The implication of Synemin gene on expression and prognostic significance in gastric cancer based on bioinformatics

doi:10.21203/rs.3.rs-3227025/v1

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The implication of Synemin gene on expression and prognostic significance in gastric cancer based on bioinformatics

https://doi.org/10.21203/rs.3.rs-3227025/v1

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Objective：Here, we explored the expression of SYNM by means of Gene Expression Omnibus (GEO) and investigated its prognostic significance as well as potential functions in gastric cancer (GC).

Methods：Toward this goal, differential gene expression analysis, univariate Cox regression, Lasso regression, best subset regression, Gene Set Enrichment Analysis (GSEA) and multivariate Cox regression were employed in GEO. For further verification, the pathological tissues of patients with gastric cancer were collected and analyzed. The expression of SYNM in GC tissues was verified by qRT-PCR, Western blotting and immunohistochemistry. Kruskal-Wallis test was used to analyze the correlation between SYNM expression and clinical characteristics. Kaplan-Meier analysis, univariate and multivariate Cox regression analysis were applied to analyzed prognostic.

Results：SYNM is underexpressed in GC in public datasets and clinical samples (P <0.01); Its expression was significantly correlated with Lauren typing, T, N, M and clinical staging (P < 0.05). Patients with high SYNM expression had poor prognosis (P < 0.01) and it was an independent prognostic factor for GC (p = 0.01). The high expression of SYNM mRNA was enriched in Extracellular matrix (ECM) receptor interaction, leukocyte transendothelial migration and Transforming growth factor β (TGF – β) signaling pathway, and CD4+ memory T cells resting were abundant.

Conclusion：SYNM was low expression in GC and it might promote the malignant development and immune evasion of GC, and patients with high expression of SYNM predicted a good prognosis.

Gastric cancer

Bioinformatics analysis

Survival prognosis

Immune infiltration

Synemin

In 2019, there were 49,008 new cases of gastric cancer (GC) worldwide, with an age-standardized incidence rate of 1.6/100,000, 30.9% lower than in 1990. In 2019, there were 27 895 deaths of GC worldwide, and the age-standardized mortality rate was 0.9/100 000, which decreased by 46.8% compared with 1990(Wen, et al. 2021). In recent years, good progress has been made in the treatment of gastric cancer. The remarkable achievement was undoubtedly due to three factors: (1) The popularization of health education. The importance of early diagnosis and treatment of GC has been improved in the whole society. (2) New screening technology for GC. The sensitivity of gastroscopy screening GC is as high as 88.6% (Hamashima, et al. 2013), which greatly improves the early diagnosis of GC. (3) Improvement of GC treatment. In recent years, surgery and standardized radiotherapy and chemotherapy guided by imaging technology have made great progress. Addtionaly, immunotherapy, such as immune checkpoint modulators, has approved for first-line treatment of gastric cancer (Fuchs, et al. 2018).

In the past 20 years, although China has made great progress in the screening and treatment of GC, the age-standardized five-year survival rate of GC in China is about 30%, which is far lower than the 60% rate in South Korea and Japan (Allemani, et al. 2018). The five-year survival rate refers to the proportion of patients who survive more than five years after various comprehensive treatments for certain tumors. There are two main reasons for the low five-year survival rate of GC patients in China :(1) the low rate of early diagnosis is due to the high requirements and high price of gastroscopy screening technology, which is still difficult to be used as a large-scale GC screening method in primary and secondary hospitals. (2) Low early diagnosis rate of GC inevitably leads to more patients with advanced cancer, and most patients with advanced cancer have distant metastasis and poor surgical treatment effect, resulting in low postoperative survival rate. So far, there is no clear consensus on surgical indications for patients with advanced GC, and there is also a lack of relevant large-sample controlled studies. If the patients with distant metastasis are not suitable for surgery, then the huge trauma and possible complications brought by the surgery itself will not play a role in the treatment, but may accelerate the death of patients due to the "double blow" of advanced cancer and surgical trauma. Therefore, to find a method or index that can evaluate the "postoperative" survival and screen prognosis of patients with advanced GC before surgery, so as to provide a "surgical indication" for clinicians as a reference. Our research group conducted a preliminary study on a small sample of patients with GC and gained some experience (Liu, et al. 2016). By using Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), this study conducted a large sample expansion study on the "postoperative" survival and prognosis assessment methods and indicators for GC patients.

