Open Access

Identification of tumor microenvironment‑associated immunological genes as potent prognostic markers in the cancer genome analysis project HOPE

  • Authors:
    • Ryota Kondou
    • Yasuto Akiyama
    • Akira Iizuka
    • Haruo Miyata
    • Chie Maeda
    • Akari Kanematsu
    • Kyoko Watanabe
    • Tadashi Ashizawa
    • Takeshi Nagashima
    • Kenichi Urakami
    • Yuji Shimoda
    • Keiichi Ohshima
    • Akio Shiomi
    • Yasuhisa Ohde
    • Masanori Terashima
    • Katsuhiko Uesaka
    • Tetsuro Onitsuka
    • Seiichiro Nishimura
    • Yasuyuki Hirashima
    • Nakamasa Hayashi
    • Yoshio Kiyohara
    • Yasuhiro Tsubosa
    • Hirohisa Katagiri
    • Masashi Niwakawa
    • Kaoru Takahashi
    • Hiroya Kashiwagi
    • Masahiro Nakagawa
    • Yuji Ishida
    • Takashi Sugino
    • Akifumi Notsu
    • Keita Mori
    • Mitsuru Takahashi
    • Hirotsugu Kenmotsu
    • Ken Yamaguchi
  • View Affiliations

  • Published online on: September 13, 2021     https://doi.org/10.3892/mco.2021.2395
  • Article Number: 232
  • Copyright: © Kondou et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Project High‑tech Omics‑based Patient Evaluation (HOPE), which used whole‑exome sequencing and gene expression profiling, was launched in 2014. A total of ~2,000 patients were enrolled until March 2016, and the survival time was observed up to July 2019. In our previous study, a tumor microenvironment immune type classification based on the expression levels of the programmed death‑ligand 1 (PD‑L1) and CD8B genes was performed based on four types: A, adaptive immune resistance; B, intrinsic induction; C, immunological ignorance; and D, tolerance. Type A (PD‑L1+ and CD8B+) exhibited upregulated features of T helper 1 antitumor responses. In the present study, survival time analysis at 5 years revealed that patients in type A had a better prognosis than those in other categories [5 year survival rate (%); A (80.5) vs. B (73.9), C (73.4) and D (72.6), P=0.0005]. Based on the expression data of 293 immune response‑associated genes, 62 specific genes were upregulated in the type A group. Among these genes, 18 specific genes, such as activated effector T‑cell markers (CD8/CD40LG/GZMB), effector memory T‑cell markers (PD‑1/CD27/ICOS), chemokine markers (CXCL9/CXCL10) and activated dendritic cell markers (CD80/CD274/SLAMF1), were significantly associated with a good prognosis using overall survival time analysis. Finally, multivariate Cox proportional hazard regression analyses of overall survival demonstrated that four genes (GZMB, HAVCR2, CXCL9 and CD40LG) were independent prognostic markers, and GZMB, CXCL9 and CD40LG may contribute to the survival benefit of patients in the immune type A group.

Introduction

Since the development of immune checkpoint blockade cancer therapy, many clinical trials of immune checkpoint therapy combined with conventional targeted therapy against solid cancers have been performed, and this treatment has achieved great success in the cancer treatment field as a novel immunotherapy (1-3). With advances in clinical cancer immunotherapeutic regimens, closely associated tumor-related parameters have been intensively investigated. These parameters are thought to be linked to the efficacy of immune checkpoint blockade therapy and the prognosis of cancer patients (4-7). However, in the tumor microenvironment, there are many factors, such as genetic, immunological (cellular or humoral), and metabolic factors, that have been demonstrated to be involved in the immunosuppressive mechanism. For example, as cellular factors, regulatory effector T cells, myeloid-derived suppressor cells (MDSCs), tumor-associated macrophages (TAMs) and cancer-associated fibroblasts (CAFs) have been reported to exhibit protumor immunosuppressive actions (8-10).

