Exploration of a novel prognostic risk signatures and immune checkpoint molecules in endometrial carcinoma microenvironment

GDF10 and IDO1 as a thyroid cancer prognostic biomarker associated with immune infiltration

Objection

The aim of this work is to screen the immune-related genes to predict the prognosis and provide a new direction of treatment for patients with thyroid cancer (THCA).

Methods

The mRNA and clinical features of THCA patients were collected from the Cancer Genome Atlas (TCGA) databases. The immune-related genes were obtained from the ImmPort databases. The bio-information methods were performed to screen the differential expression genes (DEGs) and genes related to immunity between the THCA patients and normal individuals. On this basis, the hub prognosis immunity genes were screened by Veen. The related genes were obtained by constructing the protein-protein interaction network. The enrichment analyses were performed based on the protein and protein interaction (PPI) related genes. The hub immune checkpoint was screened by correlation analysis. Finally, the hub gene and the immunity checkpoint-miRNA (or transcription factor, drug) interaction network were constructed. A drug-sensitive analysis also was performed.

Results

The GDF10 was screened. The PPI genes were enriched in the TGF-beta signaling pathway, signaling pathways regulating, the pluripotency of stem cells, Cytokine-cytokine receptor interaction, and so on. The hub immunity checkpoint IDO1 was obtained. The joint indicator of two hub genes was positively related to the thyroid differentiation score. Three interaction factors were found to be related to the two hub genes, and 7 kinds of drugs screened act on the two hub genes at the same time.

Conclusion

This work indicated that immune-related gene GDF10 and immune checkpoint IDO1 are important for the prognosis prediction of THCA patients, and immunity is involved in the proliferation, and differentiation of tumor cells.

Identification of immune related molecular subtypes and prognosis model for predicting prognosis, drug resistance in cervical squamous cell carcinoma

Background: One of the features of tumor immunity is the immunosuppressive tumor microenvironment (TME). In this study, TME gene signatures were used to define the characteristics of Cervical squamous cell carcinoma (CESC) immune subtypes and construct a new prognostic model.

Methods: Single sample gene set enrichment analysis (ssGSEA) was used to quantify pathway activity. RNA-seq of 291 CESC were obtained from the Cancer Genome Atlas (TCGA) database as a training set. Microarray-based data of 400 cases of CESC were obtained from the Gene Expression Compilation (GEO) database as an independent validation set. 29 TME related gene signatures were consulted from previous study. Consensus Cluster Plus was employed to identify molecular subtype. Univariate cox regression analysis and random survival forest (RSF) were used to establish the immune-related gene risk model based on the TCGA data set of CESC, and the accuracy of prognosis prediction was verified by GEO data set. ESTIMATE algorithm was used to perform immune and matrix scores on the data set.

Results: three molecular subtypes (C1, C2, C3) were screened in TCGA-CESC on account of 29 TME gene signatures. Among, C3 with better survival outcome had higher immune related gene signatures, while C1 with worse prognosis time had enhanced matrix related features. Increased immune infiltration, inhibition of tumor related pathways, widespread genomic mutations and prone immunotherapy were observed in C3. Furthermore, a five immune genes signature was constructed and predicted overall survival for CESC, which successfully validated in GSE44001 dataset. A positive phenomenon was observed between five hub genes expressions and methylation. Similarly, high group enriched in matrix related features, while immune related gene signatures were enriched in low group. Immune cell, immune checkpoints genes expression levels were negatively, while most TME gene signatures were positively correlated with Risk Score. In addition, high group was more sensitive to drug resistance.

Conclusion: This work identified three distinct immune subtypes and a five genes signature for predicting prognosis in CESC patients, which provided a promising treatment strategy for CESC.

Construction and verification of a novel prognostic risk model for kidney renal clear cell carcinoma based on immunity-related genes

Background: Currently, there are no useful biomarkers or prognostic risk markers for the diagnosis of kidney renal clear cell carcinoma (KIRC), although recent research has shown that both, the onset and progression of KIRC, are substantially influenced by immune-associated genes (IAGs). Objective: This work aims to create and verify the prognostic value of an immune risk score signature (IRSS) based on IAGs for KIRC using bioinformatics and public databases. Methods: Differentially expressed genes (DEGs) related to the immune systems (IAGs) in KIRC tissues were identified from The Cancer Genome Atlas (TCGA) databases. The DEGs between the tumor and normal tissues were identified using gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, a prognostic IRSS model was constructed and its prognostic and predictive performance was analyzed using survival analyses and nomograms. Kidney renal papillary cell carcinoma (KIRP) sets were utilized to further validate this model. Results: Six independent immunity-related genes (PAEP, PI3, SAA2, SAA1, IL20RB, and IFI30) correlated with prognosis were identified and used to construct an IRSS model. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer prognoses than those of patients in the low-risk group in both, the verification set (p <0.049; HR = 1.84; 95% CI = 1.02-3.32) and the training set (p < 0.001; HR = 3.12, 95% CI = 2.23-4.37). The numbers of regulatory T cells (Tregs) were significantly positively correlated with the six immunity-related genes identified, with correlation coefficients were 0.385, 0.415, 0.399, 0.451, 0.485, and 0.333, respectively (p <0.001). Conclusion: This work investigated the association between immune infiltration, immunity-related gene expression, and severity of KIRC to construct and verify a prognostic risk model for KIRC and KIRP.

