Immune signature profiling identified prognostic factors for gastric cancer

Identification of Immune-Related Gene Pair Signature to Predict Prognosis of Diffuse Large B-Cell Lymphoma Based on Bioinformatics Analyses

Since over one-third of DLBCL patients experience relapse or refractory after standard therapy, high-risk patients must be predicted. We developed a prognostic immune-related gene pairs (IRGPs) signature for DLBCL patients using bioinformatics analyses. This signature can predict the prognosis of these patients adequately, either alone or in combination with other clinical parameters. It hopes to improve the stratification and management of these patients for broad clinical applications.

MFGE8 promotes gastric cancer progression by activating the IL-6/JAK/STAT3 signaling

OBJECTIVE

Gastric cancer is malignant cancer with high morbidity and mortality worldwide. Milk fat globule EGF and factor V/VIII domain containing (MFGE8) was involved in many cancers. Nevertheless, the role of MFGE8 in gastric cancer remained indistinct. To probe the role of MFGE8 in gastric cancer and further explore the regulating mechanism.

METHODS

GEPIA was employed for analysis of MFGE8 expression and survival of gastric cancer patients. MFGE8 expression in gastric cancer was determined by immunohistochemistry, PCR, and western blot. The effect of MFGE8 on gastric cancer cells were evaluated by a series of cell function experiments. The mechanism of MFGE8 on gastric cancer was analyzed by GSEA and verified by in vitro and in vivo experiments.

RESULTS

MFGE8 was over-expressed in gastric cancer. Silence of MFGE8 suppressed cell viability, proliferated ability, migrated and invasive ability, and EMT, but accelerated cell apoptosis. The opposite results were obtained in MFGE8-overexpressed gastric cancer cells. Zinc finger and BTB domain containing 7 A (ZBTB7A) was a transcription factor of MFGE8. ZBTB7A overexpression eliminated the effect of MFGE8 on gastric cancer cells. MFGE8 activated the IL-6/JAK/STAT3 signaling. Inhibition of IL-6/JAK/STAT3 signaling by Stattic (pathway inhibitor) could eliminate the promoting effect of MFGE8 on IL-6/JAK/STAT3 signaling. In addition, MFGE8 shRNA inhibited tumor growth.

CONCLUSION

MFGE8 promoted cell proliferation, EMT progress, and tumor growth of gastric cancer by activating the IL-6/JAK/STAT3 signaling.

Identification of necroptosis-associated miRNA signature for predicting prognosis and immune landscape in stomach adenocarcinoma

PURPOSE

This study aims to identify the potential necroptosis related genes (NRGs)-associated miRNAs signature and explore the impact on the prognosis of stomach adenocarcinoma (STAD).

METHODS

Employing rigorous methodologies, we utilized univariate Cox, Lasso and multivariate Cox regression analyses to develop a prognostic signature. Kaplan-Meier (K-M) and ROC curves were applied to assess the prognostic value of signature in a training group and an independent test group. Furthermore, we conducted Gene Set Enrichment Analysis (GSEA) for enrichment of tumor-related pathways. The risk score was calculated for each patient based on the expression of miRNAs which were enrolled in the signature. Patients were stratified into high- and low-risk groups. The immune cell infiltration and immunotherapy were compared between the two groups. Finally, the diagnostic potential of the miRNA was explored by RT-qPCR.

RESULTS

We constructed a prognostic model based on 6 NRGs-associated miRNAs. K-M plots underscored superior survival outcomes in the low-risk group. GSEA results revealed the enrichment of several tumor-related pathways in the high-risk group. Notably, CD8+ T cells, Tregs and activated memory CD4+ T cells exhibited negative correlations with the risk score. Additionally, a few immune checkpoint genes, such as CTLA4, PD1 and PD-L1, were significantly upregulated in the low-risk group. Furthermore, the serum expression levels of all these 6 miRNAs were significantly elevated in STAD patients.

CONCLUSIONS

Our study identified a robust risk score derived from a signature of 6 NRGs-associated miRNAs, demonstrating high efficacy for prognosis of STAD. These results not only contributed to our understanding of STAD pathogenesis, but also held promise for potential clinical applications, particularly in the realm of personalized immunotherapy for STAD patients.

A novel immune-related risk-scoring system associated with the prognosis and response of cervical cancer patients treated with radiation therapy

Objective: The tumor microenvironment plays a critical role in the radiotherapy and immunotherapy response of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). Radioresistance is a key factor in treatment failure among patients who receive radical radiotherapy. Thus, new immune-related biomarkers associated with radiotherapy response in CESC are needed. Methods: In this study, the CIBERSORT and ESTIMATE methods were applied to determine the percentage of tumor-infiltrating cells and the number of immune components in 103 CESCs treated with radiotherapy from The Cancer Genome Atlas (TCGA) database. The main dysregulated genes were subjected to multivariate and univariate analyses. The prognostic value of this system was studied via receiver operating characteristic curve and survival analysis. For further confirmation, the biomarkers' expression levels and predictive value were validated by immunohistochemistry (IHC) and qRT-PCR. The CIBERSORT algorithm was used to calculate the compositional patterns of 22 types of immune cells in cervical cancer patients treated with radiation therapy. Results: Data for 17 radioresistant and 86 radiosensitive tumors were obtained from the The Cancer Genome Atlas database. 53 immune-related DEGs were identified. GO and KEGG analyses revealed that the DEGs were enriched in protein kinase B signaling, growth factors in cytokines, the MAPK pathway and the PI3K-Akt pathway. Then, 14 key immune-related genes built a risk scoring model were deemed prognostic in CESC with radiotherapy. The area under the curve (AUC) of the model was 0.723, and the high-risk group presented worse outcomes than the low-risk group. In addition, the high-risk group tended to have persistent tumors (p = 0.001). The high expression of WT1 and SPOUYT4 were associated with relapse, the high expression of Angiotensinogen and MIEN1 were associated with nonrelapse. Analysis of the immune microenvironment indicated that M0 macrophages, M2 macrophages, activated mast cells and resting memory CD4+ T cells were positively correlated with the risk score (p < 0.05). Conclusion: The novel immune-related risk scoring system has some advantages in predicting the prognosis and treatment response of cervical cancer patients treated with radiotherapy. Moreover, it might provide novel clues for providing targeted immune therapy to these patients.

Gastric cancer-derived LBP promotes liver metastasis by driving intrahepatic fibrotic pre-metastatic niche formation

BACKGROUND

Liver metastasis (LM) is one of the most common distant metastases of gastric cancer (GC). However, the mechanisms underlying the LM of GC (GC-LM) remain poorly understood. This study aimed to identify the tumour-secreted protein associated with GC-LM and to investigate the mechanisms by which this secreted protein remodels the liver microenvironment to promote GC-LM.

METHODS

Data-independent acquisition mass spectrometry (DIA-MS), mRNA expression microarray, quantitative real-time PCR, enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry (IHC) were performed to identify and validate the GC-secreted proteins associated with GC-LM. A modified intrasplenic injection mouse model of LM was used to evaluate the progression and tumour burden of LM in vivo. Flow cytometry, immunofluorescence (IF), western blots (WB) and IHC were performed to validate the pre-metastatic niche (PMN) formation in the pre-modelling mouse models. mRNA sequencing of PMA-treated THP-1 cells with or without lipopolysaccharide binding protein (LBP) treatment was used to identify the functional target genes of LBP in macrophages. Co-immunoprecipitation (Co-IP), WB, ELISA, IF and Transwell assays were performed to explore the underlying mechanism of LBP in inducing intrahepatic PMN formation.

RESULTS

LBP was identified as a critical secreted protein associated with GC-LM and correlated with a worse prognosis in patients with GC. LBP activated the TLR4/NF-κB pathway to promote TGF-β1 secretion in intrahepatic macrophages, which, in turn, activated hepatic satellite cells (HSCs) to direct intrahepatic fibrotic PMN formation. Additionally, TGF-β1 enhanced the migration and invasion of incoming metastatic GC cells in the liver. Consequently, selective targeting of the TGF-β/Smad signaling pathway with galunisertib demonstrated its efficacy in effectively preventing GC-LM in vivo.