Moreover, immune-type classifications that can contribute to the prediction of immune checkpoint blockade efficacy and the prognosis of cancer patients have been performed by several researchers using three main types of immunological features: PD-L1 expression level, tumor-infiltrating lymphocyte (TIL) status and tumor mutational burden (TMB) (11-16). PD-L1 is a major immune checkpoint molecule that is expressed on tumor cells or associated macrophages and is supposed to inhibit activated T cell function via PD-1/PD-L1 binding (17,18). Meanwhile, some researchers have demonstrated that the simple combination of PD-L1+ and TIL+ (CD8+) may predict a good response to immune checkpoint blockade (11,12). Others have reported that TMB is a genuine biomarker for the prediction of immune checkpoint blockade efficacy (14).

Previously, our group performed an immunological classification based on PD-L1 and CD8B gene expression levels and demonstrated that the PD-L1+ and CD8B+ groups were associated with the upregulation of cytotoxic T lymphocyte (CTL) killing-associated genes, T cell activation genes, antigen-presentation genes and dendritic cell (DC) maturation genes, and promoted T helper 1 (Th1) antitumor responses (19). However, there are few immune-type classification studies that directly evaluated cancer patient prognosis.

In the present study, we verified that the PD-L1+CD8B+ group (type A) was associated with a better prognosis [5-year overall survival time (OST)] than the other types. In addition, we identified prognostic factors responsible for the survival benefit of patients in type A based on 293 immune response-associated gene expression datasets.

Materials and methods

Patient characteristics and study design

The Shizuoka Cancer Center launched Project HOPE in 2014 using multiomics analyses including whole exome sequencing (WES) and gene expression profiling (GEP). Ethical approval for the HOPE study was obtained from the Institutional Review Board of Shizuoka Cancer Center (authorization no. 25-33). In total, 1,763 patients with tumors were enrolled until March 2016 and the survival time was observed up to July 2019.

Clinical specimens

Tumor tissue samples weighing more than 0.1 g and with a tumor content greater than 50% were dissected along with surrounding normal tissue samples by pathologists.

GEP and WES analysis

DNA and RNA isolation and the GEP and WES analyses were performed as described previously (20). RNA samples with an RNA integrity number ≥6.0 were used for microarray analysis. Labeled samples were hybridized to the SurePrint G3 Human Gene Expression 8x60 K v2 Microarray (Agilent Technologies). Microarray analysis was performed in accordance with the MIAME guidelines. For DNA data analysis, somatic mutations were identified by comparing data from tumor and corresponding blood samples. Mutations in 138 known driver genes were defined as those identified as pathogenic in the ClinVar database. Vogelstein et al (21) demonstrated that 138 genes, when altered by intragenic mutations, can promote or drive tumorigenesis. A most of tumors including colorectal cancers contain two to eight of these ʻdriver gene’ mutations and the remaining mutations are passengers that do not contribute to tumorigenesis directly. Thus, these 138 driver mutations are accepted as relevant genes to the tumorigenesis (21). Single nucleotide variants (SNVs) of the total exonic mutations for each sequenced tumor included nonsynonymous, synonymous, and indel/frameshift mutations.

Renewal of the immune response-associated gene panel

The immune response-associated gene panel was described previously (22). In the present study, the gene panel was renewed by adding 119 immunological genes (293-gene panel) as shown in Table I. The panel consisted of 114 antigen-presenting cell (APC), T cell and natural killer cell receptor (NKR) genes; 48 cytokine signal and metabolic genes; 48 tumor necrosis factor (TNF) and TNF receptor superfamily genes; 23 regulatory T cell-associated genes; and 60 IFN-g pathway genes.

Table I

Immune response-associated genes list.

Table I

Immune response-associated genes list.