Hypoxia-associated prognostic markers and competing endogenous RNA coexpression networks in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common form of non-small cell lung cancer (NSCLC). Hypoxia has been found in 50-60% of locally advanced solid tumors and is associated with poor prognosis in various tumors, including NSCLC. This study focused on hypoxia-associated molecular hallmarks in LUAD. Fifteen hypoxia-related genes were selected to define the hypoxia status of LUAD by ConsensusClusterPlus based on data from The Cancer Genome Atlas (TCGA). Then, we investigated the immune status under different hypoxia statuses. Subsequently, we constructed prognostic models based on hypoxia-related differentially expressed genes (DEGs), identified hypoxia-related microRNAs, lncRNAs and mRNAs, and built a network based on the competing endogenous RNA (ceRNA) theory. Two clusters (Cluster 1 and Cluster 2) were identified with different hypoxia statuses. Cluster 1 was defined as the hypoxia subgroup, in which all 15 hypoxia-associated genes were upregulated. The infiltration of CD4+ T cells and Tfh cells was lower, while the infiltration of regulatory T (Treg) cells, the expression of PD-1/PD-L1 and TMB scores were higher in Cluster 1, indicating an immunosuppressive status. Based on the DEGs, a risk signature containing 7 genes was established. Furthermore, three differentially expressed microRNAs (hsa-miR-9, hsa-miR-31, hsa-miR-196b) associated with prognosis under different hypoxia clusters and their related mRNAs and lncRNAs were identified, and a ceRNA network was built. This study showed that hypoxia was associated with poor prognosis in LUAD and explored the potential mechanism from the perspective of the gene signature and ceRNA theory.

Analysis of m6A modulator-mediated methylation modification patterns and the tumor microenvironment in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer. In the development and progression of LUAD, epigenetic aberration plays a crucial role. However, the function of RNA N6-methyladenosine (m⁶A) modifications in the LUAD progression is unknown. The m6A regulator modification patterns in 955 LUAD samples were analyzed comprehensively. Patterns were systematically correlated with the tumor microenvironment (TME) cell-infiltration characteristics. Using principal component analysis algorithms, the m6Ascore was generated to quantify m⁶A modification patterns in individual tumors. Then, their values for predicting prognoses and therapeutic response in LUAD patients were assessed. Three distinct m⁶A modification patterns in LUAD were identified. Among them, the prognosis of m6Acluster C was the best, while the prognosis of m⁶Acluster A was the worst. Interestingly, the characterization of TME cell infiltration and biological behavior differed among the three patterns. To evaluate m⁶A modification patterns within individual tumors, an m⁶Ascore signature was constructed. The results showed that the high m⁶Ascore group was associated with a better prognosis; tumor somatic mutations and tumor microenvironment differed significantly between the high- and low- m⁶Ascore groups. Furthermore, in the cohort with anti-CTLA-4 treatment alone, patients with a high m⁶Ascore had higher ICI scores, which indicated significant therapeutic advantage and clinical benefits.

SLC2As as diagnostic markers and therapeutic targets in LUAD patients through bioinformatic analysis

Facilitative glucose transporters (GLUTs), which are encoded by solute carrier 2A (SLC2A) genes, are responsible for mediating glucose absorption. In order to meet their higher energy demands, cancer cells are more likely than normal tissue cells to have elevated glucose transporters. Multiple pathogenic processes, such as cancer and immunological disorders, have been linked to GLUTs. Few studies, meanwhile, have been conducted on individuals with lung adenocarcinoma (LUAD) to evaluate all 14 SLC2A genes. We first identified increased protein levels of SLC2A1, SLC2A5, SLC2A6, and SLC2A9 via HPA database and downregulated mRNA levels of SLC2A3, SLC2A6, SLC2A9, and SLC2A14 by ONCOMINE and UALCAN databases in patients with LUAD. Additionally, lower levels of SLC2A3, SLC2A6, SLC2A9, SLC2A12, and SLC2A14 and higher levels of SLC2A1, SLC2A5, SLC2A10, and SLC2A11 had an association with advanced tumor stage. SLC2A1, SLC2A7, and SLC2A11 were identified as prognostic signatures for LUAD. Kaplan-Meier analysis, Univariate Cox regression, multivariate Cox regression and ROC analyses further revealed that these three genes signature was a novel and important prognostic factor. Mechanistically, the aberrant expression of these molecules was caused, in part, by the hypomethylation of SLC2A3, SLC2A10, and SLC2A14 and by the hypermethylation of SLC2A1, SLC2A2, SLC2A5, SLC2A6, SLC2A7, and SLC2A11. Additionally, SLC2A3, SLC2A5, SLC2A6, SLC2A9, and SLC2A14 contributed to LUAD by positively modulating M2 macrophage and T cell exhaustion. Finally, pathways involving SLC2A1/BUB1B/mitotic cell cycle, SLC2A5/CD86/negative regulation of immune system process, SLC2A6/PLEK/lymphocyte activation, SLC2A9/CD4/regulation of cytokine production might participate in the pathogenesis of LUAD. In summary, our results will provide the theoretical basis on SLC2As as diagnostic markers and therapeutic targets in LUAD.