CONCLUSIONS

The results of this study provide compelling evidence that serological LBP can serve as a valuable diagnostic biomarker for the early detection of GC-LM. Mechanistically, GC-derived LBP mediates the crosstalk between primary GC cells and the intrahepatic microenvironment by promoting TGF-β1 secretion in intrahepatic macrophages, which induces intrahepatic fibrotic PMN formation to promote GC-LM. Importantly, selectively targeting the TGF-β/Smad signaling pathway with galunisertib represents a promising preventive and therapeutic strategy for GC-LM.

Regulation and Functions of α6-Integrin (CD49f) in Cancer Biology

Over the past decades, our knowledge of integrins has evolved from being understood as simple cell surface adhesion molecules to receptors that have a complex range of intracellular and extracellular functions, such as delivering chemical and mechanical signals to cells. Consequently, they actively control cellular proliferation, differentiation, and apoptosis. Dysregulation of integrin signaling is a major factor in the development and progression of many tumors. Many reviews have covered the broader integrin family in molecular and cellular studies and its roles in diseases. Nevertheless, further understanding of the mechanisms specific to an individual subunit of different heterodimers is more useful. Thus, we describe the current understanding of and exploratory investigations on the α6-integrin subunit (CD49f, VLA6; encoded by the gene itga6) in normal and cancer cells. The roles of ITGA6 in cell adhesion, stemness, metastasis, angiogenesis, and drug resistance, and as a diagnosis biomarker, are discussed. The role of ITGA6 differs based on several features, such as cell background, cancer type, and post-transcriptional alterations. In addition, exosomal ITGA6 also implies metastatic organotropism. The importance of ITGA6 in the progression of a number of cancers, including hematological malignancies, suggests its potential usage as a novel prognostic or diagnostic marker and useful therapeutic target for better clinical outcomes.

Immune-related pan-cancer gene expression signatures of patient survival revealed by NanoString-based analyses

The immune system plays a central role in the onset and progression of cancer. A better understanding of transcriptional changes in immune cell-related genes associated with cancer progression, and their significance in disease prognosis, is therefore needed. NanoString-based targeted gene expression profiling has advantages for deployment in a clinical setting over RNA-seq technologies. We analysed NanoString PanCancer Immune Profiling panel gene expression data encompassing 770 genes, and overall survival data, from multiple previous studies covering 10 different cancer types, including solid and blood malignancies, across 515 patients. This analysis revealed an immune gene signature comprising 39 genes that were upregulated in those patients with shorter overall survival; of these 39 genes, three (MAGEC2, SSX1 and ULBP2) were common to both solid and blood malignancies. Most of the genes identified have previously been reported as relevant in one or more cancer types. Using Cibersort, we investigated immune cell levels within individual cancer types and across groups of cancers, as well as in shorter and longer overall survival groups. Patients with shorter survival had a higher proportion of M2 macrophages and γδ T cells. Patients with longer overall survival had a higher proportion of CD8+ T cells, CD4+ T memory cells, NK cells and, unexpectedly, T regulatory cells. Using a transcriptomics platform with certain advantages for deployment in a clinical setting, our multi-cancer meta-analysis of immune gene expression and overall survival data has identified a specific transcriptional profile associated with poor overall survival.

Bioinformatics analysis on the expression of GPX family in gastric cancer and its correlation with the prognosis of gastric cancer

Background

Gastric cancer (GC) is one of the most common cancers of the digestive tract, with the fifth-highest incidence and third highest mortality rate in the world.

Methods

In this study, the Kaplan-Meier Plotter database was used to analyze the correlation between the expression of the glutathione peroxidase (GPX) family and the clinical prognosis of gastric cancer (GC). The prognostic value of increased GPX family mRNA expression in GC patients in different clinical stages, with different differentiation degrees, in different genders and human epidermal growth factor receptor-2 (HER2) status, and treated with different therapeutic regimens was also studied.

Results

The results showed that with the increase of GPX1 and GPX2 mRNA low expression levels, the overall survival (OS) of gastric cancer patients was longer. However, when the high expression levels of GPX3, GPX5 and GPX6 mRNA increased, gastric cancer patients presented good OS, while the increase of GPX4 mRNA expression level had no significant correlation with OS in gastric cancer patients.

Conclusion

The results of this study are expected to provide a reliable basis for the clinical treatment of GC and lay a foundation for the development of a novel GC treatment approach.

An Immune Signature for Risk Stratification and Therapeutic Prediction in Helicobacter pylori-Infected Gastric Cancer

Helicobacter pylori (HP) infection is the greatest risk factor for gastric cancer (GC). Increasing evidence has clarified that tumor immune microenvironment (TIME) is closely related to the prognosis and therapeutic efficacy of HP-positive (HP+) GC patients. In this study, we aimed to construct a novel immune-related signature for predicting the prognosis and immunotherapy efficacy of HP+ GC patients. A total of 153 HP+ GC from three different cohorts were included in this study. An Immune-Related prognostic Signature for HP+ GC patients (IRSHG) was established using Univariate Cox regression, the LASSO algorithm, and Multivariate Cox regression. Univariate and Multivariate analyses proved IRSHG was an independent prognostic predictor for HP+ GC patients, and an IRSHG-integrated nomogram was established to quantitatively assessthe prognostic risk. The low-IRSHG group exhibited higher copy number load and distinct mutation profiles compared with the high-IRSHG group. In addition, the difference of hallmark pathways and immune cells infiltration between the two groups was investigated. Notably, tumor immune dysfunction and exclusion (TIDE) analysis indicated that the low-IRSHG group had a higher sensitivity to anti-PD-1 immunotherapy, which was validated by an external pabolizumab treatment cohort. Moreover, 98 chemotherapeutic drugs and corresponding potential biomarkers were identified for two groups, and several drugs with potential ability to reverse IRSHG score were identified using CMap analysis. Collectively, IRSHG may serve as a promising biomarker for survival outcome as well as immunotherapy efficacy. Furthermore, it can also help to prioritize potential therapeutics for HP+ GC patients, providing new insight for the personalized treatment of HP-infected GC.

Lactadherin: From a Well-Known Breast Tumor Marker to a Possible Player in Extracellular Vesicle-Mediated Cancer Progression

Lactadherin is a secreted glycoprotein associated with the milk fat globule membrane, which is highly present in the blood and in the mammary tissue of lactating women. Several biological functions have been associated with this protein, mainly attributable to its immunomodulatory role promoting phagocyte-mediated clearance of apoptotic cells. It has been shown that lactadherin also plays important roles in cell adhesion, the promotion of angiogenesis, and tissue regeneration. On the other hand, this protein has been used as a marker of breast cancer and tumor progression. Recently, high levels of lactadherin has been associated with poor prognosis and decreased survival, not only in breast cancer, but also in melanoma, ovarian, colorectal, and other types of cancer. Although the mechanisms responsible for the tumor-promoting effects attributed to lactadherin have not been fully elucidated, a growing body of literature indicates that lactadherin could be a promising therapeutic target and/or biomarker for breast and other tumors. Moreover, recent studies have shown its presence in extracellular vesicles derived from cancer cell lines and cancer patients, which was associated with cancer aggressiveness and worse prognosis. Thus, this review will focus on the link between lactadherin and cancer development and progression, its possible use as a cancer biomarker and/or therapeutic target, concluding with a possible role of this protein in cellular communication mediated by extracellular vesicles.

Construction of a Novel Immune-Related mRNA Signature to Predict the Prognosis and Immune Characteristics of Human Colorectal Cancer

Background: Anti-cancer immunotherapeutic approaches have gained significant efficacy in multiple cancer types. However, not all patients with colorectal cancer (CRC) could benefit from immunotherapy due to tumor heterogeneity. The purpose of this study was to construct an immune-related signature for predicting the immune characteristics and prognosis of CRC.

