Mining TCGA Database for Tumor Microenvironment-Related Genes of Prognostic Value in Hepatocellular Carcinoma

Evolutionarily conserved enhancer-associated features within the MYEOV locus suggest a regulatory role for this non-coding DNA region in cancer

The myeloma overexpressed gene (MYEOV) has been proposed to be a proto-oncogene due to high RNA transcript levels found in multiple cancers, including myeloma, breast, lung, pancreas and esophageal cancer. The presence of an open reading frame (ORF) in humans and other primates suggests protein-coding potential. Yet, we still lack evidence of a functional MYEOV protein. It remains undetermined how MYEOV overexpression affects cancerous tissues. In this work, we show that MYEOV has likely originated and may still function as an enhancer, regulating CCND1 and LTO1. Firstly, MYEOV 3' enhancer activity was confirmed in humans using publicly available ATAC-STARR-seq data, performed on B-cell-derived GM12878 cells. We detected enhancer histone marks H3K4me1 and H3K27ac overlapping MYEOV in multiple healthy human tissues, which include B cells, liver and lung tissue. The analysis of 3D genome datasets revealed chromatin interactions between a MYEOV-3'-putative enhancer and the proto-oncogene CCND1. BLAST searches and multi-sequence alignment results showed that DNA sequence from this human enhancer element is conserved from the amphibians/amniotes divergence, with a 273 bp conserved region also found in all mammals, and even in chickens, where it is consistently located near the corresponding CCND1 orthologues. Furthermore, we observed conservation of an active enhancer state in the MYEOV orthologues of four non-human primates, dogs, rats, and mice. When studying this homologous region in mice, where the ORF of MYEOV is absent, we not only observed an enhancer chromatin state but also found interactions between the mouse enhancer homolog and Ccnd1 using 3D-genome interaction data. This is similar to the interaction observed in humans and, interestingly, coincides with CTCF binding sites in both species. Taken together, this suggests that MYEOV is a primate-specific gene with a de novo ORF that originated at an evolutionarily older enhancer region. This deeply conserved putative enhancer element could regulate CCND1 in both humans and mice, opening the possibility of studying MYEOV regulatory functions in cancer using non-primate animal models.

Evaluation of vital genes correlated with CD8 + T cell infiltration as prognostic biomarkers in stomach adenocarcinoma

BACKGROUND

Infiltration of CD8 + T cells in the tumor microenvironment is correlated with better prognosis in various malignancies. Our study aimed to investigate vital genes correlated with CD8 + T cell infiltration in stomach adenocarcinoma (STAD) and develop a new prognostic model.

METHODS

Using the STAD dataset, differentially expressed genes (DEGs) were analyzed, and co-expression networks were constructed. Combined with the CIBERSORT algorithm, the most relevant module of WGCNA with CD8 + T cell infiltration was selected for subsequent analysis. The vital genes were screened out by univariate regression analysis to establish the risk score model. The expression of the viral genes was verified by lasso regression analysis and in vitro experiments.

RESULTS

Four CD8 + T cell infiltration-related genes (CIDEC, EPS8L3, MUC13, and PLEKHS1) were correlated with the prognosis of STAD. Based on these genes, a risk score model was established. We found that the risk score could well predict the prognosis of STAD, and the risk score was positively correlated with CD8 + T cell infiltration. The validation results of the gene expression were consistent with TCGA. Furthermore, the risk score was significantly higher in tumor tissues. The high-risk group had poorer overall survival (OS) in each subgroup.

CONCLUSIONS

Our study constructed a new risk score model for STAD prognosis, which may provide a new perspective to explore the tumor immune microenvironment mechanism in STAD.

GALNT5 functions as a suppressor of ferroptosis and a predictor of poor prognosis in pancreatic adenocarcinoma

Mucin-type O-glycosylation, a posttranslational modification of membrane and secretory proteins, facilitates metastasis and immune escape in tumor cells. N-acetylgalactosaminyl-transferase 5 (GALNT5), the enzyme initiating mucin-type O-glycosylation, is known to advance the progression of various tumors. Yet, the comprehensive role of GALNT5 in pan-cancer scenarios remains to be elucidated. In this research, we conducted a database-centric pan-cancer expression analysis of GALNT5. We examined its aberrant expression, assessed its prognostic implications, and explored the correlations between GALNT5 expression and factors such as ferroptosis, immune cell infiltration levels, and immune checkpoint gene expression across multiple tumor types. To substantiate GALNT5's role, we analyzed cell proliferation, migration, invasion, and ferroptosis in PAAD cells after GALNT5 knockdown. Additionally, RNA-seq was employed to discern potential downstream pathways influenced by GALNT5. Our findings indicate that GALNT5 expression is heightened in the majority of tumors, correlating with the prognosis of multiple cancers. There's a notable association between GALNT5 levels and ferroptosis-related genes, immune cell infiltration, and immune checkpoint genes. In PAAD specifically, the role of GALNT5 was further probed. Knockdown of GALNT5 curtailed the proliferation, migration, and invasion capacities of PAAD cells, concurrently promoting ferroptosis. Moreover, in vivo studies demonstrated that GALNT5 inhibition stunted PAAD tumor growth. The RNA-seq analysis unveiled inflammation and immune-centric pathways, such as the TNF signaling pathway, as potential downstream conduits of GALNT5. In conclusion, our pan-cancer study underscores GALNT5 as a potential therapeutic target for enhancing PAAD prognosis, given its strong ties with ferroptosis and immune cell infiltration. Our experiments further define GALNT5 as a novel suppressor of ferroptosis.

