Correlation of LOXL2 expression in non-small cell lung cancer with immunotherapy and clinical prognosis

doi:10.21203/rs.3.rs-2756606/v1

Background

Lung cancer is the worst and deadliest disease globally, with about 80% of cases being non-small cell lung cancer (NSCLC). NSCLC mainly consists of lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). Although chemotherapy is currently the mainstay of treatment for NSCLC, chemotherapy resistance is still an urgent problem for patients. Studies in recent years have proposed immunotherapy as a new direction for treating NSCLC.

Methods

Based on the available datasets from TCGA, the association between the LOXL2 gene and NSCLC was explored by using multiple online tools (e.g., GEIPA, Oncomine, Dreimt) and bioinformatics analysis software (R or GSEA). The immune microenvironment of the tumor was explored by calculating ImmuneScore, StromalScore, and TumorPurity of LUAD and LUSC and analyzing the infiltration of 22 immune cells in lung cancer tissues. LOXL2-related loads were obtained from the Xena database for LUSC and LUAD patients, and relevant prognostic genes were identified by analyzing survival curves. Functional and pathway enrichment analyses of prognostic, predictive genes were performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The expression of LOXL2 in NSCLC was detected by RT-qPCR.

Results

LOXL2 may involve in the progression of the course of LUAD and LUSC and is closely related to the T-lymphocyte subpopulation, T-reg cells. SEMA7A and VEGFC were identified as the genes that interact with LOXL2 and could be used as prognostic signature genes in NSCLC patients.

Conclusion

LOXL2 may become a prognostic marker and a new target for immunotherapy.

NSCLC

Bioinformatics

Immune infiltration

LOXL2

T-reg

As one of the main causes of cancer-related deaths worldwide, lung cancer accounts for twenty-six percent (1, 2) of cancer-related deaths around the world(1, 2). Non-small cell lung cancer (NSCLC) is a major component of lung cancer, accounting for approximately eighty percent of lung cancer patients. (3). Based on nearly a decade of in-depth research on the genome and signaling pathways of lung cancer, we further define NSCLC as a group of diseases with genetic and cellular heterogeneity (4) and are divided into two categories: lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) (5). In addition, early-stage NSCLC has mild symptoms and clinical manifestations while progressing rapidly, leading to difficulty in early detection. Therefore, the vast majority of patients are diagnosed in the late stage, with a doubtful prognosis and high mortality (6).

Resistance to chemotherapeutic agents such as cisplatin has been a big problem, and it is important to develop new approaches to treat NSCLC. Previous studies have found that harnessing the host's immune response to treat cancer (7). Immunotherapy has dramatically changed the treatment paradigm for NSCLC(8). The relationship between T-reg cells and the tumor immune microenvironment has been demonstrated(9). Therefore, it is of interest to explore new targets for the immunotherapy of NSCLC. The expression of LOXL2 is greatly upregulated in tumors, participating in tumor invasion and migration. However, it remains unclear whether LOXL2 is a potential target for immunotherapy of NSCLC.

In this research, we performed bioinformatics analysis of risk factors affecting the prognosis of NSCLS through differential gene expression, survival curves, tumor immune microenvironment, association with other molecules, and KEGG and GO analysis. We also confirmed high expression of LOXL2 in NSCLC by RT-qPCR analysis. The above results suggest that LOXL2 is expected to become a new target for immunotherapy in NSCLC patients.

Gene Express Data Analysis

In the TCGA database, GEPIA screened the top 500 differentially expressed genes (DEGs) in LUAD and LUSC patients, and eleven commonly related genes were obtained by intersection. Then we used Kaplan-Meier plotter2 to analyze the overall survival (OS) of LUAD and LUSC cancer patients. We divided all samples into two groups according to gene expression levels to determine the significance of each gene in predicting the patient's prognosis. Then, we drew a K-M survival chart to compare the two groups and contrast the log10HR value (HRs) and P-value. The two risk factors, LOXL2 and TLDC1, with the worst prognosis in LUAD and LUSC, were identified. Based on the LUAD and LUSC patients in the GEPIA database, we included lung cancer cases (n = 483 for LUAD; n = 486 for LUSC) as the "tumor group", and compared the expression of LOXL2 in the "tumor group" with the "control group".