Methods: RNA-sequencing data and corresponding clinical information of patients with CRC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and immune-related genes (IRGs) were downloaded from the Immunology Database and Analysis Portal (ImmPort). Then, we utilized univariate, lasso regression, and multivariate cox regression to identify prognostic IRGs and develop the immune-related signature. Subsequently, a nomogram was established based on the signature and other prognostic factors, and its predictive capacity was assessed by receiver operating characteristic (ROC) and decision curve analysis (DCA). Finally, associations between the signature and the immune characteristics of CRC were assessed.

Results: In total, 472 samples downloaded from TCGA were divided into the training cohort (236 samples) and internal validation cohort (236 samples), and the GEO cohort was downloaded as an external validation cohort (122 samples). A total of 476 differently expressed IRGs were identified, 17 of which were significantly correlated to the prognosis of CRC patients. Finally, 10 IRGs were filtered out to construct the risk score signature, and patients were divided into low- and high-risk groups according to the median of risk scores in the training cohort. The high-risk score was significantly correlated with unfavorable survival outcomes and aggressive clinicopathological characteristics in CRC patients, and the results were further confirmed in the internal validation cohort, entire TCGA cohort, and external validation cohort. Immune infiltration analysis revealed that patients in the low-risk group infiltrated with high tumor-infiltrating immune cell (TIIC) abundances compared to the high-risk group. Moreover, we also found that the immune checkpoint biomarkers were significantly overexpressed in the low-risk group.

Conclusion: The prognostic signature established by IRGs showed a promising clinical value for predicting the prognosis and immune characteristics of human CRC, which contribute to individualized treatment decisions.

Development of Tumor Mutation Burden-Related Prognostic Model and Novel Biomarker Identification in Stomach Adenocarcinoma

Background: Stomach adenocarcinoma (STAD) is one of the most common tumors. Tumor mutation burden (TMB) has been linked to immunotherapy response. We wanted to see if there was any link between TMB and cancer prognosis.

Methods: The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to obtain mutation data, gene expression profiles, and clinical data. We looked at the differences in gene expression and immune markers between low and high TMB groups, built an immune prognostic model, and created a dynamic nomograph App that may be used in the clinic. Simultaneously, We ran the immunotherapy prediction and model comparison at the same time. Finally, model gene mutation and copy number variation (CNV) were displayed. The cellular functional experiments were used to investigate the potential role of GLP2R in gastric cancer.

Results: Firstly, basic mutation information and differences in immune infiltration in STAD are revealed. Secondly, the prognostic model developed by us has good accuracy, and the corresponding dynamic nomograph Apps online and immunotherapy prediction facilitate clinical transformation. Furthermore, GLP2R knockdown significantly inhibited the proliferation, migration of gastric cancer cells in vitro.

Conclusion: Our findings imply that TMB plays a significant role in the prognosis of STAD patients from a biological perspective. GLP2R may serve as a potential target for gastric cancer.

Comprehensive molecular characterization and identification of prognostic signature in stomach adenocarcinoma on the basis of energy-metabolism-related genes

BACKGROUND

Stomach adenocarcinoma (STAD) is a leading cause of cancer deaths, but its molecular and prognostic characteristics has never been fully illustrated.

AIM

To describe a molecular evaluation of primary STAD and develop new therapies and identify promising prognostic signatures.

METHODS

We describe a comprehensive molecular evaluation of primary STAD based on comprehensive analysis of energy-metabolism-related gene (EMRG) expression profiles.

RESULTS

On the basis of 86 EMRGs that were significantly associated to patients’ progression-free survival (PFS), we propose a molecular classification dividing gastric cancer into two subtypes: Cluster 1, most of which are young patients and display more immune and stromal cell components in tumor microenvironment and lower tumor priority; and Cluster 2, which show early stages and better PFS. Moreover, we construct a 6-gene signature that can classify the prognostic risk of patients after a three-phase training test and validation process. Compared with patients with low-risk score, patients with high-risk score had shorter overall survival. Furthermore, calibration and DCA analysis plots indicate the excellent predictive performance of the 6-gene signature, and which present higher robustness and clinical usability compared with three previous reported prognostic gene signatures. According to gene set enrichment analysis, gene sets related to the high-risk group were participated in the ECM receptor interaction and hedgehog signaling pathway.

CONCLUSION

Identification of the EMRG-based molecular subtypes and prognostic gene model provides a roadmap for patient stratification and trials of targeted therapies.

Immune cell gene expression signatures in diffuse glioma are associated with IDH mutation status, patient outcome and malignant cell state, and highlight the importance of specific cell subsets in glioma biology

The tumor micro-environment (TME) plays an important role in various cancers, including gliomas. We estimated immune cell type-specific gene expression profiles in 3 large clinically annotated glioma datasets using CIBERSORTx and LM22/LM10 blood-based immune signatures and found that the proportions and estimated gene expression patterns of specific immune cells significantly varied according to IDH mutation status. When IDH-WT and IDH-MUT tumors were considered separately, cluster-of-cluster analyses of immune cell gene expression identified groups with distinct survival outcomes. We confirmed and extended these findings by applying a signature matrix derived from single-cell RNA-sequencing data derived from 19 glioma tumor samples to the bulk profiling data, validating findings from the LM22/LM10 results. To link immune cell signatures with outcomes in checkpoint therapy, we then showed a significant association of monocytic lineage cell gene expression clusters with patient survival and with mesenchymal gene expression scores. Integrating immune cell-based gene expression with previously described malignant cell states in glioma demonstrated that macrophage M0 abundance significantly correlated with mesenchymal state in IDH-WT gliomas, with evidence of a previously implicated role of the Oncostatin-M receptor and macrophages in the mesenchymal state. Among IDH-WT tumors that were enriched for the mesenchymal cell state, the estimated M0 macrophage expression signature coordinately also trended to a mesenchymal signature. We also examined IDH-MUT tumors stratified by 1p/19q status, showing that a mesenchymal gene expression signature the M0 macrophage fraction was enriched in IDH-MUT, non-codeleted tumors. Overall, these results highlight the biological and clinical significance of the immune cell environment related to IDH mutation status, patient prognosis and the mesenchymal state in diffuse gliomas.

Construction and validation of an immune-related genes prognostic index (IRGPI) model in colon cancer

BACKGROUND

Though immunotherapy has become one of the standard therapies for colon cancer, the overall effective rate of immunotherapy is very low. Constructing an immune-related genes prognostic index (IRGPI) model may help to predict the response to immunotherapy and clinical outcomes.

METHODS

Differentially expressed immune-related genes (DEIRGs) between normal tissues and colon cancer tissues were identified and used to construct the co-expression network. Genes in the module with the most significant differences were further analyzed. Independent prognostic immune-related genes (IRGs) were identified by univariate and multivariate cox regression analysis. Independent prognostic IRGs were used to construct the IRGPI model using the multivariate cox proportional hazards regression model, and the IRGPI model was validated by independent dataset. ROC curves were plotted and AUCs were calculated to estimate the predictive power of the IRGPI model to prognosis. Gene set enrichment analysis (GSEA) was performed to screen the enriched KEGG pathways in the high-risk and low-risk phenotype. Correlations between IRGPI and clinical characteristic, immune checkpoint expression, TMB, immune cell infiltration, immune function, immune dysfunction, immune exclusion, immune subtype were analyzed.