Prognostic value and potential molecular mechanism of ITGB superfamily members in hepatocellular carcinoma

We analyzed the prognostic value and potential molecular mechanisms of the members of integrin β (ITGB)superfamily in hepatocellular carcinoma (HCC) using data from The Cancer Genome Atlas (TCGA), cBioPortal, Gene Expression Profiling Interactive Analysis (GEPIA), Human Protein Atlas (HPA) HPA, Search Tool for the Retrieval of Interacting Genes/Proteins, GeneMANIA, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), TIMER and Gene set enrichment analysis (GSEA) databases. ITGB4/5 mRNA was upregulated in HCC tissues in contrast to the normal liver tissues, whereas ITGB2/3/8 levels were lower in the former. ITGB4 was the most frequently mutated ITGB gene in HCC. Receiver operating characteristic curve (ROC) analysis showed that the expression levels of ITGB2/3/4/5/7/8 had significant diagnostic value in distinguishing HCC tissues from healthy liver tissues, ITGB8 had the highest diagnostic efficacy. The ITGB1/3/6/8 were also upregulated in the HCC tissues in contrast to healthy liver tissues. The expression of ITGB8 was verified by immunohistochemistry (IHC). Furthermore, ITGB6 and ITGB7 expression levels were strongly associated with the overall survival (OS) of HCC patients. The ITGB superfamily members exhibited homology and interactions in protein structure. In addition, ITGB6 together with ITGB7 were negatively related to the infiltration of multiple immune cell populations. GSEA results showed that ITGB6 was enriched in HCC migration and recurrence, whereas ITGB7 was significantly enriched in HIPPO, TOLL and JAK-STAT signaling pathways. In conclusion, ITGB6 and ITGB7 genes are possible to be prognostic biomarkers for HCC.

PLEKHS1 drives PI3Ks and remodels pathway homeostasis in PTEN-null prostate

The PIP3/PI3K network is a central regulator of metabolism and is frequently activated in cancer, commonly by loss of the PIP3/PI(3,4)P2 phosphatase, PTEN. Despite huge research investment, the drivers of the PI3K network in normal tissues and how they adapt to overactivation are unclear. We find that in healthy mouse prostate PI3K activity is driven by RTK/IRS signaling and constrained by pathway feedback. In the absence of PTEN, the network is dramatically remodeled. A poorly understood YXXM- and PIP3/PI(3,4)P2-binding PH domain-containing adaptor, PLEKHS1, became the dominant activator and was required to sustain PIP3, AKT phosphorylation, and growth in PTEN-null prostate. This was because PLEKHS1 evaded pathway-feedback and experienced enhanced PI3K- and Src-family kinase-dependent phosphorylation of Y258XXM, eliciting PI3K activation. hPLEKHS1 mRNA and activating Y419 phosphorylation of hSrc correlated with PI3K pathway activity in human prostate cancers. We propose that in PTEN-null cells receptor-independent, Src-dependent tyrosine phosphorylation of PLEKHS1 creates positive feedback that escapes homeostasis, drives PIP3 signaling, and supports tumor progression.

Identifying TME signatures for cervical cancer prognosis based on GEO and TCGA databases

The mortality rate from cervical cancer (CESC), a malignant tumor that affects women, has increased significantly globally in recent years. The discovery of biomarkers points to a direction for the diagnosis of cervical cancer with the advancement of bioinformatics technology. The goal of this study was to look for potential biomarkers for the diagnosis and prognosis of CESC using the GEO and TCGA databases. Because of the high dimension and small sample size of the omic data, or the use of biomarkers generated from a single omic data, the diagnosis of cervical cancer may be inaccurate and unreliable. The purpose of this study was to search the GEO and TCGA databases for potential biomarkers for the diagnosis and prognosis of CESC. We begin by downloading CESC (GSE30760) DNA methylation data from GEO, then perform differential analysis on the downloaded methylation data and screen out the differential genes. Then, using estimation algorithms, we score immune cells and stromal cells in the tumor microenvironment and perform survival analysis on the gene expression profile data and the most recent clinical data of CESC from TCGA. Then, using the 'limma' package and Venn plot in R language to perform differential analysis of genes and screen out overlapping genes, these overlapping genes were then subjected to GO and KEGG functional enrichment analysis. The differential genes screened by the GEO methylation data and the differential genes screened by the TCGA gene expression data were intersected to screen out the common differential genes. A protein-protein interaction (PPI) network of gene expression data was then created in order to discover important genes. The PPI network's key genes were crossed with previously identified common differential genes to further validate them. The Kaplan-Meier curve was then used to determine the prognostic importance of the key genes. Survival analysis has shown that CD3E and CD80 are important for the identification of cervical cancer and can be considered as potential biomarkers for cervical cancer.

Role of NCF2 as a potential prognostic factor and immune infiltration indicator in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the major causes of cancer-related deaths globally. The tumor microenvironment (TME) plays a crucial role in the prognosis and treatment of HCC. Hence, it is important to exploit new biomarkers for survival surveillance and TME estimation of HCC.

