Clinical significance and prognostic role of an immune-related gene signature in gastric adenocarcinoma

Results

For the entire analysis process of this study, please see Figure 1. And for the clinicopathological data obtained from the TCGA, GSE84437, GSE26253, and the quantitative reverse transcription polymerase chain reaction (qRT-PCR) datasets, please see Table 1.

Figure 1. Entire analytical process of this study.

Table 1. Clinical information on patients in the training dataset, internal validation dataset and entire validation dataset.

Characteristic	Training dataset TCGA-STAD (n=350)	Validation dataset GSE84437 (n=431)	Validation dataset GSE26253 (n=432)	Independent dataset (n=145)
Age (y)
<62	150	213	-	53
≥62	197	218	-	59
Not available	3	-	-	-
Sex
Male	226	294	-	85
Female	124	137	-	27
Survival status
Alive (no recurrence)	208	224	255	67
Dead (recurrence)	142	207	177	45
pT stage
T1 (T1; T1a; T1b)	16 (5; 2; 9)	11	-	15 (0; 10; 5)
T2 (T2; T2a; T2b)	74 (55; 7; 12)	38	-	14
T3	161	92	-	2
T4 (T4; T4a; T4b)	95 (28; 45; 22)	290	-	81 (0; 48; 33)
TX	4	-	-	-
pN stage
N0	103	80	-	47
N1	93	187	-	37
N2	72	132	-	24
N3 (N3; N3a; N3b)	71 (25; 40; 6)	32	-	4
NX	11	-	-	-
M
M0	312	-	-	106
M1	23	-	-	6
Not available	15	-	-	-
AJCC stage
Stage I (I; IA; IB)	46 (1; 12; 33)	-	68	26 (0; 15; 11)
Stage II (II; IIA; IIB)	110 (27; 34; 49)	-	167	19 (0; 4; 15)
Stage III (III; IIIA; IIIB; IIIC)	145 (3; 58; 51; 33)	-	111 (IIIA); 19 (IIIB)	61 (0; 37; 23; 1)
Stage IV	35	-	67	6
Not available	14	-	-	-
Grade
1	9	-	-	6
2	125	-	-	29
3	207	-	-	77
Not available	9	-	-	-
Histological type
Signet ring cell type	11	-	-	-
Diffuse type	61	-	-	-
Mucinous type	19	-	-	-
Papillary type	5	-	-	-
Tubular type	67	-	-	-
NOS	187	-	-	-

Immune genes were remarkably correlated with prognosis

First, we merged the two datasets GSE84433 and GSE84426 and removed the batch effect (Supplementary Figure 1A). Then screen the matching IRGs in the TCGA, GSE84437 and ImmPort databases (Supplementary Figure 1B), and based on the 881 differentially expressed IRGs, patients in the TCGA were classified into two groups by unsupervised clustering (Figure 2A, 2C, 2D). Prognostic analysis showed that compared with cluster-2, cluster-1 had more advantages in survival (Figure 2B).

Figure 2. Unsupervised clustering analysis of 881 IRGs. (A, D, E) Classification of the TCGA-STAD cohort into two groups. (C) Landscape of the expression of 881 IRGs in the TCGA-STAD cohort. (B) Kaplan-Meier OS curves in the training cohort based on clusters.

Differential expression analysis

We conducted a differential expression analysis as to the GAC and normal specimens from the TCGA (Supplementary Figure 1C) before we ultimately obtained 378 differentially expressed IRGs.

The IBPS composed of 9 IRGs could effectively evaluate prognosis in the TCGA cohort

Through a univariate Cox survival analysis, there were 43 IRG with P < 0.05 chosen from 378 IRG for follow-up analysis (Figure 3A). Figure 3B indicated that via the least absolute shrinkage and selector operator (LASSO) regression analysis, there were 9 IRGs identified (“lambda.min” criteria). The results of multivariate COX analysis of the 9 IRGs are shown in Supplementary Table 2. Ultimately, 9 IRGs predictive of GAC patient survival, namely, ADM, APOD, CXCR4, ITGAV, NRP1, RFX5, STC1, TAP1, and ZC3HAV1, were identified. The formula of the IBPS was calculated: risk score = (0.0622 × exp of ADM) + (0.0771 × exp of APOD) + (0.1335 × exp of CXCR4) + (0.2369 × exp of ITGAV) + (0.0968 × exp of NRP1) - (0.2405 × exp of RFX5) + (0.0531 × exp of STC1) - (0.0892 × exp of TAP1) - (0.5188 × exp of ZC3HAV1). Via taking the median risk score as the cut-off point, a division of the patients in the training set into high-risk group and low-risk group was conducted (Supplementary Figure 3A). The expression levels of the 9 IRGs are shown in Supplementary Figure 2. The patients in the low-risk group had a significantly better OS than the high-risk group (Figure 4B). Besides, there was an analysis of the receiver operating characteristic (ROC) performed, for an evaluation of the IBPS's prediction of the survival in terms of its accuracy (Figure 4A). The results suggest that there is an area of 0.736 (3 years) and 0.827 (5 years), under the curve (AUC value) of IBPS, respectively, which is larger than the AUC value of the system of the TNM pathological staging (Supplementary Figure 4E).

