The correlation between the subsets of tumor infiltrating memory T cells and the expression of indoleamine 2,3-dioxygenase in gastric cancer

Malignant cell receptor-ligand subtypes guide the prediction of prognosis and personalized immunotherapy of liver cancer

OBJECTIVE

Liver cancer is a prevalent disease with a dismal prognosis. The aim of the research is to identify subgroups based on malignant cell receptor ligand gene from single-cell RNA, which might lead to customized immunotherapy for patients with liver cancer.

METHODS

Based on scRNA-seq data, we identified the receptor-ligand genes associated with prognosis and classify patients into molecular subtypes by univariate Cox regression and consensus clustering. LASSO regression was performed to construct a prognostic model, which was validated in TCGA and ICGC datasets. Immune infiltration and prediction of immunotherapy response were analyzed using ssGSEA, ESTIMATE, TIDE, and TRS score calculation. Finally, qPCR and Western blot validation of key genes and protein levels in cell lines.

RESULTS

A risk model using 16-gene expression levels predicted liver cancer patients' prognosis. The RiskScore associated significantly with tumor clinical characteristics and immunity, integrated with clinicopathological features for survival prediction. Differential expression of SRXN1 was verified in hepatocellular carcinoma and normal liver cells.

CONCLUSION

Our study utilizes single-cell analysis to investigate the communication between malignant cells and other cell types, identifying molecular subtypes based on malignant cell receptor ligand genes, offering new insights for the development of personalized immunotherapy and prognostic prediction models.

Development and validation of a ligand-receptor pairs signature to predict outcome and provide a therapeutic strategy in gastric cancer

BACKGROUND

An important factor in tumor development and progression is the tumor microenvironment (TME), which is heterogeneous. Previous studies have mainly investigated the expression profile and prognostic values of genes in gastric cancer (GC) at the cell population level but neglected the interactions and heterogeneity between cells.

METHODS

The pattern of ligand-receptor (LR) interactions was delineated on a scRNA-seq dataset containing 44,953 cells from nine GC patients and a fourth bulk RNA-seq dataset including data from 1159 GC patients. We then constructed an LR.Score scoring model to comprehensively evaluate the influence of LR-pairs on the TME, overall survival, and immunotherapy response in GC patients from several cohorts.

RESULTS

Cell communication network among 13 cell types was constructed based on the LR-pairs. We proposed a new molecular subtyping model for GC based on the LR-pairs and revealed the differences in prognosis, pathophysiologic features, mutation characteristics, function enrichment, and immunological characteristics among the three subtypes. Finally, an LR.Score model based on LR-pairs was developed and validated on several datasets.

CONCLUSIONS

Based on the selected LR-pairs, we successfully constructed a novel prediction model and observed its well performance on molecular subtyping, target and pathway screening, prognosis judging, and immunotherapy response predicting.

Glycosyltransferase GLT8D1 and GLT8D2 serve as potential prognostic biomarkers correlated with Tumor Immunity in Gastric Cancer

BACKGROUND

Glycosylation involved in various biological function, aberrant glycosylation plays an important role in cancer development and progression. Glycosyltransferase 8 domain containing 1 (GLT8D1) and GLT8D2, as members of the glycosyltransferase family proteins, are associated with transferase activity. However, the association between GLT8D1/2 and gastric cancer (GC) remains unclear. We aimed to investigate the potential prognostic value and oncogenic role of GLT8D1/2 in GC.

METHODS

The relationship between GLT8D1/2 and GC was evaluated through comprehensive bioinformatics approaches. A series of factors like gene expression patterns, Kaplan-Meier survival analyses, Cox regression analyses, prognostic nomogram, calibration curves, ROC curves, function enrichment analyses, tumor immunity association, genetic alterations, and DNA methylation were included. Data and statistical analyses were performed using R software (v3.6.3).

RESULTS

Both GLT8D1 and GLT8D2 expression were significantly upregulated in GC tissues(n = 414) compared with normal tissues(n = 210), and high expression of GLT8D1/2 was remarkably correlated with poor prognosis for GC patients. Cox regression analyses implied that GLT8D1/2 could act as independent prognostic factors in GC. Furthermore, gene function analyses indicated that multiple signaling pathways involving tumor oncogenesis and development enriched, such as mTOR, cell cycle, MAPK, Notch, Hedgehog, FGF, and PI3K-Akt signaling pathways. Moreover, GLT8D1/2 was significantly associated with immune cell infiltration, immune checkpoint genes, and immune regulators TMB/MSI.

CONCLUSION

GLT8D1/2 may serve as potential prognostic markers of poor prognosis in GC correlated with tumor immunity. The study provided an insight into identifying potential biomarkers and targets for prognosis, immunotherapy response, and therapy in GC.

Development of Prognostic Features of Hepatocellular Carcinoma Based on Metabolic Gene Classification and Immune and Oxidative Stress Characteristic Analysis

Background

The molecular classification of HCC premised on metabolic genes might give assistance for diagnosis, therapy, prognosis prediction, immune infiltration, and oxidative stress in addition to supplementing the limitations of the clinical staging system. This would help to better represent the deeper features of HCC.

Methods

TCGA datasets combined with GSE14520 and HCCDB18 datasets were used to determine the metabolic subtype (MC) using ConsensusClusterPlus. ssGSEA method was used to calculate the IFNγ score, the oxidative stress pathway scores, and the score distribution of 22 distinct immune cells, and their differential expressions were assessed with the use of CIBERSORT. To generate a subtype classification feature index, LDA was utilized. Screening of the metabolic gene coexpression modules was done with the help of WGCNA.

Results

Three MCs (MC1, MC2, and MC3) were identified and showed different prognoses (MC2-poor and MC1-better). Although MC2 had a high immune microenvironment infiltration, T cell exhaustion markers were expressed at a high level in MC2 in contrast with MC1. Most oxidative stress-related pathways are inhibited in the MC2 subtype and activated in the MC1 subtype. The immunophenotyping of pan-cancer showed that the C1 and C2 subtypes with poor prognosis accounted for significantly higher proportions of MC2 and MC3 subtypes than MC1, while the better prognostic C3 subtype accounted for significantly lower proportions of MC2 than MC1. As per the findings of the TIDE analysis, MC1 had a greater likelihood of benefiting from immunotherapeutic regimens. MC2 was found to have a greater sensitivity to traditional chemotherapy drugs. Finally, 7 potential gene markers indicate HCC prognosis.

