Figure 1.
The box diagram of distribution of standardized data. Blue bar represents GC tissues, and yellow bar represents normal tissue.
This study aimed to identify significant immune microenvironment-related competing endogenous RNA (CeRNA) regulatory axis in gastric cancer (GC). Analysis of differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and lncRNAs (DElncRNAs) was performed for the microarray datasets. After abundance analysis of immune cell's infiltration, immune-related mRNAs and lncRNAs were obtained. Meanwhile, according to the Pearson correlation coefficient between immune-related mRNAs and lncRNAs, the co-expression mRNA-lncRNA pairs were screened. Furthermore, the target genes of co-existance miRNAs were predicted, and miRNA-lncRNA pairs were identified. Finally, the lncRNA-miRNA and miRNA-mRNA relationship regulated by the same miRNA was screened. Combining with the co-expression relationship between lncRNA and mRNA, the CeRNA network was constructed. In abundance analysis of immune cell's infiltration, a total of eight immune cells were obtained, in addition, 83 immune-related DElncRNAs and 705 immune-related DEmRNAs were screened. KEGG pathway enrichment analysis showed that these mRNAs were mainly involved in PI3K-Akt signaling pathway and human papillomavirus infection, while lncRNA were relevant to gastric acid secretion. A total of 25 miRNAs were significantly associated with immune-related mRNAs, such as hsa-miR-148a-3p, hsa-miR-17-5p, and hsa-miR-25-3p. From the mRNA-miRNA-lncRNA CeRNA network, we observed that AC104389.28─miR-17-5─SMAD5 axis and LINC01133─miR-17-5p─PBLD axis played a crucial role in the development of GC. Furthermore, resting memory CD4 T cells and plasma cells were closely associated with the pathogenesis of GC, and these immune cells might be affected by the key genes. The present study identified key genes that associated with immune microenvironment in GC, providing potential molecular targets for immunotherapy of GC.
Citation: Jie Chen, Jinggui Chen, Bo Sun, Jianghong Wu, Chunyan Du. Integrative analysis of immune microenvironment-related CeRNA regulatory axis in gastric cancer[J]. Mathematical Biosciences and Engineering, 2020, 17(4): 3953-3971. doi: 10.3934/mbe.2020219
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This study aimed to identify significant immune microenvironment-related competing endogenous RNA (CeRNA) regulatory axis in gastric cancer (GC). Analysis of differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and lncRNAs (DElncRNAs) was performed for the microarray datasets. After abundance analysis of immune cell's infiltration, immune-related mRNAs and lncRNAs were obtained. Meanwhile, according to the Pearson correlation coefficient between immune-related mRNAs and lncRNAs, the co-expression mRNA-lncRNA pairs were screened. Furthermore, the target genes of co-existance miRNAs were predicted, and miRNA-lncRNA pairs were identified. Finally, the lncRNA-miRNA and miRNA-mRNA relationship regulated by the same miRNA was screened. Combining with the co-expression relationship between lncRNA and mRNA, the CeRNA network was constructed. In abundance analysis of immune cell's infiltration, a total of eight immune cells were obtained, in addition, 83 immune-related DElncRNAs and 705 immune-related DEmRNAs were screened. KEGG pathway enrichment analysis showed that these mRNAs were mainly involved in PI3K-Akt signaling pathway and human papillomavirus infection, while lncRNA were relevant to gastric acid secretion. A total of 25 miRNAs were significantly associated with immune-related mRNAs, such as hsa-miR-148a-3p, hsa-miR-17-5p, and hsa-miR-25-3p. From the mRNA-miRNA-lncRNA CeRNA network, we observed that AC104389.28─miR-17-5─SMAD5 axis and LINC01133─miR-17-5p─PBLD axis played a crucial role in the development of GC. Furthermore, resting memory CD4 T cells and plasma cells were closely associated with the pathogenesis of GC, and these immune cells might be affected by the key genes. The present study identified key genes that associated with immune microenvironment in GC, providing potential molecular targets for immunotherapy of GC.
Studies have shown that the incidence of gastric cancer (GC) is decreasing worldwide [1]. In 2018, with the emergence of more than 1, 000, 000 new cases, GC becomes the fifth most common cancer. Besides, GC is regarded as the third leading cause of cancer death with 783, 000 deaths in 2018 [2]. For Chinese people, GC continues to be the primary cause of the cancer burden [3]. For the purpose to maximally reduce locoregional recurrence of advanced GC, gastrectomy and dissection of lymph node are the standard surgery for the treatment of GC [4]. Although the serum carbohydrate antigen, pepsinogen, and carcinoembryonic antigen have been used to diagnose GC, the detection rate is only 5% in the early stage due to lack of symptoms [5]. Therefore, the exploration of developmental mechanism and therapeutic targets of GC is imperative.