Identification of cuproptosis-related subtypes, establishment of a prognostic model and tumor immune landscape in endometrial carcinoma

Cuproptosis, the mechanism of copper-dependent cell death, is distinct from all other known forms of regulated cell death and dependents on mitochondrial respiration. Cuproptosis promises to be a novel treatment, especially for tumors resistant to conventional therapies. We investigated the changes in cuproptosis-related genes (CRGs) in endometrial cancer (EC) cohorts from the merged Gene Expression Omnibus and the Cancer Genome Atlas databases, which could be divided into three distinct CRGclusters. Patients in CRGcluster C would have higher survival probability (P = 0.007), and higher levels of tumor microenvironment (TME) cell infiltration than other CRGclusters. CRG score was calculated via the results of univariate, multivariate cox analysis and least absolute shrinkage and selection operator regression analysis. Patients were divided into two risk subgroups according to the median risk score. Low-risk patients exhibited a more favorable prognosis, higher immunogenicity, and greater immunotherapy efficacy. Besides, CRG scores were strongly correlated to copy number variation, immunophenoscore, tumor mutation load, cancer stem cell index, microsatellite instability, and chemosensitivity. The c-index of our model is 0.702, which is higher than other four published model. The results proved that our model can distinguish EC patients with high-risk and low-risk and accurately predict the prognosis of EC patients. It will provide new ideas for clinical prognosis and precise treatments.

Molecular subtypes, clinical significance, and tumor immune landscape of angiogenesis-related genes in ovarian cancer

Angiogenesis is a physiological process, where new blood vessels are formed from pre-existing vessels through the mechanism called sprouting. It plays a significant role in supporting tumor growth and is expected to provide novel therapeutic ideas for treating tumors that are resistant to conventional therapies. We investigated the expression pattern of angiogenesis-related genes (ARGs) in ovarian cancer (OV) from public databases, in which the patients could be classified into two differential ARG clusters. It was observed that patients in ARGcluster B would have a better prognosis but lower immune cell infiltration levels in the tumor microenvironment. Then ARG score was computed based on differentially expressed genes via cox analysis, which exhibited a strong correlation to copy number variation, immunophenoscore, tumor mutation load, and chemosensitivity. In addition, according to the median risk score, patients were separated into two risk subgroups, of which the low-risk group had a better prognosis, increased immunogenicity, and stronger immunotherapy efficacy. Furthermore, we constructed a prognostic nomogram and demonstrated its predictive value. These findings help us better understand the role of ARGs in OV and offer new perspectives for clinical prognosis and personalized treatment.

Clinicopathological Features and Prognostic-Related Risk Factors of Gastric Signet Ring Cell Carcinoma: A Meta-Analysis

Background

Gastric signet ring cell carcinoma (SRCC) has shown a growth growing trend worldwide, but its clinicopathological features and prognostic-related risk factors have not been systematically studied. This systematic review was devoted to this.

Method

PubMed, Embase, Cochrane Library, and Web of Science databases were retrieved, and retrospective cohort studies comparing clinicopathological features and related risk factors in SRCC patients were included.

Results

In SRCC patient population, males were more than females (male, OR = 1.38, 95% CI: 1.20-1.60); N3 patients were more than N0-2 patients (N0-2, OR = 3.19, 95% CI: 1.98-5.15); M1 patients were more than M0 patients (M0, OR = 3.30, 95% CI: 1.88-5.80); patients with tumor > 5 cm were more than those with tumor (≤5 cm, OR = 7.36, 95% CI: 1.33-40.60). Patients with age < 60 years (age ≥ 60 years, OR = 1.03, 95% CI: 1.01-1.05), lymphatic vessel invasion (no, OR = 1.74, 95% CI: 1.03-2.45), T2 (T1, OR = 1.17, 95% CI: 1.07-1.28) and T4 (T1, OR = 2.55, 95% CI: 2.30-2.81) stages, and N1 (N0, OR = 1.73, 95% CI: 1.08-2.38), N2 (N0, OR = 2.24, 95% CI: 1.12-3.36), and N3 (N0, OR = 3.45, 95% CI: 1.58-5.32) stages had higher hazard ratio (HR).

Conclusion

SRCC may occur frequently in male. Age, lymphatic vessel invasion, TN, and M stage may be risk factors for poor prognoses of SRCC patients.

Crosstalk of Redox-Related Subtypes, Establishment of a Prognostic Model and Immune Responses in Endometrial Carcinoma

Simple Summary

In order to explore the role of redox as a prognostic indicator in endometrial carcinoma (EC), we detected the expression patterns of 55 redox-related genes (RRGs) in EC cohorts from public databases. Performing consensus cluster algorithm, we determined four molecular subclusters based on RRGs which had significant differences in overall survival (OS) and immune activities of EC patients. Furthermore, we developed a prognostic risk model on the basis of the redox-related subtype by stepwise Cox regression analyses. All EC patients were divided into high-risk and low-risk groups according to the median value of risk score. Our proposed model could accurately assess the clinical outcome and had favorable independent ability in EC cases. Moreover, our signature can serve as a predictor for immune status and chemotherapy sensitivity.