GroupsGenesNo. of genes
APC, T cell and NKR genesCD80, CD86, CD274 (PD-L1), PDCD1LG2 (PD-L2), ICOSLG, CD276, VTCN-1, C10orf54, B7H6, HHLA2, LGALS9, SIRPB1, TREM1, CLEC5A, SIGLEC14, CD68, CD204(MSR1), HLA-DPA, HLA-DQA, HLA-DRA, HLA-DRB1, HLA-DQA2, CD19, CD20, CD38, CD138, CD28, CTLA4, CD279 (PD-1), ICOS, BTLA, SLAMF1, HAVCR1, HAVCR2, TIMD4, TREML2, LAG3, CD247 (CD3zeta), CD4, CD8A, CD8B, CD25, FOXP3, CCR4, CD56 (NCAM1), CD3D, CD3G, CD3E, HLA-A, HLA-B, HLA-C, HLA-E, MICA, MICB, ULBP1, ULBP2, ULBP3, RAET1E, NKp44L, CLEC2D, CLEC12B, CDH1, CDH2, CDH3, CDH4, CD83, CD11b, CD11c, CD209, TIGIT, CD155, CD200, CD200R, GZMB, PRF1, CD44, CD45, CD62L, CCR7, CXCR3, CXCR4, CD69, BCL2, CD122, CD127, CD16, CD314 (NKG2D), CD335 (NCR1), TLR1, TLR2, TLR3, TLR4, TLR5, TLR6, TLR7, TLR8, TLR9, TLR10, DDX58, IFIH1, DHX58, NOD1, NOD2, CLEC4E, CLEC6A, CLEC7A, STING, TOX, TCF7, B2MG, TAP1, TAP2, NT5E (CD73), ADRA2A114
Cytokine signal and metabolic genesTGFB1, TGFB2, TGFB3, TGFBR1, TGFBR2, VEGFA, IFNA1, IFNA2, IFNB1, IL2, IL4, IL6, IFNG, IL10, IL12A, IL17A, IL23A, IDO1, ARG1, NOS2, PTGS2, AHR, TDO2, JAK2, STAT1, STAT3, STAT4, STAT5, STAT6, SOCS1, VCAM1, CCL2, CCL3, CCL4, CCL5, CCL19, CCL21, CCL22, CXCL5, CXCL8, CXCL9, CXCL10, CXCL12, CXCR2, CSF1, CSF2, CSF3, CSF1R48
TNFSF and TNFRSFTNFSF1 (LTA), TNFSF2 (TNF), TNFSF3 (LTB), TNFSF4, TNFSF5 (CD40LG), TNFSF6 (FASLG), EDA, TNFSF7 (CD70), TNFSF8, TNFSF9, TNFSF10, TNFSF11, TNFSF12, TNFSF13, TNFSF13B, TNFSF14, TNFSF15, TNFSF18, TNFRSF1A, TNFRSF1B, EDAR, TNFRSF3 (LTBR), TNFRSF4, TNFRSF5 (CD40), TNFRSF6 (FAS), TNFRSF6B, TNFRSF7(CD27), TNFRSF8(CD30), TNFRSF9, TNFRSF10A, TNFRSF10B, TNFRSF10C, TNFRSF10D, TNFRSF11A, TNFRSF11B, TNFRSF12A, TNFRSF13B, TNFRSF13C, TNFRSF14, TNFRSF16(NGFR), TNFRSF17, TNFRSF18, TNFRSF19, TNFRSF19L(RELT), TNFRSF21, TNFRSF25, TNFRSF27 (EDA2R)48
Regulatory T cell-associated genesSEMA3G, LGALS3, ENTPD1 (CD39), CCR6, CCL20, IL12RB2, CCR10, ANXA2, IL17RB, ADAM12, TMEM45A, LRRC32, LOXL1, GREB1, HRH4, CCR5, BMPR1B, SFRP1, LAMA2, ITGB1, CPE, MKI67, CDCA323
IFN-γ pathway genesIFIT1B, IFNA21, IFNW1, IFNA14, IFNA4, IFNA5, IFNA6, IFNA8, IFNE, IFIT1, IFNK, CNTFR, IFIT2, IFIT3, IL10RA, IL11RA, IL20RA, CREB3, IL12B, IL31RA, IL7R, IFI30, IFNGR1, IFRD1, IFRD2, IL22RA2, IL5RA, IRF1, IRF8, IRGM, JAK1, MX1, OAS1, PIK3CA, PRKCD, PYH1N1, PIAS4, EBI3, IFI27, IFNAR1, IFNAR2, IFNGR2, IL10RB, IL21R, IL28A, IL28RA, IL29, IL4R, IL6R, IRF2BP1, IRF3, IRF4, IRF5, LEPR, MPL, SP110, STAT2, TBX21, TYK2, SOCS360
Total 293

[i] APC, antigen-presenting cell; NKR, natural killer cell receptor; TNFSF, TNF super-family; TNFRSF, TNF receptor super-family.