In cancer development, evading immune surveillance has become a sign of biological competence [6]. In the cancerous tissues, the host immune system can recognize cancer cells and destroy them, suggesting that the evasion of tumor cells from the destruction of the immune system is significant in the process of tumor development [7]. The clinical course of the disease often determined by the ability of the tumor to evade recognition by the immune system [8]. With the discovery of infiltrating immune cells in various malignancies, immune parameters may have significance for the prognosis assessment of cancer patients [9]. Liu et al [10] revealed that immune cells contributed to determining the prognosis of GC, and high TCD68+/SCD68+ ratio and TCD8+/TFoxp3+ ratio were associated with improved overall survival, which were promising independent predictors for overall survival in GC. Additionally, tumor-infiltrating immune cells could be used reinforce the clinical outcome prediction ability of the TNM staging system and provide a convenient tool for treatment selection for patients with GC [11]. However, the relevant bio-molecular mechanisms of the immune microenvironment in GC have not been fully elucidated.
Long non-coding RNA (lncRNA) has been widely investigated in a variety of diseases [12]. Studies have shown that lncRNAs can be regarded as potential therapeutic targets for cancer treatment, which regulate signal transduction pathways involved in tumor proliferation, metastasis, migration, apoptosis, and drug resistance [13,14,15]. The lncRNA has recently received increasing attention due to it functions as competing endogenous RNA (CeRNA) to hinder miRNA functions that participate in post-transcriptional regulatory networks in tumors, revealing that lncRNA and mRNA can hinder miRNA function by virtue of shared microRNA response elements (MREs) as natural miRNA sponges [16]. Growing evidences have reported the role of CeRNA in the development of cancer. Luan et al [17] reported that lncRNA XLOC_006390 might serve as a CeRNA that reversely regulated the expression of miR-331-3p and miR-338-3p, thus promoting cervical cancer tumorigenesis and metastasis. Additionally, previous study has indicated that CeRNA might serve as diagnostic biomarkers or therapeutic targets for GC [18]. Chen et al [19] revealed that lncRNA LINC01234 contributed to GC by competitively binding with miR-204-5p to regulated core-binding factor subunit beta (CBFB) expression, suggesting these genes might be considered as novel targets for GC diagnosis and therapy. Meanwhile, lncRNA HNF1A-AS1 induced GC progression by competitively binding miR-661 with cell division cycle 34 (CDC34), and provided that non-coding RNA might promote the progression of GC by regulating the cell cycle [20]. Nevertheless, there remain insufficient comprehensive analyses on the lncRNAs and miRNAs related to GC on the basis of immune cells. In our study, microarray datasets of GC were downloaded from the public database. Then, differentially expressed mRNA (DEmRNA), differentially expressed miRNA (DEmiRNA), and differentially expressed lncRNA (DElncRNA) between GC tissues and control tissues were screened. Meanwhile, immuno-infiltration analysis was conducted to identify the differential immune cell subset between GC tissues and control tissues. The relationship between lncRNA/mRNA and immune cell was analyzed. Besides, the lncRNA-mRNA pairs and miRNA-lncRNA pairs were respectively predicted. Based on these interactions, the immune cell-related mRNA-miRNA-lncRNA CeRNA network was constructed. Importantly, the proposed immune-related network might allow a better understanding of the regulatory mechanism of CeRNA mediated by lncRNA in the pathogenesis and prognosis of GC.
After a careful review of the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database [21], we finally selected four microarray datasets (GSE65801, GSE23739, GSE26595, and GSE30073) to screen the differentially expressed genes between GC tissues and normal tissues, and raw data were downloaded in October 2019. The microarray dataset GSE65801 [22] based on GPL14550 Agilent-028004 SurePrint G3 Human GE 8x60K Microarray (Probe Name Version) platform was obtained from GEO database, and composed of the lncRNA/mRNA data of 32 GC tissues and 32 normal tissues. Among these, GC tissues were taken from untreated patients with primary gastric carcinomas, and who underwent D2 radical gastrectomy. Microarray dataset GSE23739 [23] was derived from GPL7731 platform (Agilent-019118 Human miRNA Microarray 2.0 G4470B (Feature Number version)), and contained miRNA expression data of 40 GC tissues and 40 normal tissues. The primary gastric tumors and adjacent normal gastric tissues were obtained from the National Cancer Centre Singapore, Singapore, and the Singhealth Tissue Repository. Microarray dataset GSE26595 [24] based on GPL8179 Illumina Human v2 MicroRNA expression beadchip platform was downloaded from GEO database, and included miRNA data of 60 GC tissues and eight normal tissues. The clinical features of patients in this dataset are listed in Supplementary Table 1. Furthermore, the platform for GSE30073 [25] was GPL13742 Agilent-015868 Human miRNA Microarray (miRBase release 9.0 Feature Number version)], and this dataset contained miRNA expression data of 90 GC samples and 34 normal samples. The clinico-pathological characteristics of patients are shown in Supplementary Table 2.