Abstract

Redox plays a central part in the pathogeneses and development of tumors. We comprehensively determined the expression patterns of redox-related genes (RRGs) in endometrial carcinoma (EC) cohorts from public databases and identified four different RRG-related clusters. The prognosis and the characteristics of TME cell infiltration of RRGcluster C patients were worse than those of other RRG clusters. When it comes to the gene cluster, there were great differences in clinicopathology traits and immunocyte infiltration. The RRG score was calculated by Cox analyses, and an RRG-based signature was developed. The risk score performed well in the EC cohort. Samples were separated into two risk subgroups with the standard of the value of the median risk score. Low-risk patients had a better prognosis and higher immunogenicity. In addition, RRG score was closely associated with immunophenoscore, microsatellite instability, tumor mutation burden, tumor stem cell index, copy number variation and chemotherapy sensitivity. The nomogram accurately predicted the prognosis of patients, and our model showed better performance than other published models. In conclusion, we built a prognostic model of RRGs which can help to evaluate clinical outcomes and guide more effective treatment.

Construction and validation of an immune-related LncRNA prognostic model for hepatocellular carcinoma

Herein, based on mRNA data from TCGA database, hepatocellular carcinoma (HCC) samples were subjected to a single-sample Gene Set Enrichment Analysis (ssGSEA). Then, HCC samples were finally classified into high-, middle-, and low-immunity groups using K-means consensus clustering (K = 3) according to ssGSEA scores. After the tumor microenvironment of HCC patients was further analyzed using ESTIMATE algorithm, the results indicated high immune score, stromal score, ESTIMATE score and low tumor purity in high-immunity group. HLA family genes and PD-L1(CD274) were remarkably highly expressed in high-immunity group. Immune-related lncRNAs were required by analyzing differentially expressed genes in high- and low-immunity groups. Differential expression analysis was undertaken on HCC samples, with normal samples as the control. After immune-related lncRNAs and differentially expressed lncRNAs were intersected, 321 differentially expressed immune-related lncRNAs were acquired. Later, the prognostic model based on immune-related lncRNAs was obtained following the Cox regression analysis of previous samples. According to the riskScore, the samples in TCGA-LIHC were divided into high- and low-risk groups. Kaplan-Meier survival analysis, ROC curve, and independence analysis confirmed that the immune-related lncRNAs prognostic model was an important factor independent from clinical characteristics. We further analyzed the difference in immune microenvironment and mutational landscapes in both risk groups. Prominent differences were shown in multiple immunity-related gene sets and immune cells in both groups. The mutation rate of TP53 in high-risk group was much higher than the low-risk one. All these conclusions offered references to prognostic evaluations and personalized treatments for patients with HCC.

A 13-Gene Signature Based on Estrogen Response Pathway for Predicting Survival and Immune Responses of Patients With UCEC

Background: Accumulating evidence suggests that anti-estrogens have been effective against multiple gynecological diseases, especially advanced uterine corpus endometrial carcinoma (UCEC), highlighting the contribution of the estrogen response pathway in UCEC progression. This study aims to identify a reliable prognostic signature for potentially aiding in the comprehensive management of UCEC.

Methods: Firstly, univariate Cox and LASSO regression were performed to identify a satisfying UCEC prognostic model quantifying patients’ risk, constructed from estrogen-response-related genes and verified to be effective by Kaplan-Meier curves, ROC curves, univariate and multivariate Cox regression. Additionally, a nomogram was constructed integrating the prognostic model and other clinicopathological parameters. Next, UCEC patients from the TCGA dataset were divided into low- and high-risk groups according to the median risk score. To elucidate differences in biological characteristics between the two risk groups, pathway enrichment, immune landscape, genomic alterations, and therapeutic responses were evaluated to satisfy this objective. As for treatment, effective responses to anti-PD-1 therapy in the low-risk patients and sensitivity to six chemotherapy drugs in the high-risk patients were demonstrated.

Results: The low-risk group with a relatively favorable prognosis was marked by increased immune cell infiltration, higher expression levels of HLA members and immune checkpoint biomarkers, higher tumor mutation burden, and lower copy number alterations. This UCEC prognostic signature, composed of 13 estrogen-response-related genes, has been identified and verified as effective.

Conclusion: Our study provides molecular signatures for further functional and therapeutic investigations of estrogen-response-related genes in UCEC and represents a potential systemic approach to characterize key factors in UCEC pathogenesis and therapeutic responses.

DLC1 Is a Prognosis-Related Biomarker Correlated With Tumor Microenvironment Remodeling in Endometrial Carcinoma

Background and Aim

Deleted in liver cancer 1 (DLC1) is confirmed as a metastasis suppressor gene in endometrial carcinoma (EC). However, its functional mechanisms remain unclear. This study aimed to explore the relationship between DLC1 expression and EC.

Methods

The Cancer Genome Atlas database was used for evaluating the expression of DLC1 in pan-cancer. CIBERSORT was used to assess the relationship between DLC1 and tumor immune infiltration. We applied real-time quantitative polymerase chain reaction to determine the expression of DLC1 in EC and adjacent normal tissue samples. The targeting endogenous protein levels were assessed using the dataset from the cBioPortal database.