METHODS

HCC samples data was collected from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database, and clinical samples were collected from our center. The TME of HCC were explored with ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data), ssGSEA (single sample Gene Sets Enrichment Analysis) and CIBERSORT algorithm. Differentially expressed genes were analyzed with functional enrichment analysis. Immunohistochemistry was implemented to validate the results.

RESULTS

Based on TCGA database, we found that Neutrophil Cytosolic Factor 2 (NCF2) was significantly associated with the prognosis of HCC patients, involved in immune-related biological processes of HCC and closely associated with some types of immunocompetent cells. The survival analysis based on NCF2 expression assessed by immunohistochemistry also confirmed that NCF2-positive group had a shorter relapse free survival (RFS) and overall survival (OS) than NCF2-negative group. Multivariate Cox regression revealed NCF2 expression level and lymphovascular space invasion (LVSI) were independent risk factors for HCC patients. Receiver operating characteristic curves showed that the combination of NCF2 and LVSI had higher predictive efficacy on the 1-year RFS rate and 5-year OS rate than each of them alone. Besides, the expression level of NCF2 was positively associated with M0 and M2 macrophages infiltration. Furthermore, NCF2 expression was positively correlated with CSF1, IL4, IL10, CD206, CD163, CSF1R and TGFβ1.

CONCLUSION

We proposed that higher NCF2 expression predicted an adverse prognosis and more M2 macrophages infiltration in HCC patients.

Profiling of Tumor Microenvironment Components Identifies Five Stroma-Related Genes with Prognostic Implications in Colorectal Cancer

Background: Tumor microenvironment (TME) significantly affects colorectal cancer (CRC) progression and therapeutic efficacy, particularly the infiltrating stromal components. This study profiled the TME composition of tumor tissue and identify TME-related, especially stroma-related genes having prognosis value in CRC patients. Materials and Methods: We used the ESTIMATE algorithm to assess stromal/immune component and divided 524 CRC cases of public dataset into high- and low-score groups. We analyzed the effect of the score on prognosis and extracted the differential expression genes (DEGs) between groups, which were stromal- and/or immune-related genes, and performed a prognostic investigation of the DEGs. Results: Higher stromal score correlated with poor survival, whereas the immune score was the inverse. By comparing global gene expression of cases with high vs. low stromal/immune scores, we extracted 474 stroma-related genes, 76 immune-related genes, and 498 intersection genes, which were explored by function enrichment and survival analysis. We identified the expression of five stroma-related genes (including ITGA7, PTPN14, SCG2, TNS1, and GRP) significantly associated with poorer survival, which were validated in the other two independent CRC cohorts. Conclusion: These results presented a comprehensive understanding of TME components and identified five stroma-related genes that predict poor outcomes in CRC patients.

A universal co-expression gene network and prognostic model for hepatic-biliary-pancreatic cancers identified by integrative analyses

Hepatic, biliary and pancreatic cancers are a diverse set of malignancies with poor prognoses. It is possible that common molecular mechanisms are involved in the carcinogenesis of these cancers. Here, we identified LINC01537 and seven protein-coding genes by integrative analysis of transcriptomes of mRNAs, microRNAs and long non-coding RNAs from cholangiocarcinoma, hepatocellular carcinoma and pancreatic adenocarcinoma cohorts in TCGA. A predictive model constructed from seven biomarkers was established to successfully predict the survival rate of patients, which was then further verified in external cohorts. Additionally, patients with high-risk scores in our model were prone to epithelial-mesenchymal transition. Finally, activation of the biomarker PDE2A significantly attenuated migration and epithelial-mesenchymal transition in the HepG2 liver cancer cell line.

Clinical significance for diagnosis and prognosis of POP1 and its potential role in breast cancer: a comprehensive analysis based on multiple databases

Background: Breast cancer (BC) is one of the most common cancers in women. The discovery of available biomarkers is crucial for early diagnosis and improving prognosis. The effect of POP1 in BC remains unrevealed. Our study aims to explore the expression of POP1 in BC and demonstrate its clinical significance and potential molecular mechanisms.

Methods: The Cancer Genome Atlas (TCGA) BC cohort transcriptome data and corresponding clinical information were downloaded. GSE42568 cohort, GSE162228 cohort, GSE7904 cohort, and GSE161533 cohort in the Gene Expression Omnibus (GEO) database were used as verification groups. R software and several web tools were used for statistical analysis. Moreover, the proliferation, transwell, wound healing experiments, and flow cytometry were used for in vitro investigation.

Results: Compared with normal breast tissue, POP1 expression was up-regulated in BC tissue with a higher mutation rate. POP1 had good diagnostic value for BC and could be utilized as a new marker. POP1 was significantly correlated with multiple pathways in BC and played an important role in the immune infiltration of BC. High-POP1 expression patients were more prone to be responded to immunotherapy and had a significantly higher percentage of immunotherapy response rate. Moreover, POP1 promoted proliferation and migration and inhibited apoptosis in BC cells.