RESULTS

Totally 680 DEIRGs were identified. Three independent IRGs,NR5A2, PPARGC1A and LGALS4, were independently related to survival. NR5A2, PPARGC1A and LGALS4 were used to establish the IRGPI model. Survival analysis showed that patients with high-risk showed worse survival than patients in the low-risk group. The AUC of the IRGPI model for 1-year, 3-year and 5-year were 0.584, 0.608 and 0.697, respectively. Univariate analysis and multivariate cox regression analysis indicated that IRGPI were independent prognostic factors for survival. Stratified survival analysis showed that patients with IRGPI low-risk and low TMB had the best survival, which suggested that combination of TMB and IRGPI can better predict clinical outcome. Immune cell infiltration, immune function, immune checkpoint expression and immune exclusion were different between IRGPI high-risk and low-risk patients.

CONCLUSION

An immune-related genes prognostic index (IRGPI) was constructed and validated in the current study and the IRGPI maybe a potential biomarker for evaluating response to immunotherapy and clinical outcome for colon cancer patients.

Development and Validation of an Immune-Related Gene Pairs Signature in Grade II/III Glioma

BACKGROUND

Gliomas are prevalent primary intracerebral malignant tumors. Increasing evidence indicates an association between the immune signature and Grade II/III glioma prognosis. Thus, we aimed to develop an immune-related gene pair (IRGP) signature that can be used as a prognostic tool in Grade II/III glioma.

METHODS

The gene expression levels and clinical information of Grade II/III glioma patients were collected from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. The TCGA data were randomly divided into a training cohort (n = 249) and a validation cohort (n = 162), and a CGGA dataset served as an external validation group (n = 605). IRGPs significantly associated with prognosis were selected by Cox regression. Gene set enrichment analysis and filtration were performed with the IRGPs.

RESULTS

Within a set of 1991 immune genes, 8 IRGPs including 15 unique genes that significantly affect survival constituted a gene signature. In the validation datasets, the IRGP signature significantly stratified patients with Grade II/III glioma into low- and high-risk groups (P < 0.001), and the IRGP index was found to be an independent prognostic factor through univariate and multivariate analyses (P < 0.05). Additionally, 26 functional pathways were identified through the intersection of Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) enrichment analysis.

CONCLUSION

The IRGP signature demonstrated good prognostic value for Grade II/III gliomas, which may provide new insights into individual treatment for glioma patients. The IRGPs might function through the identified 26 functional pathways.

Construction of a New Tumor Immunity-Related Signature to Assess and Classify the Prognostic Risk of Colorectal Cancer

Purpose

Although immunotherapy and checkpoint inhibitors contribute to the treatment of colorectal cancer (CRC), few patients can benefit from these treatments. Therefore, our goal was to develop a marker based on immune-related genes to predict the prognosis of patients with CRC to guide treatment strategies.

Methods

Gene expression data from colorectal cancer patients in the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas were analyzed systematically. We used Cox regression to identify immune-related genes with potential prognostic value. The expression of immune genes, infiltration level of immune cells, and several immune-related molecules were further compared between the high-risk and low-risk groups. Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analyses were used for functional analysis.

Results

Five GEO datasets were integrated into a merged GEO dataset, which showed obvious survival in StromalScore and ESTIMATEScore. WGCNA showed that 749 genes of the pink module are related to immunity, 95 of which are related to prognosis, correlating with cytokine–cytokine receptor interaction and natural killer cell-mediated cytotoxicity. Among these genes, an 11-gene signature was developed through stability selection and LASSO Cox regression. Univariate and multifactorial Cox regression analyses demonstrated that gene signature was an independent prognostic factor for predicting survival in patients with colorectal cancer. Samples from the low-risk group may be more sensitive to immunotherapy. In addition, the nomogram risk prediction model effectively predicted the prognosis of CRC patients by appropriately stratifying the risk scores.

Conclusion

In conclusion, we developed a novel immune-related gene signature that may be useful in predicting cancer progression and prognosis, thus contributing to the individualized management of colorectal cancer patients.

Novel immune-related signature for risk stratification and prognosis in prostatic adenocarcinoma

A substantial proportion of prostatic adenocarcinoma (PRAD) patients experience biochemical failure (BCF) after radical prostatectomy (RP). The immune microenvironment plays a vital role in carcinogenesis and the development of PRAD. This study aimed to identify a novel immune-related gene (IRG)-based signature for risk stratification and prognosis of BCF in PRAD. Weighted gene coexpression network analysis was carried out to identify a BCF-related module in a discovery cohort of patients who underwent RP at the Massachusetts General Hospital. The median follow-up time was 70.32 months. Random forest and multivariate stepwise Cox regression analyses were used to identify an IRG-based signature from the specific module. Risk plot analyses, Kaplan-Meier curves, receiver operating characteristic curves, univariate and multivariate Cox regression analyses, stratified analysis, and Harrell's concordance index were used to assess the prognostic value and predictive accuracy of the IRG-based signature in the internal discovery cohort; The Cancer Genome Atlas database was used as a validation cohort. Tumor immune estimation resource database analysis and CIBERSORT algorithm were used to assess the immunophenotype of PRAD. A novel IRG-based signature was identified from the specific module. Five IRGs (BUB1B, NDN, NID1, COL4A6, and FLRT2) were verified as components of the risk signature. The IRG-based signature showed good prognostic value and predictive accuracy in both the discovery and validation cohorts. Infiltrations of various immune cells were significantly different between low-risk and high-risk groups in PRAD. We identified a novel IRG-based signature that could function as an index for assessing tumor immune status and risk stratification in PRAD.

Establishment of an Immune Cell Infiltration Score to Help Predict the Prognosis and Chemotherapy Responsiveness of Gastric Cancer Patients

The immune microenvironment plays a critical role in tumor biology. The molecular profiles of immune components and related genes are of tremendous value for the study of primary resistance to immune checkpoint blockers (ICBs) for gastric cancer (GC) and serve as prognostic biomarkers to predict GC survival. Recent studies have revealed that tumor immune cell infiltration (ICI) is an indicator of the survival and responsiveness to chemotherapy in GC patients. Here, we describe the immune cell landscape based on the ESTIMATE and CIBERSORT algorithms to help separate GC into 3 ICI clusters using the unsupervised clustering method. Further in-depth analyses, such as differential expression gene (DEG) analysis and principal component analysis (PCA), help to establish an ICI scoring system. A low ICI score is characterized by an increased tumor mutation burden (TMB). The combination of the ICI score and TMB score better predicts the survival of GC patients. Analyses based on public and our own database revealed that the ICI scoring system could also help predict the survival and chemotherapy responsiveness of GC patients. The present study demonstrated that the ICI score may be an effective prognostic biomarker and predictive indicator for chemotherapy and immunotherapy.

The novel immune-related genes predict the prognosis of patients with hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the main causes of cancer deaths globally. Immunotherapy is becoming increasingly important in the cure of advanced HCC. Thus it is essential to identify biomarkers for treatment response and prognosis prediction. We searched publicly available databases and retrieved 465 samples of genes from The Cancer Genome Atlas (TCGA) database and 115 tumor samples from Gene Expression Omnibus (GEO). Meanwhile, we used the ImmPort database to determine the immune-related genes as well. Weighted gene correlation network analysis, Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were used to identify the key immune related genes (IRGs) which are closely related to prognosis. Gene set enrichment analysis (GSEA) was implemented to explore the difference of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway between Immune high- and low-risk score groups. Finally, we made a prognostic nomogram including Immune-Risk score and other clinicopathologic factors. A total of 318 genes from prognosis related modules were identified through weighted gene co-expression network analysis (WGCNA). 46 genes were strongly linked to prognosis after univariate Cox analysis. We constructed a seven genes prognostic signature which showed powerful prediction ability in both training cohort and testing cohort. 16 significant KEGG pathways were identified between high- and low- risk score groups using GSEA analysis. This study identified and verified seven immune-related prognostic biomarkers for the patients with HCC, which have potential value for immune modulatory and therapeutic targets.

Identification and Validation of a Six Immune-Related Genes Signature for Predicting Prognosis in Patients With Stage II Colorectal Cancer

Background

Immune-related genes (IRGs) play important roles in the tumor immune microenvironment and can affect the prognosis of cancer. This study aimed to construct a novel IRG signature for prognostic evaluation of stage II colorectal cancer (CRC).