Conclusion

The difference (variation) in tumor microenvironment and oxidative stress among metabolic subtypes of HCC was compared from multiple angles and levels. A complete and thorough clarification of the molecular pathological properties of HCC, the exploration of reliable markers for diagnosis, the improvement of the cancer staging system, and the guiding of individualized treatment of HCC all gain benefit greatly from molecular classification associated with metabolism.

Metabolic heterogeneity in early-stage lung adenocarcinoma revealed by RNA-seq and scRNA-seq

PURPOSE

Cancer cells maintain cell growth, division, and survival through altered energy metabolism. However, research on metabolic reprogramming in lung adenocarcinoma (LUAD) is limited METHODS: We downloaded TCGA and GEO sequencing data. Consistent clustering with the ConsensusClusterPlus package was employed to detect the scores for four metabolism-related pathways. The LUAD samples in the TCGA dataset were clustered with ConsensusClusterPlus, and the optimal number of clusters was determined according to the cumulative distribution function (CDF). The cell score for each sample in the TCGA dataset was calculated using the MCPcounter estimate function of the MCPcounter package.

RESULTS

We identified two subtypes by scoring the samples based on the 4 metabolism-related pathways and cluster dimensionality reduction. The prognosis of cluster B was obviously poorer than that of cluster A in patients with LUAD. The analysis of single-nucleotide variation (SNV) data showed that the top 15 genes in the four metabolic pathways with the most mutations were TKTL2, PGK2, HK3, EHHADH, GLUD2, PKLR, TKTL1, HADHB, CPT1C, HK1, HK2, PFKL, SLC2A3, PFKFB1, and CPT1A. The IFNγ score of cluster B was significantly higher than that of cluster A. The immune T-cell lytic activity score of cluster B was significantly higher than that of cluster A. We further identified 5 immune cell subsets from single-cell sequencing data. The top 5 marker genes of B cells were IGHM, JCHAIN, IGLC3, IGHA1, and IGKC. The C0 subgroup of monocytes had a higher pentose phosphate pathway (PPP) score than the C6 subgroup.

CONCLUSIONS

Metabolism-related subtypes could be potential biomarkers in the prognosis prediction and treatment of LUAD.

Dysfunctional epigenetic protein-coding gene-related signature is associated with the prognosis of pancreatic cancer based on histone modification and transcriptome analysis

Emerging evidence suggests that epigenetic alterations are responsible for the oncogenesis and progression of cancer. However, the role of epigenetic reprogramming in pancreatic cancer is still not clear. In this study, we used the limma R package to identify differentially expressed protein-coding genes (PCGs) between pancreatic cancer tissues and normal control tissues. The cell-type identification by the estimating relative subsets of RNA transcripts (CIBERSORT) package was used to quantify relative cell fractions in tumors. Prognostic molecular clusters were constructed using ConsensusClusterPlus analysis. Furthermore, the least absolute shrinkage and selection operator and stepAIC methods were used to construct a risk model. We identified 2351 differentially expressed PCGs between pancreatic cancer and normal control tissues in The cancer genome atlas dataset. Combined with histone modification data, we identified 363 epigenetic PCGs (epi-PCGs) and 19,010 non-epi-PCGs. Based on the epi-PCGs, we constructed three molecular clusters characterized by different expression levels of chemokines and immune checkpoint genes and distinct abundances of various immune cells. Furthermore, we generated a 9-gene model based on dysfunctional epi-PCGs. Additionally, we found that patients with high risk scores showed poorer prognoses than patients with low risk scores (p < 0.0001). Further analysis showed that the risk score was significantly related to survival and was an independent risk factor for pancreatic cancer patients. In conclusion, we constructed a 9-gene prognostic risk model based on epi-PCGs that might serve as an effective classifier to predict overall survival and the response to immunotherapy in pancreatic cancer patients.

Novel tumor necrosis factor-related long non-coding RNAs signature for risk stratification and prognosis in glioblastoma

Background

Tumor necrosis factor (TNF) is an inflammatory cytokine that can coordinate tissue homeostasis by co-regulating the production of cytokines, cell survival, or death. It widely expresses in various tumor tissues and correlates with the malignant clinical features of patients. As an important inflammatory factor, the role of TNFα is involved in all steps of tumorigenesis and development, including cell transformation, survival, proliferation, invasion and metastasis. Recent research has showed that long non-coding RNAs (lncRNAs), defined as RNA transcripts >200 nucleotides that do not encode a protein, influence numerous cellular processes. However, little is known about the genomic profile of TNF pathway related-lncRNAs in GBM. This study investigated the molecular mechanism of TNF related-lncRNAs and their immune characteristics in glioblastoma multiforme (GBM) patients.

Methods

To identify TNF associations in GBM patients, we performed bioinformatics analysis of public databases - The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). The ConsensusClusterPlus, CIBERSORT, Estimate, GSVA and TIDE and first-order bias correlation and so on approaches were conducted to comprehensively characterize and compare differences among TNF-related subtypes.

Results

Based on the comprehensive analysis of TNF-related lncRNAs expression profiles, we constructed six TNF-related lncRNAs (C1RL-AS1, LINC00968, MIR155HG, CPB2-AS1, LINC00906, and WDR11-AS1) risk signature to determine the role of TNF-related lncRNAs in GBM. This signature could divide GBM patients into subtypes with distinct clinical and immune characteristics and prognoses. We identified three molecular subtypes (C1, C2, and C3), with C2 showing the best prognosis; otherwise, C3 showing the worst prognosis. Moreover, we assessed the prognostic value, immune infiltration, immune checkpoints, chemokines cytokines and enrichment analysis of this signature in GBM. The TNF-related lncRNA signature was tightly associated with the regulation of tumor immune therapy and could serve as an independent prognostic biomarker in GBM.