Tumor Purity and Immune Cell Infiltration Analysis

We used the ESTIMATE algorithm to calculate ImmuneScore, StromalScore, and TumorPurity for LUAD and LUSC patients LOXL2 respectively. we used the R package CIBERSORT to determine the proportion of 22 TILs in LUSC and LUAD. And with GEPIA2021 we visualized gene expression profiles to explore differences in immune cell subtypes. These immune cells include neutrophils. eosinophils, mast cells activated, mast cells quiescent. dendritic cells activated, dendritic cells quiescent, macrophages M2, T cell follicular helper cells, T cell regulatory (Tregs), T cell γδ, NK cells quiescent, NK cells activated, monocytes, macrophages M0, macrophages M1, T cell CD4 memory activated, T cell CD4 memory quiescent, T cell CD4 naive, T cell CD8, plasma cells, B cell memory, and B-cell naive.

Immune Prognosis Analysis of LOXL2

ssGSEA was used to analyze the immune profile of LOXL2 in LUAD and LUSC, and the tumor tissues of LUAD and LUSC in the GEPIA database were compared to the adjacent tissues in normal lungs. Spearman correlation test was used to confirm the positive correlation between LUAD and LUSC and T-reg (LUAD, T-reg, rho = 0.311, p = 5,71e-13; LUSC, T-reg, rho = 0.494, p < 2.2 e-16).

Correlated Gene Analysis

LOXL2-associated genes from LUAD and LUSC patients were obtained using the Xena database, P-values and R-values were calculated, and DEGs were analyzed. Cross-tabulation analysis was performed on genes that were upregulated in LOXL2-associated genes in LUAD and LUSC patients. The eight cross-over genes associated with OS were extracted by univariate Cox regression analysis of the results and then the median of their characteristic scores. Patients were finally categorized as high and low risk. Patient survival was analyzed by the Kaplan-Meier method. Time-dependent receiver operating characteristic (ROC) curves were used to predict the accuracy and characteristics of clinical characteristics. The R package rms generated the nomograms.

Genetic Interaction Analysis

The search tool for interacting genes/proteins (STRING) database(http://cn.string-bd.org) was used to establish a PPI network of LOXL2.

Gene Enrichment Analysis

GO and KEGG analysis is carried out by using the R package "clusterProfiler". Gene Ontology (GO) was used for functional enrichment analysis, while the Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for pathway enrichment analysis. The effects of functional enrichment included molecular functions (MF), cellular components (CC), and biological processes (BP). The top 10 pathway enrichment was analyzed.

Statistical Analysis

Kaplan-Meier plots used HR and P-value or Cox P-value for the log-rank test. R software was used to perform all statistical analyses. P-value < 0.05 was considered statistically significant.

Cell culture

The human lung adenocarcinoma cell line A549 and human bronchial epithelial cell line were provided by Jiangsu Province Key Laboratory of Geriatrics, and cultured in DMEM (Sigma-Aldrich) supplemented with 100 µg/ml streptomycin, 100 µg/ml penicillin (Gibco), and 10% fetal bovine serum (FBS) (Invitrogen). All cells were maintained in a 5% CO2 incubator (Thermo Fisher Scientific) at 37°C. Cells in the logarithmic growth phase were used for experiments.

Rt-pcr

RT-PCR

Total RNA was extracted from the cells using Trizol reagent (Invitrogen). RNA was reverse transcribed to cDNA using Primescript™ RT Master Mix (Vazyme). Quantitative RT-PCR was performed using ChamQTM Universal SYBR QPCR Master Mix (Vazyme) and Steppe One PlusTM Real-Time PCR System (Application Biosystems, Foster City, CA, USA). The primers for LOXL2 and GAPDH (used as an internal reference) were purchased from Genscript.

