Role of the membrane spanning 4A (MS4A) gene family in lung adenocarcinoma

doi:10.21203/rs.3.rs-2228081/v1

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Role of the membrane spanning 4A (MS4A) gene family in lung adenocarcinoma

https://doi.org/10.21203/rs.3.rs-2228081/v1

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Lung adenocarcinoma, which is the second most prevalent cancer in the world, has a poor prognosis and a low five-year survival rate. The MS4A protein family is crucial to disease development and progression, particularly for cancers, allergies, metabolic disorders, autoimmune diseases, infections, and neurodegenerative disorders. However, its involvement in lung adenocarcinoma remains unclear. In this study, we found that 12 MS4A family genes were up- or downregulated in lung adenocarcinoma. Furthermore, we described the genetic variation landscape of the MS4A family in lung adenocarcinoma. Notably, according to a function enrichment study of the MS4A family, the immune response-regulating signaling pathway and receptor signaling pathway on the surface of immune response-regulating cells were both found to be associated with the binding of an immunoglobulin. According to the Kaplan–Meier curve, patients with lung adenocarcinoma having poor expression of MS4A2, MS4A4A, MS4A7, MS4A14, and MS4A15 had a low overall survival rate. These five prognostic genes are substantially associated with immune-infiltrating cells, and a prognosis model incorporating them may more accurately predict the overall survival rate of patients with lung adenocarcinoma than current models. The findings of this study may offer creative suggestions and recommendations for the identification and management of lung adenocarcinoma.

Biological sciences/Cancer

Biological sciences/Cancer/Cancer genomics

Cancer is a global public health issue and poses a major challenge to the standard of medical excellence of all nations. Lung cancer is the second most prevalent cancer worldwide and accounts for the highest cancer-related deaths in both men and women. Globally, 1796144 passed away from this illness in 2020 [1]. Small cell and non-small cell lung cancers (NSCLCs) account for the majority of lung cancer cases. Currently, chemotherapy, radiation, and surgery are the main avenues for NSCLC treatment. Given the several investigations on tumor heterogeneity, NSCLC has also been treated with molecular targeted therapy and immunotherapy. However, the 5-year survival rate of NSCLC patients is only 26% [1, 2]. Lung adenocarcinoma is a typical subtype of NSCLC. Despite the discovery of numerous immunological checkpoints and prognostic indicators, the molecular makeup of lung adenocarcinoma remains unclear. There is still an urgent need for more research into treatment targets and prognosis indicators for lung cancer.

A new gene family called MS4A has four transmembrane protein-encoding genes. Currently, the MS4A family comprises at least 16 individuals. The MS4A gene family is crucial for cell differentiation, signaling, and cell cycle control [3–6]. Previous research has suggested that members of the MS4A family, including MS4A1, MS4A3, MS4A4A, MS4A6A, MS4A7, MS4A12, and MS4A15, are associated with the onset and progression of cancers; however, the underlying mechanisms remain unknown [7–16]. According to published literature, MS4A2 is strongly associated with the prognosis of lung adenocarcinoma [17] and lung cancer brain metastases [18]. Conversely, MS4A8 is considered to have a role in the morphology and cell development of NSCLC [19]. However, further study of the expression and prognostic significance of the MS4A family in lung adenocarcinoma is needed.

We herein first evaluated the expression of the MS4A family in lung adenocarcinoma and its relationship with clinical patient prognosis and established a prognosis model. We examined the signal route used by the MS4A family and its connection with the immune microenvironment of lung adenocarcinoma to learn more about the relationship between the pathophysiology of MS4A family and the disease which would help establish a theoretical foundation for identifying lung adenocarcinoma prognostic factors and treatment.

mRNA expression of the MS4A family in patients with lung adenocarcinoma

Using the TCGA database, we first looked for MS4A family expression in lung cancer and healthy lung tissues. In lung adenocarcinoma, we discovered that the expression of one MS4A family gene was upregulated and that of 10 other genes was downregulated (Fig. 1). Compared with normal tissues, in lung adenocarcinoma, MS4A2/3/4A/6A/6E/7/8/10/14/15 expression was downregulated, whereas MS4A1 expression was upregulated.

