Abstract

   Objectives: Sm proteins (SNRPB/D1/D2/D3/E/F/G), involved in pre-mRNA
   splicing, were previously reported in the tumorigenesis of several
   cancers. However, their specific role in lung adenocarcinoma (LUAD)
   remains obscure. Our study aims to feature abnormal expressions and
   mutations of genes for Sm proteins and assess their potential as
   therapeutic targets via integrated bioinformatics analysis.

   Methods: In this research, we explored the expression pattern and
   prognostic worth of genes for Sm proteins in LUAD across TCGA, GEO,
   UALCAN, Oncomine, Metascape, David 6.8, and Kaplan-Meier Plotter, and
   confirmed its independent prognostic value via univariate and
   multivariate cox regression analysis. Meanwhile, their expression
   patterns were validated by RT-qPCR. Gene mutations and co-expression of
   genes for Sm proteins were analyzed by the cBioPortal database. The PPI
   network for Sm proteins in LUAD was visualized by the STRING and
   Cytoscape. The correlations between genes for Sm proteins and immune
   infiltration were analyzed by using the “GSVA” R package.

   Results: Sm proteins genes were found upregulated expression in both
   LUAD tissues and LUAD cell lines. Moreover, highly expressed mRNA
   levels for Sm proteins were strongly associated with short survival
   time in LUAD. Genes for Sm proteins were positively connected with the
   infiltration of Th2 cells, but negatively connected with the
   infiltration of mast cells, Th1 cells, and NK cells. Importantly, Cox
   regression analysis showed that high SNRPD1/E/F/G expression were
   independent risk factors for the overall survival of LUAD.

   Conclusion: Our study showed that SNRPD1/E/F/G could independently
   predict the prognostic outcome of LUAD and was correlated with immune
   infiltration. Also, this report laid the foundation for additional
   exploration on the potential treatment target’s role of SNRPD1/E/F/G in
   LUAD.

   Keywords: SM proteins, prognostic biomarkers, immune infiltration,
   target therapy, lung adenocarcinoma

Introduction

   Lung cancer is the most well-known sort of malignant tumor worldwide
   and is the major cause of cancer mortality ([36]Sung et al., 2021).
   Lung adenocarcinoma (LUAD) has been the most common subtype of
   Non-small cell cancer (NSCLC) ([37]Anonymous, 2015). LUAD is
   characterized by a lack of early clinical symptoms, a high rate of
   distant metastasis and drug resistance, which pose serious challenges
   to clinical treatment. Currently, treatment methods for LUAD mainly
   include surgical resection, radiotherapy, chemotherapy, immunotherapy,
   and molecular targeted therapy ([38]Reck and Rabe, 2017). The best
   treatment for lung cancer is surgical resection in the early stages of
   lung cancer. However, the early symptoms of the disease are not
   obvious, easy to be ignored, which leads to a late diagnosis. The
   treatment of advanced lung adenocarcinoma is limited, and molecular
   targeted therapy is a promising choice, as well as immunotherapy.
   However, because of the lack of effective molecular targets, most drugs
   remain ineffective in the treatment of LUAD patients, of whom the
   5-year survival rate is just 15% ([39]Chen et al., 2016). Hence, it is
   absolutely necessary to recognize effective and dependable biomarkers
   to determine poor prognoses and direct treatment strategies.

   The spliceosome is a ribonucleoprotein (RNP) with a complex ring-shaped
   structure, which mainly consists of small nuclear ribonucleoproteins
   (snRNPs), which is involved in splicing the pre rna into mature mRNA.
   Seven Sm proteins (SNRPB, SNRPD1, SNRPD2, SNRPD3, SNRPE, SNRPF, and
   SNRPG) and a small eponymous small nuclear RNA (snRNA) compose snRNPs
   ([40]Chari et al., 2008). Accurate splicing is essential for normal
   cellular functions like cell proliferation, apoptosis, migration, and
   invasion. Sm proteins, involved in the formation of anti-Sm antibodies,
   were significant diagnostic biomarkers in autoimmune diseases, such as
   systemic lupus erythematosus (SLE). Furthermore, priors works have
   illustrated that the aberrant expressions of genes for Sm proteins are
   related to some human cancers, including cervical cancer ([41]Zhu et
   al., 2020), glioblastoma ([42]Correa et al., 2016), breast cancer
   ([43]Dai et al., 2021), and hepatocellular carcinoma ([44]Zhan et al.,
   2020).

   Up to now, there have been limited studies investigating the connection
   between the abnormal expressions of genes for Sm proteins and LUAD. For
   instance, a report ([45]Liu et al., 2019) showed that SNRPB
   down-regulation inhibited the growth and metastasis of NSCLC cells via
   RAB26 down-regulation. In the study directed by [46]Valles et al.
   (2012), SNRPB and SNRPE were proved to be related to poor survival in
   LUAD patients. However, the precise functional roles of Sm proteins in
   LUAD are unclear yet. Our study aimed to present abnormal expressions
   and mutations of genes for Sm proteins and assess their potential as
   therapeutic targets via integrated bioinformatics analysis, which may
   be helpful to the treatment of LUAD patients.