Statistical analysis

Based on the expression levels of the PD-L1 and CD8B genes, we classified all 1,763 tumors enrolled in the HOPE project into 4 immune types: type A, PD-L1+CD8B+; type B, PD-L1+CD8-; type C, PD-L1-CD8B-; and type D, PD-L1-CD8B+ as described previously. A comparative analysis of the survival times between group A and the other groups was performed using the Kaplan-Meier method and Cox proportional hazards regression model. The upregulated genes derived from the 293-immune response-associated gene panel between tumor microenvironment (TME) immune type A and other types were identified using the volcano plot method with Benjamini-Hochberg correction. Upregulated immune response-associated genes with >2-fold expression differences (P<0.05) were identified. The heatmap expression data of upregulated genes in the immune type A group were investigated using GeneSpring GX software version 13.1.1 (Agilent Technologies). The association of upregulated gene expression levels with the OST was examined using the Kaplan-Meier method. A comparative analysis of the survival times between patients with low expression (less than the median) and patients with high expression (more than the median) of the identified genes in group type A (referred as to group A) was performed by the log-rank test using EZR software and Microsoft Excel. Regarding probable prognosis-associated genes identified in group A, the significance of these genes was analyzed using a multivariate Cox proportional hazards regression model with EZR software (23). Values of P<0.05 denoted statistically significant differences.

Results

Association of the overall survival time with immune types

The 1,763 pairs of tumors and adjacent normal tissues derived from different cancer types were classified into 4 immune types based on the expression levels of the PD-L1 and CD8B genes. The patient numbers with different cancer types were described previously (17). The proportions of TME immune types A, B, C and D were 39.3, 26.5, 19.1 and 15.1%, respectively. Survival time analysis at 5 years revealed that group A had a better prognosis than the other groups [5 year survival rate (%); A (80.5) vs. B (73.9), C (73.4) and D (72.6), P=0.0005] (Fig. 1).

Association of genetic mutations and immunological surface markers with overall survival

The characteristics of genetic mutations, including Vogelstein driver mutations and SNVs, and gene amplification were described previously (19). The association of the genetic mutation status of driver gene mutations, such as TP53, KRAS, EGFR, PIK3CA and BRAF mutations, or gene amplification with the OST was investigated using the log-rank test. There was no significant association of genetic parameters with the OST (Table II).

Table II

Association of immunological and genetic features with overall survival.

Table II

Association of immunological and genetic features with overall survival.

GroupCohort (case no./5yrOS)P-value
Genetic mutations  
     VogelsteinMT (1084/77.3%) vs. WT (679/74.6%)0.184
     TP53MT (729/74.5%) vs. WT (1034/77.4%)0.206
     KRASMT (299/77.7%) vs. WT (1464/75.9%)0.431
     EGFRMT (107/73.7%) vs. WT (1656/76.3%)0.215
     PIK3CAMT (169/80.5%) vs. WT (1594/75.9%)0.625
     BRAFMT (64/77.1%) vs. WT (1699/76.2%)0.912
     TMB number>20 (83/81.7%) vs. <20 (1679/76.0%)0.512
Gene amplificationa  
     All 64 genesbYes (575/75.9%) vs. No (833/75.9%)0.858
     EGFRYes (61/75.4%) vs. No (1347/75.9%)0.746
     HER2Yes (33/71.3%) vs. No (1375/76.0%)0.530

[i] aGene amplification, fold-change in expression ≥5 and copy number ≥6.

[ii] b64 amplified gene list was reported previously (20). Comparison of MST between cohorts was performed using the log-rank test. P<0.05 denote statistically significant differences. 5yrOS, 5-year overall survival rate; MT, mutated; WT, wild-type; TMB, tumor mutation burden number.

The identification of upregulated immune response-associated genes in immune type A compared with the other types

Based on the expression profile of the 293-immune response-associated gene panel, 62 upregulated immune response-associated genes (more than 2-fold and P-value 1.0E-50) were identified using volcano plots (Fig. 2).

Comparison of upregulated genes among immune types or between the poor prognosis and good prognosis cohorts

The heatmap expression data of 62 upregulated genes in group A were compared with those of the other groups. Interestingly, in group A, T cell effector activation genes (CXCL9, CXCL10, and TNFRSF9) and CTL killing genes (GZMB, CD16) showed high expression, while immune checkpoint genes such as CTLA4 and TIGIT also showed high expression levels. In contrast, in group C, T cell effector activation genes (ICOS, CD69, and CD40LG) and Th1 cytokine genes (IFNG and TNF) exhibited low expression (Fig. 3). Additionally, the upregulated T cell activation genes identified in group A showed a tendency of higher expression levels in the better survival cohort than in the poorer survival cohort, as shown in Fig. 4.