Conclusion

This analysis provides a comprehensive understanding of the role of TNF-related characters, which may improve the clinical outcome of GBM patients.

Systematic Analysis of Molecular Subtypes Based on the Expression Profile of Immune-Related Genes in Pancreatic Cancer

Immunotherapy has a good therapeutic effect and provides a new approach for cancer treatment. However, only limited studies have focused on the use of molecular typing to construct an immune characteristic index for gene expression in pancreatic adenocarcinoma (PAAD) and to assess the effectiveness of immunotherapy in patients with PAAD. Clinical follow-up data and gene expression profile of PAAD patients were retrieved from The Cancer Genome Atlas (TCGA) database. Based on 184 immune features, molecular subtypes of pancreatic cancer were found by the "ConsensusClusterPlus" package, and the association between clinical features and immune cell subtype distribution was analysed. In addition, the relationship between the proportion of immune subtypes and the expression of immune checkpoints was analysed. The CIBERSORT algorithm was introduced to evaluate the immune scores of different molecular subtypes. We used the tumor immune dysfunction and exclusion (TIDE) algorithm to assess the potential clinical effect of immunotherapy interventions on single-molecule subtypes. In addition, the oxidative stress index was constructed by linear discriminant analysis DNA (LDA), and weighted correlation network analysis was performed to identify the core module of the index and its characteristic genes. Expression of hub genes was verified by immunohistochemical analysis in an independent PAAD cohort. Pancreatic cancer is divided into three molecular subtypes (IS1, IS2, and IS3), with significant differences in prognosis between multiple cohorts. Expression of immune checkpoint-associated genes was significantly reduced in IS3 and higher in IS1 and IS2, suggesting that the three subgroups have different responsiveness to immunotherapy interventions. The results of the CIBERSORT analysis showed that IS1 exhibited the highest levels of immune infiltration, whereas the results of the TIDE analysis showed that the T-cell dysfunction score of IS1 was higher than that of IS2 and IS3. Furthermore, IS3 was found to be more sensitive to 5-FU and to have a higher immune signature index than IS1 and IS2. Based on WGCNA analysis, 10 potential gene markers were identified, and their expression at the protein level was verified by immunohistochemical analysis. Specific molecular expression patterns in pancreatic cancer can predict the efficacy of immunotherapy and influence the prognosis of patients.

The Correlation of Ten Immune Checkpoint Gene Expressions and Their Association with Gastric Cancer Development

In the immunotherapy based on immune checkpoint inhibition (IC), additional ICs are being studied to increase its effectiveness. An almost unstudied feature is the possible co-expression of ICs, which can determine the therapeutic efficacy of their inhibition. For the selection of promising ICs, information on the association of their expression with cancer development may be essential. We have obtained data on the expression correlation of ADAM17, PVR, TDO2, CD274, CD276, CEACAM1, IDO1, LGALS3, LGALS9, and HHLA2 genes in gastric cancer (GC). All but one, TDO2, have other IC genes with co-expression at some stage. At the metastatic stage, the expression of the IDO1 does not correlate with any other gene. The correlations are positive, but the expressions of the CD276 and CEACAM1 genes are negatively correlated. The expression of TDO2 and LGALS3 is associated with GC metastasis. The expression of TDO2 four-fold higher in metastatic tumors than in non-metastatic tumors, but LGALS3 was two-fold lower. The differentiation is associated with IDO1. The revealed features of TDO2, with a significant increase in expression at the metastatic stage and the absence of other IC genes with correlated expression indicates that the prospect of inhibiting TDO2 in metastatic GC. IDO1 may be considered for inhibition in low-differentiated tumors.

Establishing a Novel Gene Signature Related to Histone Modifications for Predicting Prognosis in Lung Adenocarcinoma

Background

Epigenetic modifications have been revealed to play an important role in tumorigenesis and tumor development. This study aims to analyze the role of histone modifications and the prognostic values of histone modifications in lung adenocarcinoma (LUAD). The promoters and enhancers of protein encoding genes (PCGs) were the regions of enriched histone modifications.

Methods

Expression profiles and clinical information of LUAD samples were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Histone modification data of LUAD cell lines were downloaded from Encyclopedia of DNA Elements (ENCODE) database. Limma R package was used to identify differentially expressed PCGs. To identify molecular subtypes, consensus clustering was conducted based on the expression of dysregulated PCGs with abnormal histone modifications. Univariate Cox regression analysis and stepwise Akaike information criterion (stepAIC) were utilized to establish a prognostic model.

Results

We identified a total of 699 epigenetic dysregulated genes with 122 of them significantly correlating with LUAD prognosis. We constructed three molecular subtypes (C1, C2, and C3) based on the 122 prognostic genes. C2 had the longest overall survival while C1 had the worst prognosis. In addition, three subtypes had differential immune infiltration and the response to immune checkpoint inhibitors. Moreover, we identified a risk model containing 5 epi-PCGs that had favorable performance to predict prognosis in different datasets.

Conclusions

This study further supported the critical histone modifications in LUAD development. Three subtypes may provide guidance for the immunotherapy of LUAD patients. Importantly, the prognostic model had great potential to predict LUAD prognosis.

Systematic Analysis of Molecular Subtypes and Immune Prediction Based on CD8 T Cell Pattern Genes Based on Head and Neck Cancer

CD8+ T lymphocytes, also known as cytotoxic T lymphocytes, are the most powerful antitumour cells in the human body. Patients with head and neck squamous cell carcinoma (HNSCC) in whom CD8+ T lymphocyte infiltration is high have a better prognosis. However, the clinical significance and prognostic significance of CD8+ T cell-related regulatory genes in HNSCC remain unclear, and further research is required. In total, 446 CD8+ T cell-related genes were obtained using WGCNA. It was discovered that 111 genes included within the TCGA and GSE65858 datasets were intimately linked to the patient’s prognosis. These genes were included in the subsequent analysis. According to consensus clustering analysis, HNSCC samples were classified into 3 subtypes (IC1, IC2, and IC3). There were substantial differences between the three subtypes in terms of immunological molecules, immune function, and the response to drug treatment. In addition, the 8-gene signature, which was generated premised on CD8+ T cell-related genes, exhibited stable prognostic prediction in the TCGA and GEO datasets and different HNSCC patient subgroups and independently served as a prognostic indicator for HNSCC. More importantly, the 8-gene signature effectively predicted immunotherapy response. We first constructed a molecular subtype of HNSCC based on CD8+ T cell-related genes. Between the three subtypes, there were significant differences in the prognosis, clinical features, immunological molecules, and drug treatment response. The 8-gene signature that was further constructed effectively predicted prognosis and immunotherapy response.