In the expression profile analysis of the mRNA/lncRNA, the processed and standardized probe expression matrix file (GSE65801_series_matrix.txt) was downloaded from the GEO database; meanwhile, all probes sequencing in GPL14550 Agilent-028004 SurePrint G3 Human GE 8x60K Microarray (Probe Name Version) platform were also downloaded, and human reference genome (GRCh38) was obtained from GENCODE [26] database (https://www.gencodegenes.org/releases/current.html). All probe sequences were aligned to the reference genome by using seqmap software [27]. Briefly, the uniquemap probes were reserved, then, using its position on the chromosome and positive and negative strand information, the genes corresponding to each probe were obtained according to the human gene annotation file (Release25). Specifically, reserving the probes with annotation information "protein_coding" as corresponding mRNA probes; while retaining the probes with annotation information "antisense", "sense_intronic", "lincRNA", "sense_overlapping", "3prime_overlapping_ncRNA", "bidirectional_promoter_lncRNA", "macro_lncRNA" or "processed_transcript" as the corresponding lncRNA probes. The probes matching to mRNA/lncRNA (Genesymbol) were retained, while probes that not matching to Genesymbol were removed. The average values of the different probes were selected as the final expression value of mRNA/lncRNA when different probes matching to the same gene.
In the expression profile analysis of miRNA (GSE23739, GSE26595, and GSE30070), the original probe signal value file was downloaded from GEO database, and was preprocessed by using limma [28] package, including background correction, robust multi-array average normalization, and calculation of expression value. Additionally, the annotation file corresponding to platform was downloaded. Firstly, the miRNA ID was mapped to the miRNA version (v22) of the mirBase database (http://www.mirbase.org/)[29] to obtain the miRNA name. Then, the probes were mapped to miRNA. The mean value of different probes was considered as the final expression value of miRNA when the multiple probes corresponding to the same miRNA name, while probes without corresponding miRNA name were deleted.
The classical Bayesian method in limma package (Version 3.10.3, http://www.bioconductor.org/packages/2.9/bioc/html/limma.html) [28] was applied to perform differential expression analysis, including DEmRNA and DElncRNA in GSE65801, as well as DEmiRNA in GSE23739, GSE26595, and GSE30073. The p value was adjusted by the Benjamini/Hochberg test. The screening threshold of DEmRNA and DElncRNA was adjusted p value < 0.05 and |log fold change (FC)| > 1, whereas the adjusted p value < 0.05 and |logFC| > 0.585 were considered as the thresholds of DEmiRNA.
Based on the gene expression matrix, the algorithm of CIBERSORT deconvolution [30] was utilized to estimate the abundance of immune infiltration. The gene expression feature template was provided by the LM22 dataset of CIBERSORT, which contained 22 immune-related cells. After obtaining the infiltration abundance of 22 types of cells in each sample, the samples with p value < 0.05 were selected. The differentially analysis was conducted by T test to explore the immune-related cells between GC tissues and normal tissues, and p value < 0.05 was considered as statistically significant. Besides, ggplot2 (Version: 3.2.1) in R package was used to constitute the landscape histogram of the abundance of immune cell infiltration. The boxplot in R package (version 0.3.2) was used to constitute the violin plot of differentially infiltrated immune cells.
Based on the obtained differential immune cells, the cor. test of R language was used to respectively calculate the spearman correlation coefficient between DEmRNA-immune cells and DElncRNA- immune cells, which conducted with the significance test. The immune-related mRNAs/lncRNAs were screened out with the threshold of p-value < 0.05 and correlation coefficient |r| > 0.6.
Utilizing the Clusterprofiler tool [31] in R package (Version:3.8.1, http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses was performed for the immune-related DEmRNAs, and p value < 0.05 and count ≥ 2 were defined as the cut-off criteria.
Utilizing the cor.test of R language, the Pearson correlation coefficient between the immune-related mRNAs and immune-related lncRNAs was calculated and conducted with the significance test. The threshold value of significantly correlated mRNA-lncRNA pairs was r value > 0.8 and p-value < 0.05. Cytoscape software (version 3.4.0, http://chianti.ucsd.edu/cytoscape-3.4.0/) [31] was applied to structure the co-expressed network of lncRNA-mRNA.
Meanwhile, the mRNAs were served as the potential target genes of lncRNAs, and the KEGG pathway enrichment analysis of mRNA was conducted by using ClusterProfiler in R package. P-value < 0.05 and the number of enriched genes ≥ 2 were considered as significant enrichment results, which indirectly predicted the function of lncRNA.
The up-regulated miRNAs and down-regulated miRNAs in three sets of GSE23739, GSE26595, and GSE30070 datasets were respectively intersected, and miRNAs with differences in at least two datasets were selected for further analysis. Online tool MiRWalk3.0 [32] (http://mirwalk.umm.uni-heidelberg.de/search_genes/) was used to predict the target genes for the DEmiRNAs. The mRNA that existed at least in TargetScan or miRDB was retained. Then, these genes were overlapped with immune-related mRNA to obtain miRNA-mRNA pairs, and the miRNA-target regulatory network was constructed using Cytoscape software. Similarly, ClusterProfiler was used to conduct KEGG pathway enrichment analysis of the target genes in miRNA-mRNA relationship, which was indirectly defined as the pathway enrichment analysis results of miRNA. The screening threshold of significantly enriched pathways was p-value < 0.05.