Figure 3. Identification of prognostic immune-related genes in GAC. (A) Univariate Cox regression analysis revealed 43 immune-related genes significantly associated with OS. (B) LASSO regression analysis was performed to screen the most useful prognostic genes.

Figure 4. Application of the IBPS in predicting the survival of GAC patients. Kaplan-Meier survival curves of the hub RNAs in the ceRNA network. (A) ROC curve analysis of the immune-related gene signature for the prediction of OS at 1, 2, 3, 4, and 5 years in the TCGA cohort; (B) Kaplan-Meier curves of OS in all GAC patients in the TCGA cohort based on the risk score; (C) ROC curve analysis of the immune-related gene signature and TNM stage for the prediction of OS at 3 years in the TCGA cohort; (D) ROC curve analysis of the immune-related gene signature for the prediction of OS at 1, 2, 3, 4, and 5 years in the GEO cohort; (E) Kaplan-Meier curves of OS in all GAC patients in the GEO cohort based on the risk score; (F) ROC curve analysis of the immune-related gene signature and TNM stage for the prediction of OS at 5 years in the TCGA cohort.

The IBPS could effectively evaluate prognosis in the GSE84437 cohort

In order to validate the robustness of IBPS, we also analyze it with GSE84437 dataset (n = 431). The results suggest that it can also predict the prognosis in GSE84437 (Supplementary Figures 3B, 4C, 4D).

The IBPS could effectively evaluate prognosis in different clinical subgroups

In general, patients in the low-risk group had better OS than those in the high-risk group according to different pathological stages (Supplementary Figure 4A–4C). In the stage IV subgroup (P = 0.19) (Supplementary Figure 4D), we could clearly see the same trend, but it was not statistically significant, potentially due to insufficient sample sizes.

Although the pathological stage is one of the factors that most influences survival of GAC, other factors, such as age, grade, sex, and major histological phenotype, are also contributors [8]; therefore, we grouped the GAC patients in the TCGA training cohort according to the above clinical features. According to the results, in all subgroups (older (aged ≥ 62) and younger (aged < 62), grade 2 and grade 3, males and females, and intestinal type and diffuse type), the low risk groups had better OS than the high risk groups (Supplementary Figure 5, P < 0.05). Predictably, validation of these results was observed in the GSE84437 dataset (Supplementary Figure 6). Additionally, the WHO provides detailed descriptions of histological features and classifies GAC into tubular, papillary, mucinous (MUC), mixed, and signet ring cell (SRC) types. We analyzed the performance of the IBPS among these subtypes. The results showed a notable concentration of the SRC type in high-risk scores group (Supplementary Figure 7A). Among patients with the tubular, MUC or papillary subtype, there was significantly shorter OS observed in the high-risk patients compared with the low-risk patients (Supplementary Figure 7B–7D, P < 0.05). Besides, among all GAC patients who received chemotherapy, there was longer OS time observed in the patients in the low risk groups compared with the high risk groups (Supplementary Figure 7E, P < 0.0001). According to the results presented in Supplementary Figure 7F, 7G, there was significantly worse chemotherapy effect observed in the patients with high risk score (3-year OS rate: 35%) compared with the patients with low risk score (3-year OS rate: 70%).

The IBPS could effectively evaluate prognosis in the GSE26253 cohort

For an exploration of whether the IBPS can equally work in prediction of the RFS outcome of GAC patients, a similar analysis was performed for the GSE26253 cohort (n = 432, Supplementary Figure 8A, 8B). The results showed that the IBPS could effectively evaluate the RFS outcome of GAC patients. Besides, we observed that the RFS duration of patients with advanced-stage disease was significantly shorter than that of patients with early-stage disease (Supplementary Figure 8C). It is worth noting that in pathological stage I and stage II subgroups, better RFS was seen in the patients in the low-risk groups compared with the high-risk groups (Supplementary Figure 8D, 8E, P <0.01).