Methods

Gene expression profiles and clinical data for stage II CRC patients were collected from the Cancer Genome Atlas and Gene Expression Omnibus database. Univariate, multivariate Cox regression, and least absolute shrinkage and selection operator regression were used to develop the IRG signature, namely IRGCRCII. A nomogram was constructed, and the “Cell Type Identification by Estimating Relative Subsets of RNA Transcripts” (CIBERSORT) method was used to estimate immune cell infiltration. The expression levels of genes and proteins were validated by qRT-PCR and immunohistochemistry in 30 pairs of primary stage II CRC and matched normal tissues.

Results

A total of 466 patients with stage II CRC were included, and 274 differentially expressed IRGs were identified. Six differentially expressed IRGs were detected and used to construct the IRGCRCII signature, which could significantly stratify patients into high-risk and low-risk groups in terms of disease-free survival in three cohorts: training, test, and external validation (GSE39582). Receiver operating characteristics analysis revealed that the area under the curves of the IRGCRCII signature were significantly greater than those of the OncotypeDX colon signature at 1 (0.759 vs. 0.623), 3 (0.875 vs. 0.629), and 5 years (0.906 vs. 0.698) disease-free survival, respectively. The nomogram performed well in the concordance index (0.779) and calibration curves. The high-risk group had a significantly higher percentage of infiltrated immune cells (e.g., M2 macrophages, plasma cells, resting mast cells) than the low-risk group. Finally, the results of qRT-PCR and immunohistochemistry experiments performed on 30 pairs of clinical specimens were consistent with bioinformatics analysis.

Conclusion

This study developed and validated a novel immune prognostic signature based on six differentially expressed IRGs for predicting disease-free survival and immune status in patients with stage II CRC, which may reflect immune dysregulation in the tumor immune microenvironment.

Immune gene prognostic signature for disease free survival of gastric cancer: Translational research of an artificial intelligence survival predictive system

The progress of artificial intelligence algorithms and massive data provide new ideas and choices for individual mortality risk prediction for cancer patients. The current research focused on depict immune gene related regulatory network and develop an artificial intelligence survival predictive system for disease free survival of gastric cancer.

Multi-task logistic regression algorithm, Cox survival regression algorithm, and Random survival forest algorithm were used to develop the artificial intelligence survival predictive system.

Nineteen transcription factors and seventy immune genes were identified to construct a transcription factor regulatory network of immune genes. Multivariate Cox regression identified fourteen immune genes as prognostic markers. These immune genes were used to construct a prognostic signature for gastric cancer. Concordance indexes were 0.800, 0.809, and 0.856 for 1-, 3- and 5- year survival. An interesting artificial intelligence survival predictive system was developed based on three artificial intelligence algorithms for gastric cancer. Gastric cancer patients with high risk score have poor survival than patients with low risk score.

The current study constructed a transcription factor regulatory network and developed two artificial intelligence survival prediction tools for disease free survival of gastric cancer patients. These artificial intelligence survival prediction tools are helpful for individualized treatment decision.

Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients

BACKGROUND

This study aimed to identify a series of prognostically relevant immune features by immunophenoscore. Immune features were explored using MRI radiomics features to prediction the overall survival (OS) of lower-grade glioma (LGG) patients and their response to immune checkpoints.

METHOD

LGG data were retrieved from TCGA and categorized into training and internal validation datasets. Patients attending the First Affiliated Hospital of Harbin Medical University were included in an external validation cohort. An immunophenoscore-based signature was built to predict malignant potential and response to immune checkpoint inhibitors in LGG patients. In addition, a deep learning neural network prediction model was built for validation of the immunophenoscore-based signature.

RESULTS

Immunophenotype-associated mRNA signatures (IMriskScore) for outcome prediction and ICB therapeutic effects in LGG patients were constructed. Deep learning of neural networks based on radiomics showed that MRI radiomic features determined IMriskScore. Enrichment analysis and ssGSEA correlation analysis were performed. Mutations in CIC significantly improved the prognosis of patients in the high IMriskScore group. Therefore, CIC is a potential therapeutic target for patients in the high IMriskScore group. Moreover, IMriskScore is an independent risk factor that can be used clinically to predict LGG patient outcomes.

CONCLUSIONS

The IMriskScore model consisting of a sets of biomarkers, can independently predict the prognosis of LGG patients and provides a basis for the development of personalized immunotherapy strategies. In addition, IMriskScore features were predicted by MRI radiomics using a deep learning approach using neural networks. Therefore, they can be used for the prognosis of LGG patients.

Proteomic Profiling of Gastric Signet Ring Cell Carcinoma Tissues Reveals Characteristic Changes of the Complement Cascade Pathway

Signet ring cell carcinoma (SRCC) is a histological subtype of gastric cancer with distinct features in multiple aspects compared with adenocarcinomas (ACs). The lack of a systematic molecular overview of this disease has led to slow progress in its clinical practice. In the present proteomics study, gastric tissues were collected from tumors and adjacent tissues, including 14 SRCCs and 34 ACs, and laser capture microdissection (LCM) was employed to eradicate the cellular heterogeneity of the tissues. The proteomes of tissues were profiled by data-independent acquisition (DIA) mass spectrometry (MS). Based on the over 6000 proteins quantified, univariate analysis and pathway enrichment revealed that some proteins and pathways demonstrated differences between SRCC and ACs. Importantly, the upregulation of a majority of complement-related proteins was notable for SRCC but not for ACs. A hypothesis, based on the proteomics evidence, was proposed that the complement cascade was evoked in the SRCC microenvironment upon infiltration, and the SRCC cells survived the complement cytotoxicity by secreting endogenous negative regulators. Moreover, an attempt was made to establish appropriate cell models for gastric SRCC through proteomic comparison of the 15 gastric cell lines and gastric tumors. The predictions of a supervised classifier suggested that none of these gastric cell lines qualified to mimic SRCC. This study discovered that the complement cascade is activated at a higher level in gastric SRCC than in ACs.

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LCM-DIA extracted unprecedented proteomic details of gastric in different subtypes.

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Complement cascade was found to be an SRCC-specific pathway for the first time.

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Gastric cell lines were evaluated based on proteomic features for the first time.

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Re-analyzable DIA data collected provide rich opportunity for future research.

Comprehensive Analysis of Survival-Related lncRNAs, miRNAs, and mRNAs Forming a Competing Endogenous RNA Network in Gastric Cancer

To analyze and construct a survival-related endogenous RNA (ceRNA) network in gastric cancer (GC) with lymph node metastasis, we obtained expression profiles of long non-coding RNAs (lncRNAs), mRNAs, and microRNAs (miRNAs) in GC from The Cancer Genome Atlas database. The edgeR package was used to screen differentially expressed lncRNAs, mRNAs, and miRNAs between GC patients with lymphatic metastasis and those without lymphatic metastasis. Then, we used univariate Cox regression analysis to identify survival-related differentially expressed RNAs. In addition, we used multivariate Cox regression analysis to screen lncRNAs, miRNAs, and mRNAs for use in the prognostic prediction models. The results showed that 2,247 lncRNAs, 155 miRNAs, and 1,253 mRNAs were differentially expressed between the two patient groups. Using univariate Cox regression analysis, we found that 395 lncRNAs, eight miRNAs, and 180 mRNAs were significantly related to the survival time of GC patients. We next created a survival-related network consisting of 59 lncRNAs, seven miRNAs, and 36 mRNAs. In addition, we identified eight RNAs associated with prognosis by multivariate Cox regression analysis, comprising three lncRNAs (AC094104.2, AC010457.1, and AC091832.1), two miRNAs (miR-653-5p and miR-3923), and three mRNAs (C5orf46, EPHA8, and HPR); these were used to construct the prognostic prediction models, and their risk scores could be used to assess GC patients' prognosis. In conclusion, this study provides new insights into ceRNA networks in GC and the screening of prognostic biomarkers for GC.