LOXL2 Forward primer (5´-3´): AACGAGGCGACCCTTGCAGC´

Reverse primer (5´-3´): GGGTGCGCTTGCGGTAGGTT

GAPDH Forward primer(5´-3´): CTCCTCCACCTTTGACGC

Reverse primer (5´-3´): CCACCACCCTGTTGCTGT

Expression Feature of LOXL2

We identified the top 500 DEGs in LUAD and LUSC from the TCGA databases. We crossed them to obtain eleven common DEGs, including PITX3, STAP1, DBP, FAM72B, LOXL2, ERH, ADGRD1, CTD-2626G11.2, RP11-1046B16.3, MYL2, TLDC1 (Fig. 1A). Next, we used Kaplan-Meier Plotter and GEPIA database to explore the relationship between this set of genetic changes and the survival rate of LUAD and LUDSC patients (Fig.S1). We found six protective factors: STAP1, DBP, ADGRD1, CTD-2626G11.2, 1046B16.3, MYL2 and five risk factors: PITX3, FAM72B, LOXL2, ERH, TLDC1. Among them, we focused on two risk factors LOXL2 and TLDC1, which had high-risk values in LUAD and LUSC (Fig. 1B, C). Based on the GEPIA dataset, we analyzed LOXL2 and TLDC1 in lung cancer tissues and adjacent tissues. We included lung cancer cases (n = 483 for LUAD; n = 486 for LUSC) as the "tumor group." Compared with the "control group," we found that the expression level of LOXL2 in the "tumor group" was higher (Fig. 1D, E).

Furthermore, we verified that LOXL2 was highly expressed in LUAD and LUSC by calculating the p-value, HR value, and 95% cl of the LOXL2 gene in the TCGA database (p < 0.05, Fig. 1F, Table 1).

Table 1

Univariate analysis of the correlation between LOXL2 expression and clinical features and OS in LUAD and LUSC patients.
Dataset	Pvalue	HR	95%CI	Prognostic
GSE3141	< 0.0001	3.0597	1.7798 ~ 5.2600	Poor	Go
TCGA	0.0011	1.4389	1.1574 ~ 1.7890	Poor	Go
GSE11969	0.0012	2.2213	1.3702 ~ 3.6009	Poor	Go
Stagel	0.1672	1.7682	0.7876 ~ 3.9693
Stagell	0.516	1.5434	0.4166 ~ 5.7188
Stagelll	0.0014	3.4612	1.6128 ~ 7.4280	Poor	Go
GSE67639	0.0015	1.4023	1.1375 ~ 1.7288	Poor	Go
GSE50081	0.0023	2.1125	1.3056 ~ 3.4179	Poor	Go
Stagel	0.0428	1.8640	1.0203 ~ 3.4056	Poor	Go
Stagell	0.1498	1.7766	0.8127 ~ 3.8839
GSE30219	0.0077	1.5230	1.1177 ~ 2.0753	Poor	Go
GSE5123	0.0251	2.8011	1.1376 ~ 6.8970	Poor	Go
Stagel	0.4481	1.7116	0.4269 ~ 6.8620
Stage ll	0.2313	2.4212	0.5692 ~ 10.3000
Stagelll	0.2918	3.6458	0.3290 ~ 40.3971
GSE26939	0.0293	1.8049	1.0613 ~ 3.0694	Poor	Go
Stagel	0.1121	1.8641	0.8646 ~ 4.0194
Stagelll	0.1187	2.6001	0.7828 ~ 8.6362
GSE31210	0.0522	1.9740	0.9935–3.9221
Stagel	0.0015	4.7791	1.8171 ~ 12.5698	Poor	Go
Stagell	0.3536	0.5551	0.1601 ~ 1.9252
GSE12428	0.0566	2.9957	0.9695 ~ 9.2566		Go
GSE33072	0.0910	1.6524	0.9229 ~ 2.9585		Go
GSE68465	0.1093	1.2647	0.9487 ~ 1.6860		Go
GSE68571	0.1336	1.8647	0.8261 ~ 4.2092		Go
Stagel	0.2186	1.9920	0.6646 ~ 5.9708
Stagell	0.4599	0.5556	0.1169 ~ 2.6405
GSE17710	0.2148	0.5670	0.2313 ~ 1.3897		Go
Stagel	0.6453	1.2765	0.4514 ~ 3.6101
Stagell	0.0807	0.1560	0.0194 ~ 1.2551
GSE37745	0.2954	1.2176	0.8421 ~ 1.7604		Go
Stagel	0.3817	1.2318	0.7721 ~ 1.9651
GSE37745	0.2954	1.2175	0.8421 ~ 1.7604		Go
Stagel	0.3817	1.2176	0.7721 ~ 1.9651
Stagell	0.2517	1.2318	0.7032 ~ 3.8342
Stagelll	0.0789	1.6420	0.1594 ~ 1.1054
GSE31908_GPL96	0.3379	0.4198	0.4808 ~ 8.4458		Go
Stagel	0.4208	2.0151	0.1952 ~ 49.9487
GSE31908_GPL97	0.3389	3.1225	0.0442 ~ 2.9246		Go
GSE102287	0.3431	0.3697	0.5849 ~ 4.6697		Go
GSE41271	0.3752	1.6527	0.8082 ~ 1.7588		Go
Stagel	0.0460	1.1923	1.0119 ~ 3.6844	Poor
Stagell	0.2885	1.9308	0.1872 ~ 1.6460
Stagelll	0.5018	0.5550	0.4432 ~ 1.4893
GSE14814	0.4097	0.8125	0.4067 ~ 1.4434		Go
Stagel	0.5010	0.7661	0.2710 ~ 1.8932
Stagell	0.0585	0.7163	0.4185 ~ 2.0663
GSE13213	0.5543	0.9299	0.6420 ~ 2.2847		Go
Stagelll	0.1295	1.2111	0.7902 ~ 6.3195
GSE10245	0.5596	2.2347	0.2447 ~ 2.1422		Go
GSE29013	0.5720	0.7240	0.2015 ~ 2.4230		Go
Stagel	0.8679	0.6987	0.0916 ~ 7.5108
Stagelll	0.7513	0.8295	0.1581 ~ 3.7854
GSE11117	0.6797	0.7736	0.4787 ~ 3.0952		Go
StagelV	0.7480	1.2173	0.3764 ~ 3.8970
GSE5843	0.6938	1.2112	0.3011 ~ 2.2229		Go
Stagel	0.6080	0.8181	0.2301 ~ 2.0696
GSE19188	0.6975	0.6901	0.4482 ~ 1.7106		Go
GSE4573	0.7531	0.8756	0.5204 ~ 1.6039		Go
Stagel	0.9703	1.0146	0.4719 ~ 2.1815
Stagell	0.2390	1.8968	0.6522 ~ 5.5165
Stagelll	0.2249	0.4603	0.1315 ~ 1.6114
Poepman	0.9458	1.0227	0.5350 ~ 1.9551		Go
combined	< 0.0001	1.3787	1.2564 ~ 1.5130	Poor
Note:cutoff:upper25% vs other 75%