The IBPS could effectively evaluate prognosis in the qRT-PCR group

For a validation of the IBPS in terms of its robustness, we conducted the same analysis with the qRT-PCR validation cohort (n = 145) (Figure 5A, 5B). The AUCs for the prediction of 3- and 4-year survival by the IBPS reached 0.769 and 0.831, respectively (Figure 5C), which were larger compared to the system of the traditional TNM pathological staging (Figure 5D, 5E). Furthermore, when applied to subcategories of patients with GAC in different pathological stages at the time of diagnosis and other different clinical subgroups, the risk score was predictive of significantly different OS outcomes (Supplementary Figure 9, P < 0.05). We compared the expression of 9 IRGs in gastric cancer tissues to their corresponding normal tissues (Figure 5F). We also compared the differential expression of 9 IRGs between the two groups (Figure 5G).

Figure 5. Validation of the prognostic performance of the immune-related gene signature in an independent group based on 145 frozen tissues. (A) Distribution of the risk score, survival status, and gene expression panel. (B) Kaplan-Meier curves of OS in all GAC patients based on the risk score. (C) ROC curve analysis of the immune-related gene signature for the prediction of OS at 2, 3, and 4 years in the independent cohort. (D) ROC curve analysis for the prediction of OS at 3 years in an independent cohort based on multiple clinical features. (E) ROC curve analysis for the prediction of OS at 4 years in an independent cohort based on multiple clinical features. (G) The expression of 9 immune genes in gastric cancer tissues and their corresponding normal tissues. (F) We also compared the differential expression of 9 immune genes between the high- and low-risk groups.

Patients in the High-risk group had the below features, namely, low immune scores, distinctive immune cell proportions, and different immune checkpoint profiles

To explore the potential mechanism between the IBPS and OS in GAC patients, we performed multiple analyses related to the immune profile. The stromal, immune, and ESTIMATE scores showed significant differences between the two groups (Supplementary Figure 10A, 10B). Kaplan-Meier analysis of data on GAC patients in the TCGA cohort showed that different stromal and ESTIMATE scores produced differential OS outcomes (Supplementary Figure 10C, 10E). And, we obtained similar results in the GSE84437 cohort (Supplementary Figure 10D, 10F).

Since there was a close relation of the risk score with the immune infiltration score, we analyzed the high-risk group and the low-risk group in terms of their differences in immune cell infiltration and immune checkpoints. The results obtained from the TCGA and Gene Expression Omnibus (GEO) datasets are shown in Supplementary Figure 11A. M1 macrophages, M0 macrophages, memory CD4 T cells, M2 macrophages, and CD8 T cells constituted a large proportion of the GAC-infiltrating immune cells. Besides, the two group showed most of the differential immune cells, which were predominantly M1 macrophages, monocytes, CD8 T cells, follicular helper T cells, memory CD4 T cells, and M2 macrophages (Supplementary Figure 11B, 11C). Furthermore, as observed, the 9 IRGs had a Pearson correlation with varied immune cells (Supplementary Figure 12). We also compared the differences in 49 immune checkpoints, and the results obtained from the TCGA and GEO datasets are shown in Supplementary Figure 13A, 13B, respectively. Overall, the expression of VTCN1, ENTPD1, and FGL1 was obviously upregulated while that of LGALS9 was dramatically downregulated in the high-risk group of patients in both cohorts (Supplementary Figure 14, P < 0.05). Besides, good correlations were observed between the 9 IRGS and various differentially expressed immune checkpoints, especially those for TAP1 and CXCR4 (Supplementary Figure 15).

Patients in the High-risk group were characterized with a low tumor mutational burden (TMB)

Then, we used the maftools software package to analyze the difference in the distribution of somatic mutations between the low-risk score group and the high-risk score group in the TCGA-STAD cohort. Supplementary Figure 16A shows the top 30 gene mutations in the TCGA-STAD cohort. The top 30 gene mutations in the high-risk score group and the low-risk score group are shown in Figure 6A, 6B, respectively. Somatic mutations were altered in 355 of 427 samples (83.14%). The well-known oncogenes TP53, ERBB2, EGFR, FGFR2, MET, and KRAS [9] in GAC were altered in 182 of 427 samples (42.62%) (Supplementary Figure 16B), especially TP53 (Figure 6C, 6D). Kaplan-Meier curve analysis suggests that patients with a high TMB have significantly better OS than patients with a low TMB (Figure 6E). And there was lower TMB observed in the high risk score group compared to the low risk score group (P < 0.0001, Figure 6F). What's more, the high TMB group had lower risk scores compared to the low TMB group (Figure 6G). Besides, we explored that the mutational rates of the 9 IRGs in the IBPS were very low (Supplementary Figure 16C), supporting their use as diagnostic or prognostic biomarkers.