Synemin (SYNM), a type Ⅵ intermediate filament protein, mainly exists in stem cells and progenitor cells, as well as differentiated muscle cells and endothelial cells (Snider and Omary 2014). This protein was found to form connections to the extracellular matrix (ECM) and was a subunit of the network of intermediate filament, providing important structural support in muscle. However, there are few studies on the role of SYNM in tumor cell transformation. Recently, studies have found that hypomethylation of SYNM is associated with aggressive forms of breast cancer and colorectal neoplastic lesions (Noetzel, et al. 2010,Ashktorab, et al. 2020). Moreover, SYNM has been previously identified as novel regulator of cancer cell therapy resistance and DNA double strand break (DSB) repair by non-homologous end joining repair(Deville, et al. 2020,Deville, et al. 2020). By using bioinformatics, Qianqian Wang et al found that SYNM was correlated with platinum response status and prognosis of ovarian cancer patients(Wang, et al. 2019). The above examples clearly demonstrate that SYNM would play an vitol role in the progression of systemic malignancies. However, the role of SYNM in GC has not been reported so far. Therefore, we explored the correlation between expression levels of SYNM in GC and the clinical characteristics and prognosis of GC patients through the cancer public database, and explored the mechanism of SYNM affecting the occurrence and development of GC.

Datasets sources and processing

Based on our research purpose, microarray dataset that met the following criteria was downloaded from subsequent analysis: (1) The dataset must be derived from tumor and normal tissue of patients with GC; (2) The dataset must contain whole-genome mRNA expression microarray data; (3) The dataset must be from Homo sapiens and all of them are from Asia; (4) The sample size of dataset must be ≥ 100; (5) The dataset must contain clinical information such as gender, age, clinical staging and survival time. Finally, one microarray dataset GSE66229 which includes 400 GC patients with corresponding clinical information was downloaded. The chip platform is Affymetrix Human Genome U133 Plus 2.0 Array.

Differential genes expression analysis of GC

The series matrix file and platform were downloaded to convert the probes into the corresponding gene symbol, using Practical Extraction and Report Language (Perl) (https://www.perl.org/) scripts. For multiple probes corresponding to the same gene, the final value of the gene was replaced by the average value. The "impute" package and "limma" package in R language were used to complement and correct the data. The difference was considered significant when |logFC| (fold change) was ≥ 0.5 and false discovery rate (FDR) was < 0.05.

Selection of prognosis ‑related genes

1. Survival analysis

First, the expression data of differential genes were combined with the survival time and status of patients. Then we used the "survival" package in R language to screen the differential genes and the criteria were p value < 0.01, p.adjust < 0.01.

2. Univariate Cox regression

The gene expression data filtered by survival analysis were screened by univariate Cox regression analysis in the "survival" package of R language. The criteria were p value < 0.01, p.adjust < 0.01.

3. Least absolute shrinkage and selection operator (Lasso) regression

In order to prevent overfitting, we applied Lasso regression between univariate Cox regression and multivariate Cox regression. The "glmnet" package and "survival" package in R language were used to construct the model for the gene expression data filtered by univariate Cox regression analysis. In order to reduce the error, the cross-validation method was adopted: for the given value of λ, cross-validation was carried out, and the λ value with the minimum cross-validation error was selected. Then according to the value obtained, the model can be re fitted with all the data.

4. Best subset regression(BSR)

BSR is a regression modeling method that takes all combinations of all alternative independent variables as a subset of the model, which is filtered by a weighting function of fitting accuracy and number of parameters: minimum information criterion (AIC) = 2(number of model parameters)-2ln(maximum likelihood function of the model). The "glmulti" package in R language was used to combine the genes screened by Lasso regression and the model with the minimum AIC was taken as the optimal subset.