RT-qPCR analysis showed that compared with the HBE cell line, the LOXL2 mRNA level was significantly higher in the A549 cell line (p < 0.0001, Fig. 1G).

LOXL2-Related Immune Cell Infiltration

We performed the immune score, stromal score, and tumor purity determination of LOXL2 (Fig. 2A-F), and the results showed that LOXL2 is negatively correlated with an immune score, positively correlated with a stromal score, and negatively associated with tumor purity. Considering that the tumor immune microenvironment plays a significant role in the development of tumourigenesis, we then used the R package CIBERSORT to determine the ratio of 22 TILs in LUAD and LUSC patients (Fig. 2G-H, Fig.S2A-B).

LOXL2 was positively correlated with T-reg

We characterized the immunology profile of LUAD and LUSC samples with low LOXL2 and high LOXL2 expression by ssGSEA. Found that LOXL2 in LUAD and LUSC patients is associated with T-reg. GEPIA, to compare LUAD and LUSC tumor tissues, adjacent tissues, and normal lungs (Fig. 3A-B), found highly expressed T-reg in tumor tissues. In addition, Spearman Correlation Text confirmed the positive correlation between LUAD Fig. 3C and LUSC Fig. 3D and T-reg.(LUAD, T-reg,rho = 0.311,p = 5,71e-13;LUSC,T-reg,rho = 0.494,p < 2.2e-16.All these results indicated that LOXL2 might potentially regulate immune infiltration and the response to immunotherapy.

Correlated Gene Expression

Furthermore, we evaluated the association of fifty-two corresponding bases of LUAD (Fig. 4A, Table 2) and ten related genes of LUSC (Fig.S3, Table 3) with LOXL2. Based on the intersection of the related genes of LUAD and LUSC and LOXL2, we identified eight common genes (Fig. 4B): PDGFRA, SEMA7A, FGF5, CMTM3, UCN2, MANF, PDGFC, VEGFC. Among them, LOXL2, SEWA7A, FGF5, UCN2, and VEGFC were positively correlated with LUAD; while LOXL2, SEMA7A, and VEGFC were positively correlated with LUSC (all p < 0.05, Fig. 4C).