Figure 6. Tumor somatic mutational landscape of the IBPS. (A) The top 35 gene mutations in the high-risk score group. (B) The top 35 gene mutations in the low-risk score group. (C, D) Mutations in the commonly mutated genes TP53, ERBB2, EGFR, FGFR2, MET, and KRAS in the high-risk and low-risk score groups, respectively. (E) Kaplan-Meier curves of OS in all GAC patients based on TMB. (F) Difference in TMB between the high- and low-risk groups. (G) Difference in risk scores between the high and low TMB groups.

The IBPS is an independent risk factor for GAC patients

Through univariate and multivariate Cox regression analysis of various clinical factors in the TCGA cohort (Table 2), we screened out the independent prognostic risk factors such as age, histological type, TNM stage, IBPS and so on. The specific variables significantly related to survival included age > 62 years (hazard ratio (HR)=1.89, P< 0.001), the MUC type (HR=0.25, P= 0.02), stage IV (HR=3.86, P=0.0005), stage III (HR=2.28, P=0.017), and a rising risk score (HR=2.31, P<0.0001). Besides, the results obtained from the GSE84437 cohort confirmed the value of the IBPS for OS (P = 0.0037) in GAC patients (Table 3). Additionally, Supplementary Table 3 showed that the IBPS was also an independent risk factor for RFS (P=0.0300). As expected, there were similar results observed in the qRT-PCR cohort (Supplementary Table 4).

Table 2. Univariable and multivariable Cox regression analyses of the IBPS and correlations with OS in the TCGA cohort.

Variable	Overall survival
	Univariate Cox				Multivariate Cox
	p value	HR	95% CI		p value	HR	95% CI
Age
≥62 vs <62	0.0075	1.6050	1.1350-2.2700		0.0009	1.8900	1.2976-2.7528
Sex
Male vs Female	0.1760	1.2780	0.8954-1.8250		0.4200	1.1630	0.8055-1.6800
Histological type
NOS
Signet ring cell type	0.0565	2.0307	0.9806-4.2054		0.6783	1.1780	0.5438-2.5505
Diffuse type	0.2685	0.7689	0.4827-1.2247		0.1812	0.7087	0.4278-1.1741
Mucinous type	0.0152	0.2395	0.0755-0.7592		0.0206	0.2520	0.0785-0.8094
Papillary type	0.5446	1.4284	0.4507-4.5277		0.1640	2.4470	0.6940-8.6276
Tubular type	0.2558	0.7702	0.4910-1.2083		0.7323	0.9184	0.5640-1.4957
TNM stage
I
II	0.1857	1.5830	0.8017-3.1280		0.1553	1.6560	0.8260-3.3188
III	0.0144	2.2300	1.1734-4.2380		0.0170	2.2790	1.1584-4.4817
IV	0.0004	3.6980	1.7921-7.6310		0.0005	3.8550	1.7978-8.2680
NA	0.0002	5.1930	2.1954-12.2840		0.0008	4.7050	1.9007-11.6448
Grade
1
2	0.4690	1.6870	0.4097-6.9480		0.2812	2.2500	0.5148-9.8341
3	0.2910	2.1300	0.5240-8.6590		0.1229	3.2190	0.7289-14.2170
NA	0.2280	2.8420	0.5201-15.5360		0.0662	5.4690	0.8923-33.5155
Risk score
Increasing	<0.0001	2.4650	1.7420-3.4880		<0.0001	2.3050	1.5836-3.3562
IBPS, immune gene set-based prognostic signature; HR, hazard ratio; CI, confidence interval.

Table 3. Univariable and multivariable Cox regression analyses of the IBPS and correlations with OS in the GSE84437 cohort.

Variable	Overall survival
	Univariate Cox				Multivariate Cox
	p value	HR	95% CI		p value	HR	95% CI
Age
≥62 vs <62	0.0022	1.5387	1.1670-2.0290		0.0007	1.6171	1.2235-2.1370
Sex
Male vs Female	0.1660	1.2395	0.9151-1.6790		0.2367	1.2038	0.8854-1.6370
T stage
1
2	0.9463	0.9474	0.1968-4.5610		0.7378	0.7624	0.1577-3.7330
3	0.1904	2.5859	0.6238-10.7200		0.4621	1.7180	0.4016-7.2680
4	0.0547	3.9247	0.9729-15.8330		0.2187	2.4322	0.5900-10.0260
N stage
0
1	0.1020	1.4487	0.9287-2.2600		0.2378	1.3131	0.8354-2.0640
2	<0.0001	2.9315	1.8826-4.5650		0.0004	2.2971	1.4547-3.6270
3	<0.0001	3.7702	2.1294-6.6750		0.0027	2.4691	1.3683-4.4550
Risk score
Increasing	<0.0001	1.8140	1.3730-2.3970		0.0037	1.5264	1.1474-2.0310
IBPS, immune gene set-based prognostic signature; HR, hazard ratio; CI, confidence interval.