According to the previously obtained immune-related lncRNAs and DEmiRNAs, the Miranda (v3.3a) [33] software (parameters: -sc 140, -en -20), was used to obtain the miRNA-lncRNA relationship. Combined with the miRNA-mRNA pairs, the lncRNA-miRNA-mRNA pairs regulated by the same miRNA were screened. Meanwhile, based on the correlation coefficients > 0.95 of co-expressed lncRNA-mRNA pairs, the lncRNA-miRNA-mRNA pairs were further screened to establish the CeRNA network. Among these, lncRNA and mRNA with positive co-expression relationship regulated by the same miRNA were CeRNA with each other.
In the GSE65801, a total of 42545 probes were downloaded from the platform. Compared to the genome, 32054 probes were obtained. Among these, 5159 lncRNA probes were retained, corresponding to 2870 lncRNA; while 24890 mRNA probes were reserved, corresponding to 16325 mRNA. In the GSE23739, a total of 15744 probes were obtained from the platform, among these, 11570 probes were human miRNA probes, and these probes corresponding to 713 miRNA. In the GSE26595, a total of 1146 miRNA probes were extracted from the platform, which corresponding to 348 miRNA. In the GSE30070, a total of 2296 miRNA probes were obtained, which corresponding to 407 miRNA. Distribution of standardized data showed that the median was roughly on the same horizontal line (Figure 1), which could be used for the further analysis.
According to the screening criteria, a total of 1637 DEmRNAs (868 up-regulated and 769 down-regulated) and 218 DElncRNAs (81 up-regulated and 137 down-regulated) were identified in GSE65801; total 218 DEmiRNAs, including 104 DEmiRNAs (60 up-regulated and 44 down-regulated) in GSE23739, 62 DEmiRNAs (34 up-regulated and 28 down-regulated) in GSE26595, and 52 DEmiRNAs (27 up-regulated and 25 down-regulated) in GSE30070, were screened between GC tissues and normal tissues. The bidirectional clustering heatmap (Figure 2) and volcano map (Figure 3) of DEmRNA, DElncRNA, and miRNAs were displayed.
According to the screening threshold, a total of 48 samples (24 tumor samples and 24 normal samples) were selected for the analysis of infiltrating abundance. The histograms of abundance of immune cell infiltration in each sample are shown in Figure 4A. Meanwhile, eight types of immune cells, including plasma cells, naive CD4 T cells, CD8 T cells, gamma delta T cells, resting memory CD4 T cells, M0 Macrophages, M2 Macrophages, and activated dendritic cells, had significantly differential infiltration between tumor and normal tissues (Figure 4B).
Correlation analysis was conducted between mRNA/lncRNA with eight immune cells. According to the threshold, a total of 83 immune-related lncRNAs and 705 immune-related mRNAs were screened. KEGG pathway enrichment analysis of 705 immune-related mRNA showed that a total of 33 pathways were obtained (Figure 5). These mRNAs were mainly involved in pathways such as PI3K-Akt signaling pathway, human papillomavirus infection, and Neuroactive ligand-receptor interaction.
The co-expression of DEmRNA and DElncRNA was performed, a total of 3558 significantly co-expression mRNA-lncRNA pairs, 432 DEmRNAs, and 72 DElncRNAs were obtained. The network of co-expressed mRNA-lncRNA pairs with Pearson correlation > 0.9 is displayed in Figure 6A.
KEGG pathway enrichment analysis was performed on target genes of 72 lncRNAs. According to the threshold, 49 enriched pathways were obtained. As shown in Figure 6B, the top 10 enrichment pathway were exhibited. These lncRNA primarily participated in gastric acid secretion, tyrosine metabolism, collecting duct acid secretion, and drug metabolism-cytochrome P450.
According to the intersection analysis, there were 28 miRNA (14 up-regulated, 14 down-regulated) were selected at least in two databases (Figure 7C). The mirwalk3.0 was used to predict the target genes of 28 miRNAs, and 4088 miRNA-mRNA relationships were obtained. Through an intersection analysis of 705 immune-related mRNAs, 163 miRNA-mRNA pairs (25 miRNAs and 106 mRNAs) were finally obtained (Figure 7B). Besides, KEGG pathway enrichment analysis of 25 miRNAs was performed, the enrichment pathways of 3 miRNA (hsa-miR-148a-3p, hsa-miR-17-5p, and hsa-miR-25-3p) are shown in Figure 7A. Among these, hsa-miR-17-5p was principally involved in ras signaling pathway and breast cancer pathways.
A total of 576 lncRNA-miRNA relationship pairs were predicted which composed of 69 lncRNAs and 27 miRNAs. Finally, a number of 206 lncRNA-miRNA-mRNA relationships were obtained that contained 135 lncRNA-miRNA pairs, 47 miRNA-mRNA pairs, and 122 lncRNA-mRNA relationships. Meanwhile, Cytoscape was used to construct the CeRNA network, which consisted of 15 miRNAs, 27 mRNAs, and 35 lncRNAs (Figure 8). Among these, several mRNAs targeted by hsa-miR-17-5p, such as epidermal growth factor receptor (EGFR), E2F transcription factor 3 (E2F3), phenazine biosynthesis like protein domain containing (PBLD), and SMAD Family Member 5 (SMAD5). Moreover, lncRNAs such as LINC00534 (degree = 24), DRAIC (degree = 20), and AC104389.28 (degree = 18) with higher degrees, and might be considered as hub nodes.
