Identification of 7 key genes as biomarkers for prognosis and metastasis of gastric cancer

doi:10.21203/rs.3.rs-1896419/v1

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Identification of 7 key genes as biomarkers for prognosis and metastasis of gastric cancer

https://doi.org/10.21203/rs.3.rs-1896419/v1

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Background

Gastric cancer is one of the most common tumors in the GI system worldwide. The refractory nature of gastric cancer and the increasing risk of recurrence and metastasis with inadequate treatment have led to numerous reports of death due to recurrence and metastasis of gastric cancer. In turn, potential mechanisms and potential biomarkers for the prevention of cancer metastasis have become a major task in clinical cancer research.

Methods

Differential genes (DEGs) up- and down-regulated in the GEO dataset (GSE118897) were identified, and 145 gastric cancer patients from the TCGA database were evaluated for differential gene prognosis and the impact of high expression of these genes in gastric cancer tissues on survival of gastric cancer patients was explored.

Results

A total of 90 DEGs were identified, of which 33 were expressed up-regulated and 57 were expressed down-regulated. By screening genes that were highly expressed in tumor tissues and had poor prognosis for gastric cancer patients, seven key genes were finally screened, including CEACAM7, THBS2, TREM2, MFAP2, BGN, MMP1 and NUSAP1. The expression levels of these genes were shown to correlate with overall survival by survival analysis. These genes are involved in biological processes such as cell proliferation and division, while being reflected in the KEGG-enriched pathway with a higher cell cycle and inflammatory response. Further analysis revealed that all seven genes can affect the proliferation and metastasis of cancer cells, with significant correlation with increasing expression, suggesting a possible significant implication for the prevention and targeted treatment of gastric cancer.

Gastric cancer (GC)

Bioinformatic analysis

Prognosis analysis

Metastasis

Diagnosis of metastasis

Gastric cancer (GC) is a major problem affecting human health, it is the second leading cause of cancer death, after lung cancer. It is the most common malignant tumor of the gastrointestinal tract in endemic areas of Latin American countries such as Japan and Mexico, with a very high mortality rate and an average 5-year survival rate of less than 20%, which is due to late diagnosis and early clinical asymptomatic only in a few countries, especially Japan, where extensive early detection programs have been established to detect and treat tumors before they invade the stomach, with a 5-year survival rate of up to 90% ^[1–3]. One study found the highest incidence in East Asia (Korea, China, etc.) with an annual incidence rate of 40 to 60 per 100,000 inhabitants. The incidence of gastric cancer in the population is gradually becoming younger, and there is an unexplained increase in the incidence of gastric cancer in the younger population (mostly under 40 years of age), which is closely related to the lifestyle habits of young people, such as Helicobacter pylori infection, staying up late, and high-sugar diet, which can directly or indirectly lead to the development of gastric malignancies. The pathological studies about gastric cancer have been initially explored in depth,but it does not mean that the clinical practice of predicting metastasis and recurrence of gastric cancer is mature.In order to further explore the potential mechanisms and biomarkers of tumor cell metastasis,predicting recurrence of gastric cancer is the key work in cancer research and also the significance of this study ^{[4, 5]}.

The discovery of mutated genes (expression abnormalities) in GC and the development of effective treatment strategies are key factors in improving patient prognosis. In recent years, the application of high throughput platform In gene expression (GE) has attracted a lot of attention and sustained breakthroughs in clinical research such as finding oncogenes, prognosis prediction, and new target drug discovery, highlighting an important position in clinical research. In this work, we analyzed mRNA microarray datasets to obtain differential genes (DEGs) between GC tissues and normal tissues. We further explored the development of GC through functional enrichment of DEGs and analysis of gene expression in individual samples, combined with survival analysis and clustering analysis to identify key genes in GC(Fig. 1), through which we identified specific differential genes that may serve as signature genes for gastric cancer development or metastasis. Microarray technology allows us to study the expression status of multiple genes simultaneously, see Fig. 1. in addition, clustering analysis can help us to identify important pathways and genes for further studies ^[6–8].