5. Multivariate Cox regression

Finally, multivariate Cox regression analysis was performed on the gene combination of the best subset. The criteria were p value < 0.05, p.adjust < 0.05.

qRT-PCR

In order to extract total RNA, the tissues had been pre-processed. Through the utilization of a Prime Script RT reagent kit the cDNA was synthe-sized. Quantitative PCR was carried out with a 7500 real-time PCR system. GAPDH served as an internal control, and fold change had been calculated using the 2 − ΔΔCT technique. Primer sequences are as follows:

SYNM forward: 5′-ACCGACTCACTACTACAGAGGG-3′

SYNM reverse: 5′-CGAGCTGTGATTGAAACTTGC-3′

GAPDH forward: 5′-ACAACTTTGGTATCGTGGAAGG-3′

GAPDH reverse: 5′-GCCATCACGCCACAGTTTC-3′

Western blotting

According to the manufacturer's recommendations, protein from cells or tissues was extracted using RIPA buffer. Equal amounts of protein were separated by SDS–acrylamide gel and transferred into a PVDF membrane. After blocking, the membrane was incubated with the following primary human monoclonal antibodies: anti-SYNM and anti-β-catenin. Then, the blots were washed with TBS-Tween 20 buffer and incubated with the horseradish peroxidase-conjugated secondary antibodies at room temperature for 1 hour. The signal was visualized by enhanced chemiluminescence (ECL).

Immunohistochemistry

120 paired tumor tissue samples and matched adjacent normal samples were collected from patients in the first Affiliated Hospital of Shihezi University School of Medicine. All patients wrote informed consent. This study was approved by the ethics committee of the first Affiliated Hospital of Shihezi University School of Medicine. GC tissues and paired adjacent tissues were sectioned into paraffin slices after fixation, dehydration, and embedding. Envision two-step method was applied for immunohistochemistry staining. The expression levels of SYNM in tissues were examined by two independent pathologists. The scoring criteria are shown in Table 1:

Table 1

Scoring criteria for SYNM immunohistochemical staining results
Percentage of positive cells	Score	Staining intensity	Score		Evaluation results *
Percentage of positive cells	Score	Staining intensity	Score	Product	Score
< 5%	0	no signal detectable	0	0–1	0/-
6–25%	1	weak positive signal	1	2–4	1+
26–50%	2	clear positive signal	2	5–8	2+
51–75%	3	strong positive signal	3	9–12	3+
76–100%	4
Note: Immunohistochemical evaluation result = score of percentage of positive cells in all cells × staining intensity

Gene set enrichment analysis

Gene set enrichment analysis (GSEA) is a computational method that assesses whether a prior defined set of genes shows statistical significance and concordant differences between two biological states. To investigate the role of SYNM gene in GC, the package cluster Profiler was used to conduct single-gene GSEA analysis. P value < 0.05 and FDR < 25% were regarded as the cut-off criteria.

Immune cell infiltration analysis

CIBERSORT (https://cibersort.stanford.edu/) was used to evaluate proportions of tumor-infiltrating lymphocytes in a mixed cell population. A total of 22 immune cell types in this tool were used to estimate relative abundance of immune cell infiltration between low- and high-expression groups.

Statistical analysis

In this study, SPSS 22.0 and R Studio software were used for analysis. Kruskal-Wallis H test was used to compare the mRNA expression of SYNM between tumor and normal tissue, compare the correlation between high and low expression of SYNM in GC and clinical characteristics of GC patients; Chi-square test and Fisher's exact test were used to compare SYNM expression between tumor and normal tissue. Kaplan-Meier was used to analyze the relationship between high and low expression of SYNM and postoperative prognosis of GC.

Identification of DEGs

According to the above screening criteria, a total of 156 differential genes were screened out, including 38 up-regulated genes and 118 down-regulated genes (see Table S1 for details).