Despite the decreased incidence of GC, the prognosis of GC remains poor. Recently, the introduction of modern immunotherapy, especially the use of immune checkpoint inhibitors, has improved the prognosis of many types of cancers [34]. Therefore, understanding the complex relationship between host's immune microenvironment and tumors, as well as the immune-related molecular mechanisms that lead to development and progression of tumor will be helpful to identify prognostic immune markers. In this study, we found eight types of immune cells were closely associated with the development of GC, such as CD8 T cells, naï ve CD4 T cells, resting memory CD4 T cells, gamma delta T cells, M0 Macrophages, and M2 Macrophages. A total of 83 immune-related lncRNAs and 705 immune-related mRNAs were obtained. KEGG pathway enrichment analysis showed that these mRNAs were mainly involved in PI3K-Akt signaling pathway and human papillomavirus infection, while lncRNA were relevant to gastric acid secretion. Meanwhile, 25 miRNAs were significantly associated with immune-related mRNAs, such as hsa-miR-148a-3p, hsa-miR-17-5p, and hsa-miR-25-3p. From the mRNA-miRNA-lncRNA CeRNA network, we observed that AC104389.28─miR-17-5─SMAD5 axis and LINC01133─miR-17-5p─PBLD axis played crucial roles in the development of GC.
Drosophila mothers against decapentaplegic (SMADs) gene had three subclasses, [receptor-regulated SMADs (R-SMADs), common mediator SMADs, and inhibitory SMADs], which had been found in mammals [35]. R-SMADs could be subdivided into two subtypes according to the process of phosphorylation, which could be generated by transforming growth factor-beta or by bone morphogenetic protein. Via probably up-regulated R-SMADs (SMADs1, 5, 8), BMP mediated apoptosis actively in the limb of embryonic [36]. The study also showed that SMAD5 was mediated apoptosis of gastric epithelial cells caused by H. pylori infection [37]. In GC cells, apoptosis was generally inhibited, although proliferation was still accelerating [37]. MiR-17-5p was belonged to miR-17-92 cluster [38]. Compared with normal tissues, the expression level of miR-17-5p was higher in pancreatic cancer, colorectal cancer, glioma, hepatocellular carcinoma, basal cell carcinoma, and GC [38]. By targeting p21 and tumor protein p53-induced nuclear protein 1(TP53INP1), lack of miR-17-5p would influence the growth of AGS cells, in which cell cycle was arrested and apoptosis was increased. Whereas overexpression of miR-17-5p promoted the progression of cell cycle and inhibited the apoptosis of GC cells [39]. In our study, SMAD5 was down-regulated, while miR-17-5p was up-regulated in GC tissues compared with control tissues. Meanwhile, miR-17-5p was bound to lncRNA AC104389.28. Moreover, we observed that SMAD5 and AC104389.28 were associated with the resting memory CD4 T cells. Rohr-Udilova and colleague [40] reported the relationship between resting memory CD4 T cells and hepatocellular carcinoma (HCC), indicating the number of this immune cell was increased in HCC tissues compared with health tissues. Furthermore, Jiang et al [41] showed that resting memory CD4 T cells were relevant to the prognosis of GC, and it was significantly higher in the high-risk group compared with the low-risk group. Taken together, a hypothesis was presented that AC104389.28 might function as a CeRNA in regulating SMAD5 expression of GC through competitively binding to miR17-5p, and SMAD5 played important role in the development of GC by affecting the number of resting memory CD4 T cells.
PBLD, also termed as mitogen-activated protein kinase activator with WD-40 repeats (MAWD) binding protein, was identified from a human liver cDNA library firstly [42]. The transcription activated by TGF-β was inhibited by MAWD, which contributed to protein/protein interactions [43]. More recently, the study proved that down-regulated PBLD could cause gastric carcinogenesis [44]. Through inhibiting TGF-β1-induced Epithelial-Mesenchymal Transition (EMT), the growth and invasion in GC cell-lines were negatively regulated by PBLD [43]. Meanwhile, the previous study showed that miR-17-5p had a pro-proliferative function in the development of cancer cells which was highly regulated in GC tissues [45]. In this study, miR-17-5p was regulated by lncRNA LINC01133. We also found both PBLD and LINC01133 were connected with plasma cells. Plasma cells provided protective immunity against recurring sources of infection [46]. Previous study found that multiple myeloma was characterized by the cloning and amplification of malignant plasma cells [47]. Although there was no literature directly reported the relationship between plasma cells and GC, the role of plasma cells in the development of cancer cannot be ignored. Therefore, we predicted that LINC01133 might function as a CeRNA in regulating PBLD expression of GC through competitively binding to miR-17-5p, and these genes might affect the plasma cells in GC tissues.
In conclusion, a series of bioinformatics analyses were conducted with immune microenviroment in GC patients. Several CeRNA mechanisms were proposed in this study. AC104389.28 might function as a CeRNA in regulating SMAD5 expression through competitively binding to miR-17-5p. Meanwhile, LINC01133 might affect the expression of PBLD with the competitively binding to miR-17-5p. However, these results should be validated and supported by further experimental studies.