Mutation Landscape Of The Ms4a Family In Lung Adenocarcinoma

Using the Gene Set Cancer Analysis (GSCA) website, we examined the prevalence of somatic mutations and copy number variants in the MS4A family. Consequently, we discovered that 99/113 lung adenocarcinoma specimens (87.61%) had gene alterations (Fig. 2A-2B). We discovered that “missense mutations” was the most prevalent variant category, “SNPs” was the most prevalent variant type, and “C > A” was the most prevalent SNV type (see Fig. 2A for more details). Among the 18 genes, MS4A14 was the most predominantly mutated gene, followed by MS4A4A, MS4A3, and MS4A1.

Functional Enrichment Analysis Of The Ms4a Family

To identify the function of MS4A, we used the R program to evaluate the pathways involving the MS4A family. Through Gene Ontology (GO) analysis, we found the MS4A family to be predominantly involved in the immune response-regulating signaling pathway and immune response-regulating cell surface receptor signaling pathway and found it to be associated with immunoglobulin binding (Fig. 3A). Through KEGG analysis, the MS4A gene family was found to be related to the occurrence of asthma and to participate in the FcεRI signaling pathway (Fig. 3B).

High Expression Of Ms4a2/4a/7/14/15 Is Beneficial To The Survival Of Lung Adenocarcinoma Patients

To forecast the predictive value of the MS4A family of genes in lung adenocarcinoma, we used the online database Kaplan–Meier plotter. Then, based on the statistical significance of MS4A's mRNA expression level, we discovered five genes having a prognostic value. Poorer overall survival was strongly associated with lower expression of MS4A2 (P = 0.00038), MS4A4A (P = 0.049), MS4A7 (P = 0.018), MS4A14 (P = 0.0099), and MS4A15 (P = 0.00033) (Fig. 4A-E).

Construction Of Ms4a Family Prognostic Gene Model

To learn more about the function of prognostic genes in lung adenocarcinoma, five prognostic MS4A genes were used to create a model using LASSO Cox regression analysis. The following were the findings: risk score = (-0.1357) *MS4A2 + (0.2448) * MS4A4A + (-0.2276) * MS4A7 + (-0.1091) * MS4A14+ (-0.067) * MS4A15 (Fig. 5A-5B). Lung adenocarcinoma patients were divided into two groups based on the risk score. The risk score distribution, survival status, and the expression of these five genes are presented in Fig. 5C. The level of gene expression declines as the risk score rises, and consequently, patients have shorter lives. The Kaplan–Meier curve, as seen in Fig. 5D, demonstrates that patients with lung adenocarcinoma who have high risk scores have a worse prognosis than those who have low risk scores (median time = 3.3 years vs. 4.9 years, P = 0.00217). ROC analysis revealed that the AUC values at 1, 3, and 5 years were 0.677, 0.587, and 0.577, respectively (Fig. 5E).

Construction Of Nomogram Model

Using the prognostic gene model, we identified the influence of five prognostic genes on the survival rate of lung adenocarcinoma patients. However, several factors affect the prognosis in cancers. To further investigate the effect of prognostic genes and clinical parameters, such as age, sex, and clinical stage, on the overall survival of patients with lung adenocarcinoma, we built a model using nomograms. According to the single-factor Cox regression analysis, MS4A2/4A/7/14/15 are protective factors in lung adenocarcinoma, whereas staging is a risk factor (Fig. 6A). Multivariate Cox regression analysis revealed that the prognosis is significantly influenced by clinical stage and MS4A2, suggesting that the prognosis of lung adenocarcinoma patients is influenced by MS4A2 expression independently of the pT, pN, and pM stages (Fig. 6B). A nomogram incorporating variables with appreciable differences to the prognosis was created based on the findings of the multivariate analysis. We discovered that the 3- and 5-year overall survival rates could be reasonably predicted when compared to the ideal model of the entire cohort (Fig. 6C-6D).