Screening of prognosis related DEGs

According to the above screening conditions, a total of 80 differential genes were screened out to be related to the survival of GC (see Table S2 for details). According to univariate Cox regression analysis, a total of 58 target genes were screened out (see Table S3 for details). Nine significant genes with non-zero coefficients were selected from 58 target genes by Lasso regression, which were ATP1A2 (ATPase), CASQ2 (Calsequestrin 2), HOXA13 (Homeobox A13), MMP12 (Matrix metallopeptidase 12), MYOT (Myotilin), PLEKHS1 (Pleckstrin homology domain containing), SYNM, SYNPO2 (Synaptopodin 2) and TAC1 (Tachykinin). As shown in Fig. 1A, when λ value is about − 3.6, 9 parameters are selected and the part of Lasso regression model may be abnormally minimum. Then ATP1A2, CASQ2, HOXA13, MMP12, MYOT, PLEKHS1, SYNM, SYNPO2 and TAC1 were divided into five groups and their AIC values were calculated respectively. Among them, the combination with the lowest AIC value was ATP1A2, CASQ2, HOXA13, MMP12, SYNM and TAC1 (see Table S4 for details). Finally, ATP1A2, CASQ2, MMP12, SYNM and TAC1 were screened out by multivariate Cox regression (Fig. 1B). These five genes will become potential prognostic markers of GC, among which SYNM is the gene we are interested in studying. The mRNA expression of SYNM in normal tissues was significantly higher than that in tumor tissues (P < 0.001, Fig. 1C). Kaplan-Meier showed that the survival rate of patients with high expression of SYNM mRNA was significantly lower than that of patients with low expression (P < 0.001, Fig. 1D). Age [HR = 1.031, 95%CI (1.017–1.045), P < 0.001], Lauren typing [HR = 1.461, 95%CI (1.119–1.907), p = 0.005], lymph node metastasis (N) [HR = 1.374, 95%CI (1.059–1.783), p = 0.017], distant metastasis (M) [HR = 2.101, 95% CI (1.306–3.381), p = 0.002], clinical staging [HR = 1.565, 95%CI (1.095–2.236), p = 0.014], the mRNA expression of SYNM [HR = 1.382, 95%CI (1.148–1.664), P < 0.001] were independent prognostic factors of GC by univariate and multivariate Cox regression analysis (Fig. 1F, G). In addition, we also analyzed the relationship between SYNM mRNA expression and different clinical characteristics and the results showed that SYNM mRNA expression was higher in females than in males (P < 0.05, Fig. 2H); The mRNA expression of SYNM in intestinal GC was higher than that in diffuse GC (P < 0.01, Fig. 2H); SYNM mRNA expression increased with T, N and Stage staging (P < 0.001, P < 0.05, P < 0.001, Fig. 2H).

SYNM expression is low in GC tissues

In order to verify the results of bioinformatics analysis, the mRNA expression of SYNM in GC tissues and normal tissues was verified by qRT-PCR. SYNM mRNA expression in normal tissues was significantly higher than that in GC tissues (P < 0.01, Fig. 2E). Then, Western blotting was performed to quantitatively detect protein expression level. SYNM expression level in normal tissues was significantly higher than that in GC tissues (P < 0.01, Fig. 2D). Finally, the protein expression level of SYNM was detected qualitatively and locationally by immunohistochemistry. According to the specification of SYNM antibody, SYNM was positively expressed in human myocardium tissue (Fig. 2A), proving the effectiveness of SYNM antibody. Then we detected SYNM protein expression in 120 pairs of cancer tissues and normal tissues of GC patients. As shown in Fig. 2B and C, we observed diffuse cytoplasmic staining in normal tissues, while most cytoplasmic and nuclear staining were not observed in GC tissues, and light cytoplasmic staining was found in a few tumor tissues. As shown in Table 2, the positive rate of SYNM expression in GC was 47.5% (57/120) and the negative rate was 52.5% (63/120). The positive rate of SYNM expression in normal tissues was 100% (120/120) and the negative rate was 0% (0/120). SYNM expression level in normal tissues was higher (P < 0.001). In conclusion, SYNM is low expressed in GC tissues, which is consistent with bioinformatics analysis results.