Materials and Methods

Data Source and Flow Chart

   The [47]GSE40791 dataset data performed by [48]GPL570 ([49]Zhang et
   al., 2012) were downloaded from the Gene Expression Omnibus (GEO)
   database ([50]https://www.ncbi.nlm.nih.gov/geo/) ([51]Clough and
   Barrett, 2016). [52]GSE40791 included 100 non-tumor lung tissues and 94
   lung adenocarcinoma tissues (N = 100, T = 94). The TGCA-LUAD data (N =
   59, T = 535) were downloaded from the TGCA database
   ([53]https://portal.gdc.cancer.gov) ([54]Tomczak et al., 2015). A flow
   chart of this study procedure is shown in [55]Figure 1. The
   characteristics of 535 patients, including their ages, genders, smoking
   history, TNM stages, pathologic stages, primary therapy outcome,
   residual tumor and anatomic neoplasm subdivision, are presented in
   [56]Supplementary Table S1.

FIGURE 1.

   [57]FIGURE 1
   [58]Open in a new tab

   Flow chart of the present study.

Comparison of Genes for Sm Proteins Expression Levels Between LUAD and
Corresponding Normal Tissue

   The Oncomine database ([59]https://www.oncomine.org) ([60]Rhodes et
   al., 2004) was used to obtain the data of mRNA expressions for Sm
   proteins in various cancers, and these data were analyzed via Student’s
   t-test. And we defined the following threshold: p-value<0.001, fold
   change >2 and 10% of most highly ranked genes. Furthermore, we compared
   expression levels of genes for Sm proteins between lung adenocarcinoma
   samples and normal samples in [61]GSE40791. Moreover, the transcription
   levels of these genes were validated in the GEPIA 2 database
   ([62]http://gepia2.cancer-pku.cn) ([63]Tang et al., 2017), including
   tumor and normal samples from the TCGA and the Genotype-Tissue
   Expression Project (GTEx) database ([64]https://gtexportal.org/home/)
   and the TCGA-LUAD data performed by R software.

Cell Culture and RT-qPCR

   BEAS-2B cell line (human bronchial epithelial cell line) was purchased
   from the American Type Culture Collection (Manassas, VA, United
   States), A549 cell line was purchased from the Type Culture Collection
   of the Chinese Academy of Sciences, Shanghai, China. Two types of cells
   were cultured in DMEM with 10% FBS, penicillin (50 U/ml), and
   streptomycin (50 U/ml). The cells were incubated at 37°C with 5% CO[2].

   Total RNA of the above cells was extracted using TRIzol (Thermo, United
   States), and first-strand complementary DNA (cDNA) synthesis from total
   RNA was carried out using the GoScript Reverse Transcription System
   (Promega, United States). RT-qPCR was conducted using an AriaMx
   Real-Time PCR machine (Agilent Technologies, United States) with TB
   GreenPremix ExTaq II (Takara Bio, Japan). The primer sequences are
   shown in [65]Supplementary Table S2. RT-qPCR cycle conditions: 3 min at
   95°C, 40 cycles of 15 s for 95°C and 60 s for 60°C. Data was normalized
   to the house-keeping gene GAPDH. The relative gene expression was
   performed by the 2^−ΔΔCt method ([66]Livak and Schmittgen, 2001).

Correlations Between Genes for Sm Proteins and Clinicopathological Parameters

   In our study, the transcription levels of genes for Sm proteins in TP53
   mutation of LUAD patients were analyzed with the lung adenocarcinoma
   dataset using the UALCAN database ([67]http://ualcan. [68]path.uab.edu)
   ([69]Chandrashekar et al., 2017). Moreover, we explored the correlation
   between genes for Sm proteins gene expressions and tumor stages via the
   “Expression DIY module” of GEPIA 2 and selected 50 neighboring genes
   related to genes for Sm proteins using the “Similar Genes Detection”
   module.

Survival Analysis

   The Kaplan-Meier plotter ([70]http://kmplot.com) ([71]Gyorffy et al.,
   2013; [72]Nagy et al., 2021), including mRNA expression data and
   patients’ clinical information from GEO and TCGA, is a powerful online
   tool to further verify the prognostic value of genes in several
   cancers. The overall survival (OS), free progression (FP), and
   post-progression survival (PPS) curves of genes for Sm proteins in LUAD
   were shown via the Kaplan-Meier plotter. Meanwhile, we also explored if
   the dyregulation of genes for Sm proteins had any impacts on the OS of
   LUAD patients with smoke history by using the Kaplan Meier plotter. In
   addition, univariate and multivariate cox regression analysis of the
   TGCA-LUAD data was performed using the “survival” and the “survminer” R
   package.

Gene Mutations and Co-Expression Analysis

   In this study, the lung adenocarcinoma dataset (TCGA, Firehose Legacy),
   including data from 230 complete samples of 586 patients, was selected
   and visualized as the map of gene mutations, expression heatmap, and
   co-expression map of genes for Sm proteins using the cBioPortal
   database ([73]http://www. [74]cbioportal.org) ([75]Cerami et al., 2012;
   [76]Gao et al., 2013). The z-Score threshold was set to ±1.8.