Identification of epigenetic dysregulation gene markers and immune landscape in kidney renal clear cell carcinoma by comprehensive genomic analysis

Kidney cancer is one the most lethal cancers of the urinary system, but current treatments are limited and its prognosis is poor. This study focused on kidney renal clear cell carcinoma (KIRC) and analyzed the relationship between epigenetic alterations and KIRC prognosis, and explored the prognostic significance of these findings in KIRC patients. Based on multi-omics data, differentially expressed histone-modified genes were identified using the R package limma package. Gene enhancers were detected from data in the FANTOM5 database. Gene promoters were screened using the R package ChIPseeker, and the Bumphunter in the R package CHAMP was applied to screen differentially methylated regions (DMR). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) functional enrichment analysis of genes was performed using the R package clusterProfiler. We identified 51 dysregulated epigenetic protein coding genes (epi-PCGs) from 872 epi-PCGs, and categorized three molecular subtypes (C1, C2, and C3) of KIRC samples with significantly different prognosis. Notably, among the three molecular subtypes, we found a markedly differential immune features in immune checkpoints, cytokines, immune signatures, and immune cell distribution. C2 subtype had significantly lower enrichment score of IFNγ, cytotoxic score (CYT), and angiogenesis. In addition, an 8-gene signature containing 8 epi-PCGs (ETV4, SH2B3, FATE1, GRK5, MALL, HRH2, SEMA3G, and SLC10A6) was developed for predicting KIRC prognosis. Prognosis of patients with a high 8-gene signature score was significantly worse than those with a low 8-gene signature score, which was also validated by the independent validation data. The 8-gene signature had a better performance compared with previous signatures of KIRC. Overall, this study highlighted the important role of epigenetic regulation in KIRC development, and explored prognostic epi-PCGs, which may provide a guidance for exploiting further pathological mechanisms of KIRC and for developing novel drug targets.

Triple-Negative Breast Cancer Analysis Based on Metabolic Gene Classification and Immunotherapy

Triple negative breast cancer (TNBC) has negative expression of ER, PR and HER-2. TNBC shows high histological grade and positive rate of lymph node metastasis, easy recurrence and distant metastasis. Molecular typing based on metabolic genes can reflect deeper characteristics of breast cancer and provide support for prognostic evaluation and individualized treatment. Metabolic subtypes of TNBC samples based on metabolic genes were determined by consensus clustering. CIBERSORT method was applied to evaluate the score distribution and differential expression of 22 immune cells in the TNBC samples. Linear discriminant analysis (LDA) established a subtype classification feature index. Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves were generated to validate the performance of prognostic metabolic subtypes in different datasets. Finally, we used weighted correlation network analysis (WGCNA) to cluster the TCGA expression profile dataset and screen the co-expression modules of metabolic genes. Consensus clustering of the TCGA cohort/dataset obtained three metabolic subtypes (MC1, MC2, and MC3). The ROC analysis showed a high prognostic performance of the three clusters in different datasets. Specifically, MC1 had the optimal prognosis, MC3 had a poor prognosis, and the three metabolic subtypes had different prognosis. Consistently, the immune characteristic index established based on metabolic subtypes demonstrated that compared with the other two subtypes, MC1 had a higher IFNγ score, T cell lytic activity and lower angiogenesis score, T cell dysfunction and rejection score. TIDE analysis showed that MC1 patients were more likely to benefit from immunotherapy. MC1 patients were more sensitive to immune checkpoint inhibitors and traditional chemotherapy drugs Cisplatin, Paclitaxel, Embelin, and Sorafenib. Multiclass AUC based on RNASeq and GSE datasets were 0.85 and 0.85, respectively. Finally, based on co-expression network analysis, we screened 7 potential gene markers related to metabolic characteristic index, of which CLCA2, REEP6, SPDEF, and CRAT can be used to indicate breast cancer prognosis. Molecular classification related to TNBC metabolism was of great significance for comprehensive understanding of the molecular pathological characteristics of TNBC, contributing to the exploration of reliable markers for early diagnosis of TNBC and predicting metastasis and recurrence, improvement of the TNBC staging system, guiding individualized treatment.

Construction of Metabolic Molecular Classification and Immune Characteristics for the Prognosis Prediction of Ovarian Cancer

Background

Ovarian cancer (OC) is a malignant tumor that seriously threatens women's health. Molecular classification based on metabolic genes can reflect the deeper characteristics of ovarian cancer and provide support for prognostic evaluation and the guidance of individualized treatment.

Method

The metabolic subtypes were determined by consensus clustering and CDF. We used the ssGSEA method to calculate the IFNγ score of each patient. The CIBERSORT method was used to evaluate the score distribution and differential expression of 22 immune cells, and LDA was applied to establish a subtype classification feature index. The Kaplan-Meier and ROC curves were generated to validate the prognostic performance of metabolic subtypes in different cohorts. WGCNA was used to screen the coexpression modules associated with metabolic genes.

Results

We obtained three metabolic subtypes (MC1, MC2, and MC3). MC2 had the best prognosis, and MC1 and MC3 had poor prognoses. Consistently, MC2 subtype had higher T cell lytic activity and lower angiogenesis, IFNγ, T cell dysfunction, and rejection scores. TIDE analysis showed that MC2 patients were more likely to benefit from immunotherapy; MC1 patients were more sensitive to immune checkpoint inhibitors and traditional chemotherapy drugs. The multiclass AUCs based on the RNASeq and GSE cohorts were 0.93 and 0.84, respectively. Finally, we screened 11 potential gene markers related to the metabolic characteristic index that could be used to indicate the prognosis of OC.