Immune Cell Infiltration Of Prognostic Genes Of The Ms4a Family In Lung Adenocarcinoma

From GO analysis findings, we learned that the MS4A family is involved in the immune regulation pathway and that the immunological microenvironment is crucial for cancer initiation and development. Using the TIMER database, we discovered a strong association between immune-infiltrating cells and MS4A family prognostic genes in lung adenocarcinoma. We noted a positive correlation between the prognostic genes (MS4A2/4A/7/14/15) and the quantity of immune-infiltrating cells (B cells, CD4 + T cells, CD8 + T cells, macrophages, neutrophils, and dendritic cells; Fig. 7A-E).

Validation Of Prognostic Genes In Lung Adenocarcinoma By Real-time Quantitative Pcr

To confirm the expression of prognostic genes of the MS4A family in lung adenocarcinoma, we used RT-qPCR to identify the mRNA expression levels of prognostic genes in lung adenocarcinoma and paired neighboring normal lung tissues. Among the nine pairs of lung adenocarcinoma and neighboring tissues obtained by us, MS4A2 expression in lung adenocarcinoma tissues was considerably downregulated in four pairs and upregulated in five pairs (Fig. 8A). In one set of samples with upregulated expression, the relative expression of MS4A2 in lung adenocarcinoma is more than 300 times that in normal lung tissue (Supplementary Fig. 1A-I), and therefore, it was disregarded. In eight sample pairs, the expression of MS4A4A and MS4A7 was dramatically reduced, whereas no significant variation in the expression level was noted in one pair (Fig. 8B-8C, Supplementary Fig. 2A-2I and Supplementary Fig. 3A-3I). Compared with normal lung tissues, MS4A14 and MA4A15 expression levels were significantly downregulated in six pairs of lung adenocarcinoma tissues and markedly upregulated in the remaining three pairs (Fig. 8D-8E, Supplementary Fig. 4A-4I and Supplementary Fig. 5A-5I).

Members of the MS4A family have similar structures and roles. Previous reports indicate that the MS4A protein predominantly interacts with several immunological receptors and controls signaling pathways [5, 6]. The well-known members of the MS4A family, namely MS4A1 (CD20), MS4A2 (FcεRIβ), and MS4A3 (HTm4), all have a significant role in cancer initiation and development. However, it is unknown how most members of this family contribute to lung adenocarcinoma.

To determine this, we started with exploring how the MSA4 family was expressed in lung adenocarcinoma. Using the TCGA database, we discovered that 12/18 genes in the MS4A family had differential expression. Based on our research of the prevalence of copy number variation and somatic mutation in the MS4A family, we found that the majority of the MS4A family members had gene mutations in lung adenocarcinoma. Specifically, MS4A14, MS4A4A, MS4A1, and MS4A3 genes had the highest prevalence of mutations. Additionally, we performed a function enrichment analysis. The findings corroborated prior research by Mattiola and Eon Kuek and revealed that the MS4A family was primarily involved in the receptor signaling pathway on the surface of immune response-regulating cells and the immune response-regulating signaling pathway, which was associated with immunoglobulin binding [5, 6]. According to their research, the MS4A family is instrumental in humoral immunity, IgE signal transduction, T cell proliferation control [20–22]. According to prognostic analyses, patients with low expression levels of MS4A2, MS4A4A, MS4A7, MS4A14, and MS4A15 had a worse prognosis. The overall survival of patients with lung adenocarcinoma was positively correlated with the expression level of these genes. Then, to better predict the overall survival of lung cancer patients, we built a prognostic model incorporating the five prognostic genes. Using the LASSO Cox regression analysis and prediction nomogram, we found that the model could predict 3- and 5-year overall survival with reasonable accuracy. During immune infiltration analyses, we discovered that prognostic genes and immune-infiltrating cells had a statistically significant positive correlation. This suggests that immunological modulation by the MS4A family contributes to the onset and progression of lung adenocarcinoma.