Association of the upregulated gene expression level with the overall survival time

The association of 62 upregulated genes in group A with the OST was analyzed by the log-rank test using EZR software. Ultimately, 18 genes were found to be significantly associated with prognosis (Table III). Memory T cell markers such as PD-1, CD27 and ICOS, as well as activated effector T cell genes (GZMB, CXCL10 and CD40LG) and mature DC marker genes (CD80 and SLAMF1), were selected as prognostic factors. Interestingly, immune checkpoint marker genes, such as HAVCR2 and TIGIT, were also verified as prognostic markers; however, the HAVCR2 gene was demonstrated to be a poor prognostic marker, although it was upregulated in group A.

Table III

Probable prognostic genes identified from 62 upregulated genes.

Table III

Probable prognostic genes identified from 62 upregulated genes.

Probe nameFold-changeGene symbol5yrOS (%)a Positive. vs. NegativeLog-rank P-value
A_23_P1176024.401GZMB80.7 vs. 71.7 1.44x10-4
A_24_P4115612.005HAVCR274.1 vs. 78.3 2.03x10-3
A_23_P3712154.31ICOS80.5 vs. 71.9 2.14x10-3
A_23_P184525.751CXCL980.5 vs. 71.8 3.06x10-3
A_23_P4201962.024SOCS179.6 vs. 72.8 3.44x10-3
A_23_P1364052.388PDCD180.3 vs. 72.1 3.6x10-3
A_24_P3030914.874CXCL1080.5 vs. 71.9 4.76x10-3
A_23_P984103.159CD3G79.8 vs. 72.7 1.47x10-2
A_23_P4208632.004NOD279.1 vs. 73.3 1.82x10-2
A_33_P32506802.608CD40LG78.6 vs. 74.0 2.52x10-2
A_33_P33755413.117CD3D79.7 vs. 72.7 2.6x10-2
A_23_P626472.012SLAMF179.7 vs. 72.6 2.6x10-2
A_24_P3200332.167CD8079.2 vs. 73.2 2.96x10-2
A_23_P480882.628CD2779.9 vs. 72.7 3.31x10-2
A_23_P4167472.052CD3E78.9 vs. 73.6 3.64x10-2
A_33_P33420564.335TIGIT79.3 vs. 73.2 4.16x10-2
A_23_P3384793.96CD27478.6 vs. 73.8 4.33x10-2
A_23_P417652.526IRF179.2 vs. 73.0 4.53x10-2

[i] aThe 5yrOS between positive (higher expression than the median level) and negative (lower expression than the median level) groups were compared using the log-rank test using EZR software. Ultimately, 18 genes were found to be significantly associated with prognosis of patients with cancer. Only the HAVCR2 gene demonstrated a negative association with prognosis. 5yrOS, 5-year overall survival.

Identification of probable prognostic genes using multivariate Cox hazards regression analysis

To evaluate the prognostic value of the genes, 18 probable prognostic genes identified using the Kaplan-Meier method from 62 upregulated genes in group A were analyzed by the Cox proportional hazards regression model. In particular, the multivariate analysis demonstrated that four upregulated genes, namely, GZMB, HAVCR2, CXCL9 and CD40LG, maintained their significance (P<0.05), as shown in Table IV. The survival curves of these four significant genes were drawn with the Kaplan-Meier method, and the OST was compared between the group that was higher-than-the median-level and the group that was lower-than-the median-level, as shown in Fig. 5. The upregulation of GZMB, CXCL9 and CD40LG gene expression might be linked to better prognosis in group A patients.

Table IV

Cox proportional hazards regression analysis of overall survival in upregulated genes.

Table IV

Cox proportional hazards regression analysis of overall survival in upregulated genes.

