The Construction And Analysis of ceRNA Network And Patterns of Immune Infiltration In Lung Adenocarcinoma

doi:10.21203/rs.3.rs-611562/v1

Background: Lung adenocarcinoma (LUAD) is the most common one of lung cancer. Competitive endogenous RNA (ceRNA) may be closely associated with tumor progression. However, studies on ceRNAs and immune cells in LUAD are scarce.

Method: The profiles of gene expression and clinical data of LUAD patients were extracted from the TCGA database and separated into two categories: LUADs and adjacent normal tissues. Bioinformatics methods were used to evaluate differentially expressed genes (DEGs) and form a ceRNA network. Preliminary verification of clinical specimens was employed to detect the expressions of key biomarkers at the tissue level. Cox and Lasso regressions were used to screen the key genes and halt overfitting, upon which the prognosis prediction nomograms were formed. The interconnection between the risk score and immune components was evaluated using the CIBERSORT algorithm, while the conformation of notable tumor-infiltrating immune cells (TIICs) in the LUAD tissues of the high- and the low-risk group was assessed using the RNA transcript relative subgroup for identifying the tissue types. Finally, co-expression study was used to examine the interconnection between the key genes in the ceRNA networks and the immune cells.

Result: A ceRNA network of 115 RNAs was established, and nine key genes were screened to construct a Cox proportional-hazard model for making a prognostic nomogram. This risk-assessment model might serve as an unconstrained factor to forecast the prognosis of LUAD, and it was consistent with the preliminary verification of clinical specimens. The CIBERSORT algorithm was used to screen four highly expressed LUAD-related immune infiltrating cells, identifying five immune cells with significant differences in the LUAD tissues of the high- and low-risk groups. Besides, two pairs of biomarkers associated with the growth of LUAD were found, i.e., E2F7 and macrophage M1 (R = 0.419, p = 1.4e^-08) and DBF4 and macrophage M1 (R = 0.282, p < 2.2 e^-16); they appeared to be co-expressed.

Conclusion: This study identified several important ceRNAs (E2F7 and BNF4) and TIICs (macrophage M1), which might be related to the development and prognosis of LUAD. The established risk-assessment model might help better clinical management.

Cancer Biology

Oncology

Competitive endogenous RNA(ceRNA)

Lung adenocarcinoma(LUAD)

MicroRNA

Risk-assessment model

Tumor-infiltrating immune cells(TIICs)

Lung cancer (carcinoma) is one of the most prevalent tumors, with rapid progression, high metastatic potential, high morbidity, and fatality ^[1]. The World Health Organization reported in 2018 that incidences and death rates of lung cancer were 11.6% and 18.4% respectively. There are two main subtypes of lung carcinoma, namely, small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). Pathologically, there are several subtypes of NSCLC, namely, lung adenocarcinoma (LUAD), squamous carcinoma (LUSD), and adenosquamous carcinoma (AC). Among these NSCLCs, LUAD is the most prevalent subtype in histology. It shows a rapid progression mainly due to the high metastatic nature of the tumor, and this is the most common reason for treatment failure^[2]. LUAD is one of the most critical malignancies, which is often detected at the progressive stage with a poor clinical prognosis^[3]. Numerous genetic and molecular biomarkers, such as protein-coding and non-coding genes are often employed as diagnostic and remedial goals for developing LUAD^{[4, 5, 6, 7, 8]}. Among these biomarkers, the competitive endogenous RNAs (ceRNAs) and tumor-infiltrating immune cells (TIICs) are potentially the most important ones, as they influence tumor development. However, studies on the ceRNAs are scarce with poor clinical prognosis, and the factors related to the LUAD progression remain largely unknown.

The ceRNA network assumes that RNA transcripts containing microRNA (miRNA) response elements could seal miRNA from other targets that share the same miRNA response elements, thus, modulating its expression and harmonising life processes ^[9]. Earlier studies found that the ceRNA networks were involved in the development, metastasis, and prognosis of lung cancers, such as linc00665 and miR-98^[10]. Besides, the presence of TIICs, especially the leukocytes, witnesses the immune system activities, and they should assume a major function in the development, progression, and metastasis of neoplastic cells, including LUAD^{[11, 12, 13]}. However, only a handful of investigations focused on the regulatory mechanism between the ceRNA network and TIICs, but not LUAD^[14].