Table 2

The association of fifty-two corresponding bases of LUAD) with LOXL2
GENE1	GENE2	P	R
LOXL2	PDGFRA	2.97444E-20	0.36848778
LOXL2	GDF11	2.49057E-14	0.308106677
LOXL2	SEMA4F	2.31871E-18	0.350596022
LOXL2	IL1RN	4.10483E-17	0.385049349
LOXL2	CCL21	9.49702E-19	0.35435584
LOXL2	SEMA7A	2.8837E-58	0.599078545
LOXL2	FGF5	3.26866E-26	0.418530278
LOXL2	CMTM3	8.7196E-62	0.613532508
LOXL2	SECTM1	2.27652E-15	0.319714484
LOXL2	HDGF	3.45809E-45	0.537939759
LOXL2	GDF7	1.51068E-17	-0.342533156
LOXL2	UCN2	1.5078E-16	0.371179367
LOXL2	MANF	2.4182E-42	0.522782038
LOXL2	PDGFC	1.18991E-18	0.398061079
LOXL2	ADM	3.65267E-40	0.510629978
LOXL2	VEGFC	3.21155E-45	0.538106606
LOXL2	STC1	2.41538E-69	0.659673055
LOXL2	GIP	6.12859E-12	0.336351246
LOXL2	TOR2A	3.65868E-16	0.32825584
LOXL2	TGFA	5.19121E-15	0.366262391
LOXL2	AIMP1	1.3148E-23	0.397702146
LOXL2	ESM1	1.50638E-46	0.569273722
LOXL2	GREM1	4.3189E-51	0.567128278
LOXL2	SEMA6B	1.78133E-43	0.554551206
LOXL2	CD320	8.90038E-25	0.407236163
LOXL2	LTBP3	1.33309E-16	0.332859725
LOXL2	BMP1	2.52567E-83	0.688311411
LOXL2	SEMA4C	5.06991E-51	0.566800334
LOXL2	CXCL8	7.83508E-26	0.452239551
LOXL2	APLN	4.08522E-20	0.367221915
LOXL2	ANGPTL7	3.20052E-15	-0.318092326
LOXL2	CCL11	3.29126E-16	0.328742125
LOXL2	SAA1	6.0212E-17	0.336430152
LOXL2	VEGFB	2.92379E-25	0.411089378
LOXL2	CLCF1	5.6556E-24	0.400722429
LOXL2	DEFB103B	1.09719E-15	0.323157274
LOXL2	RABEP2	9.27753E-14	0.301500619
LOXL2	BMP8A	4.29787E-12	0.337930853
LOXL2	CSF1	4.10798E-12	0.338131359
LOXL2	JAG2	5.90689E-31	0.453085117
LOXL2	SEMA4B	3.73141E-58	0.618957825
LOXL2	ANGPTL5	7.44501E-11	-0.324968647
LOXL2	SEMA4D	1.59881E-11	0.332036618
LOXL2	FAM3C	1.75315E-35	0.512438649
LOXL2	GMFB	2.65536E-18	0.350020547
LOXL2	NMB	8.22844E-11	0.324502476
LOXL2	S100A6	8.25088E-21	0.415408511
LOXL2	DEFA1	3.48669E-14	0.306432507
LOXL2	CKLF	1.41157E-25	0.413583014
LOXL2	MIF	2.19098E-20	0.412079957
LOXL2	CCL16	1.58955E-15	-0.321414289
LOXL2	CCL3	1.29679E-10	0.322372806

Table 3

The association of ten related genes of LUSC with LOXL2
GENE1	GENE2	P	R
LOXL2	PDGFRA	5.46926E-10	0.32491428
LOXL2	SEMA7A	7.77056E-30	0.457736861
LOXL2	FGF5	1.305E-30	463270539
LOXL2	CMTM3	4.11249E-30	45972208
LOXL2	CCN1	1.83492E-13	306935752
LOXL2	UCN2	4.35921E-13	0.302312857
LOXL2	MANF	1.4595E-11	0.331374895
LOXL2	PDGFC	1.75505E-23	0.408057926
LOXL2	VEGFC	7.30389E-17	0.393951854
LOXL2	BMP3	3.60237E-13	-0.303338747

Prediction significance of SEMA7A and VEGFC in LUAD and LUSC

The common genes SEMA7A and VEGFC related to LUAD, LUSC, and LOXL2 were used as independent factors for cox survival analysis (Fig. 5A-D). SEMA7A (Log-rank p = 0.016) and VEGFC (Log-rank p < 0.01) were prognostic factors of LUAD. SEMA7A (Log-rank p = 0.0017) and VEGFC (Log-rank p = 0.003) were the predictive factors of LUSC.