2.1 Microarray data

In this study, gene expression profiles (GSE118897) of GC clinical samples were obtained from the GEO database (http://www.ncbi.nlm.nih.gov/geo/), and these RNA fragments were based on the GPL16686 platform (Affymetrix Human Gene 2.0 ST Array). gse118897 The array data consisted of 20 pairs of GC tissue and normal tissue, and the analysis results were obtained from R software (version: 4.0.5). Expression profile data and corresponding clinical information of RNA sequencing data from 145 gastric cancer tumors were obtained using The Cancer Genome Atlas (TCGA) dataset (https://portal.gdc.com), and genes associated with GC prognosis were obtained by analysis.

2.2 Clinical data screening and analysis

The dataset used contains data from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) in the downloadable data format MINiML. Data correction was performed using R software (version 4.0.5) for the GC clinical sample gene expression profile (GSE118897) by adding missing values and removing duplicates. The probe names of the microarray GSE118897 matrix data were converted to gene names by the platform file GPL16686 and evaluated for the samples, and adjusted P values were analyzed in GEO to correct for false positive results. "P < 0.05 and log2 (fold change) > 1 or log2 (fold change) < -1" was defined as a threshold mRNA differential expression screen ^{[9, 10]}.

2.3 Obtaining differentially expressed genes (DEGs)

Heat map and volcano map of gene expression distribution in normal and GC tissue samples were plotted using the phatmap and vioplot packages in R software. The Limma package was used to screen the differentially expressed genes (DEGs) with the screening criteria of | log2FC |> 1 and adjusted P < 0.05 [9] to obtain the expression of genes in each sample.

2.4 Enrichment analysis of DEGs

The obtained DEGs were further collated and analyzed, and the analysis of DEGs was divided into (GO) enrichment analysis including: biological processes (BP), cellular components (CC), and molecular functions (MF). To understand the relevant functions of each DEGs, we performed GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEGs using R software, and plotted bubble maps of GO analysis and KEGG pathway maps for up- and down-regulation of differential genes, respectively, with adjusted P < 0.05 was considered statistically significant.

2.5 Gene screening for prognostic differences in gastric cancer

To further analyze the relationship between the expression of DEGs in normal and GC tissues and the prognosis of GC patients, raw counts of RNA sequencing data and corresponding clinical information for 145 gastric cancer tumors were obtained from The Cancer Genome Atlas (TCGA) dataset (https://www.cancer.gov/) by expression analysis of highly expressed genes in tumor tissues. Log rank was used to examine KM survival analysis to compare survival differences between the above samples, and timeROC analysis was performed to compare the predictive accuracy and risk scores of the differential genes. All the above analysis methods and R packages were run using (version: 4.0.5) R software and p < 0.05 was considered statistically significant ^{[11, 22]}.

3.1 Data processing in GC patient tissue and normal tissue samples

By performing between-sample analysis of the gene expression profile of GC clinical samples (GSE118897), the median line in the box line plot was at the same position and the tumor tissue (10) was at the same level as the normal tissue (10) in the sample comparison, where the numbers in the plot are even for normal tissue and odd for gastric cancer tissue, Fig. 2.

3.2 Screening of differentially expressed genes

To further investigate the intrinsic differences between GC tissues and normal tissues, we performed Fold change and P-value correction on GSE118897 gene microarray data and then plotted volcanoes. 90 DEGs were obtained (normal control/GC group) of which 33 were up-regulated and 57 were down-regulated in the normal group compared to the tumor group as shown in Table 1. The differential gene expression between the two groups is shown in Fig. 3. The expression of differential genes in the two groups is shown in Fig. 3. The differential genes were analyzed by R software and heat map plotting was performed using Limma package as shown in Fig. 4 to obtain the expression trends of differential genes in different tissues.