In addition, an investigation was performed as to the IBPS in terms of its comprehensive prognostic value in all groups by prognostic meta-analysis (n = 893). According to the results, the IBPS was a significant risk factor for OS in GAC patients (combined HR = 2.218, 95% CI = 1.804–2.727, P < 0.0001) (Supplementary Figure 17).

Multiple immune and tumor-related pathways associated with the IBPS

GSEA identified 20 significant KEGG pathways associated with the risk score, including cell adhesion molecules (CAMs), the MAPK signaling pathway, DNA replication, nucleotide excision repair, the cell cycle, cytokine-receptor interaction, the P53 signaling pathway, mismatch repair, ECM-receptor interaction, and pathways in cancer (Supplementary Figure 18).

Abbreviations

GAC: gastric adenocarcinoma; TCGA: The Cancer Genome Atlas; GEO: Gene Expression Omnibus; ROC: receiver operating characteristic; OS: overall survival; AUC: area under the curve; KEGG: Kyoto Encyclopedia of Genes and Genomes; qRT-PCR: quantitative real-time reverse transcription polymerase chain reaction.

Author Contributions

M.-R., Z.-T.T. and L.-Y.J. conceived the project and designed the experiments. W.-Z. and W.-Y.X. carried out the experiments. M.-R, L.-K.H., and Z.-N.N. contributed equally to this work. L.-Y.J., Z.-T.T., and M.-R. wrote the manuscript. M.-R, Z.-N.N., and L.-K.H. carried out the statistical analysis and assisted in collecting tissue samples. Z.-T.T. contributed to manuscript revision. All authors provided suggestions during manuscript preparation and read the final version.

Acknowledgment

The authors would like to thank the efforts of staff of the National Center for Biotechnology Information, National Cancer Institute, and Department of Colorectal Surgery of National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Funding

This work was supported by grants from the National Natural Science Foundation of China (81502075) and the Foundation of Science and Technology of Sichuan Province (2019YJ0635). The funders had no role in the study design or implementation.

Editorial Note

This corresponding author has a verified history of publications using a personal email address for correspondence