This study aimed to construct a ceRNA network risk assessment model that could be used for prognosis prediction and immune infiltration analysis of LUAD. A LUAD-related ceRNA network was established based on gene expression profiles to screen for key LUAD genes, and the prognostic values of these genes were determined using a Cox proportional-hazard model. Also, this study estimated the proportion of TIICs in LUAD tissues in the high- and low-risk groups using the Cell Type Identification Estimation of Relative Subpopulation RNA Transcription (CIBERSORT) algorithm. Besides, co-expression analysis was used to forecast the occurrence and growth of LUAD. The mechanism of LUAD occurrence and development was also deliberated. The risk-assessment model and the established mechanism of regulation might provide us with potentially novel insight for predicting the clinical occurrence and development of LUAD.

Source of Data and Analysis of Disparate Gene Expression

In this study, data were extracted from the USA database The Cancer Genome Atlas (TCGA; https://gdc-portal.nci.nih.gov/). These data encompassed the clinical information of LUAD patients and the profiles of long non-coding RNA (lncRNA), messenger RNA (mRNA), and microRNA (miRNA). Data were organised by different expression profiles, ID conversion, filtering, merging, correction, and clinical information. For each patient, demography (e.g., age, sex, race, etc.), survival endpoints (patients alive or lost, times to die, and times to the final follow-up), and the histology and tumor stage of the LUAD were also acquired. Genes that showed no expression in LUAD (i.e., no detections of expression for these genes in both the test and control groups) were sieved. Differentially expressed RNAs, including Differentially Expressed LncRNAs (DELs), Differentially Expressed miRNAs (DEMs) and Differentially Expressed mRNAs (DEGs) were determined using the edgeR method. The down- or up-regulated genes, | log 2 fold change (log₂FC) | > 1.0, and the false discovery rate (FDR) < 0.05 were considered as thresholds.

Construction of LUAD-associated ceRNA networks and identification of genes associated with prognosis

Before the analysis of basic statistics, experimentally validated information on miRNA-mRNA interactions based on miRTarBase was downloaded from the experimental modules of the database Lncbase v.2, along with the lncRNA-miRNA interaction. All interactions were validated by direct molecular mechanisms, such as luciferase reporter experiments and immunoprecipitation. To simultaneously regulate lncRNAs and mRNAs, the hypergeometric testing and correlation analysis of miRNAs was performed to obtain DEM-DEG interaction pairs. Axes of DEM-DEG were obtained to build the ceRNA network and visualised using the software Cytoscape (version 3.6.1; www.cytoscape.org/). The two-sided univariate Cox regression was analysed using the software survival package in R (version 3.5.1, Institute for Statistics and Mathematics, Vienna, Austria; www.r-project.org) at the significance level 0.05 to screen for the overall survival (OS) and disease-free survival (DFS) related to DEMs and DEGs.

To test if the modelings of these genes were different, the key genes were screened with Cox and Lasso regressions to create a prognostic prediction line graph. The Cox model encompassed all elements of the ceRNA network, and the Lasso regression was used to halt the overfitting of the multifaceted model. The prognostic values of all biomarkers were determined using the Cox proportional-hazard model. A columnar graph was created with the multivariate model to forecast the survival of LUAD patients. Calibration curves and receiver operating characteristic curves (ROCs) were then used to assess the precision and discriminatory power of the nomogram. Finally, each biomarker was validated using the Kaplan-Meier survival analysis.

Immune Risk Assessment of Key RNAs, CIBERSORT Estimation, and the Validation of Initial Clinical Specimens

Univariate and multivariate Cox regressions for age, sex, tumor stage, TNM, and risk score were performed to examine if the constructed immune risk-score model was unconstrained by clinicopathological parameters. The CIBERSORT algorithm (citation) was used to identify cell types by estimating the relative subset of RNA transcripts of 22 cell types to determine the relationship between risk score and immune components in LUAD. Cell types in samples with a CIBERSORT output of p < 0.05 were further identified by determining the relative subset of RNA transcripts. The Wilcoxon rank-sum test was employed to examine if the proportion between high- and low-risk groups in LUAD tissue was different by identifying the immune-infiltrating cells. Expressions of the key genes in the tissues were verified through searching over the database The Human Protein Atlas (https://www.proteinatlas.org/). The immunohistochemical results of key genes in ceRNA were compared.

Statistical Analyses

The software R was used to perform all statistical analyses, such as preprocessCore package, edgeR package, rms package, and ggplot2 package. Only a two-sided p value < 0.05 was considered statistically significant.