Table 1

Up-regulated and down-regulated differential genes
Gene	Up/Down	Gene	Up/Down	Gene	Up/Down
CCDC110	Up	CLDN1	Down	MSLN	Down
DUSP19	Up	RTKN2	Down	TICRR	Down
LRRC17	Up	CKAP2L	Down	CXCL9	Down
MYRIP	Up	SHCBP1	Down	DEPDC1B	Down
CCKAR	Up	RAD54L	Down	FRAS1	Down
INSIG1	Up	CENPI	Down	BGN	Down
MT1B	Up	PRC1	Down	DTL	Down
DNER	Up	SPAG5	Down	CCNE1	Down
AQP4	Up	TRIP13	Down	IGF2BP3	Down
BVES	Up	APOC1	Down	MYBL2	Down
ZNF385B	Up	KIF2C	Down	SFN	Down
CBLIF	Up	F2RL2	Down	BAAT	Down
TMED6	Up	MELK	Down	CXCL10	Down
CPA2	Up	TMEM158	Down	TRIM31	Down
HOMER2	Up	MMP1	Down	CHI3L1	Down
C16ORF89	Up	TOP2A	Down	PBK	Down
ETNPPL	Up	CDK1	Down	VIL1	Down
TRIM50	Up	PLK1	Down	TRIM15	Down
FREM2	Up	MYEOV	Down	CEACAM7	Down
ANGPTL3	Up	NUSAP1	Down	LGR5	Down
CCKBR	Up	CDC6	Down	CCL20	Down
MT1A	Up	MMP12	Down	OLFM4	Down
MT1G	Up	TREM2	Down	NMUR2	Down
CA9	Up	ESM1	Down	CXCL8	Down
DPT	Up	SKA3	Down
FGA	Up	NDC80	Down
ERO1B	Up	MKI67	Down
CLIC6	Up	EPHB2	Down
MAMDC2	Up	MFAP2	Down
GKN1	Up	BUB1	Down
ALB	Up	THBS2	Down
MFSD4A	Up	IQGAP3	Down
CAPN13	Up	KNL1	Down

3.3 Function and pathway enrichment analyses of DEGs

Functional enrichment analysis of 90 differential genes was performed using R software, and the results are shown in Fig. 5. dEGs are mainly involved in cellular value addition and division, and their biological processes include DNA replication, nuclear transcription, mRNA metabolism, etc. Molecular functions include protein binding and chemokine activity. KEGG pathway enrichment analysis showed that DEGs were mainly involved in TNF signaling pathway, IL-17 signaling pathway, cell cycle, P53 signaling pathway, mineral uptake and other processes (Fig. 3b), among which the highly expressed down-regulated genes in gastric cancer tissues were found to be mainly involved in cell proliferation and division by GO enrichment analysis, which may be related to cancer cell metastasis This may be related to cancer cell metastasis, tumor tissue growth. The enrichment analysis of KEGG pathway revealed significant differences in inflammation-related pathway and cell cycle pathway, and the results of the enrichment analysis showed that 57 differentially expressed genes in GC tissues may have genes that promote the regulation of cancer cell value addition and metastasis risk.

3.4 Further screening of prognostic key genes for gastric cancer using statistical analysis

To further elucidate the relationship between the highly expressed genes in GC and prognosis, we passed 57 selected DEGs through expression analysis and prognostic assessment in GSE118897, and we found that high expression of CEACAM7, THBS2, TREM2, MFAP2, BGN, MMP1 and NUSAP1 (Table 2)was associated with prognosis in GC patients, and CEACAM7, THBS2, TREM2, MFAP2, BGN, MMP1 and NUSAP1 were significantly overexpressed in the GC group compared with the control group, and the results are shown in Fig. 6. In order to verify the prognostic significance of these genes, we performed ROC curves and AUC analysis of the seven differential genes at different times, and all seven differential genes were statistically significant as shown in Fig. 7. The clinical data of 145 GC patients in the TCGA dataset were used to analyze the expression and survival time and survival status of the seven differential genes, and the trend of gene expression in the scatter plot was from low to high, as shown in Fig. 8. By analyzing 145 gastric cancer patients in the TCGA database, we found that CEACAM7, THBS2, TREM2, MFAP2, BGN, MMP1 and NUSAP1 were highly expressed in tumor tissue while affecting patient survival see Fig. 9.