Results

DLC1 expression negatively correlated with the clinical characteristics (clinical stage, histologic grade) and positively correlated with the survival of patients with uterine corpus EC (UCEC). The gene set enrichment analysis displayed that the low-expression DLC1 group was enriched in metabolic pathways. Concomitantly, the high-expression DLC1 group was enriched in tumor immune-related activities. The CIBERSORT analysis showed that the number of resting memory CD4 T cells and resting mast cells positively correlated with DLC1 expression, while the number of macrophages M2 had a negative correlation, indicating that DLC1 played a key role in mediating immune cell infiltration. The target gene validation confirmed that DLC1 expression was downregulated in tumor samples. The target protein level was consistently downregulated in tumor samples.

Conclusions

DLC1 levels might be useful in predicting the prognosis of patients with UCEC and especially governing the status of tumor microenvironment transition from immune-dominant to metabolic-dominant. The findings shed a different light on the immune therapeutics of UCEC.

Immunomodulatory potential of natural products from herbal medicines as immune checkpoints inhibitors: Helping to fight against cancer via multiple targets

Immunotherapy sheds new light to cancer treatment and is satisfied by cancer patients. However, immunotoxicity, single‐source antibodies, and single‐targeting stratege are potential challenges to the success of cancer immunotherapy. A huge number of promising lead compounds for cancer treatment are of natural origin from herbal medicines. The application of natural products from herbal medicines that have immunomodulatory properties could alter the landscape of immunotherapy drastically. The present study summarizes current medication for cancer immunotherapy and discusses the potential chemicals from herbal medicines as immune checkpoint inhibitors that have a broad range of immunomodulatory effects. Therefore, this review provides valuable insights into the efficacy and mechanism of actions of cancer immunotherapies, including natural products and combined treatment with immune checkpoint inhibitors, which could confer an improved clinical outcome for cancer treatment.

A Novel Kinase Inhibitor AX-0085 Inhibits Interferon-γ-Mediated Induction of PD-L1 Expression and Promotes Immune Reaction to Lung Adenocarcinoma Cells

In this study, we describe a novel kinase inhibitor AX-0085 which can suppress the induction of PD-L1 expression by Interferon-γ (IFN-γ) in lung adenocarcinoma (LUAD) cells. AX-0085 effectively blocks JAK2/STAT1 signaling initiated by IFN-γ treatment and prevents nuclear localization of STAT1. Importantly, we demonstrate that AX-0085 reverses the IFN-γ-mediated repression of T cell activation in vitro and enhances the anti-tumor activity of anti-PD-1 antibody in vivo when used in combination. Finally, transcriptomic analyses indicated that AX-0085 is highly specific in targeting the IFN-γ-pathway, thereby raising the possibility of applying this reagent in combination therapy with checkpoint inhibitor antibodies. It may be particularly relevant in cases in which PD-L1-mediated T cell exhaustion leads to immunoevasive phenotypes.

Identification of an Autophagy-Related Risk Signature Correlates With Immunophenotype and Predicts Immune Checkpoint Blockade Efficacy of Neuroblastoma

Neuroblastoma is one of the malignant solid tumors with the highest mortality in childhood. Targeted immunotherapy still cannot achieve satisfactory results due to heterogeneity and tolerance. Exploring markers related to prognosis and evaluating the immune microenvironment remain the major obstacles. Herein, we constructed an autophagy-related gene (ATG) risk model by multivariate Cox regression and least absolute shrinkage and selection operator regression, and identified four prognostic ATGs (BIRC5, GRID2, HK2, and RNASEL) in the training cohort, then verified the signature in the internal and external validation cohorts. BIRC5 and HK2 showed higher expression in MYCN amplified cell lines and tumor tissues consistently, whereas GRID2 and RNASEL showed the opposite trends. The correlation between the signature and clinicopathological parameters was further analyzed and showing consistency. A prognostic nomogram using risk score, International Neuroblastoma Staging System stage, age, and MYCN status was built subsequently, and the area under curves, net reclassification improvement, and integrated discrimination improvement showed more satisfactory prognostic predicting performance. The ATG prognostic signature itself can significantly divide patients with neuroblastoma into high- and low-risk groups; differentially expressed genes between the two groups were enriched in autophagy-related behaviors and immune cell reactions in gene set enrichment analysis (false discovery rate q -value < 0.05). Furthermore, we evaluated the relationship of the signature risk score with immune cell infiltration and the cancer-immunity cycle. The low-risk group was characterized by more abundant expression of chemokines and higher immune checkpoints (PDL1, PD1, CTLA-4, and IDO1). The risk score was significantly correlated with the proportions of CD8+ T cells, CD4+ memory resting T cells, follicular helper T cells, memory B cells, plasma cells, and M2 macrophages in tumor tissues. In conclusion, we developed and validated an autophagy-related signature that can accurately predict the prognosis, which might be meaningful to understand the immune microenvironment and guide immune checkpoint blockade.