Identifying Genes with Markedly Different Expression

A total of 694 samples, including 535 LUADs and 59 adjacent normal tissues, were downloaded from TCGA with a total of 19,413 mRNAs, 13,709 lncRNAs, and 1,479 miRNAs identified between LUADs and paraneoplastic normal tissues (Figure 1). Using |log₂FC| > 1.0 and FDR < 0.05 as threshold value, variations were detected in 2,994 DEGs (1,649 up- and 1,345 down-regulated; Figures 1B and 1C), 198 expressions of individual miRNAs (111 up- and 87 down-regulated; Figures 1D and 1E), and 214 lncRNAs (163 up- and 51 down-regulated; Figures 1F and 1G).

Construction of the ceRNA network and identification of DEMs and DEGs associated with prognosis

The ceRNA network was constructed in this study with 115 genes (seven lncRNAs, 15 miRNAs, and 93 mRNAs) for examining the pattern of differential gene regulation (Figure 2A ). Table 1 shows the ceRNA network with a hypergeometric p -value < 0.05 as the cut-off value. In addtional, the results of gene survival analysis were screened for prognostic significance of ceRNA nodes with p < 0.05 as the cut-off value, so this study demonstrated the survival curve plots of two of these genes, SNHG3 and CCT6A, respectively(Figure 2B-C). Both curves show that the expression levels of SNHG3 and CCT6A were critically associated with the overall survival in patients with LUAD.

Table 1

Results of hypergeometric test and correlation analysis of ceRNAs network
lncRNAs	Genes	miRNAs	Correlation p	Hypergeometric test p
PVT1	MRPL24	hsa-miR-20a-5p,hsa-miR-93-5p	1.12E-19	0.01085
SNHG1	GPI	hsa-miR-326,hsa-miR-330-5p	1.09E-05	0.00117
MAGI2-AS3	CDC14A	hsa-miR-374b-5p,hsa-miR-374a-5p	2.96E-19	0.00662
AC074117.1	E2F2	hsa-let-7a-5p,hsa-let-7i-5p,hsa-let-7c-5p,hsa-let-7b-5p,hsa-let-7d-5p, hsa-miR-98-5p	1.10E-16	0.00022
H19	ESCO2	hsa-miR-130a-3p,hsa-miR-454-3p,hsa-miR-130b-3p	0.004980968	0.00117
H19	E2F2	hsa-miR-4295,hsa-miR-130a-3p,hsa-miR-454-3p,hsa-miR-130b-3p,hsa-miR-3666	1.05E-07	0.02452
H19	CPS1	hsa-miR-29c-3p,hsa-miR-29b-3p,hsa-miR-29a-3p	6.30E-06	0.00060
H19	E2F7	hsa-miR-29c-3p,hsa-miR-29b-3p,hsa-miR-4295,hsa-miR-130a-3p,hsa-miR-454-3p,hsa-miR-130b-3p,hsa-miR-3666,hsa-miR-29a-3p	1.92E-07	0.00140
H19	GPR37	hsa-miR-29c-3p,hsa-miR-29b-3p,hsa-miR-29a-3p	0.00643677	0.00060
H19	CCT6A	hsa-miR-130a-3p,hsa-miR-454-3p,hsa-miR-130b-3p	1.84E-05	0.01734
H19	SLC16A1	hsa-miR-29c-3p,hsa-miR-29b-3p,hsa-miR-29a-3p	6.26E-06	0.01102

Risk assessment analysis of the prognostic value of key RNAs in ceRNA networks

In this study, 38 genes in the ceRNA network were analysed with the univariate Cox regression, and nine genes were screened and tested with the multivariate Cox regression. Nine genes (DBF4, CPS1, CDC14A, CCT6A, SLC16A1, E2F7, GPR37, SNHG3, Hsa-miR-326) in the ceRNA network were merged into the Cox proportional-risk model to assess its prognostic value (Figure 3A). A nomogram was established using the risk value score of the prediction model and clinical variables to project the overall survival probability of LUAD patients at the first, third, and fifth years (Figure 3B). After multivariate cox analysis, Lasso regression was perfromed to assess the stability of the genetic model (Figures 3C and 3D). The ROC curves and calibration curves (Figures 3E and 3F) showed good accuracy for the area under the curve (AUC) and calibration of the nomogram. The AUC values for one-year, three-year, and five-year survival were 0.718, 0.705, and 0.707, respectively.

Preliminary Verification of Clinical Specimens

To verify the expression of key genes in tissues, we searched through The Human Protein Atlas database (https://www.proteinatlas.org/). Immunohistochemical results showed that CCT6A, CDC14A, DBF4, and SLC16A1 proteins were obviously different in diagnosing normal tissues and LUAD (Figure 4).