Table 2

SYNM expression in GC and normal adjacent tissues
Group	n	-	1+	2+	3+	Positive rate	Χ²	P
Normal	120	0(0%)	81(67.5%)	38(31.7%)	1(0.8%)	100%	136.433	< 0.001
Tumor	120	63(52.5%)	57(47.5%)	0(0%)	0(0%)	47.5%

The relationship between SYNM expression level and clinical features and prognosis of GC

After confirming the low expression of SYNM in GC, we further studied the correlation between SYNM expression and clinicopathological data of GC patients. As shown in Table 3, SYNM expression was correlated with Lauren classification (p = 0.014), tumor invasion (T) (p = 0.012), N (P < 0.001), M (P < 0.001), clinical staging (P < 0.001). Further, patients with high SYNM expression had poor survival prognosis by survival curve (P < 0.001, Fig. 1E), which was consistent with bioinformatics analysis.

Table 3

Correlation between SYNM expression and clinical characteristics of GC patients
Characteristic	n	Low expression	High expression	P value
Gender
Male	95	48(50.5%)	47(49.5%)	0.399
Female	25	15(60.0%)	10(40.0%)
Age(yr)
< 64	60	35(58.3%)	25(41.7%)	0.201
≥ 64	60	28(46.7%)	32(53.3%)
Lauren typing
Diffuse	41	26(63.4%)	15(36.6%)	0.014
Intestinal	57	22(38.6%)	35(61.4%)
Mixed	22	15(68.2%)	7(31.8%)
T
T1	14	11(78.6%)	3(21.4%)	0.012
T2	17	10(58.8%)	7(41.2%)
T3	68	28(41.2%)	40(58.8%)
T4	21	14(66.7%)	5(23.3%)
N
N0	49	39(79.6%)	10(20.4%)	< 0.001
N1	17	10(58.8%)	7(41.2%)
N2	21	7(33.3%)	14(66.7%)
N3	33	7(21.2%)	26(78.8%)
M
M0	108	63(58.3%)	45(41.7%)	< 0.001
M1	12	0(0.0%)	12(100.0%)
Clinical staging
Ⅰ	22	17(77.3%)	5(22.7%)	< 0.001
Ⅱ	41	30(73.2%)	11(26.8%)
Ⅲ	45	16(35.6%)	29(64.4%)
Ⅳ	12	0(0.0%)	12(100.0%)

Potential biological functions of SYNM

In order to understand the mechanism of SYNM affecting the occurrence and development of GC, we analyzed related pathways in SYNM high-risk groups by GSEA in GSE66229 and The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) respectively. We found that the samples with the overexpression of SYNM were enriched in ECM receptor interaction, leukocyte transendothelial migration and Transforming growth factor β (TGF – β) signaling pathway, and were verified in TCGA with consistent results (Fig. 3A). Therefore, SYNM may affect the development of GC through the above signal pathways.

Distribution of infiltrating immune cells

Since those samples were enriched in leukocyte transendothelial migration, we used CIBERSORT to explore 22 kinds of immune cell infiltration between SYNM mRNA high and low expression groups in GSE6629. As shown in Fig. 3B, the low expression groups showed significant infiltration by B cells naive, T cells CD4 memory activated, NK cells resting, M0 macrophages, dendritic cells activated, and neutrophils compared to high expression groups (P < 0.001, P < 0.001, p = 0.045, P < 0.001, p = 0.027, p = 0.003). Reciprocally, the high expression groups showed significant infiltration by T cells CD4 memory resting, T cells follicular helper, and dendritic cells resting compared to low expression groups(p = 0.004, p = 0.028, p = 0.002).In order to break the limitation of single database and further improve the credibility of proportion analysis of immune cell infiltration, we used TCGA for independent analysis again. The low expression groups showed significant infiltration by plasma cells, T cells CD4 memory activated, T cells follicular helper, NK cells resting, M0 macrophages, mast cells activated, and neutrophils compared to high expression groups (p = 0.006, p = 0.001, p = 0.002, P < 0.001, p = 0.002, p = 0.002, P < 0.001), while the high expression groups showed significant infiltration by T cells CD4 memory resting, regulatory T cells (Tregs), NK cells activated, monocytes, and mast cells resting compared to low expression groups (P < 0.001, p = 0.004, p = 0.041, p = 0.003, P < 0.001). In conclusion, the infiltration of T cells CD4 memory resting was abundant in the high expression group of SYNM mRNA, while the infiltration of T cells CD4 memory activated, NK cells resting, M0 macrophages and neutrophils was abundant in the low expression group. Therefore, SYNM may also be involved in the regulation of tumor immune microenvironment cells.