Conclusion

Molecular subtypes related to metabolism are crucial to comprehensively understand the molecular pathological characteristics related to metabolism for OC development, explore reliable markers for prognosis, improve the OC staging system, and guide personalized treatment.

CD8+ T Cell-Associated Gene Signature Correlates With Prognosis Risk and Immunotherapy Response in Patients With Lung Adenocarcinoma

The presence of infiltrating CD8⁺ T lymphocytes in the tumor microenvironment of lung adenocarcinoma (LUAD) is correlated with improved patient prognosis, but underlying regulatory mechanisms remain unknown. To identify biomarkers to improve early diagnosis and treatment of LUAD, we downloaded 13 immune cell line-associated datasets from the GEO database. We identified CD8⁺ T cell-associated genes via weighted correlation network analysis. We constructed molecular subtypes based on CD8⁺ T cell-associated genes and constructed a multi-gene signature. We identified 252 CD8⁺ T cell-associated genes significantly enriched in immune function-related pathways and two molecular subtypes of LUAD (immune cluster 1 [IC1] and IC2) using our CD8⁺ T cell-associated gene signature. Patients with the IC2 subtype had a higher tumor mutation burden and lower immune infiltration scores, whereas those with the IC1 subtype were more sensitive to immune checkpoint inhibitors. Prioritizing the top candidate genes to construct a 10-gene signature, we validated our model using independent GSE and TCGA datasets to confirm its robustness and stable prognostic ability. Our risk model demonstrated good predictive efficacy using the Imvigor210 immunotherapy dataset. Thus, we established a novel and robust CD8⁺ T cell-associated gene signature, which could help assess prognostic risk and immunotherapy response in LUAD patients.

Expression of Immune Checkpoints in Malignant Tumors: Therapy Targets and Biomarkers for the Gastric Cancer Prognosis

To increase the effectiveness of anticancer therapy based on immune checkpoint (IC) inhibition, some ICs are being investigated in addition to those used in clinic. We reviewed data on the relationship between PD-L1, B7-H3, B7-H4, IDO1, Galectin-3 and -9, CEACAM1, CD155, Siglec-15 and ADAM17 expression with cancer development in complex with the results of clinical trials on their inhibition. Increased expression of the most studied ICs—PD-L1, B7-H3, and B7-H4—is associated with poor survival; their inhibition is clinically significant. Expression of IDO1, CD155, and ADAM17 is also associated with poor survival, including gastric cancer (GC). The available data indicate that CD155 and ADAM17 are promising targets for immune therapy. However, the clinical trials of anti-IDO1 antibodies have been unsatisfactory. Expression of Galectin-3 and -9, CEACAM1 and Siglec-15 demonstrates a contradictory relationship with patient survival. The lack of satisfactory results of these IC inhibitor clinical trials additionally indicates the complex nature of their functioning. In conclusion, in many cases it is important to analyze the expression of other participants of the immune response besides target IC. The PD-L1, B7-H3, B7-H4, IDO1 and ADAM17 may be considered as candidates for prognosis markers for GC patient survival.

Expression of Indoleamine 2, 3-dioxygenase 1 (IDO1) and Tryptophanyl-tRNA Synthetase (WARS) in Gastric Cancer Molecular Subtypes

AIMS

Developments in genomic pathology have led to novel molecular classification schemes in gastric cancers. Two of these new subtypes, Epstein-Barr virus (EBV)-associated and microsatellite instability-high (MSI-H), are associated with a dominant T-cell-mediated immune response. The roles of the immune modulators, indoleamine 2, 3-dioxygenase 1 (IDO1) and tryptophanyl-tRNA synthetase (WARS), have not been investigated in the context of this classification.

METHODS AND RESULTS

Using in situ hybridization and immunohistochemistry we subclassified 421 primary gastric adenocarcinomas into 5 subtypes, EBV-associated, epithelial to mesenchymal transition, MSI-H, p53-aberrant, and p53-wildtype tumors. Tumor-infiltrative lymphocytes were counted and protein expression of IDO1 and WARS was graded on tissue microarrays of these 421 tumors. High tumor-infiltrative lymphocytes as well as high expression of both IDO1 and WARS was found in EBV and MSI-H tumors. The prognostic effects of IDO1 and WARS expression were tumor subtype dependent. Although high expression levels of IDO1 and WARS were associated with poor prognosis in p53-aberrant, p53-wildtype, and all cancers combined, WARS expression was associated with better prognosis in MSI tumors.

CONCLUSIONS

The immunomodulators, IDO1 and WARs, are upregulated and have prognostic significance in EBV-associated and MSI-H tumors. Novel therapies targeting these proteins should be considered in the treatment of these patients.

The footprint of kynurenine pathway in every cancer: a new target for chemotherapy

Treatment of cancers has always been a challenge for physicians. Typically, several groups of anti-cancer medications are needed for effective management of an invasive and metastatic cancer. Recently, therapeutic potentiation of immune system markedly improved treatment of cancers. Kynurenine pathway has an interwoven correlation with immune system. Kynurenine promotes T Reg (regulatory) differentiation, which leads to increased production of anti-inflammatory cytokines and suppression of cytotoxic activity of T cells. Overactivation of kynurenine pathway in cancers provides an immunologically susceptible microenvironment for mutant cells to survive and invade surrounding tissues. Interestingly, kynurenine pathway vigorously interacts with other molecular pathways involved in tumorigenesis. For instance, kynurenine pathway interacts with phospoinosisitide-3 kinase (PI3K), extracellular signal-regulated kinase (ERK), Wnt/β-catenin, P53, bridging integrator 1 (BIN-1), cyclooxygenase 2 (COX-2), cyclin-dependent kinase (CDK) and collagen type XII α1 chain (COL12A1). Overactivation of kynurenine pathway, particularly overactivation of indoleamine 2,3-dioxygenase (IDO) predicts poor prognosis of several cancers such as gastrointestinal cancers, gynecological cancers, hematologic malignancies, breast cancer, lung cancer, glioma, melanoma, prostate cancer and pancreatic cancer. Furthermore, kynurenine increases the invasion, metastasis and chemoresistance of cancer cells. Recently, IDO inhibitors entered clinical trials and successfully passed their safety tests and showed promising therapeutic efficacy for cancers such as melanoma, brain cancer, renal cell carcinoma, prostate cancer and pancreatic cancer. However, a phase III trial of epacadostat, an IDO inhibitor, could not increase the efficacy of treatment with pembrolizumab for melanoma. In this review the expanding knowledge towards kynurenine pathway and its application in each cancer is discussed separately.