Conclusions: POP1 expression was up-regulated in BC and was associated with a poor prognosis. Patients with high-POP1 expression were more likely to be responded to immunotherapy. Our study can provide a potential marker POP1 for BC, which is beneficial in the diagnosis and treatment of BC.

Identification of Candidate Therapeutic Target Genes and Profiling of Tumor-Infiltrating Immune Cells in Pancreatic Cancer via Integrated Transcriptomic Analysis

Pancreatic cancer (PC) has a dismal prognosis despite advancing scientific and technological knowledge. The exploration of novel genes is critical to improving current therapeutic measures. This research is aimed at selecting hub genes that can act as candidate therapeutic target genes and as prognostic biomarkers in PC. Gene expression profiles of datasets GSE101448, GSE15471, and GSE62452 were extracted from the GEO database. The “limma” package was performed to select differentially expressed genes (DEGs) between PC and normal tissue samples in each dataset. Robust rank aggregation (RRA) algorithm was conducted to integrate multiple expression profiles and identify robust DEGs. GO analysis and KEGG analysis were conducted to identify the functional correlation of the DEGs. The CIBERSORT algorithm was conducted to estimate the immune cell composition of each tissue sample. STRING and Cytoscape were used to establish the protein-protein interaction (PPI) network. The cytoHubba plugin in Cytoscape was performed to identify hub genes. Survival analysis based on hub gene expression was performed with clinical information from TCGA database. 566 robust DEGs (338 upregulated genes and 226 downregulated genes) were identified. Tumor tissue had a higher infiltration of resting dendritic cells and tumor-associated macrophages (TAM), including M0, M1, and M2 macrophages, while infiltration levels of B memory cells, plasma cells, T cells CD8, T follicular helper cells, and NK cells in normal tissue were relatively higher. GO terms and KEGG pathway analysis results revealed enrichment in tumor-associated pathways, including the extracellular matrix organization, cell−substrate adhesion cytokine−cytokine receptor interaction, calcium signaling pathway, and glycine, serine, and threonine metabolism, to name a few. Finally, FN1, MSLN, PLAU, and VCAN were selected as hub genes. High expression of FN1, MSLN, PLAU, and VCAN in PC significantly correlated with poor prognosis. Integrated transcriptomic analysis was used to provide new insights into PC pathogenesis. FN1, MSLN, PLAU, and VCAN may be considered as novel biomarkers of PC.

Weighted Gene Coexpression Network Analysis Identifies TBC1D10C as a New Prognostic Biomarker for Breast Cancer

Background

Immune checkpoint inhibitors are a promising therapeutic strategy for breast cancer (BRCA) patients. The tumor microenvironment (TME) can downregulate the immune response to cancer therapy. Our study is aimed at finding a TME-related biomarker to identify patients who might respond to immunotherapy.

Method

We downloaded raw data from several databases including TCGA and MDACC to identify TME hub genes associated with overall survival (OS) and the progression-free interval (PFI) by WGCNA. Correlations between hub genes and either tumor-infiltrating immune cells or immune checkpoints were conducted by ssGSEA.

Result

TME-related green and black modules were selected by WGCNA to further screen hub genes. Random forest and univariate and multivariate Cox regressions were applied to screen hub genes (MYO1G, TBC1D10C, SELPLG, and LRRC15) and construct a nomogram to predict the survival of BRCA patients. The C-index for the nomogram was 0.713. A DCA of the predictive model revealed that the net benefit of the nomogram was significantly higher than others and the calibration curve demonstrated a good performance by the nomogram. Only TBC1D10C was correlated with both OS and the PFI (both p values < 0.05). TBC1D10C also had a high positive association with tumor-infiltrating immune cells and common immune checkpoints (PD-1, CTLA-4, and TIGIT).

Conclusion

We constructed a TME-related gene signature model to predict the survival probability of BRCA patients. We also identified a hub gene, TBC1D10C, which was correlated with both OS and the PFI and had a high positive association with tumor-infiltrating immune cells and common immune checkpoints. TBC1D10C may be a new biomarker to select patients who may benefit from immunotherapy.

Identifying Potential Biomarkers of Prognostic Value in Colorectal Cancer via Tumor Microenvironment Data Mining

Colorectal cancer (CRC) is a common cancer that has increased rapidly worldwide in the past decades with a relatively high mortality rate. An increasing body of evidence has highlighted the importance of infiltrating immune and stromal cells in CRC. In this study, based on gene expression data of CRC patients in TCGA database we evaluated immune and stromal scores in tumor microenvironment using ESTIMATE method. Results showed there was potential correlation between these scores and the prognosis, and that patients with higher immune score and lower stromal score had longer survival time. We found that immune score was correlated with clinical characteristics including tumor location, tumor stage, and survival time. Specifically, the right-sided colon cancer had markedly elevated immune score, compared to left-sided colon cancer and rectal cancer. These results might be useful for understanding tumor microenvironment in colorectal cancer. Through the differential analysis we got a list of genes significantly associated with immune and stromal scores. Gene Set Enrichment and protein-protein interaction network analysis were used to further illustrate these differentially expressed genes. Finally, 15 hub genes were identified, and three (CXCL9, CXCL10 and SELL) of them were validated with favorable outcomes in CRC patients. Our result suggested that these tumor microenvironment related genes might be potential biomarkers for the prognosis of CRC.