References

• 1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020; 70:7–30. https://doi.org/10.3322/caac.21590 [PubMed]
• 2. Verdecchia A, Francisci S, Brenner H, Gatta G, Micheli A, Mangone L, Kunkler I, and EUROCARE-4 Working Group. Recent cancer survival in Europe: a 2000-02 period analysis of EUROCARE-4 data. Lancet Oncol. 2007; 8:784–96. https://doi.org/10.1016/S1470-2045(07)70246-2 [PubMed]
• 3. Fuse N, Kuboki Y, Kuwata T, Nishina T, Kadowaki S, Shinozaki E, Machida N, Yuki S, Ooki A, Kajiura S, Kimura T, Yamanaka T, Shitara K, et al. Prognostic impact of HER2, EGFR, and c-MET status on overall survival of advanced gastric cancer patients. Gastric Cancer. 2016; 19:183–91. https://doi.org/10.1007/s10120-015-0471-6 [PubMed]
• 4. Zhang J, Wu Y, Lin YH, Guo S, Ning PF, Zheng ZC, Wang Y, Zhao Y. Prognostic value of hypoxia-inducible factor-1 alpha and prolyl 4-hydroxylase beta polypeptide overexpression in gastric cancer. World J Gastroenterol. 2018; 24:2381–91. https://doi.org/10.3748/wjg.v24.i22.2381 [PubMed]
• 5. Thompson ED, Zahurak M, Murphy A, Cornish T, Cuka N, Abdelfatah E, Yang S, Duncan M, Ahuja N, Taube JM, Anders RA, Kelly RJ. Patterns of PD-L1 expression and CD8 T cell infiltration in gastric adenocarcinomas and associated immune stroma. Gut. 2017; 66:794–801. https://doi.org/10.1136/gutjnl-2015-310839 [PubMed]
• 6. Jones JO, Smyth EC. Gastroesophageal cancer: Navigating the immune and genetic terrain to improve clinical outcomes. Cancer Treat Rev. 2020; 84:101950. https://doi.org/10.1016/j.ctrv.2019.101950 [PubMed]
• 7. Sunakawa Y, Stremitzer S, Cao S, Zhang W, Yang D, Wakatsuki T, Ning Y, Yamauchi S, Stintzing S, Sebio A, El-Khoueiry R, Matsusaka S, Parekh A, et al. Association of variants in genes encoding for macrophage-related functions with clinical outcome in patients with locoregional gastric cancer. Ann Oncol. 2015; 26:332–39. https://doi.org/10.1093/annonc/mdu542 [PubMed]
• 8. Ajani JA, Lee J, Sano T, Janjigian YY, Fan D, Song S. Gastric adenocarcinoma. Nat Rev Dis Primers. 2017; 3:17036. https://doi.org/10.1038/nrdp.2017.36 [PubMed]
• 9. Chia NY, Tan P. Molecular classification of gastric cancer. Ann Oncol. 2016; 27:763–69. https://doi.org/10.1093/annonc/mdw040 [PubMed]
• 10. Crew KD, Neugut AI. Epidemiology of gastric cancer. World J Gastroenterol. 2006; 12:354–62. https://doi.org/10.3748/wjg.v12.i3.354 [PubMed]
• 11. Ajani JA, D’Amico TA, Almhanna K, Bentrem DJ, Chao J, Das P, Denlinger CS, Fanta P, Farjah F, Fuchs CS, Gerdes H, Gibson M, Glasgow RE, et al. Gastric Cancer, Version 3.2016, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2016; 14:1286–312. https://doi.org/10.6004/jnccn.2016.0137 [PubMed]
• 12. Wang M, Li Z, Peng Y, Fang J, Fang T, Wu J, Zhou J. Identification of immune cells and mRNA associated with prognosis of gastric cancer. BMC Cancer. 2020; 20:206. https://doi.org/10.1186/s12885-020-6702-1 [PubMed]
• 13. Qiu XT, Song YC, Liu J, Wang ZM, Niu X, He J. Identification of an immune-related gene-based signature to predict prognosis of patients with gastric cancer. World J Gastrointest Oncol. 2020; 12:857–76. https://doi.org/10.4251/wjgo.v12.i8.857 [PubMed]
• 14. Cheng P. A prognostic 3-long noncoding RNA signature for patients with gastric cancer. J Cell Biochem. 2018; 119:9261–69. https://doi.org/10.1002/jcb.27195 [PubMed]
• 15. Yang Y, Qu A, Zhao R, Hua M, Zhang X, Dong Z, Zheng G, Pan H, Wang H, Yang X, Zhang Y. Genome-wide identification of a novel miRNA-based signature to predict recurrence in patients with gastric cancer. Mol Oncol. 2018; 12:2072–84. https://doi.org/10.1002/1878-0261.12385 [PubMed]
• 16. Peng Y, Wu Q, Wang L, Wang H, Yin F. A DNA methylation signature to improve survival prediction of gastric cancer. Clin Epigenetics. 2020; 12:15. https://doi.org/10.1186/s13148-020-0807-x [PubMed]
• 17. Zhao E, Zhou C, Chen S. A signature of 14 immune-related gene pairs predicts overall survival in gastric cancer. Clin Transl Oncol. 2021; 23:265–74. https://doi.org/10.1007/s12094-020-02414-7 [PubMed]