Chemoresistance was correlated with elevated expression and activity of indoleamine 2,3-dioxygenase in breast cancer

BACKGROUND

Indoleamine 2,3-dioxygenase (IDO) catalyses degradation of the essential amino acid tryptophan leading to the production of immunosuppressive kynurenine and tryptophan exhausting. IDO expression and activity contribute to aggressive tumor growth, inferior therapeutic gain and poor prognosis. The aim of this study was to explore the association between chemoresistance and IDO expression, activity in breast cancer METHODS: Immunohistochemistry was applied for evaluating IDO expression in biopsy tissues. Serum IDO activity was examined via High-performance liquid chromatography (HPLC). Western blots (WB), HPLC and Real-time PCR (RT-PCR) were used to analyze IDO protein, IDO enzyme activity and IDO gene expression in original and paclitaxel-resistant cells respectively. Logistic regression and survival analysis were applied to explore the association between chemoresistance and IDO expression, activity in breast cancer.

RESULTS

IDO expression in tumor tissues was associated with serum IDO activity (P = 0.004). Both IDO expression in tumor and serum activity were associated with clinical tumor stage, node stage and estrogen receptor (ER) status (all P < 0.05); clinical response and pathologic complete response (pCR) to NAC were both related to IDO expression and activity prior NAC (all P < 0.05). Multivariate analysis showed IDO activity before NAC was the only independent factor affected pCR (P = 0.032). ROC curves showed that the IDO expression and activity had discriminative ability for predicting the clinical response and pCR. In the prognostic analysis, patients with high IDO expression had significantly impaired overall survival (5 year survival rate: 53.57% vs 80.00%) and progression-free survival (5 year survival rate: 46.43% vs 72.00%, P = 0.031 and P = 0.046). In vitro, significantly increased IDO protein, IDO mRNA expression and IDO enzyme activity in paclitaxel-resistant cells were demonstrated in comparing of sensitive cells.

CONCLUSION

IDO expression and activity associated with advanced breast cancer, poor response to neoadjuvant chemotherapy and prognosis. IDO expression and activity were significantly increased in paclitaxel-resistant breast cancer cells.

CD4/CD8 + T cells, DC subsets, Foxp3, and IDO expression are predictive indictors of gastric cancer prognosis

BACKGROUND

The tumor microenvironment represents an abnormal niche containing numerous factors, such as T cells, dendritic cells (DCs), regulatory T cells (Tregs), and indoleamine 2,3-dioxygenase (IDO), involved in maintaining immune homeostasis and tolerance. All these factors may influence the choice of therapy and the clinical outcomes.

METHODS

Flow cytometry was performed to identify CD4+/CD8 + T cells and DCs, and immunohistochemistry was used to evaluate IDO and Forkhead Box P3 (Foxp3) expression; these experiments were performed in order to explore the clinical and prognostic significance of CD4/CD8 + T cells, DCs, Tregs, and IDO expression in gastric carcinoma.

RESULTS

Smaller tumor size was correlated with higher expression levels of peripheral CD4 + T cells (P = .003) and CD8 + T cells (P = .002), and lower IDO expression (P = .044) in tumors. Well-differentiated gastric carcinomas displayed higher peripheral (P = .029) and tumor-infiltrating CD4 + T cell (P = .009) populations and a higher tumor-infiltrating DC1/DC2 ratio (P = .048). Gastric cancer in the early T stages exhibited higher populations of peripheral DC2s (P = .044) and a higher tumor-infiltrating DC1/DC2 ratio (P = .012). Gastric cancer at the N0 stage had lower tumor-infiltrating DC2s (P = .032) and a higher DC1/DC2 ratio (P = .037). IDO expression was positively correlated with tumor-infiltrating Foxp3 + Tregs (P < .001) as well as DC2s (P < .001), whereas it was negatively correlated with the tumor-infiltrating CD4/CD8 + T cell ratio (P = .023). Tumor-infiltrating Foxp3 + Treg was positively correlated with tumor-infiltrating DC2s (r²  = 0.772; P < .001). At T, N, and TNM stages, the expression levels of peripheral DC2s, tumor-infiltrating DC1/DC2 ratios, Foxp3 + Tregs, and IDO were significantly correlated with prognosis (P < .05). The T stage and peripheral DC2s were significant risk factors for overall survival.

CONCLUSION

Immunocompetent cells and humoral immune factors, including DC2s, CD4+/CD8 + T cells, Foxp3 + Tregs, and IDO, interact with each other to compose a complex community of tumor immune microenvironment, ultimately affecting tumor progression and survival of gastric cancer.

A positive feedback between IDO1 metabolite and COL12A1 via MAPK pathway to promote gastric cancer metastasis

Background

IDO1 (Indoleamine 2,3-dioxygenase 1) inhibits host anti-tumor immune response by exhausting tryptophan in tumor microenvironment, but the pathogenic mechanisms of IDO1 in gastric cancer (GC) cells need to be further explored.

Methods

The aim of this study was to use CCLE (Cancer Cell Line Encyclopedia) transcriptomic data of GC cell lines for WGCNA (Weighted Gene Co-expression Network Analysis) analysis, and explore the potential functions and mechanisms of IDO1 in GC progression in vitro and in vivo.