Bioinformatics analysis and experimental verification of five metabolism-related lncRNAs as prognostic models for hepatocellular carcinoma

BACKGROUND

The number of patients with hepatocellular carcinoma (HCC) is showing a growing trend all over the world. The metabolic microenvironment has been shown to play a key role in the pathogenesis of HCC in recent studies. The expression of metabolites and metabolic processes in tumor cells can be regulated by gene regulation mediated by long non-coding RNAs (lncRNAs), the abnormal expression of which is closely related to tumor occurrence and metastasis. However, the fundamental mechanism of applying metabolism-related lncRNAs to predicting HCC is still unclear.

METHODS

With the complete RNA sequence data and clinical data obtained from The Cancer Genome Atlas database and metabolism-related genes downloaded from the Kyoto Encyclopedia of Genes and Genomes database, with false discovery rate < 0.001, log fold change > 1.5 selected as the screening criteria for lncRNA, the relationship between the expression level of metabolism-related LncRNAs (MRLs) and the overall survival rate was determined by the Univariate Cox regression analyses with the establishment of the metabolic prognosis model by the application of Multivariate Cox regression analyses, revealing the different biological processes and signaling pathways in both high-risk groups and low-risk groups by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and gene set enrichment analysis, leading the expression of lncRNA to be assessed by the reverse transcription-polymerase chain reaction results.

RESULTS

The overall survival rate of HCC patients is significantly correlated with signature of 5-MRLs. The prognosis characteristics of lncRNA reveal the relatively high death rate of patients in the high-risk groups, with the predicted signals by functional and pathway enrichment analysis related to biosynthesis, metabolic process, and metabolic pathway, with the prognostic characteristics of 5-MRLs by the combined analysis showing that it is an independent factor of HCC superior to the traditional clinical indicators in predicting the prognosis. A trend of high-expression was shown in MRLs in tumors by reverse transcription-polymerase chain reaction.

CONCLUSION

The new 5-MRLs as potential biomarkers provide more powerful prognostic information for HCC patients. In the future clinical treatment of HCC, it will provide doctors with more methods.

A predictive and prognostic model for hepatocellular carcinoma with microvascular invasion based TCGA database genomics

Background

Microvascular invasion (MVI) adversely affects postoperative long-term survival outcomes in patients with hepatocellular carcinoma (HCC). There is no study addressing genetic changes in HCC patients with MVI. We first screened differentially expressed genes (DEGs) in patients with and without MVI based on TCGA data, established a prediction model and explored the prognostic value of DEGs for HCC patients with MVI.

Methods

In this paper, gene expression and clinical data of liver cancer patients were downloaded from the TCGA database. The DEG analysis was conducted using DESeq2. Using the least absolute shrinkage and selection operator, MVI-status-related genes were identified. A Kaplan-Meier survival analysis was performed using these genes. Finally, we validated two genes, HOXD9 and HOXD10, using two sets of HCC tissue microarrays from 260 patients.

Results

Twenty-three MVI-status-related key genes were identified. Based on the key genes, we built a classification model using random forest and time-dependent receiver operating characteristic (ROC), which reached 0.814. Then, we performed a survival analysis and found ten genes had a significant difference in survival time. Simultaneously, using two sets of 260 patients’ HCC tissue microarrays, we validated two key genes, HOXD9 and HOXD10. Our study indicated that HOXD9 and HOXD10 were overexpressed in HCC patients with MVI compared with patients without MVI, and patients with MVI with HOXD9 and 10 overexpression had a poorer prognosis than patients with MVI with low expression of HOXD9 and 10.

Conclusion

We established an accurate TCGA database-based genomics prediction model for preoperative MVI risk and studied the prognostic value of DEGs for HCC patients with MVI. These DEGs that are related to MVI warrant further study regarding the occurrence and development of MVI.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09047-1.

Immunogenomic Landscape Analysis of Prognostic Immune-Related Genes in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death. HBV infection is an important risk factor for the tumorigenesis of HCC, given that the inflammatory environment is closely related to morbidity and prognosis. Consequently, it is of urgent importance to explore the immunogenomic landscape to supplement the prognosis of HCC. The expression profiles of immune-related genes (IRGs) were integrated with 377 HCC patients to generate differentially expressed IRGs based on the Cancer Genome Atlas (TCGA) dataset. These IRGs were evaluated and assessed in terms of their diagnostic and prognostic values. A total of 32 differentially expressed immune-related genes resulted as significantly correlated with the overall survival of HCC patients. The Gene Ontology functional enrichment analysis revealed that these genes were actively involved in cytokine-cytokine receptor interaction. A prognostic signature based on IRGs (HSPA4, PSME3, PSMD14, FABP6, ISG20L2, TRAF3, NDRG1, NRAS, CSPG5, and IL17D) stratified patients into high-risk versus low-risk groups in terms of overall survival and remained as an independent prognostic factor in multivariate analyses after adjusting for clinical and pathologic factors. Several IRGs (HSPA4, PSME3, PSMD14, FABP6, ISG20L2, TRAF3, NDRG1, NRAS, CSPG5, and IL17D) of clinical significance were screened in the present study, revealing that the proposed clinical-immune signature is a promising risk score for predicting the prognosis of HCC.