Table 2

Identification of 7 key genes in gastric cancer by bioinformatics analysis
Gene ID	Ensembl ID	Gene Description
CEACAM7	ENSG00000007306	This gene encodes a cell surface glycoprotein and member of the carcinoembryonic antigen (CEA) family of proteins.
THBS2	ENSG00000186340	The protein encoded by this gene belongs to the thrombospondin family.
TREM2	ENSG00000095970	This gene encodes a membrane protein that forms a receptor signaling complex with the TYRO protein tyrosine kinase binding protein.
MFAP2	ENSG00000117122	Microfibrillar-associated protein 2 is a major antigen of elastin-associated microfibrils and a candidate for involvement in the etiology of inherited connective tissue diseases.
BGN	ENSG00000182492	This gene encodes a member of the small leucine-rich proteoglycan (SLRP) family of proteins.
MMP1	ENSG00000196611	This gene encodes a member of the peptidase M10 family of matrix metalloproteinases (MMPs).
NUSAP1	ENSG00000137804	NUSAP1 is a nucleolar-spindle-associated protein that plays a role in spindle microtubule organization.

GC is still a disease with a high prevalence and mortality rate. Its treatment is mostly based on surgery, supplemented by radiotherapy, chemotherapy, targeted therapy, and gene therapy. However, its survival rate is still very low, with a 5-year survival rate still below 30% ^[12]. In this regard, it is important to study GC progression as well as pathogenesis in order to guide drug interventions, suggest prognosis or improve survival. The rapid development of platforms focusing on high-throughput detection of gene expression in disease progression has provided new research directions and new preventions for cancer diagnosis, treatment and prognosis. In this study, a total of 90 DEGs were screened, including 33 up-regulated genes and 57 down-regulated genes. These up-regulated genes are mainly involved in ion transport in biological processes and response to viruses, while the down-regulated genes are mainly involved in cell proliferation and division. Among these genes, seven genes were highly expressed in tumor tissues and significantly associated with poor overall survival in GC patients, including CEACAM7, THBS2, TREM2, MFAP2, BGN, MMP1 and NUSAP1. The pathogenesis of cancer is multifactorial and is associated with the interaction of genetic, environmental and lifestyle factors. Among them CEACAM7 gene it is a member of a specific CEA gene family of carcinoembryonic antigen (CEA) family genes, CEA cell adhesion molecule-7 (CEACAM-7), which is regulating normal cell differentiation ^[13].Abnormalities in CEACAM-7 expression have been shown to occur early in colorectal tumorigenesis; changes in the expression of adenomas, proliferative polyps ^{[13, 14]} .

So far, there is little information on the expression of CEACAM7 in gastric non-neoplastic and neoplastic lesions. Meanwhile, related studies have shown that the expression of CEACAM7 is closely related to the differentiation of gastric cancer. the expression of CEACAM7 gradually increases during the development of gastric cancer and can be used as a prognostic predictor for gastric cancer patients after surgery ^{[15, 21]}. THBS2 is platelet-responsive protein 2, a protein that may regulate the cell surface properties of mesenchymal cells and is involved in cell adhesion and migration, and some studies have shown that that patients with low THBS2 expression have a better prognosis and may be studied as a signature protein for gastric cancer prognosis ^[15]. Trigger receptor expressed on myeloid cells 2 (TREM2) is a member of the immunoglobulin superfamily, which can form a complex of signaling pathways with TYRO protein tyrosine kinase binding proteins on the cell membrane, and one study found significant differences in the expression of TREM2 in gastric cancer and paraneoplastic tissues, and TREM2 in prognostic role in GC found that TREM2 inhibits T cell proliferation and is a negative regulator of tumor immunity,It was also found that TREM2 expression was higher in lung cancer patients than in healthy individuals, and TREM2 expression may be a potent prognostic biomarker for GC ^[16].