Results

The higher expression level of IDO1 was identified in 4 out of 7 GC cell lines. Increased IDO1 expression strongly promoted cell migration via its metabolite kynurenine and was associated with pathways of immune activation according to GSEA (Gene Set Enrichment Analysis). The functions of IDO1 were closely associated with extracellular matrix, collagen metabolic and catabolic process by WGCNA analysis. Among five hub genes (AXL, SGCE, COL12A1, ANTXR1, LOXL2), COL12A1 and LOXL2 were upregulated in GC tissues. IDO1 disclosed positive correlation with six collagen genes by coefficient matrix diagram. Knockdown of IDO1 decreased the expression of LOXL2, COL6A1, COL6A2 and COL12A1 in GC cells in both mRNA and protein levels. Of them, knockdown of COL12A1 inhibited cell migration more apparently than knockdown of others. IDO1 and COL12A1 revealed synergistic efficacy on promoting cell migration via a positive feedback sustained by MAPK pathway. This bioprocess was mediated by IDO1 metabolite kynurenine and integrin β1. A popliteal lymph nodemetastasis model was established for verifying metastatic promotion of IDO1 and COL12A1 in GC.

Conclusions

IDO1 and COL12A1 synergistically promoted GC metastasis. The novel findings suggested that both IDO1 and COL12A1 may be promising targets on anti-cancer treatment in GC.

Electronic supplementary material

The online version of this article (10.1186/s13046-019-1318-5) contains supplementary material, which is available to authorized users.

Role of indoleamine 2,3-dioxygenase in pathology of the gastrointestinal tract

Indoleamine 2,3-dioxygenase (IDO) has the most important role in modulation of tryptophan-dependent effects in the gastrointestinal tract, including modulation of intestinal immune response. An increased IDO activity maintains immune tolerance and attenuates ongoing inflammation but allows immune escape and uncontrolled growth of gastrointestinal tumors. Accordingly, IDO represents a novel therapeutic target for the treatment of inflammatory and malignant diseases of the gastrointestinal tract. In this review article, we summarize current knowledge about molecular and cellular mechanisms that are involved in IDO-dependent effects. We provide a brief outline of experimental and clinical studies that increased our understanding of how enhanced IDO activity: controls host-microbiota interactions in the gut; regulates detrimental immune response in inflammatory disorders of the gastrointestinal system; and allows immune escape and uncontrolled growth of gastrointestinal tumors. Additionally, we present future perspectives regarding modulation of IDO activity in the gut as possible new therapeutic approaches for the treatment of inflammatory and malignant diseases of the gastrointestinal system.

Outlook: Immunotherapy in Gastrointestinal Carcinoma - Innovative Strategies

Immune checkpoint inhibitors are emerging as a therapeutic approach for patients with advanced or metastatic gastrointestinal malignancies following the recent Food and Drug Administration and Asian approvals for colorectal, gastric, and hepatocellular carcinoma. As discussed in earlier articles, phase I-II trials demonstrate quite positive clinical activity, particularly in patients with immunogenic cancer subtypes. This outreach paper discusses some of the next innovative immunotherapy strategies under development. Here, tumor-associated macrophages, myeloid-derived suppressor cells, and regulatory T cells are increasingly coming into focus as new targets. Besides the well described use of checkpoint inhibitors, blockade of 'Wnt' or Csf1R signaling pathways as well as combinatorial treatment strategies offer promising examples for overcoming immune silencing within the resistant tumor microenvironment.

The rationale of indoleamine 2,3-dioxygenase inhibition for cancer therapy

Indoleamine 2,3-dioxygenase (IDO, also referred to as IDO1) has been demonstrated to be a normal endogenous mechanism of acquired peripheral immune tolerance in vivo. In the field of oncology, IDO expression and/or activity has been observed in several cancer types and has usually been associated with negative prognostic factors and worse outcome measures. This manuscript reviews current available data on the role of IDO in cancer and the current results obtained with IDO inhibition, both in animal models and in phase 1 and 2 clinical trials in humans. Preliminary results with IDO inhibitors, usually combined with other anti-cancer drugs, seem encouraging. Further studies are needed to clarify the conditions in which IDO inhibitors can be of value as an anti-cancer strategy. In addition, further research should address whether the expression of IDO in tissue or blood can be a marker to select patients who can benefit most from IDO inhibition.

Serum metabolomic profiling of prostate cancer risk in the prostate, lung, colorectal, and ovarian cancer screening trial

Background:

Two recent metabolomic analyses found serum lipid, energy, and other metabolites related to aggressive prostate cancer risk up to 20 years prior to diagnosis.

Methods:

We conducted a serum metabolomic investigation of prostate cancer risk in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial that included annual serum total prostate-specific antigen measurement and digital rectal examination. This nested study included 380 cases diagnosed post-screening and 380 controls individually matched to cases on age, race, study centre, and blood-collection date (median time to diagnosis, 10 years (range 4.4–17 years)). Sera were analysed on a high-resolution accurate mass platform of ultrahigh-performance liquid and gas chromatography/mass spectroscopy that identified 695 known metabolites. Logistic regression conditioned on the matching factors estimated odds ratios (OR) and 95% confidence intervals of risk associated with an 80th percentile increase in the log-metabolite signal.

Results:

Twenty-seven metabolites were associated with prostate cancer at P<0.05. Pyroglutamine, gamma-glutamylphenylalanine, phenylpyruvate, N-acetylcitrulline, and stearoylcarnitine showed the strongest metabolite-risk signals (ORs=0.53, 0.51, 0.46, 0.58, and 1.74, respectively; 0.001⩽P⩽0.006). Findings were similar for aggressive disease (peptide chemical class, P=0.03). None of the P-values were below the threshold of Bonferroni correction, however.

Conclusions:

A unique metabolomic profile associated with post-screening prostate cancer is identified that differs from that in a previously studied, unscreened population.

The emerging role of immunotherapy in gastric and esophageal adenocarcinoma

Gastric and esophageal adenocarcinomas are aggressive malignancies. Systemic therapy for these tumors relies primarily on cytotoxic chemotherapy but outcomes remain poor. In recent years, immunotherapy has emerged as a new, promising therapeutic approach for a variety of solid tumors. Characterization of gastroesophageal cancers has revealed genomic and immune features of these tumors that may predict response to immunotherapy. Indeed, preliminary results from the initial trials of immune checkpoint inhibitors have been encouraging, with objective response rates of 20% in heavily pretreated patient populations. Based on these results, additional trials of single-agent checkpoint inhibitors as well as combinations with chemotherapy and targeted therapies are currently ongoing. Further work to identify predictive biomarkers will be crucial for the successful implementation of immunotherapy.