Identification of tumor microenvironment-related prognostic genes in colorectal cancer based on bioinformatic methods

Colorectal cancer (CRC) ranks fourth among the deadliest cancers globally, and the progression is highly affected by the tumor microenvironment (TME). This study explores the relationship between TME and colorectal cancer prognosis and identifies prognostic genes related to the CRC microenvironment. We collected the gene expression data from The Cancer Genome Atlas (TCGA) and calculated the scores of stromal/immune cells and their relations to clinical outcomes in colorectal cancer by the ESTIMATE algorithm. Lower immune scores were significantly related to the malignant progression of CRC (metastasis, p = 0.001). We screened 292 differentially expressed genes (DEGs) by dividing CRC cases into high and low stromal/immune score groups. Functional enrichment analyses and protein-protein interaction (PPI) networks illustrated that these DEGs were closely involved in immune response, cytokine-cytokine receptor interaction, and chemokine signaling pathway. Six DEGs (FABP4, MEOX2, MMP12, ERMN, TNFAIP6, and CHST11) with prognostic value were identified by survival analysis and validated in two independent cohorts (GSE17538 and GSE161158). The six DEGs were significantly related to immune cell infiltration levels based on the Tumor Immune Estimation Resource (TIMER). The results might contribute to discovering new diagnostic and prognostic biomarkers and new treatment targets for colorectal cancer.

Combinatorial therapy in tumor microenvironment: Where do we stand?

The tumor microenvironment plays a pivotal role in tumor initiation and progression by creating a dynamic interaction with cancer cells. The tumor microenvironment consists of various cellular components, including endothelial cells, fibroblasts, pericytes, adipocytes, immune cells, cancer stem cells and vasculature, which provide a sustained environment for cancer cell proliferation. Currently, targeting tumor microenvironment is increasingly being explored as a novel approach to improve cancer therapeutics, as it influences the growth and expansion of malignant cells in various ways. Despite continuous advancements in targeted therapies for cancer treatment, drug resistance, toxicity and immune escape mechanisms are the basis of treatment failure and cancer escape. Targeting tumor microenvironment efficiently with approved drugs and combination therapy is the solution to this enduring challenge that involves combining more than one treatment modality such as chemotherapy, surgery, radiotherapy, immunotherapy and nanotherapy that can effectively and synergistically target the critical pathways associated with disease pathogenesis. This review shed light on the composition of the tumor microenvironment, interaction of different components within tumor microenvironment with tumor cells and associated hallmarks, the current status of combinatorial therapies being developed, and various growing advancements. Furthermore, computational tools can also be used to monitor the significance and outcome of therapies being developed. We addressed the perceived barriers and regulatory hurdles in developing a combinatorial regimen and evaluated the present status of these therapies in the clinic. The accumulating depth of knowledge about the tumor microenvironment in cancer may facilitate further development of effective treatment modalities. This review presents the tumor microenvironment as a sweeping landscape for developing novel cancer therapies.

Identification of a tumor microenvironment-related seven-gene signature for predicting prognosis in bladder cancer

Background

Accumulating evidences demonstrated tumor microenvironment (TME) of bladder cancer (BLCA) may play a pivotal role in modulating tumorigenesis, progression, and alteration of biological features. Currently we aimed to establish a prognostic model based on TME-related gene expression for guiding clinical management of BLCA.

Methods

We employed ESTIMATE algorithm to evaluate TME cell infiltration in BLCA. The RNA-Seq data from The Cancer Genome Atlas (TCGA) database was used to screen out differentially expressed genes (DEGs). Underlying relationship between co-expression modules and TME was investigated via Weighted gene co-expression network analysis (WGCNA). COX regression and the least absolute shrinkage and selection operator (LASSO) analysis were applied for screening prognostic hub gene and establishing a risk predictive model. BLCA specimens and adjacent tissues from patients were obtained from patients. Bladder cancer (T24, EJ-m3) and bladder uroepithelial cell line (SVHUC1) were used for genes validation. qRT-PCR was employed to validate genes mRNA level in tissues and cell lines.

Results

365 BLCA samples and 19 adjacent normal samples were selected for identifying DEGs. 2141 DEGs were identified and used to construct co-expression network. Four modules (magenta, brown, yellow, purple) were regarded as TME regulatory modules through WGCNA and GO analysis. Furthermore, seven hub genes (ACAP1, ADAMTS9, TAP1, IFIT3, FBN1, FSTL1, COL6A2) were screened out to establish a risk predictive model via COX and LASSO regression. Survival analysis and ROC curve analysis indicated our predictive model had good performance on evaluating patients prognosis in different subgroup of BLCA. qRT-PCR result showed upregulation of ACAP1, IFIT3, TAP1 and downregulation of ADAMTS9, COL6A2, FSTL1,FBN1 in BLCA specimens and cell lines.

Conclusions

Our study firstly integrated multiple TME-related genes to set up a risk predictive model. This model could accurately predict BLCA progression and prognosis, which offers clinical implication for risk stratification, immunotherapy drug screen and therapeutic decision.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08447-7.