Through this study to identify genes which expressed in gastric cancer samples and their matched normal tissues, and from these genes to find out the genes related to the survival and prognosis of GC patients, then the univariate Cox regression analysis, in order to prevent excessive fitting, univariate Cox regression analysis and multivariate Cox regression analysis in the design of the Lasso regression and BSR. Finally, five genes related to the prognosis of GC patients were obtained, namely ATP1A2, CASQ2, MMP12, SYNM and TAC1. Among them, we focused on SYNM and found that the mRNA expression level of SYNM in normal tissues was significantly higher than that in cancer tissues, which was also proved by qRT-PCR. Western Blotting and immunohistochemistry further proved that SYNM expression in cancer tissues was significantly lower than that in normal tissues. Univariate and multivariate Cox regression analysis showed that SYNM was a risk factor in GC. The expression of SYNM was significantly correlated with Lauren typing, T, N, M and clinical staging. In addition, Kaplan-Meier method also proved that patients with high expression of SYNM had a poor prognosis, suggesting that SYNM was involved in the development of GC.

In order to explore how SYNM affects the development of GC, GSEA analysis found that the samples with the overexpression of SYNM were enriched in ECM receptor interaction, leukocyte transendothelial migration and TGF - β signaling pathways. ECM is the basic and core component of all tissues and organs and is necessary for the existence of multicellular organisms. From the initial stage of development to death of an organism, it regulates the activity process of every cell in the body, such as survival, growth, migration and differentiation, and is crucial for maintaining normal homeostasis (Najafi, et al. 2019). Integrin is one of the extracellular matrix receptors that can drive multiple stem cell functions, including tumor initiation, epithelial plasticity, metastatic reactivation, and resistance to oncogene- and immune-targeted therapies (Cooper and Giancotti 2019). Although current studies have found that the interaction between integrin and SYNM is limited to the sarcomemma (Hijikata, et al. 2008), our study was the first to find that SYNM may affect the development of GC through the interaction between integrin and other molecules.

According to GSEA analysis, it was found that the samples with high expression of SYNM mRNA were enriched in leukocyte transendothelial migration. Therefore, CIBERSORT was used to conclude that, compared with the low expression group of SYNM mRNA, T cells CD4 + memory resting in the high expression group were abundant in infiltration. Similarly, we verified it again in TCGA and the results were consistent. T cells CD4 + memory are an important immune cell in human immune system. CD4 is mainly expressed in helper T cells and can bind to non-peptide regions of MHC class Ⅱ molecules and participate in signal transduction of T cell antigen receptor (TCR) antigen recognition. In tumor immunity, CD4 + T cells can activate CD8 + T cells through various mechanisms to differentiate into Cytotoxic T lymphocytes (CTL), and meanwhile maintain and strengthen the anti-tumor reaction of CTL cells. On the other hand, even in the absence of CD8 + T cells, CD4 + T cells can also directly kill tumor cells by IFN-γ mechanism.