Increased expression of IDO associates with poor postoperative clinical outcome of patients with gastric adenocarcinoma

Clinical significance of 2,3-dioxygenase (IDO) has been studied in types of tumors, but the role that IDO played in gastric adenocarcinoma (GAC) is still unclear. Here, we aim to investigate the prognostic value of IDO expression in patients with GAC. We examined intratumoral IDO expression in retrospectively enrolled 357 patients with GAC undergoing gastrectomy at Zhongshan Hospital of Fudan University in 2008 by immunohistochemical staining. The Kaplan-Meier method and Cox regression models were used to evaluate the prognostic value of IDO expression and its association with clinical pathological factors. We generated a predictive nomogram by integrating IDO expression with the TNM staging system for overall survival of GAC patients. High expression of intratumoral IDO predicted a dismal outcome. Intratumoral IDO expression gave a further discrimination for the prognosis of GAC patients. By Cox multivariate analysis, IDO expression was defined as an independent prognosticator. The generated nomogram performed well in predicting the 3- and 5-year overall survival of GAC patients. Conclusively, IDO is a potential prognostic biomarker for overall survival of patients with GAC after gastrectomy.

Therapeutic targeting of inflammation and tryptophan metabolism in colon and gastrointestinal cancer

Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer death in the United States. Cytotoxic therapies cause significant adverse effects for most patients and do not offer cure in many advanced cases of CRC. Immunotherapy is a promising new approach to harness the body's own immune system and inflammatory response to attack and clear the cancer. Tryptophan metabolism along the kynurenine pathway (KP) is a particularly promising target for immunotherapy. Indoleamine 2,3-dioxygenase 1 (IDO1) is the most well studied of the enzymes that initiate this pathway and it is commonly overexpressed in CRC. Herein, we provide an in-depth review of how tryptophan metabolism and KP metabolites shape factors important to CRC pathogenesis including the host mucosal immune system, pivotal transcriptional pathways of neoplastic growth, and luminal microbiota. This pathway's role in other gastrointestinal (GI) malignancies such as gastric, pancreatic, esophageal, and GI stromal tumors is also discussed. Finally, we highlight how currently available small molecule inhibitors and emerging methods for therapeutic targeting of IDO1 might be applied to colon, rectal, and colitis-associated cancer.

Role of indoleamine 2,3-dioxygenase in health and disease

IDO1 (indoleamine 2,3-dioxygenase 1) is a member of a unique class of mammalian haem dioxygenases that catalyse the oxidative catabolism of the least-abundant essential amino acid, L-Trp (L-tryptophan), along the kynurenine pathway. Significant increases in knowledge have been recently gained with respect to understanding the fundamental biochemistry of IDO1 including its catalytic reaction mechanism, the scope of enzyme reactions it catalyses, the biochemical mechanisms controlling IDO1 expression and enzyme activity, and the discovery of enzyme inhibitors. Major advances in understanding the roles of IDO1 in physiology and disease have also been realised. IDO1 is recognised as a prominent immune regulatory enzyme capable of modulating immune cell activation status and phenotype via several molecular mechanisms including enzyme-dependent deprivation of L-Trp and its conversion into the aryl hydrocarbon receptor ligand kynurenine and other bioactive kynurenine pathway metabolites, or non-enzymatic cell signalling actions involving tyrosine phosphorylation of IDO1. Through these different modes of biochemical signalling, IDO1 regulates certain physiological functions (e.g. pregnancy) and modulates the pathogenesis and severity of diverse conditions including chronic inflammation, infectious disease, allergic and autoimmune disorders, transplantation, neuropathology and cancer. In the present review, we detail the current understanding of IDO1’s catalytic actions and the biochemical mechanisms regulating IDO1 expression and activity. We also discuss the biological functions of IDO1 with a focus on the enzyme's immune-modulatory function, its medical implications in diverse pathological settings and its utility as a therapeutic target.

The subsets of dendritic cells and memory T cells correspond to indoleamine 2,3-dioxygenase in stomach tumor microenvironment

The abnormal distributions of memory T cells (Tm) and dendritic cells (DC) in stomach cancer are not well understood. Indoleamine 2,3-dioxygenase (IDO), produced by DC, may be an important enzyme affecting function and proliferation of Tm. In this study, IDO expression was examined by immunohistochemical staining. The subsets of Tm and DC were counted by flow cytometry. The percentages of CD4 + Tm and CD4 + central Tm (Tcm) were lower in tumor tissues than in normal tissues (P < 0.05), while the CD4 + effector Tm (Tem) and CD8 + Tem percentages were higher in tumor tissues (P < 0.05). The ratio of myeloid DC (DC1)/plasmacytoid DC (DC2) was significantly lower in tumor tissues (P = 0.009). The high expression of IDO was more frequently observed in tumor tissues (P = 0.001). The percentages of CD4 + Tm and CD8 + Tm were positively associated with DC1 percentage and ratio of DC1/DC2 (P < 0.05). The higher CD8 + Tcm percentage was associated with higher DC2 percentage (P = 0.025). The patients with high IDO expression had significantly lower CD4 + Tm (P = 0.012) and CD8 + Tm percentages (P = 0.033), but higher CD8 + Tem percentage (P < 0.01). Concerning on clinicopathologic features, the higher DC2 percentage was associated with larger tumor size (P = 0.019). The CD4 + Tm and CD8 + Tem percentages were significantly associated with clinical stage and lymph node metastasis; the high IDO expressions were significantly associated with deeper tumor invasion (P = 0.016) and lymph node metastasis (P = 0.038). Thus, DC subsets, Tm subsets, and IDO expression were correlated with each other. They were associated with the established clinicopathologic features, such as tumor size, depth of invasion, lymph node metastasis, and clinical stage.