Expression of lipid transport-associated genes in lipid-deprived cancer cells

Cancer cells are known to significantly alter their lipid profiles in response to changes in extracellular lipid availability. Recent studies have shown that in response to lipid deprivation, cancer cells display significant changes in their cellular lipid homeostasis. These changes have been linked to the modulation of de novo lipid synthesis pathways that are markedly altered under lipid-deprived growth conditions. However, the effects of such environment on intracellular lipid trafficking-that could also affect cellular lipid homeostasis-have not been widely investigated. The presented work studies the effect of lipid deprivation on expression of genes for lipid transport proteins (LTPs) in cancer cell lines.

A Novel Immune and Stroma Related Prognostic Marker for Invasive Breast Cancer in Tumor Microenvironment: A TCGA Based Study

BACKGROUND

Breast cancer (BC) is the most frequent cancer in women. The tumor microenvironment (TME), consisting of blood vessels, immune cells, fibroblasts, and extracellular matrix, plays a pivotal role in tumorigenesis and progression. Increasing evidence has emphasized the importance of TME, especially the immune components, in patients with BC. Nevertheless, we still lack a deep understanding of the correlation between tumor invasion and TME status.

METHODS

Transcriptome and clinical data were retrieved from The Cancer Genome Atlas (TCGA) database. ESTIMATE algorithm was applied for quantifying stromal and immune scores. Then we screened out the differentially expressed genes (DEGs) through the intersection analysis. Furthermore, the establishment of protein-protein interaction (PPI) network and univariate COX regression analysis were utilized to determine the core genes in DEGs. In addition, we also performed Gene Set Enrichment Analysis (GSEA) and CIBERSORT analysis to distinguish the function of crucial gene expression and the proportion of tumor-infiltrating immune cells (TICs), respectively.

RESULTS

A total of 1178 samples (112 normal samples and 1066 tumor samples) were extracted from TCGA for calculation, and 226 DEGs were obtained from this assessment. Further intersection analysis revealed eight key genes, including ITK, CD3E, CCL19, CD2, SH2D1A, CD5, SLAMF6, SPN, which were proven to correlate with BC status. Moreover, ITK was picked out for further study. The results illustrated that high expression of BC patients had a more prolonged overall survival (OS) time than ITK low expression BC patients (p = 0.009), and ITK expression also presented the statistical significance in age, TNM staging, tumor size classification, and metastasis classification. Additionally, GSEA and CIBERSORT analysis indicated that ITK expression had an association with immune activity in TME.

CONCLUSION

ITK may be a potential indicator for prognosis prediction in patients with BC, and its biological behavior may promote our understanding of the molecular mechanism of tumor progression and targeted therapy.

Six Immune Associated Genes Construct Prognostic Model Evaluate Low-Grade Glioma

Background

The immunotherapy of Glioma has always been a research hotspot. Although tumor associated microglia/macrophages (TAMs) proves to be important in glioma progression and drug resistance, our knowledge about how TAMs influence glioma remains unclear. The relationship between glioma and TAMs still needs further study.

Methods

We collected the data of TAMs in glioma from NCBI Gene Expression Omnibus (GEO) that included 20 glioma samples and 15 control samples from four datasets. Six genes were screened from the Differential Expression Gene through Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein-protein interaction (PPI) network and single-cell sequencing analysis. A risk score was then constructed based on the six genes and patients' overall survival rates of 669 patients from The Cancer Genome Atlas (TCGA). The efficacy of the risk score in prognosis and prediction was verified in Chinese Glioma Genome Atlas (CGGA).

Results

Six genes, including CD163, FPR3, LPAR5, P2ry12, PLAUR, SIGLEC1, that participate in signal transduction and plasma membrane were selected. Half of them, like CD163, FPR3, SIGLEC1, were mainly expression in M2 macrophages. FPR3 and SIGLEC1 were high expression genes in glioma associated with grades and IDH status. The overall survival rates of the high risk score group was significantly lower than that of the low risk score group, especially in LGG.

Conclusion

Joint usage of the 6 candidate genes may be an effective method to diagnose and evaluate the prognosis of glioma, especially in Low-grade glioma (LGG).

An Immune-Related Gene Panel for Preoperative Lymph Node Status Evaluation in Advanced Gastric Cancer

Background and Aim: Gastric cancer (GC) is the common leading cause of cancer-related death worldwide. Immune-related genes (IRGs) may potentially predict lymph node metastasis (LNM). We aimed to develop a preoperative model to predict LNM based on these IRGs. Methods: In this paper, we compared and evaluated three machine learning models to predict LNM based on publicly available gene expression data from TCGA-STAD. The Pearson correlation coefficient (PCC) method was utilized to feature selection according to its relationships with LN status. The performance of the model was assessed using the area under the curve (AUC) and F1 score. Results: The Naive Bayesian model showed better performance and was constructed based on 26 selected gene features, with AUCs of 0.741 in the training set and 0.688 in the test set. The F1 score in the training set and test set was 0.652 and 0.597, respectively. Furthermore, Naive Bayesian model based on 26 IRGs is the first diagnostic tool for the identification of LNM in advanced GC. Conclusion: These results indicate that our new methods have the value of auxiliary diagnosis with promising clinical potential.

Expression and Prognostic Analysis of Integrins in Gastric Cancer

Background

Integrins are involved in the biological process of a variety of cancers, but their importance in the diagnosis and prognosis of gastric cancer (GC) is still unclear. Therefore, this study aimed at exploring the significance of ITG gene expression in GC to evaluate its diagnosis and prognosis.

Methods

GEPIA data were used to evaluate the mRNA expression of ITG genes in GC patients. The prognostic value of these genes was assessed by analyzing their mRNA expression using the Kaplan–Meier curve. The biological function of ITG genes was evaluated by GC tissue sequencing combined with GSEA bioinformatics. Based on the sequencing data, ITGA5 with the largest expression difference was selected for verification, and RT-PCR was used to verify its mRNA expression level in 40 pairs of GC and normal tissues.

Results

ITG (A2, A3, A4, A5, A6, A11, AE, AL, AM, AV, AX, B1, B2, B4, B5, B6, and B8) was highly expressed in GC tissues, while ITGA8 was low, compared with their expression in normal tissues. RNA-seq data shows that ITG (A2, A5, A11, AV, and B1) expression was associated with poor prognosis and overall survival. In addition, combined with the results of GC tissue mRNA sequencing, it was further found that the differentially expressed genes in the ITGs genes. ITGA5 was highly expressed in GC tissues compared with its expression in normal tissues, as evaluated by qRT–PCR (P < 0.001) and ROC (P < 0.001, AUC (95% CI) = 0.747 (0.641–0.851)), and confirmed that ITGA5 expression was a potential diagnostic marker for GC. Bioinformatics analysis revealed that the signaling pathway involved in ITGA5 was mainly enriched in focal adhesion, ECM-receptor interaction, and PI3K-AKT and was mainly involved in biological processes such as cell adhesion, extracellular matrix, and cell migration.

Conclusion

This study suggested that ITGs were associated with the diagnosis and prognosis of GC and discovered the prognostic value and biological role of ITGA5 in GC. Thus, ITGA5 might be used as a potential diagnostic marker for GC.