This work was supported by The National Natural Science Foundation of China (NSFC)(Program No. 81702356).
The authors declare that they have no competing interests.
| Characteristic | Group 1 (n = 60) | miR-196b high (n = 28) | miR-196b low (n = 29) | P value | HOXA10 high (n = 29) | HOXA10 low (n = 28) | P value |
| Age, yr | |||||||
| Median (range) | 63 (32–83) | 66 (34–78) | 58 (32–83) | 0.134 | 64 (34–83) | 60 (32–78) | 0.134 |
| > 65 | 15 (52) | 9 (32) | 15 (52) | 9 (32) | |||
| Sex | |||||||
| Male | 41 (68) | 19 (68) | 20 (69) | 0.928 | 18 (62) | 21 (75) | 0.294 |
| Histologic type | |||||||
| Diffuse | 27 (45) | 9 | 17 | 0.066 | 8 | 18 | 0.007 |
| Intestinal | 23 (38) | 14 | 9 | 16 | 7 | ||
| Mixed | 10 (17) | 5 | 3 | 5 | 3 | ||
| T stage | |||||||
| T1/T2 | 26 (43) | 10 (36) | 14 (48) | 0.337 | 11 (38) | 13 (46) | 0.516 |
| T3/T4 | 34 (57) | 18 (64) | 15 (52) | 18 (62) | 15 (54) | ||
| N stage | |||||||
| N0/N1 | 36 (60) | 15 (54) | 19 (66) | 0.358 | 16 (55) | 18 (64) | 0.483 |
| N2/N3 | 24 (40) | 13 (46) | 10 (34) | 13 (45) | 10 (36) | ||
| AJCC stage1 | |||||||
| Ⅰ/Ⅱ | 23 (38) | 11 (39) | 12 (41) | 0.872 | 11 (38) | 12 (43) | 0.705 |
| Ⅲ/Ⅳ | 37 (62) | 17 (61) | 17 (59) | 18 (62) | 16 (57) | ||
| 1. Based on the American Joint Committee on Cancer staging manual 6th edition. HOXA10: Homeobox A10; AJCC: American Joint Committee on Cancer. | |||||||
DownLoad:
CSV
| Gastric cancer patient Training set | Proof-of-principle test set (responder) | Healthy volunteer | |
| Number | 82 | 8 | 34 |
| Age - yr | |||
| Median | 56 | 56 | 48 |
| Interquartile range | (44–63) | (44–58) | (43–57) |
| Sex - no. (%) | |||
| Male | 64 (78.0%) | 7 (87.5%) | 23 (67.6%) |
| Female | 18 (22.0%) | 1 (12.5%) | 11 (32.4%) |
| Performance status (PS) - no. (%) | |||
| ECOG1 PS 0 or 1 | 73 (89.0%) | 8 (100%) | |
| ECOG PS 2 or 3 | 9 (11.0%) | 0 | |
| Lauren's intestinal | 34 (41.5%) | 3 (37.5%) | |
| Lauren's diffuse | 48 (58.5%) | 5 (62.5%) | |
| Location of primary lesion - no. (%) | |||
| Upper 1/3 | 11 (13.4%) | 1 (12.5%) | |
| Middle 1/3 | 18 (22.0%) | 5 (62.5%) | |
| Lower 1/3 | 43 (52.4%) | 1 (12.5%) | |
| Entire stomach | 10 (12.2%) | 1 (12.5%) | |
| Chemotherapy regimen - no. (%) | |||
| Cisplatin/Fluorouracil | 80 (97.6%) | 8 (100%) | |
| Cisplatin/Capecitabine | 2 (2.4%) | 0 | |
| *Relative dose intensity - % | |||
| Median | 81.2 | 76.6 | |
| Interquartile range | (75.3–87.3) | (64.7-84.9) | |
| Number of chemotherapy cycles | |||
| Median | 4 | 10 | |
| Interquartile range | (2–5) | (7–11) | |
| Chemotherapy response (WHO criteria) -no (%) | |||
| PR2 | 16 (24.6%) | 6 (100%) | |
| SD3 | 25 (38.5%) | ||
| PD4 | 24 (36.9%) | ||
| Unmeasurable | 16 | 2 | |
| Unevaluable | 1 | ||
| Second-line chemotherapy | 55 (67.1%) | 6 (75.0%) | |
| Median follow-up for survivors | 35.5 months | - | |
| Overall survival - mo. | |||
| Median | 8.2 | 16 | |
| Interquartile range | (6.8-10.5) | (11.3-26.7) | |
| Time to progression - mo. | |||
| Median | 3.1 | 8.2 | |
| Interquartile range | (2.5–3.9) | (4.3–21.2) | |
| 1Eastern Cooperative Oncology Group, 2partial response, 3stable disease, 4progressive disease. *Relative dose intensity *Mean of relative dose intensities of cisplatin and fluorouracil. Dose intensity is defined as the amount of drug administered per unit of time, expressed as milligrams per square meter per week. Relative dose intensity is defined as the actual dose intensity relative to the planned dose intensity of each drug. | |||