VariableHazard ratio (95% CI)P-value
GZMB0.628 (0.496-0.795) 1.11x10-4
HAVCR21.848 (1.479-2.309) 6.63x10-8
CXCL90.778 (0.613-0.988)0.0393
CD40LG0.792 (0.642-0.977)0.0292

[i] From probable prognosis-associated genes identified in group A, the significance of those genes was analyzed using multivariate Cox proportional hazards regression model in the EZR software. P<0.05 denoted statistically significant differences.

Discussion

With advances in genome analysis technologies such as NGS- and single-cell RNA sequencing, probable immunological factors belonging to the TME and associated with prognosis have been more intensively, specifically and accurately investigated (24-26). Beyond the already-known TME factors that might be responsible for the efficacy of cancer immunotherapy, such as positive PD-L1 expression, a high mutational burden and an advanced TIL status, more specific and dynamic biomarkers associated with the immune response have been reported (27-29). Recently, Kumagai et al demonstrated using cytometry by time of flight (CyTOF) analysis based on single-cell RNA-seq that a balance between PD-1+CD8+ T cells and PD-1+CD4+FoxP3+ Treg cells is a critical determinant of the response to anti-PD-1/PD-L1 blockade therapy (29).

Previously, we reported an efficient immunological classification based on PD-L1 and CD8B gene expression levels and demonstrated that immune type A (PD-L1+CD8B+) was associated with the Th1 T cell and NK cell activation pathways, dendritic cell maturation and cancer-apoptosis activation signals and showed the highest score in immune-activation signaling pathways by means of Ingenuity Pathways Analysis (IPA) software (19). Similar studies have been conducted that showed antitumor immunological features in PD-L1+CD8+ cohort (11,12).

However, there have been few studies that have performed a long-term follow-up of overall survival in cancer patients belonging to the immune type classifications described above. Ock et al classified similarly solid tumors into specific immune types based on PD-L1 and CD8 gene expression data derived from The Cancer Genome Atlas (TCGA) database and compared the survival time between immune types; however, the temporary difference in 3-year survival time in type A finally disappeared in the 5-year comparison (12).

In the current study, we followed 1,763 patients with tumors up to 70 months after registration in the project HOPE study. Survival time analysis at 5 years revealed that group A had a better prognosis than the other groups, as shown in Fig. 1. There are some concerns regarding the temporary results of the present survival analysis: i) Miscellaneous cancer patients across various histology groups were included, and ii) there were various clinical courses, including different types of therapies and response statuses. However, despite different clinical courses in individual patients, the immunological status at cancer diagnosis can be determined temporarily in terms of the OST, and could be a reference parameter for therapeutic design because some immunological mechanisms are involved in tumor regression after or even during chemo- and radiation therapy (30-33).

In the present study, the impressive findings were that memory T cell markers (central ~ effector memory), such as PD-1, CD27 and ICOS, were selected as prognostic factors. In addition to effector-activated CTLs and NK cells, memory marker+ T cells should be considered crucial factors because i) PD-1+ T cells can achieve a good balance between good and poor responses by immune checkpoint blockade (27), and ii) effector memory T cells that proliferate by the stimulation of antigen-presenting cells, can be differentiated into activated effector CTLs (34). Another important observation was that T cell exhaustion marker genes such as HAVCR22 (TIM3) and TIGIT were included as prognostic markers. However, HAVCR2 was found to be a negative prognostic marker, suggesting that it did not contribute to the good prognosis of patients in immune group A. Very recently, Simon et al demonstrated that a high frequency of the PD-1+TIGIT+ (double-positive) CD8+ T cell subset in peripheral blood can be a good predictive marker for a good response to anti-PD-1 therapy (35). Therefore, these cells should be prolonged by anti-PD-1/PD-L1 blockade to maintain the antitumor effect, which could contribute to the good prognosis in cancer patients belonging to immune type A.

Additionally, based on prognostic factor profiling in immune group A, the upregulation of the CD80, CD274 and SLAMF1(36) genes might suggest the presence of mature dendritic cells in the TME. Interestingly, Schetters et al demonstrated that anti-PD-1 immune checkpoint blockade induced mature monocyte-derived dendritic cells in the TME (37), which means that the presence of mature dendritic cells in the tumor site could be a key factor in the prediction of ICB efficacy.