Identification of immune-related genes as prognostic factors in bladder cancer

Bladder cancer is one of the most common cancers worldwide. The immune response and immune cell infiltration play crucial roles in tumour progression. Immunotherapy has delivered breakthrough achievements in the past decade in bladder cancer. Differentially expressed genes and immune-related genes (DEIRGs) were identified by using the edgeR package. Gene ontology annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed for functional enrichment analysis of DEIRGs. Survival-associated IRGs were identified by univariate Cox regression analysis. A prognostic model was established by univariate COX regression analysis, and verified by a validation prognostic model based on the GEO database. Patients were divided into high-risk and low-risk groups based on the median risk score value for immune cell infiltration and clinicopathological analyses. A regulatory network of survival-associated IRGs and potential transcription factors was constructed to investigate the potential regulatory mechanisms of survival-associated IRGs. Nomogram and ROC curve to verify the accuracy of the model. Quantitative real-time PCR was performed to validate the expression of relevant key genes in the prognostic model. A total of 259 differentially expressed IRGs were identified in the present study. KEGG pathway analysis of IRGs showed that the “cytokine-cytokine receptor interaction” pathway was the most significantly enriched pathway. Thirteen survival-associated IRGs were selected to establish a prognostic index for bladder cancer. In both TCGA prognostic model and GEO validation model, patients with high riskscore had worse prognosis compared to low riskscore group. A high infiltration level of macrophages was observed in high-risk patients. OGN, ELN, ANXA6, ILK and TGFB3 were identified as hub survival-associated IRGs in the network. EBF1, WWTR1, GATA6, MYH11, and MEF2C were involved in the transcriptional regulation of these survival-associated hub IRGs. The present study identified several survival-associated IRGs of clinical significance and established a prognostic index for bladder cancer outcome evaluation for the first time.

Development and validation of an immune-related gene prognostic model for stomach adenocarcinoma

Purpose: Stomach adenocarcinoma (STAD) is one of the most common malignant tumors, and its occurrence and prognosis are closely related to inflammation. The aim of the present study was to identify gene signatures and construct an immune-related gene (IRG) prognostic model in STAD using bioinformatics analysis.

Methods: RNA sequencing data from healthy samples and samples with STAD, IRGs, and transcription factors were analyzed. The hub IRGs were identified using univariate and multivariate Cox regression analyses. Using the hub IRGs, we constructed an IRG prognostic model. The relationships between IRG prognostic models and clinical data were tested.

Results: A total of 289 differentially expressed IRGs and 20 prognostic IRGs were screened with a threshold of P<0.05. Through multivariate stepwise Cox regression analysis, we developed a prognostic model based on seven IRGs. The prognostic model was validated using a GEO dataset (GSE 84437). The IRGs were significantly correlated with the clinical outcomes (age, histological grade, N, and M stage) of STAD patients. The infiltration abundances of dendritic cells and macrophages were higher in the high-risk group than in the low-risk group.

Conclusions: Our results provide novel insights into the pathogenesis of STAD. An IRG prognostic model based on seven IRGs exhibited the predictive value, and have potential application value in clinical decision-making and individualized treatment.

Comprehensive analysis of a 14 immune-related gene pair signature to predict the prognosis and immune features of gastric cancer

BACKGROUND

As a new method for predicting tumor prognosis, the predictive effect of immune-related gene pairs (IRGPs) has been confirmed in several cancers, but there is no comprehensive analysis of the clinical significance of IRGPs in gastric cancer (GC).

METHOD

Clinical and gene expression profile data of GC patients were obtained from the GEO database. Based on the ImmPort database, differentially expressed immune-related gene (DEIRG) events were determined by a comparison of GC samples and adjacent normal samples. Cox proportional regression was used to construct an IRGP signature, and its availability was validated using three external validation datasets. In addition, we explored the association between clinical data and immune features and established a nomogram to predict outcomes in GC patients.

RESULT

A total of 88 DEIRGs were identified in GC from the training set, which formed 3828 IRGPs. Fourteen overall survival (OS)-related IRGPs were used to construct the prognostic signature. As a result, patients in the high-risk group exhibited poorer OS compared to those in the low-risk group. In addition, the fraction of CD8+ T cells, plasma cells, CD4 memory activated T cells, and M1 macrophages was higher in the high-risk group. Expression of two immune checkpoints, CD276 and VTCN1, was significantly higher in the high-risk group as well. Based on the independent prognostic factors, a nomogram was established and showed excellent performance.

CONCLUSION

The 14 OS-related IRGP signature was associated with OS, immune cells, and immune checkpoints in GC patients, and it could provide the basis for related immunotherapy.

Transcriptome Profiling Reveals New Insights into the Immune Microenvironment and Upregulation of Novel Biomarkers in Metastatic Uveal Melanoma

Simple Summary

Uveal melanoma (UM) is a rare aggressive eye cancer. Although treatment of the eye tumour is successful, about 50% of UM patients develop a relapse of their cancer in the liver. At present, such advanced disease is not curable. A better understanding of the metastatic UM (mUM) in the liver is essential to improve patient survival. This study examines both the response of immune cells within the liver to the UM secondaries (metastases), as well as the expression of various proteins by the UM cells. Our study demonstrates that there is a limited immune response to the mUM, but reveals that a certain type of reactive immune cell: a protumourigenic subset of macrophage is dominant within the mUM. Our research also reveals novel proteins within the mUM, which are specific to these cells and therefore may be targetable in future therapies.

Abstract

Metastatic uveal melanoma (mUM) to the liver is incurable. Transcriptome profiling of 40 formalin-fixed paraffin-embedded mUM liver resections and 6 control liver specimens was undertaken. mUMs were assessed for morphology, nuclear BAP1 (nBAP1) expression, and their tumour microenvironments (TME) using an “immunoscore” (absent/altered/high) for tumour-infiltrating lymphocytes (TILs) and macrophages (TAMs). Transcriptomes were compared between mUM and control liver; intersegmental and intratumoural analyses were also undertaken. Most mUM were epithelioid cell-type (75%), amelanotic (55%), and nBAP1-ve (70%). They had intermediate (68%) or absent (15%) immunoscores for TILs and intermediate (53%) or high (45%) immunoscores for TAMs. M2-TAMs were dominant in the mUM-TME, with upregulated expression of ANXA1, CD74, CXCR4, MIF, STAT3, PLA2G6, and TGFB1. Compared to control liver, mUM showed significant (p < 0.01) upregulation of 10 genes: DUSP4, PRAME, CD44, IRF4/MUM1, BCL2, CD146/MCAM/MUC18, IGF1R, PNMA1, MFGE8/lactadherin, and LGALS3/Galectin-3. Protein expression of DUSP4, CD44, IRF4, BCL-2, CD146, and IGF1R was validated in all mUMs, whereas protein expression of PRAME was validated in 10% cases; LGALS3 stained TAMs, and MFGEF8 highlighted bile ducts only. Intersegmental mUMs show differing transcriptomes, whereas those within a single mUM were similar. Our results show that M2-TAMs dominate mUM-TME with upregulation of genes contributing to immunosuppression. mUM significantly overexpress genes with targetable signalling pathways, and yet these may differ between intersegmental lesions.

Clinicopathological Significance of Neuropilin 1 Expression in Gastric Cancer: A Meta-Analysis

Background

Neuropilin 1 (NRP1) is involved in tumorigenesis, development, invasion, and metastasis by promoting angiogenesis of tumors. The study is aimed at evaluating the correlation between the expression of NRP1 protein and clinicopathological features of gastric cancer by meta-analysis.

Methods

The published studies were searched in databases including CNKI, Wanfang, Chongqing VIP, Web of Science, and PubMed online. Clinical case studies were included to compare the correlation between NRP1 protein expression and clinicopathological characteristics of gastric cancer. The quality of the included literatures was evaluated by NOS scale. Meta-analysis was performed by Stata software to calculate the odds ratio (OR) and 95% confidence interval (CI).

Results

A total of 12 studies were included in this analysis, involving 1,225 patients with gastric cancer. The analysis indicated that the expression of NRP1 protein in gastric cancer tissues was lower in the group of early stage versus advanced stage (OR = 0.128, 95%CI = 0.059 − 0.277, P ≤ 0.001), tumor size less than 5 cm versus more than 5 cm (OR = 0.443, 95%CI = 0.310 − 0.632, P ≤ 0.001), TNM stage I-II group versus stage III-IV patients (OR = 0.736, 95%CI = 0.589 − 0.919, P = 0.007), well to medium differentiation group versus poor differentiation group (OR = 0.735, 95%CI = 0.632 − 0.854, P ≤ 0.001), and nonlymph node metastasis group versus lymph node metastasis group (OR = 0.667, 95%CI = 0.522 − 0.854, P ≤ 0.001). The expression of NRP1 protein in gastric cancer was not related to gender, age, and Laurèn's classification.

