(9): e107468

[46]

ShiX, DongA, JiaX, et al.. Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on T-cell marker genes to predict prognosis and therapeutic response in lung squamous cell carcinoma. Front Immunol, 2022, 13: 992990

[47]

ZhuangY, LiS, LiuC, et al.. Identification of an Individualized Immune-Related Prognostic Risk Score in Lung Squamous Cell Cancer. Front Oncol, 2021, 11: 546455

[48]

FuD, ZhangB, YangL, et al.. Development of an Immune-Related Risk Signature for Predicting Prognosis in Lung Squamous Cell Carcinoma. Front Genet, 2020, 11: 978

[49]

PuJ, TengZ, YangW, et al.. Construction of a prognostic model for lung squamous cell carcinoma based on immune-related genes. Carcinogenesis, 2023, 44(2): 143-152

[50]

ZhangX, XiaoJ, FuX, et al.. Construction of a Two-Gene Immunogenomic-Related Prognostic Signature in Lung Squamous Cell Carcinoma. Front Mol Biosci, 2022, 9: 867494

[51]

LiR, LiuX, ZhouXJ, et al.. Identification of a Prognostic Model Based on Immune-Related Genes of Lung Squamous Cell Carcinoma. Front Oncol, 2020, 10: 1588

[52]

LaiJ, YangS, ChuS, et al.. Determination of a prediction model for therapeutic response and prognosis based on chemokine signaling-related genes in stage I-III lung squamous cell carcinoma. Front Genet, 2022, 13: 921837

[53]

ZhaiWY, DuanFF, ChenS, et al.. An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Squamous Carcinoma. Front Cell Dev Biol, 2022, 10: 770550

[54]

SchaafsmaE, FugleCM, WangX, et al.. Pan-cancer association of HLA gene expression with cancer prognosis and immunotherapy efficacy. Br J Cancer, 2021, 125(3): 422-432

[55]

QinS, XuL, YiM, et al.. Novel immune checkpoint targets: moving beyond PD-1 and CTLA-4. Mol Cancer, 2019, 18(1): 155

[56]

ShibelR, SarfsteinR, NagarajK, et al.. The Olfactory Receptor Gene Product, OR5H2, Modulates Endometrial Cancer Cells Proliferation via Interaction with the IGF1 Signaling Pathway. Cells, 2021, 10(6): 1483

[57]

ChenP, WangW, LiuR, et al.. Olfactory sensory experience regulates gliomagenesis via neuronal IGF1. Nature, 2022, 606(7914): 550-556

[58]

Prieto-GodinoLL, RytzR, BargetonB, et al.. Olfactory receptor pseudo-pseudogenes. Nature, 2016, 539(7627): 93-97

[59]

PolisenoL. Pseudogenes: newly discovered players in human cancer. Sci Signal, 2012, 5(242): re5

[60]

WolfeCJ, KohaneIS, ButteAJ. Systematic survey reveals general applicability of “guilt-by-association” within gene coexpression networks. BMC Bioinformatics, 2005, 6: 227

[61]

ZhuY, GongL, WeiCL. Guilt by association: EcDNA as a mobile transactivator in cancer. Trends Cancer, 2022, 8(9): 747-758

[62]

AdlerP, KoldeR, KullM, et al.. Mining for coexpression across hundreds of datasets using novel rank aggregation and visualization methods. Genome Biol, 2009, 10(12): R139

[63]

CalonA, LonardoE, Berenguer-LlergoA, et al.. Stromal gene expression defines poor-prognosis subtypes in colorectal cancer. Nat Genet, 2015, 47(4): 320-329

[64]

FuR, HanCF, NiT, et al.. A ZEB1/p53 signaling axis in stromal fibroblasts promotes mammary epithelial tumours. Nat Commun, 2019, 10(1): 3210

[65]

YangL, PangY, MosesHL. TGF-beta and immune cells: an important regulatory axis in the tumor microenvironment and progression. Trends Immunol, 2010, 31(6): 220-227