Immune checkpoint proteins (PD-L1 and CTLA-4) in endometrial carcinoma: prognostic role and correlation with CD4+/CD8+ tumor infiltrating lymphocytes (TILs) ratio

Developing the prognostic aspects of endometrial carcinoma through shedding light on immune check point proteins (PD-L1 and CTLA-4) together with Tumor-Infiltrating Lymphocytes (TILs) may help finding new targets for immunotherapy, especially for advanced cases. This study aimed to study the immunohistochemical expression of PD-L1 and CTLA-4 in correlation with tumor infiltrating lymphocytes (TILs) in series of endometrial carcinomas. CTLA-4 showed notably higher frequency of expression in the studied cases than PD-L1. However, both showed significant association across different histopathological subtypes. PD-L1 immunohistochemical expression in studied endometrial carcinomas was significantly associated with low CD4⁺/CD8⁺ratio, high tumor grades, presence of lymph node metastasis and higher tumor stage. CTLA-4 immunohistochemical expression in studied endometrial carcinomas was significantly associated with low CD4⁺/CD8⁺ ratio and high tumor grades but not with tumor stage. Both PD-L1 & CTLA-4 are expressed in subset of endometrial carcinomas with more prevalence of the latter. Both immune checkpoint proteins have significant correlation with different prognostic clinicopathological parameters together with TILs (CD4 & CD8 and their ratio). Increased activated cytotoxic T lymphocytes and PD-L1 expression in endometrial carcinomas may suggest identification of patients' subset of tumors that can be candidates for treatment with immunotherapy.

Comprehensive Analysis of the Immune Infiltrates of Pyroptosis in Kidney Renal Clear Cell Carcinoma

Kidney renal clear cell carcinoma (KIRC) has long been identified as a highly immune-infiltrated tumor. However, the underlying role of pyroptosis in the tumor microenvironment (TME) of KIRC remains poorly described. Herein, we systematically analyzed the prognostic value, role in the TME, response to ICIs, and drug sensitivity of pyroptosis-related genes (PRGs) in KIRC patients based on The Cancer Genome Atlas (TCGA) database. Cluster 2, by consensus clustering for 24 PRGs, presented a poor prognosis, likely because malignancy-related hallmarks were remarkably enriched. Additionally, we constructed a prognostic prediction model that discriminated well between high- and low-risk patients and was further confirmed in external E-MTAB-1980 cohort and HSP cohort. By further analyzing the TME based on the risk model, higher immune cell infiltration and lower tumor purity were found in the high-risk group, which presented a poor prognosis. Patients with high risk scores also exhibited higher ICI expression, indicating that these patients may be more prone to profit from ICIs. The sensitivity to anticancer drugs that correlated with model-related genes was also identified. Collectively, the pyroptosis-related prognosis risk model may improve prognostic information and provide directions for current research investigations on immunotherapeutic strategies for KIRC patients.

An immune-related prognostic signature for thyroid carcinoma to predict survival and response to immune checkpoint inhibitors

Thyroid carcinoma (THCA) is the most common endocrine malignancy, and its incidence is increasing worldwide. Several studies have explored whether the tumor immune microenvironment and immune-related genes (IRGs) influence the prognosis of patients with THCA and can be used to predict the response to immune checkpoint inhibitors (ICIs). We developed an IRG prognostic/risk signature using a bioinformatics method, and its predictive capacity was validated in patients in the test set and the total set. Subsequently, we analyzed the correlation between this IRG prognostic signature and tumor-infiltrating immune cells, tumor mutation burden (TMB), and immune checkpoint protein expression in patients with THCA. With a multivariate analysis, the IRG prognostic signature, which comprised eight IRGs, was identified as an independent prognostic factor. High-risk patients had poor overall survival compared with low-risk patients. Plasma cells, monocytes, and dendritic cells infiltrated differently according to the IRG prognostic signature. The low-risk group had a higher TMB and immunophenoscore (IPS), which indicated a better response to ICIs. The qRT-PCR validated eight IRGs with differential expression in thyroid cancer and normal tissues. We conclude that the IRG prognostic signature may be a useful tool to predict survival and response to ICIs. However, further testing is required to assess the predictive capacity of this IRG prognostic signature.

Identification of an eleven-miRNA signature to predict the prognosis of endometrial cancer

Endometrial cancer (EC) is the most common gynecological malignancy. Recent studies have uncovered miRNA acted a striking role in predicting the prognosis of multiple tumors. Over 500 EC samples were selected from the Cancer Genome Atlas (TCGA) database. Univariate, LASSO and multivariate Cox regression analysis were employed to screen out the prognosis-involved miRNAs. Kaplan-Meier (K-M) and time-dependent receiver operation characteristic (ROC) curves were conducted to reveal survival analysis and assess the accuracy of the signature. The independence of the model was verified via univariate and multivariate Cox regression analysis. Besides, qRT-PCR was conducted to testified the expression of 11 miRNAs in 16 paired tissues. A total of 514 specimens were randomly divided into the training set and the testing set, then an 11 miRNAs-based signature were determined which divided the patients into high-risk group and low-risk group. The survival was markedly different and the ROC curve exhibited a precise prediction. Meanwhile, the univariate and multivariate Cox regression analysis verified the miRNAs-based model was an independent indicator of EC. Moreove, the prediction ability of this model with clinicopathological features was more efficient. Finally, functional enrichment analysis demonstrated these miRNAs were associated with the occurrence and progression of cancer. Additionally, hsa-mir-216b, hsa-mir-363, hsa-mir-940 and hsa-mir-1301 were highly expressed in EC tissues in contrast to normal tissues through qRT-PCR. Importantly, the eleven-miRNA signature was full of robust ability to predict the prognosis of EC.