Risk-scoring models as independent predictors for LUAD prognosis

The risk scores and survival status of LUAD patients are shown in Figures 5A and 5B, respectively, and LUAD patients were separated into high- and low-risk groups using the risk scores. To investigate whether the constructed risk score model was independent of clinicopathological parameters, the univariate and multivariate Cox regression analysis for age, sex, stage, TNM, and risk score were performed. In the univariate Cox model, pathologic T, N stage, pathologic stage, and high-risk score were related to poor survival (Figure 5C), and Figure 5D shows that in the multivariate Cox model, the risk score was the sole parameter that could independently predict the overall survival. The Kaplan-Meier survival curve was constructed for the high- and low-risk groups with a statistically meaningful difference in the survival between the two groups (p < 0.001), and The low-risk group had a superior OS than that the high-risk group (Figure 5E).

The Composition of TIICs between the LUAD high- and low-risk scores

As illustrated in Figure 6, the histogram (Figure 6A) and heat maps (Figure 6B) were generated from the CIBERSORT algorithm for assessing the composition of significant TIICs in LUAD tissues of high- and low-risk groups. In both groups, the T-cell CD4 memory arrest, T-cell CD8, macrophage M0, and macrophage M2 were significantly overexpressed in LUAD tissues. The Wilcoxon rank-sum test showed that five immune cell components, i.e., B-cell naïve (p = 0.002), T-cell CD4 naïve (p= 0.028), T-cell gamma δ (p = 0.029), monocytes (p = 0.049), and macrophage M1 (p = 0.010), were significantly different in immune cell components in LUAD tissues between the high- and low-risk groups (Figure 6B).

The Co-expression of Genes and Immune Cells for Prognosis

To explore the role of key genes in the ceRNA network and tumor immune cells together on the prognosis of LUAD, co-expression analysis of key genes in the ceRNA network and significantly different TIICs using LUAD samples from high- and low-risk groups were performed and found that the E2F7 gene and macrophage M1 (the correlation coefficient, R = 0.42, p < 2.2e^-16), DBF4 and macrophage M1 (R = 0.28, p = 1.4e^-08) displayed a positive correlation (Figure 7).

In this study, a bioinformatics approach was used to predict lung cancer progression and metastasis based on RNA sequencing data extracted from the TCGA database, and 93 DE mRNAs, seven DE lncRNAs, and 15 DE miRNAs were found to be dysregulated in LUAD tissues. The relationship between differentially expressed RNAs in the ceRNA network and the clinical prognosis of LUAD patients showed that key genes reasonably well predicted the prognosis of LUAD. Besides, the Cox proportional-risk model might serve as an unconstrained factor for predicting the prognosis of LUAD. In the nomogram maps of the nine key genes (hsa-miR-326, DBF4, CPS1, CDC14A, CCT6A, SLC16A1, E2F7, GPR37, SNHG3), the AUC values would estimate the clinical diagnosis of LUAD. Also, four (macrophage M0, macrophage M2, T-cell CD4 memory dormancy, and B-cell naïve) highly expressed LUAD-associated immune infiltrating cells between LUAD and cancer paracellular were identified via the CIBERSORT algorithm, and five (B-cell naïve, T-cell CD4 naïve, T-cell gamma delta, monocytes, macrophage M1) immune cells were significantly different in LUAD of high- and low-risk groups. Additionally, two pairs of important biomarkers related to the growth of LUAD were identified, and initial clinical specimen validation showed that E2F7 and macrophage M1 (R = 0.42, p < 2.2e^-16), and DBF4 and macrophage M1 (R = 0.28, p =1.4e^-08) were significantly associated in co-expression, and their associated mechanisms would be crucial in predicting LUAD prognosis.

In recent years, miRNA is one of the hotspots in tumor research, largely because aberrant expression in miRNA is related to tumorigenesis and progression of multiple tumors ^[15]. This study predicted that 15 DE miRNAs were linked to the development of LUAD. In particular, aberrant expression of hsa-miR-326 (miR-326) is involved in a variety of pathological processes, including endometrial cancer, gastric cancer, lung cancer, osteosarcoma, pulmonary fibrosis, and breast cancer, and, thus, it has emerged as a biomarker for identifying cancer, treatment, and prognosis^{[16, 17, 18, 19, 20, 21]}. As a repressor of the Hedgehog signaling pathway, miR-326 controls the growth of cerebellar neuronal progenitors and cancer cells^[22]. It is also expressed in patients with type I diabetics and leukemia^{[23, 24, 25]}. Meanwhile, low miR-326 expression in gastric tumor is linked to clinical stage, tumor depth, lymph node metastasis, and distant metastasis; it is a relatively poor unconstrained prognostic factor for gastric tumor^[26]. In glioblastoma tissues, miR-326 was downregulated and involved in tumorigenesis and progression of glioma, and low expression of miR-326 was correlated with clinicopathological factors and prognosis of glioma patients^[27]. However, studies on hsa-miR-326 and the development of LUAD are generally scarce.