[66]

DantoingE, PitonN, SalaünM, et al.. Anti-PD1/PD-L1 Immunotherapy for Non-Small Cell Lung Cancer with Actionable Oncogenic Driver Mutations. Int J Mol Sci, 2021, 22(12): 6288

[67]

MazieresJ, DrilonA, LusqueA, et al.. Immune checkpoint inhibitors for patients with advanced lung cancer and oncogenic driver alterations: results from the IMMUNOTARGET registry. Ann Oncol, 2019, 30(8): 1321-1328

[68]

XuX, YangY, LiuX, et al.. NFE2L2/KEAP1 Mutations Correlate with Higher Tumor Mutational Burden Value/PD-L1 Expression and Potentiate Improved Clinical Outcome with Immunotherapy. Oncologist, 2020, 25(6): e955-e963

[69]

YouH, Xu-MonetteZY, WeiL, et al.. Genomic complexity is associated with epigenetic regulator mutations and poor prognosis in diffuse large B-cell lymphoma. Oncoimmunology, 2021, 10(1): 1928365

[70]

BrennanK, ShinJH, TayJK, et al.. NSD1 inactivation defines an immune cold, DNA hypomethylated subtype in squamous cell carcinoma. Sci Rep, 2017, 7(1): 17064

[71]

BourqueJ, KousnetsovR, HawigerD. Roles of Hopx in the differentiation and functions of immune cells. Eur J Cell Biol, 2022, 101(3): 151242

[72]

RenH, LiW, LiuX, et al.. Identification and Validation of an 6-Metabolism-Related Gene Signature and Its Correlation With Immune Checkpoint in Hepatocellular Carcinoma. Front Oncol, 2021, 11: 783934

[73]

LuchtelRA, DasariS, OishiN, et al.. Molecular profiling reveals immunogenic cues in anaplastic large cell lymphomas with rearrangements. Blood, 2018, 132(13): 1386-1398

[74]

XuJ, ChenS, LiangJ, et al.. Schlafen family is a prognostic biomarker and corresponds with immune infiltration in gastric cancer. Front Immunol, 2022, 13: 922138

[75]

WangQ, ZhouD, WuF, et al.. Immune Microenvironment Signatures as Biomarkers to Predict Early Recurrence of Stage Ia-b Lung Cancer. Front Oncol, 2021, 11: 680287

[76]

WangZ, LiangS, LianX, et al.. Identification of proteins responsible for adriamycin resistance in breast cancer cells using proteomics analysis. Sci Rep, 2015, 5: 9301

[77]

WheelerHE, GamazonER, FrisinaRD, et al.. Variants in and Other Mendelian Deafness Genes Are Associated with Cisplatin-Associated Ototoxicity. Clin Cancer Res, 2017, 23(13): 3325-3333

[78]

ZhengX, WangX, ChengX, et al.. Single-cell analyses implicate ascites in remodeling the ecosystems of primary and metastatic tumors in ovarian cancer. Nat Cancer, 2023, 4(8): 1138-1156

[79]

O’NeillRE, CaoX. Co-stimulatory and co-inhibitory pathways in cancer immunotherapy. Adv Cancer Res, 2019, 143: 145-194

[80]

AndersonAC, JollerN, KuchrooVK. Lag-3, Tim-3, and TIGIT: Co-inhibitory Receptors with Specialized Functions in Immune Regulation. Immunity, 2016, 44(5): 989-1004

[81]

KlempnerSJ, FabrizioD, BaneS, et al.. Tumor Mutational Burden as a Predictive Biomarker for Response to Immune Checkpoint Inhibitors: A Review of Current Evidence. Oncologist, 2020, 25(1): e147-e159

[82]

RizviNA, HellmannMD, SnyderA, et al.. Cancer immunology. tMutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science, 2015, 348(6230): 124-128

AI Summary AI Mindmap
PDF

88

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/