Identification of methylation-driven genes prognosis signature and immune microenvironment in uterus corpus endometrial cancer

Background

Uterus corpus endometrial cancer (UCEC) is the main malignant tumor in gynecology, with a high degree of heterogeneity, especially in terms of prognosis and immunotherapy efficacy. DNA methylation is one of the most important epigenetic modifications. Studying DNA methylation can help predict the prognosis of cancer patients and provide help for clinical treatment. Our research aims to discover whether abnormal DNA methylation can predict the prognosis of UCEC and reflect the patient's tumor immune microenvironment.

Patients and methods

The clinical data, DNA methylation data, gene expression data and somatic mutation data of UCEC patients were all downloaded from the TCGA database. The MethylMix algorithm was used to integrate DNA methylation data and mRNA expression data. Univariate Cox regression analysis, Multivariate Cox regression analysis, and Lasso Cox regression analysis were used to determine prognostic DNA methylation-driven genes and to construct an independent prognostic index (MDS). ROC curve analysis and Kaplan–Meier survival curve analysis were used to evaluate the predictive ability of MDS. GSEA analysis was used to explore possible mechanisms that contribute to the heterogeneity of the prognosis of UCEC patients.

Results

3 differential methylation-driven genes (DMDGs) (PARVG, SYNE4 and CDO1) were considered as predictors of poor prognosis in UCEC. An independent prognostic index was finally established based on 3 DMDGs. From the results of ROC curve analysis and survival curve analysis, MDS showed excellent prognostic ability in TCGA-UCEC. A new nomogram based on MDS and other prognostic clinical indicators has also been successfully established. The C-index of the nomogram for OS prediction was 0.764 (95% CI = 0.702–0.826). GSEA analysis suggests that there were differences in immune-related pathways among patients with different prognosis. The abundance of M2 macrophages and M0 macrophages were significantly enhanced in the high-risk group while T cells CD8, Eosinophils and Neutrophils were markedly elevated in the low-risk group. Meanwhile, patients in the low-risk group had higher levels of immunosuppressant expression, higher tumor mutational burden and immunophenoscore (IPS) scores. Joint survival analysis revealed that 7 methylation-driven genes could be independent prognostic factors for overall survival for UCEC.

Conclusion

We have successfully established a risk model based on 3 DMDGs, which could accurately predict the prognosis of patients with UCEC and reflect the tumor immune microenvironment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02038-z.

Bioinformatic profiling identifies prognosis-related genes in the immune microenvironment of endometrial carcinoma

Endometrial carcinoma (EC) is a common malignancy of female genital system which exhibits a unique immune profile. It is a promising strategy to quantify immune patterns of EC for predicting prognosis and therapeutic efficiency. Here, we attempted to identify the possible immune microenvironment-related prognostic markers of EC. We obtained the RNA sequencing and corresponding clinical data of EC from TCGA database. Then, 3 immune scores based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm were computed. Correlation between above ESTIMATE scores and other immune-related scores, molecular subtypes, prognosis, and gene mutation status (including BRCA and TP53) were further analyzed. Afterwards, gene modules associated with the ESTIMATE scores were screened out through hierarchical clustering analysis and weighted gene co-expression network analysis (WGCNA). Differentially expressed analysis was performed and genes shared by the most relevant modules were found out. KEGG pathway enrichment analysis was conducted to explore the biological functions of those genes. Survival analysis was carried out to identify prognostic immune-related genes and GSE17025 database was further used to confirm the correlation between immune-related genes and the ImmuneScore. The immune-related scores based on ESTIMATE algorithm was closely related to the immune microenvironment of EC. 3 gene modules that had the closest correlations with 3 ESTIMATE scores were obtained. 109 immune-related genes were preliminarily found out and 29 pathways were significantly enriched, most of which were associated with immune response. Univariate survival analysis revealed that there were 14 genes positively associated with both OS and PFS. Among which, 11 genes showed marked correlations with ImmuneScore values in GSE17025 database. Our current study profiled the immune status and identified 14 novel immune-related prognostic biomarkers for EC. Our findings may help to investigate the complicated tumor microenvironment and develop novel individualized therapeutic targets for EC.

Identification of an immune signature to predict poor clinical outcome in cervical cancer

Aim: To explore tumor immune microenvironment and identify immune prognostic-related circRNAs in cervical cancer. Materials & methods: RNA-seq in combination with bioinformatics were performed to establish a prognostic risk model and a circRNAs-miRNAs-CXCL8 network. Results: High-risk group correlated with poor survival outcome, and had lower PD-1 immunogenicity. Additionally, CXCL8 could distinguish normal tissue, low- and high-risk tumor tissues, the expression of which showed an increasing trend among the three groups. RNA-seq and bioinformatics indicated that circRNAs like hsa_circ_0025721 might upregulate CXCL8 through sponging miRNAs including hsa-miR-4428. Conclusion: We constructed an immune risk model related with CD8 T cells to predict the cervical cancer patients' prognosis and explored the abnormal expression mechanism of CXCL8 through the ceRNA mechanism.