Recently, MFAP2 has been found to regulate the deposition of pro-elastin into microfibrils and participate in the formation of elastic fibers. It is thought to be a co-expressed gene of the NF-kB/Snail/YY1/RKIP circuit, with upregulated expression in tumor tissues; the extent of this upregulation is specific evidence of lymph node metastasis, and MFAP2 expression is significantly elevated in gastric tumor tissues compared to adjacent normal tissues, correlating with poor prognosis in GC patients ^[17].In a study by Chen et al. BGN overexpression was found to be associated with GC poor prognosis and resulted in elevated levels of immune infiltration of cytotoxic cells, DCs, macrophages, neutrophils, Th17 cells, and Th2 cells ^[18], revealing that BGN may be a potential GC biomarker. Metallo-matrix proteinase 1 (MMP1),a member of the zinc-dependent endopeptidase family, has been shown to be closely associated with the migration and invasion of many cancers ^[19]. Recent studies have found that MMP1 can also promote the proliferation of cancer cells, and in a study by Liu et al. it was demonstrated that MMP1 can promote the proliferation and metastasis of hepatocellular carcinoma cells ^[20], while the poor prognostic outcome of high MMP1 expression in gastric cancer tissues in the present study suggests that MMP1 may become a signature gene for GC prognosis. NUSAP1 is a binding protein of Yes-associated protein1 (YAP1), which is a major player in the Hippo pathway and has an important role in tumor initiation, progression and metastasis in many cancers, including GC. The pro-oncogenic effect of NUSAP1 on tumor cell growth, migration and invasion was found to be mainly regulated by YAP1. Meanwhile, the aberrant expression of NUSAP1 and YAP1 was highly correlated in tumor cell lines and tissues. Therefore the role of NUSAP1 in the development of gastric carcinogenesis is significant, and thus a new target for GC therapy ^[20].

The current study identified seven key genes that may be associated with the development of GC by bioinformatics methods. The results of the current study provide valuable indicators for biomarker studies of GC metastasis. The current study provides a comparative study of gene expression in normal and GC tissues. The current study used TCGA and KM Plotter data to validate the initial identification of tumor biomarker genes of research value. Finally, we identified seven genes (CEACAM7, THBS2, TREM2, MFAP2, BGN, MMP1 and NUSAP1) associated with poor GC prognosis for further study, which may be potential targets for GC therapy and may be useful biomarkers for diagnosis and prognosis. However, since our study was performed on the basis of data analysis, further experiments are needed to confirm the results of such predictions in GC.

Ethical Approval

This study does not involve ethical approval.

Consent to Participate

All authors of this study Consent to participate.

Consent to Publish

All authors of this study Consent to Publish.

Author Contribution

FSY: methodology, software, validation, writingoriginaldraft, writing-review & editing.QF: project administration. LJ: visualization.LB:supervision.LK: project administration, supervision. DYP: project administration, funding acquisition.

Funding

This work was supported by the Natural Science Foundation of China of Liaoning Province (2020-MS-246); the Scientific Research Funding Project of the Liaoning Provincial Department of Education (LJKZ0980,LZ2019069, LZ2020016) and the technology platform project of the Educational Department of Liaoning Province-evaluation and transformation of traditional Chinese and Western medicine in critical disease; National Training Program for College Students’ Innovation and Entrepreneurship (202110161006X, 202010161004X, 201910161064, S202010161001X, 2017161025).

Competing Interests

The authors made no disclosures. The authors declare no conflict of interest.

Availability of data and materials

The data and materials in this study are available and feasible.

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No competing interests reported.

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Identification of 7 key genes as biomarkers for prognosis and metastasis of gastric cancer

Status:

Version 1

Abstract

Background

Methods

Results

Figures

1. Introduction

2. Material And Methods

2.1 Microarray data

2.2 Clinical data screening and analysis

2.3 Obtaining differentially expressed genes (DEGs)

2.4 Enrichment analysis of DEGs

2.5 Gene screening for prognostic differences in gastric cancer

3. Results

3.1 Data processing in GC patient tissue and normal tissue samples

3.2 Screening of differentially expressed genes

3.3 Function and pathway enrichment analyses of DEGs

3.4 Further screening of prognostic key genes for gastric cancer using statistical analysis

4. Discussion

5. Conclusions

Declarations

References

Additional Declarations

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