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Figures(8) / Tables(2)
Jie Chen, Jinggui Chen, Bo Sun, Jianghong Wu, Chunyan Du. Integrative analysis of immune microenvironment-related CeRNA regulatory axis in gastric cancer[J]. Mathematical Biosciences and Engineering, 2020, 17(4): 3953-3971. doi: 10.3934/mbe.2020219
| Characteristic | Group 1 (n = 60) | miR-196b high (n = 28) | miR-196b low (n = 29) | P value | HOXA10 high (n = 29) | HOXA10 low (n = 28) | P value |
| Age, yr | |||||||
| Median (range) | 63 (32–83) | 66 (34–78) | 58 (32–83) | 0.134 | 64 (34–83) | 60 (32–78) | 0.134 |
| > 65 | 15 (52) | 9 (32) | 15 (52) | 9 (32) | |||
| Sex | |||||||
| Male | 41 (68) | 19 (68) | 20 (69) | 0.928 | 18 (62) | 21 (75) | 0.294 |
| Histologic type | |||||||
| Diffuse | 27 (45) | 9 | 17 | 0.066 | 8 | 18 | 0.007 |
| Intestinal | 23 (38) | 14 | 9 | 16 | 7 | ||
| Mixed | 10 (17) | 5 | 3 | 5 | 3 | ||
| T stage | |||||||
| T1/T2 | 26 (43) | 10 (36) | 14 (48) | 0.337 | 11 (38) | 13 (46) | 0.516 |
| T3/T4 | 34 (57) | 18 (64) | 15 (52) | 18 (62) | 15 (54) | ||
| N stage | |||||||
| N0/N1 | 36 (60) | 15 (54) | 19 (66) | 0.358 | 16 (55) | 18 (64) | 0.483 |
| N2/N3 | 24 (40) | 13 (46) | 10 (34) | 13 (45) | 10 (36) | ||
| AJCC stage1 | |||||||
| Ⅰ/Ⅱ | 23 (38) | 11 (39) | 12 (41) | 0.872 | 11 (38) | 12 (43) | 0.705 |
| Ⅲ/Ⅳ | 37 (62) | 17 (61) | 17 (59) | 18 (62) | 16 (57) | ||
| 1. Based on the American Joint Committee on Cancer staging manual 6th edition. HOXA10: Homeobox A10; AJCC: American Joint Committee on Cancer. | |||||||
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| Gastric cancer patient Training set | Proof-of-principle test set (responder) | Healthy volunteer | |
| Number | 82 | 8 | 34 |
| Age - yr | |||
| Median | 56 | 56 | 48 |
| Interquartile range | (44–63) | (44–58) | (43–57) |
| Sex - no. (%) | |||
| Male | 64 (78.0%) | 7 (87.5%) | 23 (67.6%) |
| Female | 18 (22.0%) | 1 (12.5%) | 11 (32.4%) |
| Performance status (PS) - no. (%) | |||
| ECOG1 PS 0 or 1 | 73 (89.0%) | 8 (100%) | |
| ECOG PS 2 or 3 | 9 (11.0%) | 0 | |
| Lauren's intestinal | 34 (41.5%) | 3 (37.5%) | |
| Lauren's diffuse | 48 (58.5%) | 5 (62.5%) | |
| Location of primary lesion - no. (%) | |||
| Upper 1/3 | 11 (13.4%) | 1 (12.5%) | |
| Middle 1/3 | 18 (22.0%) | 5 (62.5%) | |
| Lower 1/3 | 43 (52.4%) | 1 (12.5%) | |
| Entire stomach | 10 (12.2%) | 1 (12.5%) | |
| Chemotherapy regimen - no. (%) | |||
| Cisplatin/Fluorouracil | 80 (97.6%) | 8 (100%) | |
| Cisplatin/Capecitabine | 2 (2.4%) | 0 | |
| *Relative dose intensity - % | |||
| Median | 81.2 | 76.6 | |
| Interquartile range | (75.3–87.3) | (64.7-84.9) | |
| Number of chemotherapy cycles | |||
| Median | 4 | 10 | |
| Interquartile range | (2–5) | (7–11) | |
| Chemotherapy response (WHO criteria) -no (%) | |||
| PR2 | 16 (24.6%) | 6 (100%) | |
| SD3 | 25 (38.5%) | ||
| PD4 | 24 (36.9%) | ||
| Unmeasurable | 16 | 2 | |
| Unevaluable | 1 | ||
| Second-line chemotherapy | 55 (67.1%) | 6 (75.0%) | |
| Median follow-up for survivors | 35.5 months | - | |
| Overall survival - mo. | |||
| Median | 8.2 | 16 | |
| Interquartile range | (6.8-10.5) | (11.3-26.7) | |
| Time to progression - mo. | |||
| Median | 3.1 | 8.2 | |
| Interquartile range | (2.5–3.9) | (4.3–21.2) | |
| 1Eastern Cooperative Oncology Group, 2partial response, 3stable disease, 4progressive disease. *Relative dose intensity *Mean of relative dose intensities of cisplatin and fluorouracil. Dose intensity is defined as the amount of drug administered per unit of time, expressed as milligrams per square meter per week. Relative dose intensity is defined as the actual dose intensity relative to the planned dose intensity of each drug. | |||