Next, we built a PPI network to understand the mode of interaction of LOXL2 (Fig. 5E). The PPI network comprised 31 nodes, showing the relationship between LOXL2, SEMA7A, and VEGFC. The results prove that SEMA7A and VEGFC are the prognostic factors of LUAD and LUSC.

Enrichment of LOXL2 -Correlated Gene

GO enrichment analysis in terms of biological processes (BP), cellular components (CC) and molecular functions (MF) revealed that the significant regulatory processes of LOXL2 on BP were axonogenesis, axon guidance, and neuronal projection guidance (Fig. 6A). For CC, alterations in LOXL2 most clearly controlled processes in the semaphorin receptor complex and the collagen-containing extracellular matrix (Fig. 6B). In the results shown for MF, semaphorin receptor activity was the most responsive to regulation by LOXL2, which was most clearly associated with different sites of the regulation (Fig. 6C).

KEGG pathway enrichment of the LOXL2 interactive gene showed that axon guidance, focal adhesion, and PI3K-Akt signaling pathway were enriched pathways (Fig. 6D).

Therapeutic targets and mechanisms of drugs

Volcano plots showed the priorities of different drugs in the DREAMT database and after batch correction (Fig. 7A). Then we analyzed the marketing status of all oncology drugs (Fig. 7B). A large proportion of experimental drugs are approved, mainly including dopamine receptor antagonists, cyclooxygenase inhibitors, serotonin receptor antagonists, glucocorticoid receptor antagonists, adrenergic receptor agonist, adrenergic receptor antagonists, and bacterial cell wall synthesis inhibitor (Fig. 7C, Table 4).

Table 4

Common tumor treatment drugs on the market.
drug_name	cefuroxime	calcifediol	benzocaine	cefazolin	mesoridazine	tacrolimus	rimantadine	doxorubicin	cefuroxime	guanethidine
drug_pubchem_id	5361202		2337		4078	445643	5071	31703	5361202	3518
summary	cefuroxime boosts case type compared to reference type	calcifediol boosts case type compared to reference type	benzocaine boosts case type compared to reference type	cefazolin boosts case type compared to reference type	mesoridazine boosts case type compared to reference type	tacrolimus boosts case type compared to reference type	rimantadine boosts case type compared to reference type	doxorubicin boosts case type compared to reference type	cefuroxime boosts case type compared to reference type	guanethidine boosts case type compared to reference type
FDR	0.001652893	0.001788909	0.001841621	0.001851852	0.001901141	0.002028398	0.002325581	0.002421308	0.001623377	0.001767
tau	99.98135372	99.98135372	99.98135372	99.98135372	99.98135372	99.98135372	99.98135372	99.98135372	99.96270744	99.96271
drug_specificity_score		0.554270368			0.646051397	0.615489277		0.696242451
drug_source_db	LINCS	LINCS	LINCS	LINCS	LINCS	LINCS	LINCS	LINCS	LINCS	LINCS
drug_source_name	BRD-K63641886	BRD-K77175907	BRD-K75466013		BRD-A14395271	BRD-K65261396		BRD-A52530684		BRD-M18219129
drug_status	APPROVED	APPROVED	APPROVED	APPROVED	APPROVED	APPROVED	APPROVED	APPROVED	APPROVED	APPROVED
drug_moa	bacterial cell wall synthesis inhibitor	vitamin D receptor agonist	sodium channel blocker	bacterial cell wall synthesis inhibitor	dopamine receptor antagonist	calcineurin inhibitor	antiviral, RNA synthesis inhibitor	topoisomerase inhibitor	bacterial cell wall synthesis inhibitor	adrenergic inhibitor
drug_target_gene_names		VDR	SCN10A	PON1	HTR2A, DRD2	FKBP1A		TOP2A		SLC6A2
drug_target_gene_ids		7421	6336	5444	3356, 1813	2280		7153		6530