DNA methylation-based lung adenocarcinoma subtypes can predict prognosis, recurrence, and immunotherapeutic implications

The marked heterogeneity of lung adenocarcinoma (LUAD) makes its diagnosis and treatment difficult. In addition, the aberrant DNA methylation profile contributes to tumor heterogeneity and alters the immune response. We used DNA methylation array data from publicly available databases to establish a predictive model for LUAD prognosis. Thirty-three methylation sites were identified as specific prognostic biomarkers, independent of patients' clinical characteristics. These methylation profiles were used to identify potential drug candidates and study the immune microenvironment of LUAD and response to immunotherapy. When compared with the high-risk group, the low-risk group had a lower recurrence rate and favorable prognosis. The tumor microenvironment differed between the two groups as reflected by the higher number of resting dendritic cells and a lower number of monocytes and resting mast cells in the low-risk group. Moreover, low-risk patients reported higher immune and stromal scores, lower tumor purity, and higher expression of HLA genes. Low-risk patients responded well to immunotherapy due to higher expression of immune checkpoint molecules and lower stemness index. Thus, our model predicted a favorable prognosis and increased overall survival for patients in the low-risk methylation group. Further, this model could provide potential drug targets to develop effective immunotherapies for LUAD.

Identification of a Novel Tumor Microenvironment-Associated Eight-Gene Signature for Prognosis Prediction in Lung Adenocarcinoma

Background

Lung cancer has become the most common cancer type and caused the most cancer deaths. Lung adenocarcinoma (LUAD) is one of the major types of lung cancer. Accumulating evidence suggests the tumor microenvironment is correlated with the tumor progress and the patient's outcome. This study aimed to establish a gene signature based on tumor microenvironment that can predict patients' outcomes for LUAD.

Methods

Dataset TCGA-LUAD, downloaded from the TCGA portal, were taken as training cohort, and dataset GSE72094, obtained from the GEO database, was set as validation cohort. In the training cohort, ESTIMATE algorithm was applied to find intersection differentially expressed genes (DEGs) among tumor microenvironment. Kaplan-Meier analysis and univariate Cox regression model were performed on intersection DEGs to preliminarily screen prognostic genes. Besides, the LASSO Cox regression model was implemented to build a multi-gene signature, which was then validated in the validation cohorts through Kaplan-Meier, Cox, and receiver operating characteristic curve (ROC) analyses. In addition, the correlation between tumor mutational burden (TMB) and risk score was evaluated by Spearman test. GSEA and immune infiltrating analyses were conducted for understanding function annotation and the role of the signature in the tumor microenvironment.

Results

An eight-gene signature was built, and it was examined by Kaplan-Meier analysis, revealing that a significant overall survival difference was seen. The eight-gene signature was further proven to be independent of other clinico-pathologic parameters via the Cox regression analyses. Moreover, the ROC analysis demonstrated that this signature owned a better predictive power of LUAD prognosis. The eight-gene signature was correlated with TMB. Furthermore, GSEA and immune infiltrating analyses showed that the exact pathways related to the characteristics of eight-genes signature, and identified the vital roles of Mast cells resting and B cells naive in the prognosis of the eight-gene signature.

Conclusion

Identifying the eight-gene signature (INSL4, SCN7A, STAP1, P2RX1, IKZF3, MS4A1, KLRB1, and ACSM5) could accurately identify patients' prognosis and had close interactions with Mast cells resting and B cells naive, which may provide insight into personalized prognosis prediction and new therapies for LUAD patients.

The Potential of Five Immune-Related Prognostic Genes to Predict Survival and Response to Immune Checkpoint Inhibitors for Soft Tissue Sarcomas Based on Multi-Omic Study

Low response rates to immunotherapy have been reported in soft tissue sarcoma (STS). There are few predictive biomarkers of response, and the tumor immune microenvironment associated with progression and prognosis remains unclear in STS. Gene expression data from the Cancer Genome Atlas were used to identify the immune-related prognostic genes (IRPGs) and construct the immune gene-related prognostic model (IGRPM). The tumor immune microenvironment was characterized to reveal differences between patients with different prognoses. Furthermore, somatic mutation data and DNA methylation data were analyzed to understand the underlying mechanism leading to different prognoses. The IGRPM was constructed using five IRPGs (IFIH1, CTSG, STC2, SECTM1, and BIRC5). Two groups (high- and low-risk patients) were identified based on the risk score. Low-risk patients with higher overall survival time had higher immune scores, more immune cell infiltration (e.g., CD8 T cell and activated natural killer cells), higher expression of immune-stimulating molecules, higher stimulating cytokines and corresponding receptors, higher innate immunity molecules, and stronger antigen-presenting capacity. However, inhibition of immunity was observed in low-risk patients owing to the higher expression of immune checkpoint molecules and inhibiting cytokines. High-risk patients had high tumor mutation burden, which did not significantly influence survival. Gene set enrichment analysis further revealed that pathways of cell cycle and cancers were activated in high-risk patients. DNA methylation analysis indicated that relative high methylation was associated with better overall survival. Finally, the age, mitotic counts, and risk scores were independent prognostic factors for STS. Five IRPGs performed well in risk stratification of patients and are candidate biomarkers for predicting response to immunotherapy. Differences observed through the multi-omic study of patients with different prognoses may reveal the underlying mechanism of the development and progression of STS, and thereby improve treatment.