Identification and immunoprofiling of key prognostic genes in the tumor microenvironment of hepatocellular carcinoma

Tumor microenvironment (TME) is involved in the occurrence and development of hepatocellular carcinoma (HCC), and immune cells in the TME have been implicated in its progression and treatment. However, the association of genes involved in the TME with HCC prognosis remains unclear. Thus, in this study, we obtained transcriptomic and clinicopathological data of patients with HCC from The Cancer Genome Atlas to identify key genes in TME associated with HCC prognosis. Stromal and immune cell scores were calculated using the ESTIMATE method, and differentially expressed genes (DEGs) were determined. We identified 830 DEGs, which were further subjected to survival analyses and functional enrichment analysis. Next, we identified prognostic TME-associated DEGs, established a protein-protein interaction (PPI) network, and performed Cox analysis.Consequently, four key prognostic genes (CXCL5, CXCL8, IL18RAP, and TREM2) associated with TME, were identified, in which CXCL5 and IL18RAP may be potential independent prognostic factors. Age, clinical stage, N stage, and risk score were also determined as significant prognostic variables. CIBERSORT was used to predict the constitution and relative content of the immune cells, wherein M0 macrophages were the most closely related to the key genes. In conclusion, CXCL5, CXCL8, IL18RAP, and TREM2 were associated with HCC prognosis and were important for immune cell invasion into the TME. Additionally, IL18RAP expression may contribute toward favorable prognosis in patients with HCC. Consequently, these genes may serve as potential biomarkers and immunotherapeutic targets for HCC.

Prognostic value of tumour microenvironment-related genes by TCGA database in rectal cancer

Rectal cancer is a common malignant tumour and the progression is highly affected by the tumour microenvironment (TME). This study intended to assess the relationship between TME and prognosis, and explore prognostic genes of rectal cancer. The gene expression profile of rectal cancer was obtained from TCGA and immune/stromal scores were calculated by Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. The correlation between immune/stromal scores and survival time as well as clinical characteristics were evaluated. Differentially expressed genes (DEGs) were identified according to the stromal/immune scores, and the functional enrichment analyses were conducted to explore functions and pathways of DEGs. The survival analyses were conducted to clarify the DEGs with prognostic value, and the protein‐protein interaction (PPI) network was performed to explore the interrelation of prognostic DEGs. Finally, we validated prognostic DEGs using data from the Gene Expression Omnibus (GEO) database by PrognoScan, and we verified these genes at the protein levels using the Human Protein Atlas (HPA) databases. We downloaded gene expression profiles of 83 rectal cancer patients from The Cancer Genome Atlas (TCGA) database. The Kaplan‐Meier plot demonstrated that low‐immune score was associated with worse clinical outcome (P = .034), metastasis (M1 vs. M0, P = .031) and lymphatic invasion (+ vs. ‐, P < .001). A total of 540 genes were screened as DEGs with 539 up‐regulated genes and 1 down‐regulated gene. In addition, 60 DEGs were identified associated with overall survival. Functional enrichment analyses and PPI networks showed that the DEGs are mainly participated in immune process, and cytokine‐cytokine receptor interaction. Finally, 19 prognostic genes were verified by GSE17536 and GSE17537 from GEO, and five genes (ADAM23, ARHGAP20, ICOS, IRF4,MMRN1) were significantly different in tumour tissues compared with normal tissues at the protein level. In summary, our study demonstrated the associations between TME and prognosis as well as clinical characteristics of rectal cancer. Moreover, we explored and verified microenvironment‐related genes, which may be the potential key prognostic genes of rectal cancer. Further clinical samples and functional studies are needed to validate this finding.

Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune‐related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune‐related genes was identified. Functional analysis and the protein–protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune‐related biomarkers for metastatic melanoma.

Identification of Prognostic Genes in the Tumor Microenvironment of Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. The efficacy of immunotherapy usually depends on the interaction of immunomodulation in the tumor microenvironment (TME). This study aimed to explore the potential stromal-immune score-based prognostic genes related to immunotherapy in HCC through bioinformatics analysis.

Methods: ESTIMATE algorithm was applied to calculate the immune/stromal/Estimate scores and tumor purity of HCC using the Cancer Genome Atlas (TCGA) transcriptome data. Functional enrichment analysis of differentially expressed genes (DEGs) was analyzed by the Database for Annotation, Visualization, and Integrated Discovery database (DAVID). Univariate and multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed for prognostic gene screening. The expression and prognostic value of these genes were further verified by KM-plotter database and the Human Protein Atlas (HPA) database. The correlation of the selected genes and the immune cell infiltration were analyzed by single sample gene set enrichment analysis (ssGSEA) algorithm and Tumor Immune Estimation Resource (TIMER).

Results: Data analysis revealed that higher immune/stromal/Estimate scores were significantly associated with better survival benefits in HCC within 7 years, while the tumor purity showed a reverse trend. DEGs based on both immune and stromal scores primarily affected the cytokine–cytokine receptor interaction signaling pathway. Among the DEGs, three genes (CASKIN1, EMR3, and GBP5) were found most significantly associated with survival. Moreover, the expression levels of CASKIN1, EMR3, and GBP5 genes were significantly correlated with immune/stromal/Estimate scores or tumor purity and multiple immune cell infiltration. Among them, GBP5 genes were highly related to immune infiltration.

Conclusion: This study identified three key genes which were related to the TME and had prognostic significance in HCC, which may be promising markers for predicting immunotherapy outcomes.