Considering that immunological conditions are varied and complicated in the TME, the status of patients with cancer is volatile and undetermined before the start of treatment. Most likely, immune type group A (PD-L1+CD8+) could be a good candidate to elicit neoantigen-specific T cell reactions and result in an improved prognosis in cancer patients. Efficient combination therapy with chemo- and radiation therapy should be explored for these types of cancer cohorts in the future.

Acknowledgements

Not applicable.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the National Bioscience Database Center repository (accession no. hum0127; https://humandbs.biosciencedbc.jp/en/).

Authors' contributions

RK and YA participated equally in the design of the study and drafting of the manuscript, and were responsible for completing the study. AS, YO, MTe, KUe, TO, SN, YH, NH, YK, YT, HKat, MNi, KT, HKas, MNa and YI were responsible for the clinical work, including the collection of clinical samples. TN, YS, KUr, KO, AI, HM, CM, AK, KW and TA participated in the design of the experiments and performed the genetic analysis. TS contributed to the pathological diagnosis. AN and KM contributed to data analysis and interpretation and confirmed the authenticity of all the raw data. MTa, HKe and KY designed the current study, revised the manuscript critically for important intellectual content and gave final approval of the version to be published by taking responsibility for the content. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The Shizuoka Cancer Center launched Project HOPE based on multiomics analyses, including WES and GEP. Ethics approval for the HOPE study was obtained from the institutional review board at the Shizuoka Cancer Center (authorization no. 25-33). Written informed consent was obtained from all patients enrolled in the study.

Patient consent for publication

Written informed consent was obtained from all patients for the publication of any associated data and accompanying images.

Competing interests

The authors declare that they have no competing interests.

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November-2021
Volume 15 Issue 5

Print ISSN: 2049-9450
Online ISSN:2049-9469

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Copy and paste a formatted citation
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Spandidos Publications style
Kondou R, Akiyama Y, Iizuka A, Miyata H, Maeda C, Kanematsu A, Watanabe K, Ashizawa T, Nagashima T, Urakami K, Urakami K, et al: Identification of tumor microenvironment‑associated immunological genes as potent prognostic markers in the cancer genome analysis project HOPE. Mol Clin Oncol 15: 232, 2021
APA
Kondou, R., Akiyama, Y., Iizuka, A., Miyata, H., Maeda, C., Kanematsu, A. ... Yamaguchi, K. (2021). Identification of tumor microenvironment‑associated immunological genes as potent prognostic markers in the cancer genome analysis project HOPE. Molecular and Clinical Oncology, 15, 232. https://doi.org/10.3892/mco.2021.2395
MLA
Kondou, R., Akiyama, Y., Iizuka, A., Miyata, H., Maeda, C., Kanematsu, A., Watanabe, K., Ashizawa, T., Nagashima, T., Urakami, K., Shimoda, Y., Ohshima, K., Shiomi, A., Ohde, Y., Terashima, M., Uesaka, K., Onitsuka, T., Nishimura, S., Hirashima, Y., Hayashi, N., Kiyohara, Y., Tsubosa, Y., Katagiri, H., Niwakawa, M., Takahashi, K., Kashiwagi, H., Nakagawa, M., Ishida, Y., Sugino, T., Notsu, A., Mori, K., Takahashi, M., Kenmotsu, H., Yamaguchi, K."Identification of tumor microenvironment‑associated immunological genes as potent prognostic markers in the cancer genome analysis project HOPE". Molecular and Clinical Oncology 15.5 (2021): 232.
Chicago
Kondou, R., Akiyama, Y., Iizuka, A., Miyata, H., Maeda, C., Kanematsu, A., Watanabe, K., Ashizawa, T., Nagashima, T., Urakami, K., Shimoda, Y., Ohshima, K., Shiomi, A., Ohde, Y., Terashima, M., Uesaka, K., Onitsuka, T., Nishimura, S., Hirashima, Y., Hayashi, N., Kiyohara, Y., Tsubosa, Y., Katagiri, H., Niwakawa, M., Takahashi, K., Kashiwagi, H., Nakagawa, M., Ishida, Y., Sugino, T., Notsu, A., Mori, K., Takahashi, M., Kenmotsu, H., Yamaguchi, K."Identification of tumor microenvironment‑associated immunological genes as potent prognostic markers in the cancer genome analysis project HOPE". Molecular and Clinical Oncology 15, no. 5 (2021): 232. https://doi.org/10.3892/mco.2021.2395