• 18. Kim TS, da Silva E, Coit DG, Tang LH. Intratumoral Immune Response to Gastric Cancer Varies by Molecular and Histologic Subtype. Am J Surg Pathol. 2019; 43:851–60. https://doi.org/10.1097/PAS.0000000000001253 [PubMed]
• 19. Lee HH, Song KY, Park CH, Jeon HM. Undifferentiated-type gastric adenocarcinoma: prognostic impact of three histological types. World J Surg Oncol. 2012; 10:254. https://doi.org/10.1186/1477-7819-10-254 [PubMed]
• 20. Zhang C, Zhang Z, Li F, Shen Z, Qiao Y, Li L, Liu S, Song M, Zhao X, Ren F, He Q, Yang B, Fan R, Zhang Y. Large-scale analysis reveals the specific clinical and immune features of B7-H3 in glioma. Oncoimmunology. 2018; 7:e1461304. https://doi.org/10.1080/2162402X.2018.1461304 [PubMed]
• 21. Kim HW, Kim JH, Lim BJ, Kim H, Kim H, Park JJ, Youn YH, Park H, Noh SH, Kim JW, Choi SH. Sex Disparity in Gastric Cancer: Female Sex is a Poor Prognostic Factor for Advanced Gastric Cancer. Ann Surg Oncol. 2016; 23:4344–51. https://doi.org/10.1245/s10434-016-5448-0 [PubMed]
• 22. Torre LA, Siegel RL, Ward EM, Jemal A. Global Cancer Incidence and Mortality Rates and Trends--An Update. Cancer Epidemiol Biomarkers Prev. 2016; 25:16–27. https://doi.org/10.1158/1055-9965.EPI-15-0578 [PubMed]
• 23. Li J, Lee Y, Li Y, Jiang Y, Lu H, Zang W, Zhao X, Liu L, Chen Y, Tan H, Yang Z, Zhang MQ, Mak TW, et al. Co-inhibitory Molecule B7 Superfamily Member 1 Expressed by Tumor-Infiltrating Myeloid Cells Induces Dysfunction of Anti-tumor CD8⁺ T Cells. Immunity. 2018; 48:773–86.e5. https://doi.org/10.1016/j.immuni.2018.03.018 [PubMed]
• 24. Cai XY, Wang XF, Li J, Dong JN, Liu JQ, Li NP, Yun B, Xia RL, Qin J, Sun YH. High expression of CD39 in gastric cancer reduces patient outcome following radical resection. Oncol Lett. 2016; 12:4080–86. https://doi.org/10.3892/ol.2016.5189 [PubMed]
• 25. Zhang Y, Qiao HX, Zhou YT, Hong L, Chen JH. Fibrinogen-like-protein 1 promotes the invasion and metastasis of gastric cancer and is associated with poor prognosis. Mol Med Rep. 2018; 18:1465–72. https://doi.org/10.3892/mmr.2018.9097 [PubMed]
• 26. Cho SJ, Kook MC, Lee JH, Shin JY, Park J, Bae YK, Choi IJ, Ryu KW, Kim YW. Peroxisome proliferator-activated receptor γ upregulates galectin-9 and predicts prognosis in intestinal-type gastric cancer. Int J Cancer. 2015; 136:810–20. https://doi.org/10.1002/ijc.29056 [PubMed]
• 27. Marabelle A, Fakih M, Lopez J, Shah M, Shapira-Frommer R, Nakagawa K, Chung HC, Kindler HL, Lopez-Martin JA, Miller WH Jr, Italiano A, Kao S, Piha-Paul SA, et al. Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study. Lancet Oncol. 2020; 21:1353–65. https://doi.org/10.1016/S1470-2045(20)30445-9 [PubMed]
• 28. Zhong G, Liu L, Fan T, Fan P, Ji H. Degradation of transcription factor RFX5 during the inhibition of both constitutive and interferon gamma-inducible major histocompatibility complex class I expression in chlamydia-infected cells. J Exp Med. 2000; 191:1525–34. https://doi.org/10.1084/jem.191.9.1525 [PubMed]
• 29. Todorova T, Bock FJ, Chang P. Poly(ADP-ribose) polymerase-13 and RNA regulation in immunity and cancer. Trends Mol Med. 2015; 21:373–84. https://doi.org/10.1016/j.molmed.2015.03.002 [PubMed]
• 30. Gao Y, Li S, Xu D, Chen S, Cai Y, Jiang W, Zhang X, Sun J, Wang K, Chang B, Wang F, Hong M. Prognostic value of programmed death-1, programmed death-ligand 1, programmed death-ligand 2 expression, and CD8(+) T cell density in primary tumors and metastatic lymph nodes from patients with stage T1-4N+M0 gastric adenocarcinoma. Chin J Cancer. 2017; 36:61. https://doi.org/10.1186/s40880-017-0226-3 [PubMed]
• 31. Delgoffe GM, Woo SR, Turnis ME, Gravano DM, Guy C, Overacre AE, Bettini ML, Vogel P, Finkelstein D, Bonnevier J, Workman CJ, Vignali DA. Stability and function of regulatory T cells is maintained by a neuropilin-1-semaphorin-4a axis. Nature. 2013; 501:252–56. https://doi.org/10.1038/nature12428 [PubMed]