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| Characteristic | Group 1 (n = 60) | miR-196b high (n = 28) | miR-196b low (n = 29) | P value | HOXA10 high (n = 29) | HOXA10 low (n = 28) | P value |
| Age, yr | |||||||
| Median (range) | 63 (32–83) | 66 (34–78) | 58 (32–83) | 0.134 | 64 (34–83) | 60 (32–78) | 0.134 |
| > 65 | 15 (52) | 9 (32) | 15 (52) | 9 (32) | |||
| Sex | |||||||
| Male | 41 (68) | 19 (68) | 20 (69) | 0.928 | 18 (62) | 21 (75) | 0.294 |
| Histologic type | |||||||
| Diffuse | 27 (45) | 9 | 17 | 0.066 | 8 | 18 | 0.007 |
| Intestinal | 23 (38) | 14 | 9 | 16 | 7 | ||
| Mixed | 10 (17) | 5 | 3 | 5 | 3 | ||
| T stage | |||||||
| T1/T2 | 26 (43) | 10 (36) | 14 (48) | 0.337 | 11 (38) | 13 (46) | 0.516 |
| T3/T4 | 34 (57) | 18 (64) | 15 (52) | 18 (62) | 15 (54) | ||
| N stage | |||||||
| N0/N1 | 36 (60) | 15 (54) | 19 (66) | 0.358 | 16 (55) | 18 (64) | 0.483 |
| N2/N3 | 24 (40) | 13 (46) | 10 (34) | 13 (45) | 10 (36) | ||
| AJCC stage1 | |||||||
| Ⅰ/Ⅱ | 23 (38) | 11 (39) | 12 (41) | 0.872 | 11 (38) | 12 (43) | 0.705 |
| Ⅲ/Ⅳ | 37 (62) | 17 (61) | 17 (59) | 18 (62) | 16 (57) | ||
| 1. Based on the American Joint Committee on Cancer staging manual 6th edition. HOXA10: Homeobox A10; AJCC: American Joint Committee on Cancer. | |||||||
| Gastric cancer patient Training set | Proof-of-principle test set (responder) | Healthy volunteer | |
| Number | 82 | 8 | 34 |
| Age - yr | |||
| Median | 56 | 56 | 48 |
| Interquartile range | (44–63) | (44–58) | (43–57) |
| Sex - no. (%) | |||
| Male | 64 (78.0%) | 7 (87.5%) | 23 (67.6%) |
| Female | 18 (22.0%) | 1 (12.5%) | 11 (32.4%) |
| Performance status (PS) - no. (%) | |||
| ECOG1 PS 0 or 1 | 73 (89.0%) | 8 (100%) | |
| ECOG PS 2 or 3 | 9 (11.0%) | 0 | |
| Lauren's intestinal | 34 (41.5%) | 3 (37.5%) | |
| Lauren's diffuse | 48 (58.5%) | 5 (62.5%) | |
| Location of primary lesion - no. (%) | |||
| Upper 1/3 | 11 (13.4%) | 1 (12.5%) | |
| Middle 1/3 | 18 (22.0%) | 5 (62.5%) | |
| Lower 1/3 | 43 (52.4%) | 1 (12.5%) | |
| Entire stomach | 10 (12.2%) | 1 (12.5%) | |
| Chemotherapy regimen - no. (%) | |||
| Cisplatin/Fluorouracil | 80 (97.6%) | 8 (100%) | |
| Cisplatin/Capecitabine | 2 (2.4%) | 0 | |
| *Relative dose intensity - % | |||
| Median | 81.2 | 76.6 | |
| Interquartile range | (75.3–87.3) | (64.7-84.9) | |
| Number of chemotherapy cycles | |||
| Median | 4 | 10 | |
| Interquartile range | (2–5) | (7–11) | |
| Chemotherapy response (WHO criteria) -no (%) | |||
| PR2 | 16 (24.6%) | 6 (100%) | |
| SD3 | 25 (38.5%) | ||
| PD4 | 24 (36.9%) | ||
| Unmeasurable | 16 | 2 | |
| Unevaluable | 1 | ||
| Second-line chemotherapy | 55 (67.1%) | 6 (75.0%) | |
| Median follow-up for survivors | 35.5 months | - | |
| Overall survival - mo. | |||
| Median | 8.2 | 16 | |
| Interquartile range | (6.8-10.5) | (11.3-26.7) | |
| Time to progression - mo. | |||
| Median | 3.1 | 8.2 | |
| Interquartile range | (2.5–3.9) | (4.3–21.2) | |
| 1Eastern Cooperative Oncology Group, 2partial response, 3stable disease, 4progressive disease. *Relative dose intensity *Mean of relative dose intensities of cisplatin and fluorouracil. Dose intensity is defined as the amount of drug administered per unit of time, expressed as milligrams per square meter per week. Relative dose intensity is defined as the actual dose intensity relative to the planned dose intensity of each drug. | |||