Notably, MS4A2, an intensively examined MS4A family member, is a crucial part of high-affinity IgE. In agreement with the findings of Ly et al., MS4A2 has low expression in lung adenocarcinomas and is associated with a bad prognosis [17]. Their findings show that mast cells affect the development of lung cancer and that high MS4A2 expression on stromal mast cells is a positive prognostic sign for the survival of early lung cancer patients. We discovered that as per the prognostic model and nomogram analysis findings, MS4A2 is a protective gene in lung adenocarcinoma and an independent factor impacting prognosis; it is also considerably positively associated with immune-infiltrating cells. Then, using RT-qPCR, we further confirmed MS4A2 expression in lung adenocarcinoma. Notably, only three of the nine sample pairs gathered herein showed reduced expression of MS4A2. In agreement with other research, our bioinformatics study showed that the level of MS4A2 was low in lung adenocarcinoma. This could be attributed to the small sample size of this study. Another influencing factor could have been the primers we created because we only chose a small portion of the MS4A2 mRNA, and this may not accurately reflect all functions of MS4A2. As the MS4A family’s first identified ion channel, MS4A2 can function as a calcium channel [23, 24], which is associated with the development of numerous cancers [25]. Although this could be a processes through which MS4A2 contributes to lung adenocarcinoma, more research is required to precisely determine the underlying pathway and mechanism.

Much of the MS4A4A research has been focused on the hematopoietic system [5]. Numerous recent studies have revealed a connection between MS4A4A and the incidence of Alzheimer's disease [26, 27]. We discovered from the TCGA database that MS4A4A expression was downregulated in lung adenocarcinoma and that the overall survival rate of lung adenocarcinoma patients with regulated MS4A4A expression was poor. The expression level of MS4A4A in lung adenocarcinoma was confirmed using RT-qPCR. Eight out of nine sample pairs demonstrated downregulated MS4A4A expression, which was in line with our expectations. Previous research revealed that MS4A4A was upregulated in gastric and ovarian cancer [12, 28], which was associated with a poor prognosis. In addition, some investigations revealed that the expression of MS4A4A and MS4A7 was upregulated in gliomas and that overall patient survival was low [13]. Additionally, MS4A4A was identified to be a surface marker of M2 macrophages, indicating that inhibiting MS4A4A could prevent the growth and operation of M2 macrophages [29]. These findings in lung adenocarcinoma contradict our predictions. However, some studies have indicated that MS4A4A can boost dectin-1-dependent NK cell-mediated anti-tumor metastasis and stimulate mast cell activation, which may be connected to the function of MS4A4A in lung adenocarcinoma [30, 31].

During the study of the MS4A family, it was discovered using PCR amplification that MS4A7 is primarily expressed in B cells and monocytes in hematopoietic cell lines. In addition, MS4A7 is present in non-hematopoietic cell types, such as those found in the colon, thymus, lung, and other organs [4, 6]. Few reports on MS4A7 in tumors have been published so far, mainly in cases of esophageal and gastric cancer [32, 33]. In their research, it has been discovered that the poor prognosis of these two cancers is associated with high MS4A7 expression. Even in lung adenocarcinoma, MS4A7 has been reported to be a predictor of poor survival [14]. In our study, however, the low expression of MS4A7 in lung adenocarcinoma suggests a bad prognosis. Our findings were further supported by the outcomes of RT-qPCR tests performed on lung cancer tissues and healthy lung tissues. In addition, the immune infiltration analysis revealed a strong correlation between MS4A7 and immune cells, particularly macrophages and dendritic cells, in lung adenocarcinoma. In contrast to our prediction results, which may be attributable to the various databases and analysis techniques used by us, Yu Luo et al. [14] reported MS4A7 as a predictor of poor lung adenocarcinoma; however, they did not analyze the expression level of MS4A7 in lung adenocarcinoma. The bad prognosis associated with high MS4A7 expression in gastric and esophageal cancers may be associated with the ability of MS4A7 to control tumor growth in lung adenocarcinoma through various other mechanisms; however, the specific mechanism of MS4A7 in lung adenocarcinoma remains to be confirmed.

Recent investigations have demonstrated that MS4A14 is highly expressed in renal clear cell carcinoma and that individuals with high MS4A14 expression had lower overall survival rates [34]. Conversely, patients with low expression of MSA14 in lung adenocarcinoma reportedly have a bad prognosis. The prognosis model has enabled us to determine that MS4A14 is a lung adenocarcinoma protective factor that is favorably correlated with patient survival time. However, the biological functions of MS4A14 remain poorly understood, and more research is required to determine how MS4A14 affects lung adenocarcinoma.