Considerable patients with non-small cell lung cancer are already at an advanced stage when diagnosed, and most of those diagnosed in the early stage eventually relapse and may develop metastatic lesions, despite recent advances in treatment (10). Nowadays, tumor immunotherapy has developed rapidly and has attracted increasing attention due to its effectiveness (11, 12). Our research aimed to explore new targets to enhance immunotherapy and prognostic indicators in NSCLC patients. We, therefore, first took intersections through the 500 DEGs in LUAD and LUSC patients and performed survival curve analysis on the common 11 genes. In addition, expression levels of the 11 genes in para-cancer and lung cancer tissues were compared, and finally, LOXL2 was identified.

The primary role of the lysyl oxidase family is the cross-linking of elastin and collagen in the outer matrix, thus promoting the stability of the extracellular matrix. Among them, lysyl oxidase-like 2 (LOXL2) was regarded as a gene that the upregulation promotes tumor infiltration and metastasis (13). Aberrant expression of LOXL2 in various tumors was associated with epithelial-mesenchymal transition (EMT), metastasis, poor prognosis, chemo-radiotherapy resistance, and tumor progression (14). Our previous study showed that LOX and LOXL2 are directly regulated by mir − 00 /ZEB1 axis and LOXL2 may serve as a new target for lung cancer therapy (15).Our research has evaluated whether LOXL2 is a novel target for immunotherapy of NSCLC.

Therefore, we calculated the immune, matrix, and tumor purity of LUAD and LUSC and confirmed that LOXL2 was related to immunity of NSCLC. We found that LOXL2 is related to T-reg by analyzing the degree of immune infiltration and the proportion of immune T cells. There is a positive correlation between LOXL2 and T-reg. Moreover, Tregs promote immune suppression in malignant tumors by suppressing the immune response to cancer cells (16, 17). In patients with early-stage NSCLC, increased circulating and tumor-infiltrating Tregs are associated with a poorer prognosis and a higher risk of recurrence (18, 19). Therefore, we could use LOXL2 as a new candidate marker to treat patients with LUAD and LUSC and predict the prognosis.

Next, we searched for the related molecules of LOXL2 and performed multi-factor COX survival analysis to identify two prognostic factors SMEATA and VEGFC. Among them, SEMA7A, a glycosylphosphatidylinositol-anchored (GPI-anchored) glycoprotein on the plasma membrane, was a possible therapeutic target for patients with EGFR-TKI-resistant lung adenocarcinoma (21). VEGFC, a member of the vascular endothelial growth factor/platelet-derived growth factor family, promotes endothelial cell proliferation and angiogenesis. VEGFC is involved in lymphatic metastasis of tumor, which is a feature of poor tumor prognosis (22). These two prognostic factors confirmed the suggestive prognostic role of LOXL2 in NSCLC.

This study has some limitations. Our study was a retrospective not a prospective analysis, and we analyzed the role of LOXL2 in NSCLC by bioinformatics analysis without exploring the mechanism.

In conclusion, our results indicate prognostic role of LOXL2 in NSCLC patients. The analysis of the tumor immune microenvironment suggests the possibility of LOXL2 as a new target for immunotherapy of NSCLC. The results of molecular interactions reveal that SEMA7A and VEGFC could be prognostic factors for NSCLC.

Acknowledgements

Not applicable

Funding
This work received no funding.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, GSE33532.( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33532)

Authors’ contributions

HY C designed experiments and interpreted results. JQ W,M M conceived the project. LL L performed experiments. The manuscript was written by HY C，LL L and ,LL Z,YX Z. ZB Touch-ups and revisions to articles.

Ethics approval and consent to participate

Not applicable

Patient consent for publication

Not applicable

Competing interests
The authors declare that they have no competing interests.

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No competing interests reported.

Correlation of LOXL2 expression in non-small cell lung cancer with immunotherapy and clinical prognosis

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Gene Express Data Analysis

Tumor Purity and Immune Cell Infiltration Analysis

Correlated Gene Analysis

Genetic Interaction Analysis

Gene Enrichment Analysis

Statistical Analysis

Cell culture

Rt-pcr

Results

Expression Feature of LOXL2

LOXL2-Related Immune Cell Infiltration

LOXL2 was positively correlated with T-reg

Correlated Gene Expression

Therapeutic targets and mechanisms of drugs

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1