TGF - β is a secreted cytokine that regulates cell proliferation, migration, and the differentiation of a plethora of different cell types (Hao, et al. 2019). During the early stages of tumorigenesis, TGF - β acts as a tumor suppressorby inducing cytostasis and the apoptosis of normal and premalignant cells. For example, telomere shortening, cell senescence and apoptosis through regulating human telomerase reverse transcriptase (hTERT) in the breast cancer MCF-7 cell line (Cassar, et al. 2017). However, at later stages, when cancer cells have acquired oncogenic mutations and/or have lost tumor suppressor gene function, cells are resistant to TGF - β - induced growth arrest. Caja, L et al. found that TGF - β stimulated myofibroblast percent and invasion rate in tumor-associated fibroblasts (CAFs) that increase tumor invasion (Caja, et al. 2018). Besides, TGF - β functions as a tumor promotor by stimulating tumor cells to undergo the so-called epithelial-mesenchymal transition (EMT). Cancer cells with an intact TGF - β pathway can avert its pro-apoptotic effect by somehow decoupling EMT from apoptosis, which in turn allows the cancer cells to use EMT for tumorigenic advantage. It can lead to metastasis and chemotherapy resistance (David and Massagué 2018). Additionally, tumor cells evade this immune surveillance through immune evasion of TGF - β. The example of regulation of tumor metastasis by TGF - β/SMAD signaling was found to be achieved by impairing the activity of tumor-infiltrating T cells (Mariathasan, et al. 2018,Tauriello, et al. 2018). Functional studies have demonstrated that selective deletion of SMAD4 in NK cells impedes NK cell homeostasis and maturation, thereby reducing murine cytomegalovirus clearance (Wang, et al. 2018). TGF - β blocks immune surveillance by inhibiting migration and inducing apoptosis of antigen-presenting cells, such as dendritic cells (DCs), whose function is to mature and stimulate T cells during the immune response (Hao, et al. 2019).

In conclusion, public data, qRT-PCR, Westren blotting and immunohistochemistry were used in this study to prove that SYNM is underexpressed in GC. We used clinical samples to further verify that SYNM high expression was closely related to Lauren typing, T, N, M, clinical staging and poor prognosis. The results of enrichment analysis and immune cell infiltration analysis indicated that SYNM plays a role in the mechanisms closely related to tumor progression and immune microenvironment. At present, there are no reports to determine whether it affects the progression of GC through the above pathways, and further studies are needed.

Author Contributions：Conceived and designed the analysis: Feng Pan, Wen-Jie Zhang; Collected the data: Xu-Dong Zhang, Xiao-Ning Li, Hai-Kang Cui, Xi Yang; Contributed data or analysis tools: Feng Pan, Yong-Qiang Zhang, Xu-Dong Zhang, Xiao-Ning Li, Hai-Kang Cui, Xi Yang; Performed the analysis: Feng Pan, Yong-Qiang Zhang, Xu-Dong Zhang, Xiao-Ning Li, Hai-Kang Cui, Xi Yang; Wrote the paper: Feng Pan; Revise the manuscript critically for important intellectual content: Lan Yan, Wen-Jie Zhang.

FUNDING: This study was funded by the National Science and Technology Support Program of China (No. 2009BAI82B02) and the National Natural Science Foundation of China (No. 81260301).

Data availability: The original data can be provided by the corresponding author upon reasonable request.

Declarations

Conflict of interest: All the authors have no relevant confict interests to disclose.

Ethical Statement: All patients provided informed consent before surgery. The ethics committee of the First Affiliated Hospital, Shihezi University School of Medicine approved this retrospective study (IRB: 2018-067-01).

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No competing interests reported.

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The implication of Synemin gene on expression and prognostic significance in gastric cancer based on bioinformatics

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Abstract

Figures

Introduction

Materials and methods

Datasets sources and processing

Differential genes expression analysis of GC

qRT-PCR

Western blotting

Immunohistochemistry

Gene set enrichment analysis

Immune cell infiltration analysis

Statistical analysis

Results

Identification of DEGs

Screening of prognosis related DEGs

SYNM expression is low in GC tissues

The relationship between SYNM expression level and clinical features and prognosis of GC

Potential biological functions of SYNM

Distribution of infiltrating immune cells

Discussion

Declarations

References

Additional Declarations

Supplementary Files

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