The MS4A family is closely related to calcium channels [5, 6], and MS4A15, which controls the level of calcium ions to coordinate lipid remodeling and prevent iron death, has recently been shown to be present in the endoplasmic reticulum [35]. According to several studies, MS4A15 is upregulated in ovarian cancer and can encourage the proliferation of ovarian cancer cells both in vivo and in vitro [15]. In our experiment, we found the expression of MS4A15 in lung cancer to be downregulated relative to that in normal lung tissues, indicating a negative prognosis for patients. Since MS4A15 is found in the endoplasmic reticulum, it possibly controls iron death and the structure and function of the mitochondria to influence the onset and progression of malignancies.

This study has some limitations. In vivo and in vitro tests are primarily lacking, and the mechanism behind the involvement of the MS4A family in lung adenocarcinoma remains to be identified.

In summary, we thoroughly examined the expression and prognosis of the MS4A family in lung adenocarcinoma and identified five MS4A family genes with prognostic value. Additionally, we found a strong association between prognostic genes and immune infiltration, and prognostic genes may influence lung adenocarcinoma development via calcium channels.

Identification of differentially expressed MS4As

RNA-sequencing expression (level 3) profiles and associated clinical data for lung adenocarcinoma were retrieved from the TCGA database (https://portal.gdc.com; Table S1) [36, 37]. R version 4.0.3 was used to apply all analysis techniques and packages.

Mutation Analysis Of The Ms4a Family

We used the online database GSCA (http://bioinfo.life.hust.edu.cn/GSCA/#/mutation) to study the gene mutation landscape of the MS4A family in lung adenocarcinoma, and we used the TCGA database to gather SNV data from 113 lung cancer samples for analysis. Seven different mutation types were examined: Missense_ Mutation, Nonsense_ Mutation, Frame_ Shift_ Ins, Splice_ Site, Frame_ Shift_ Del, In_ Frame_ Del, and In_ Frame_ Ins. These mutations were called detrimental mutations.

Functional Enrichment Analysis

We performed a functional enrichment study with clusterprofiler v4 2.0 and org Hs eg db v3. 14.0 and created a bubble diagram. If there were numerous notable entries among them, the top 20 were automatically shown in the figure (Fig. 3A).

Prognostic Analysis Of Differentially Expressed Genes

To assess the predictive significance of the mRNA expression of MS4A family members in patients with lung adenocarcinoma, the Kaplan–Meier plotter (http://kmplot.com/analysis/) was used. This plotter can help compare the 30K gene (mRNA, miRNA, and protein) expression and survival rate associated with 21 tumor types, including breast cancer, ovarian cancer, lung cancer, and gastric cancer. The primary goal is to locate and validate biomarkers using a meta-analysis. The data primarily come from TCGA, EGA, and GEO [38]. On the basis of the median expression (high expression and low expression), patient samples were split into two groups in the Kaplan–Meier plotter, and their outcomes were assessed using the Kaplan–Meier survival map, risk ratio (HR) of 95% confidence interval (CI), and log-rank P value. A P value of 0.05 or lower was considered to indicate a statistically significant difference.

Construction Of Five Gene Prognostic Models

Lung adenocarcinoma RNA-sequencing expression (level 3) profiles and associated clinical data (Table S2) were downloaded (https://portal.gdc.com) from the TCGA dataset. Samples with clinical information were retained while converting counts data to TPM and normalizing the data log2 (TPM + 1). Consequently, a total of 516 samples were collected for analysis. The survival differences between healthy individuals and patients with lung adenocarcinoma were tested using log-rank tests, and the predictive model’s accuracy was evaluated using timeROC (v0.4) analysis [39–41]. R (foundation for statistical computing 2020) version 4.0.3 was used to implement all analysis techniques and R packages. A P value of 0.05 was regarded to indicate statistical significance.