Immune infiltration of the tumor microenvironment is an important factor affecting the immune response and prognosis. Macrophages, which are crucial in the metastatic process, are a major component of TIICs and often trigger local inflammation^[28]. In this study, macrophages in tumor masses were divided into M1 and M2 types. In the early stages of tumor appearance, macrophages either phagocytose individual tumor cells or act as antigen presenting cells(APCs) to trigger an immune response of CD8+ T cells. Eventually, when CD8+ T cells are unable to generate an immune effect, tumor-associated macrophages (TAMs) would secrete growth factors to stimulate tumor growth or angiogenesis^[29]. However, macrophage infiltration in the tumor stroma is a negative prognostic factor for LUAD^[30]. In NSCLC, TAMs would stimulate tumor metastasis via the TGF- inhibitor/SOX9 axis^[31]. Specifically, the M2 subtype stimulates the invasion of lung cancer cells, while the M1 subtype suppresses tumor angiogenesis^[32]. More importantly, macrophages are a key factor in the metastatic process of LUAD ^[33].

E2F7 is a member of the family of E2F transcription factors (E2Fs), with important roles in cell proliferation, differentiation, and apoptosis. There are eight genes in the E2F family, designated as E2F1 - E2F8 in the order of discovery. E2F7 abnormalities seem to have a crucial function in the growth of cancer cells. In breast-cancer patients treated with tamoxifen, E2F7 overexpression is associated with a high likeliood of recurrence and poor prognosis by competing with E2F1 to suppress miR-15a/16 clustering ^[34]. The acquisition of the E2F7 function counteracted the effects of miR-30a-5p on cell propagation and metastasis^[35]. As for E2F7, it was upregulated in NSCLC tissues, correlated with poor prognosis, and inhibited cell propagation, migration, invasion, development of cancer, EMT, and AKT pathways in NSCLC cells by targeting miR-935 ^[36]. However, related studies are still scarce.`

In summary, this study analysed DE mRNAs, lncRNAs, and miRNAs in LUAD cancer and para cancer using an integrative biological approach. A ceRNA network of lncRNA-miRNA-mRNA ceRNA was constructed, uncovering a potentially new regulatory mechanism. The relationship between DE mRNAs, lncRNAs, and miRNAs in the ceRNA network and a Cox proportional-risk model in ceRNAs was examined to predict LUAD prognosis. This risk-assessment model could serve as an independent factor to predict LUAD prognosis. In LUAD tissues, five immune cell types with significant differences were identified using the CIBERSORT algorithm and co-expression analysis, revealing significant correlations between E2F7 and macrophage M1 (R = 0.42, p < 2.2e^-16) and DBF4 and macrophage M1 (R = 0.28, p = 1.4e^-08). These two pairs of co-expressed genes and their associated mechanisms would play an important role in predicting LUAD prognosis.

Acknowledgements

We appreciated the authors of the data series and the authors of the databases used

in this article. Their efforts provided great convenience for our research.

Availability of data and materials

The following information was supplied regarding data availability:

All the original data in this study were downloaded from the public databases including TCGA (https://portal.gdc.cancer.gov/).

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

(I)Conception and design:Jinglong Li, Hongguang Bao;

(II)Administrative support: Hongguang Bao;

(III)Provision of study materials or patients: Xiaocheng Dong, Yunfeng Dai;

(IV)Collection and assembly of data: Shaoshen Chen, Enliang Zhao;

(V)Data analysis and interpretation: Yunlong Liu, Wenyao Liu;

(VI)Manuscript writing: Yunlong Liu, Enliang Zhao;

(VII)Final approval of manuscript: Hongguang Bao.

Funding

Heilongjiang provincial higher education institutions basic scientific research business expense research project, 2019-kyywf-1211.

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No competing interests reported.

The Construction And Analysis of ceRNA Network And Patterns of Immune Infiltration In Lung Adenocarcinoma

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Abstract

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Introduction

Materials And Methods

Result

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Journal Publication

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