• 32. Bartosh TJ, Ylöstalo JH, Bazhanov N, Kuhlman J, Prockop DJ. Dynamic compaction of human mesenchymal stem/precursor cells into spheres self-activates caspase-dependent IL1 signaling to enhance secretion of modulators of inflammation and immunity (PGE2, TSG6, and STC1). Stem Cells. 2013; 31:2443–56. https://doi.org/10.1002/stem.1499 [PubMed]
• 33. Xiang Z, Zhou ZJ, Xia GK, Zhang XH, Wei ZW, Zhu JT, Yu J, Chen W, He Y, Schwarz RE, Brekken RA, Awasthi N, Zhang CH. A positive crosstalk between CXCR4 and CXCR2 promotes gastric cancer metastasis. Oncogene. 2017; 36:5122–33. https://doi.org/10.1038/onc.2017.108 [PubMed]
• 34. Mari L, Hoefnagel SJ, Zito D, van de Meent M, van Endert P, Calpe S, Sancho Serra MD, Heemskerk MH, van Laarhoven HW, Hulshof MC, Gisbertz SS, Medema JP, van Berge Henegouwen MI, et al. microRNA 125a Regulates MHC-I Expression on Esophageal Adenocarcinoma Cells, Associated With Suppression of Antitumor Immune Response and Poor Outcomes of Patients. Gastroenterology. 2018; 155:784–98. https://doi.org/10.1053/j.gastro.2018.06.030 [PubMed]
• 35. Ling A, Löfgren-Burström A, Larsson P, Li X, Wikberg ML, Öberg Å, Stenling R, Edin S, Palmqvist R. TAP1 down-regulation elicits immune escape and poor prognosis in colorectal cancer. Oncoimmunology. 2017; 6:e1356143. https://doi.org/10.1080/2162402X.2017.1356143 [PubMed]
• 36. Do Carmo S, Jacomy H, Talbot PJ, Rassart E. Neuroprotective effect of apolipoprotein D against human coronavirus OC43-induced encephalitis in mice. J Neurosci. 2008; 28:10330–38. https://doi.org/10.1523/JNEUROSCI.2644-08.2008 [PubMed]
• 37. McLachlan GJ, Bean RW, Ng SK. Clustering. Methods Mol Biol. 2017; 1526:345–62. https://doi.org/10.1007/978-1-4939-6613-4_19 [PubMed]
• 38. Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics. 2010; 26:1572–73. https://doi.org/10.1093/bioinformatics/btq170 [PubMed]
• 39. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26:139–40. https://doi.org/10.1093/bioinformatics/btp616 [PubMed]
• 40. Runa F, Hamalian S, Meade K, Shisgal P, Gray PC, Kelber JA. Tumor microenvironment heterogeneity: challenges and opportunities. Curr Mol Biol Rep. 2017; 3:218–29. https://doi.org/10.1007/s40610-017-0073-7 [PubMed]
• 41. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015; 12:453–57. https://doi.org/10.1038/nmeth.3337 [PubMed]
• 42. Janakiram M, Chinai JM, Zhao A, Sparano JA, Zang X. HHLA2 and TMIGD2: new immunotherapeutic targets of the B7 and CD28 families. Oncoimmunology. 2015; 4:e1026534. https://doi.org/10.1080/2162402X.2015.1026534 [PubMed]
• 43. Ward-Kavanagh LK, Lin WW, Šedý JR, Ware CF. The TNF Receptor Superfamily in Co-stimulating and Co-inhibitory Responses. Immunity. 2016; 44:1005–19. https://doi.org/10.1016/j.immuni.2016.04.019 [PubMed]
• 44. Chrétien S, Zerdes I, Bergh J, Matikas A, Foukakis T. Beyond PD-1/PD-L1 Inhibition: What the Future Holds for Breast Cancer Immunotherapy. Cancers (Basel). 2019; 11:628. https://doi.org/10.3390/cancers11050628 [PubMed]
• 45. Wang J, Sanmamed MF, Datar I, Su TT, Ji L, Sun J, Chen L, Chen Y, Zhu G, Yin W, Zheng L, Zhou T, Badri T, et al. Fibrinogen-like Protein 1 Is a Major Immune Inhibitory Ligand of LAG-3. Cell. 2019; 176:334–47.e12. https://doi.org/10.1016/j.cell.2018.11.010 [PubMed]
• 46. Wang J, Sun J, Liu LN, Flies DB, Nie X, Toki M, Zhang J, Song C, Zarr M, Zhou X, Han X, Archer KA, O’Neill T, et al. Siglec-15 as an immune suppressor and potential target for normalization cancer immunotherapy. Nat Med. 2019; 25:656–66. https://doi.org/10.1038/s41591-019-0374-x [PubMed]
• 47. Li W, Liu J, Zhao H. Identification of a nomogram based on long non-coding RNA to improve prognosis prediction of esophageal squamous cell carcinoma. Aging (Albany NY). 2020; 12:1512–26. https://doi.org/10.18632/aging.102697 [PubMed]
• 48. Isik A, Ramanathan R. Approaches to the treatment of pilonidal sinus disease, clinical practice in 2019. Int Wound J. 2020; 17:508–09. https://doi.org/10.1111/iwj.13265 [PubMed]
• 49. Isik A, Kurnaz E, Isik N. Intermammary Pilonidal Disease. Galician Medical Journal. 2019; 26. https://doi.org/10.21802/gmj.2019.2.11