Conclusion

The expression of NRP1 protein in gastric cancer is closely correlated to clinical stage, tumor size, TNM stage, differentiation, and lymph node metastasis.

Cancer Stem Cells-Origins and Biomarkers: Perspectives for Targeted Personalized Therapies

The use of biomarkers in diagnosis, therapy and prognosis has gained increasing interest over the last decades. In particular, the analysis of biomarkers in cancer patients within the pre- and post-therapeutic period is required to identify several types of cells, which carry a risk for a disease progression and subsequent post-therapeutic relapse. Cancer stem cells (CSCs) are a subpopulation of tumor cells that can drive tumor initiation and can cause relapses. At the time point of tumor initiation, CSCs originate from either differentiated cells or adult tissue resident stem cells. Due to their importance, several biomarkers that characterize CSCs have been identified and correlated to diagnosis, therapy and prognosis. However, CSCs have been shown to display a high plasticity, which changes their phenotypic and functional appearance. Such changes are induced by chemo- and radiotherapeutics as well as senescent tumor cells, which cause alterations in the tumor microenvironment. Induction of senescence causes tumor shrinkage by modulating an anti-tumorigenic environment in which tumor cells undergo growth arrest and immune cells are attracted. Besides these positive effects after therapy, senescence can also have negative effects displayed post-therapeutically. These unfavorable effects can directly promote cancer stemness by increasing CSC plasticity phenotypes, by activating stemness pathways in non-CSCs, as well as by promoting senescence escape and subsequent activation of stemness pathways. At the end, all these effects can lead to tumor relapse and metastasis. This review provides an overview of the most frequently used CSC markers and their implementation as biomarkers by focussing on deadliest solid (lung, stomach, liver, breast and colorectal cancers) and hematological (acute myeloid leukemia, chronic myeloid leukemia) cancers. Furthermore, it gives examples on how the CSC markers might be influenced by therapeutics, such as chemo- and radiotherapy, and the tumor microenvironment. It points out, that it is crucial to identify and monitor residual CSCs, senescent tumor cells, and the pro-tumorigenic senescence-associated secretory phenotype in a therapy follow-up using specific biomarkers. As a future perspective, a targeted immune-mediated strategy using chimeric antigen receptor based approaches for the removal of remaining chemotherapy-resistant cells as well as CSCs in a personalized therapeutic approach are discussed.

A novel prognostic signature of immune-related genes for patients with colorectal cancer

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers with an estimated 1.8 million new cases worldwide and associated with high mortality rates of 881 000 CRC-related deaths in 2018. Screening programs and new therapies have only marginally improved the survival of CRC patients. Immune-related genes (IRGs) have attracted attention in recent years as therapeutic targets. The aim of this study was to identify an immune-related prognostic signature for CRC. To this end, we combined gene expression and clinical data from the CRC data sets of The Cancer Genome Atlas (TCGA) into an integrated immune landscape profile. We identified a total of 476 IRGs that were differentially expressed in CRC vs normal tissues, of which 18 were survival related according to univariate Cox analysis. Stepwise multivariate Cox proportional hazards analysis established an immune-related prognostic signature consisting of SLC10A2, FGF2, CCL28, NDRG1, ESM1, UCN, UTS2 and TRDC. The predictive ability of this signature for 3- and 5-year overall survival was determined using receiver operating characteristics (ROC), and the respective areas under the curve (AUC) were 79.2% and 76.6%. The signature showed moderate predictive accuracy in the validation and GSE38832 data sets as well. Furthermore, the 8-IRG signature correlated significantly with tumour stage, invasion, lymph node metastasis and distant metastasis by univariate Cox analysis, and was established an independent prognostic factor by multivariate Cox regression analysis for CRC. Gene set enrichment analysis (GSEA) revealed a relationship between the IRG prognostic signature and various biological pathways. Focal adhesions and ECM-receptor interactions were positively correlated with the risk scores, while cytosolic DNA sensing and metabolism-related pathways were negatively correlated. Finally, the bioinformatics results were validated by real-time RT-qPCR. In conclusion, we identified and validated a novel, immune-related prognostic signature for patients with CRC, and this signature reflects the dysregulated tumour immune microenvironment and has a potential for better CRC patient management.

Neuropilin1 Expression Acts as a Prognostic Marker in Stomach Adenocarcinoma by Predicting the Infiltration of Treg Cells and M2 Macrophages

Neuropilin1 (NRP1) plays a critical role in tumor progression and immune responses. Although the roles of NRP1 in various tumors have been investigated, the clinical relevance of NRP1 expression in stomach adenocarcinoma (STAD) has not been studied. To investigate the use of NRP1 as a prognostic biomarker of STAD, we analyzed NRP1 mRNA expression and its correlation with patient survival and immune cell infiltration using various databases. NRP1 mRNA expression was significantly higher in STAD than normal tissues, and Kaplan-Meier survival analysis showed that NRP1 expression was significantly associated with poor prognosis in patients with STAD. To elucidate the related mechanism, we analyzed the correlation between NRP1 expression and immune cell infiltration level. In particular, the infiltration of immune-suppressive cells, such as regulatory T (Treg) cells and M2 macrophage, was significantly increased by NRP1 expression. In addition, the expression of interleukin (IL)-35, IL-10, and TGF-β1 was also positively correlated with NRP1 expression, resulting in the immune suppression. Collectively in this study, our integrated analysis using various clinical databases shows that the significant correlation between NRP1 expression and the infiltration of Treg cells and M2 macrophage explains poor prognosis mechanism in STAD, suggesting the clinical relevance of NRP1 expression as a prognostic biomarker for STAD patients.

Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in Lower-Grade Glioma

Objective

Despite several clinicopathological factors being integrated as prognostic biomarkers, the individual variants and risk stratification have not been fully elucidated in lower grade glioma (LGG). With the prevalence of gene expression profiling in LGG, and based on the critical role of the immune microenvironment, the aim of our study was to develop an immune-related signature for risk stratification and prognosis prediction in LGG.

Methods

RNA-sequencing data from The Cancer Genome Atlas (TCGA), Genome Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA) were used. Immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort). Univariate, multivariate cox regression, and Lasso regression were employed to identify differentially expressed immune-related genes (DEGs) and establish the signature. A nomogram was constructed, and its performance was evaluated by Harrell’s concordance index (C-index), receiver operating characteristic (ROC), and calibration curves. Relationships between the risk score and tumor-infiltrating immune cell abundances were evaluated using CIBERSORTx and TIMER.

Results

Noted, 277 immune-related DEGs were identified. Consecutively, 6 immune genes (CANX, HSPA1B, KLRC2, PSMC6, RFXAP, and TAP1) were identified as risk signature and Kaplan–Meier curve, ROC curve, and risk plot verified its performance in TCGA and CGGA datasets. Univariate and multivariate Cox regression indicated that the risk group was an independent predictor in primary LGG. The prognostic signature showed fair accuracy for 3- and 5-year overall survival in both internal (TCGA) and external (CGGA) validation cohorts. However, predictive performance was poor in the recurrent LGG cohort. The CIBERSORTx algorithm revealed that naïve CD4⁺ T cells were significant higher in low-risk group. Conversely, the infiltration levels of M1-type macrophages, M2-type macrophages, and CD8⁺T cells were significant higher in high-risk group in both TCGA and CGGA cohorts.

Conclusion

The present study constructed a robust six immune-related gene signature and established a prognostic nomogram effective in risk stratification and prediction of overall survival in primary LGG.