Constructed Protein-Protein Interaction Network and Selected Hub Genes

   The tool of STRING ([77]https://string-db.org/) ([78]Szklarczyk et al.,
   2019) was used to construct the protein-protein interaction (PPI)
   network between the seven genes for Sm proteins and their 50 frequently
   neighboring genes. All PPI pairs with a combined score of >0.4 were
   extracted. Then, we used the “CytoHubba” plugin (v0.1) ([79]Chin et
   al., 2014) of Cytoscape (v3.8.2) ([80]https://cytoscape.org)
   ([81]Otasek et al., 2019) to identify hub genes in that PPI network.
   Furthermore, the online tool of Metascape ([82]http://metascape.org)
   ([83]Zhou et al., 2019) was used to analyze the MCODE components of
   that PPI network.

GO and KEGG Enrichment Analyses

   Functional enrichment analyses, including gene ontology (GO) analysis
   comprising cellular component (CC), molecular function (MF), and
   biological process (BP), and Kyoto encyclopedia of genes and genomes
   (KEGG) pathway analysis, were performed via the tool of DAVID 6.8
   ([84]https://david.ncifcrf.gov) ([85]Huang da et al., 2009).
   Subsequently, the specific enriched terms for GO and KEGG enrichment
   analysis were visualized using the “clusterProfiler” package ([86]Yu et
   al., 2012) in R software. Moreover, we presented the network of
   enriched terms colored by cluster ID using the online tool of
   Metascape.

Associations Between Genes for Sm Proteins and Immune Infiltration

   Finally, we explore the associations between genes for Sm proteins and
   24 immune cell types in TGCA-LUAD. We investigated dendritic cell (DC),
   activated dendritic cell (aDC), immature dendritic cell
   (iDC),plasmacytoid dendritic cell (pDC), B cells, CD8^+ T cells,
   Cytotoxic T cells, T cells, T helper cells, T help 1 (Th1) cells, Th17
   cells, Th2 cells, T central memory (Tcm), T effector memory (Tem), T
   follicular helper (Tfh), T gamma delta (Tgd), regulatory T Cell (Treg),
   eosinophils, macrophages, mast cells, neutrophils, natural killer (NK)
   cells, NK CD56^bright cells and NK CD56^dim cells ([87]Bindea et al.,
   2013), and the above associations was performed using single-sample
   Gene Sets Enrichment Analysis (ssGSEA) algorithm of R package “GSVA”
   ([88]Hanzelmann et al., 2013) and lollipop charts were produced using R
   package “ggplot2.”

Statistical Analysis

   One-way Analysis of Variance (ANOVA) in the GEPIA 2 database, the
   log-rank test in Kaplan-Meier survival analysis, and the Cox
   proportional risk models in univariate and multivariate analyses, were
   used in statistical analysis. R software (v4.0.2)
   ([89]http://www.r-project.org) was used for analysis in this study.
   RT-qPCR data was performed in GraphPad Prism (v9.0.2) (San Diego, CA,
   United States) and presented as the mean ± S.D. Student’s T-test was
   used for statistical analyses between the data pairs where appropriate.
   *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001 were considered
   to represent statistical significance.

Results

The mRNA Levels of Sm Proteins Were Significantly Increased in Lung Cancer
Tissues and LUAD Cell Lines

   From [90]Figure 2A and [91]Table 1, we could see that the mRNA levels
   of SNRPB/D1/D2/E/G were higher in lung cancer than in non-cancer
   tissues. The results were also consistent with the mRNA levels of Sm
   proteins were upregulated in LUAD compared to normal lung tissues
   ([92]Figure 2B). Moreover, the relative mRNA level of all Sm proteins
   in A549 cell line is higher than the relative mRNA level of them in
   normal lung cell lines, BEAS-2B ([93]Figure 2C). The transcription
   levels of genes for Sm proteins considerably increased in data of GEPIA
   2 (TCGA-LUAD & GTEx), TCGA-LUAD data and pair LUAD data ([94]Figures
   3A–C) match the above results.

FIGURE 2.

   [95]FIGURE 2
   [96]Open in a new tab

   The transcription levels of genes for Sm proteins in lung cancer
   tissues and lung cell lines. (A) The genes for Sm proteins in different
   human cancers (ONCOMINE). (B) Box plots of genes for Sm proteins
   expression between lung cancer and normal tissues in [97]GSE40791. (C)
   Bar charts of mRNA level of genes for Sm proteins between BEAS-2B and
   A549 cell line. Students T-test was performed to assess the statistical
   significance. A p value of <0.05 was regarded as statistically
   significant.

TABLE 1.

   The mRNA levels of Sm proteins were significantly higher in lung cancer
   than in normal lung tissues (ONCOMINE).
   Lung cancer VS normal lung tissue Fold change p-value T-test References