Construction Of Nomogram

The lung adenocarcinoma RNA-sequencing expression (level 3) profiles and the associated clinical data (Table S3) were downloaded from the TCGA dataset (https://portal.gdc.com). Univariate and multivariate Cox regression analyses were used to choose the appropriate phrases to construct the nomogram [42]. Through the “forestplot” R package, the forest plot was used to display the P value, HR, and 95% CI of each variable [43, 44]. A nomogram based on the outcomes of the multivariate Cox proportional hazards analysis was created to forecast the overall recurrence over the next 5 years.

Immune Infiltration Analysis

The TIMER web server (https://cistrome.shinyapps.io/timer/) is a comprehensive resource for the systematic investigation of immune infiltrates in various cancer types [45, 46]. Six immunological infiltrates (B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, and dendritic cells) can be estimated by TIMER. In this study, the relationship between prognostic genes and immune infiltrating cells was examined using the “Gene Module.” We used the “Connection Module” to assess the correlation between prognostic genes and other genes in the immune-infiltrating environment of thyroid cancer.

Rna Extraction And Rt-qpcr

Nine patients with lung adenocarcinoma had their normal and cancerous lung tissues removed at the First Hospital of Jilin University, and total RNA was isolated using TRIzol (Genstar, China). We used the Uni kit (Transgen, China) to reverse transcribe RNA. Then, we performed RT-qPCR (real-time quantitative polymerase chain reaction) to determine the degree of cDNA expression using 2× RealStar Green Fast Mixture (Genestar) as an internal control. We used 2-Ct to determine the relative gene expression level, and GraphPad 8.0 was used to display the results. Table S4 enlists the primer sequences used for RT-qPCR. Each patient provided written informed consent and agreed to participate in the trial. The research methodologies followed the guidelines outlined in the Helsinki Declaration. The research protocol was approved by the First Hospital of Jilin University ethics committee.

Acknowledgments

We were grateful for the technical assistance provided by the Central Laboratory at the First Hospital of Jilin University for conducting our tests. We appreciate Professor Huanfa Yi's advice on this project.

Author contributions

Z.J.Z. analyzed and organize data, H.P.L. did the experiment, and H.G. determined the experimental design and research strategy.

Competing interests

The author(s) declare no competing interests.

Data Availability Statement

The articles/supplementary materials provide the original contributions suggested in the study; more information is available on reasonable request to the corresponding author.

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No competing interests reported.

TableS1.csv
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Supplementary1.tif
Figure 1. Expression level of MS4A2 mRNA in normal lung tissues and lung adenocarcinoma.
Supplementary2.tif
Figure 2. Different expression levels of MS4A4A in normal lung tissues and lung adenocarcinoma.
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Figure 3. Differential expression of MS4A7 in normal lung samples and lung adenocarcinoma.
Supplementary4.tif
Figure 4. Expression levels of MS4A14 in normal lung tissue and lung adenocarcinoma.
Supplementary5.tif
Figure 5. Expression levels of MS4A15 in normal lung tissue and lung adenocarcinoma.

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Role of the membrane spanning 4A (MS4A) gene family in lung adenocarcinoma

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Abstract

Figures

Introduction

Results

mRNA expression of the MS4A family in patients with lung adenocarcinoma

Mutation Landscape Of The Ms4a Family In Lung Adenocarcinoma

Functional Enrichment Analysis Of The Ms4a Family

High Expression Of Ms4a2/4a/7/14/15 Is Beneficial To The Survival Of Lung Adenocarcinoma Patients

Construction Of Ms4a Family Prognostic Gene Model

Construction Of Nomogram Model

Immune Cell Infiltration Of Prognostic Genes Of The Ms4a Family In Lung Adenocarcinoma

Validation Of Prognostic Genes In Lung Adenocarcinoma By Real-time Quantitative Pcr

Discussion

Methods

Identification of differentially expressed MS4As

Mutation Analysis Of The Ms4a Family

Functional Enrichment Analysis

Prognostic Analysis Of Differentially Expressed Genes

Construction Of Five Gene Prognostic Models

Construction Of Nomogram

Immune Infiltration Analysis

Rna Extraction And Rt-qpcr

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1