Bioinformatics analysis to screen key prognostic genes in the breast cancer tumor microenvironment

Increasing evidence has shown that the tumor microenvironment (TME) plays an important role in tumor occurrence and development and can also affect patient prognosis. In this study, we screened key prognostic genes in the breast cancer (BC) TME by analyzing the immune and stromal scores of tumor samples to detect differentially expressed genes (DEGs) and also constructed a TME-related prognostic model. First, we obtained mRNA-Seq and related clinical information for patients with BC from The Cancer Genome Atlas (TCGA) and calculated the stromal and immune scores of tumor tissues using the ESTIMATE algorithm. Next, we performed functional enrichment analysis and generated protein-protein interaction networks from the DEGs that were highly related to the TME. Finally, Cox proportional hazards regression analysis was performed on BC datasets from TCGA, and analyses were conducted on infiltrating immune cells and the human protein atlas. Together, these analyses indicated that the KLRB1 and SIT1 genes could be used as independent prognostic factors for BC, while risk score, age, and clinical stage could be used as prognostic factors. In summary, we found that the prognosis of BC is closely related to immune regulation in the TME.

CMTM6 is positively correlated with PD-L1 expression and immune cells infiltration in lung squamous carcinoma

BACKGROUND

The aim of this study was to clarify the association between CMTM6 and PD-L1 expression as well as microenvironment in lung squamous carcinoma (LUSC).

MATERIAL AND METHODS

Using Spearman's correlation and Tumor Immune Estimation Resource (TIMER), we analyzed the relationship between CMTM6 and PD-L1 mRNA in LUSC. Immunohistochemistry (IHC) assay was applied to validate the correlation between CMTM6 and PD-L1 protein level in 80 LUSC samples originated from Shandong Provincial Hospital. Then, using The Cancer Genome Atlas (TCGA) database and fisher test, we analyzed the differential mutation genes in high and low CMTM6 expression group. TISIDB was used to explore the distribution of CMTM6 across immune- and molecular-subtypes. TCGA database and Gene Set variation analysis (GSVA) were used to analyze the relationship between CMTM6 and immune genes, immune related pathways.

RESULT

Positive correlation between CMTM6 and PD-L1 in mRNA and protein level was found in LUSC patients. More gene mutations were found in CMTM6 high expression group compared with low expression group. Meanwhile, we also found the correlation between CMTM6 expression and molecular subtypes, immune genes, immune related pathways. Furthermore, our result revealed that B cells memory, T cells memory testing, T cells folicular helper, macrophages M0, macrophages M1 and neutrophils varied significantly between patients with CMTM6 high and low expression group. Finally, we found that CMTM6 expression was positively related to CD8 + T cell, macrophage, neutrophil and dendtritic cell (all, P < 0.05) and negatively related to CD4 + T cell (P = 0.018).

CONCLUSION

CMTM6 is positively associated with PD-L1 expression and correlates with infiltration of immune cells in microenvironment of lung squamous carcinoma.

Comprehensive analysis of prognostic tumor microenvironment-related genes in osteosarcoma patients

Background

Tumor microenvironment (TME) plays an important role in malignant tumors. Our study aimed to investigate the effect of the TME and related genes in osteosarcoma patients.

Methods

Gene expression profiles and clinical data of osteosarcoma patients were downloaded from the TARGET dataset. ESTIMATE algorithm was used to quantify the immune score. Then, the association between immune score and prognosis was studied. Afterward, a differential analysis was performed based on the high- and low-immune scores to determine TME-related genes. Additionally, Cox analyses were performed to construct two prognostic signatures for overall survival (OS) and disease-free survival (DFS), respectively. Two datasets obtained from the GEO database were used to validate signatures.

Results

Eighty-five patients were included in our research. The survival analysis indicated that patients with higher immune score have a favorable OS and DFS. Moreover, 769 genes were determined as TME-related genes. The unsupervised clustering analysis revealed two clusters were significantly related to immune score and T cells CD4 memory fraction. In addition, two signatures were generated based on three and two TME-related genes, respectively. Both two signatures can significantly divide patients into low- and high-risk groups and were validated in two GEO datasets. Afterward, the risk score and metastatic status were identified as independent prognostic factors for both OS and DFS and two nomograms were generated. The C-indexes of OS nomogram and DFS nomogram were 0.791 and 0.711, respectively.

Conclusion

TME was associated with the prognosis of osteosarcoma patients. Prognostic models based on TME-related genes can effectively predict OS and DFS of osteosarcoma patients.

TERT mutations correlate with higher TMB value and unique tumor microenvironment and may be a potential biomarker for anti-CTLA4 treatment

Immune checkpoint inhibitors (ICIs) have recently changed therapeutic paradigms for patients across multiple cancer types. However, current biomarkers cannot accurately predict responses to ICIs. Telomerase reverse transcriptase (TERT) mutations lead to an aberrant upregulation of TERT expression, and ultimately allow telomere maintenance, thus supporting immortalization of cancer cells. This study aimed to investigate whether the TERT mutation is a potential predictor of ICI treatment across all cancer types. TERT mutations positively correlated with a higher tumor mutational burden (TMB) value, neoantigen load, and tumor purity. Lymphocyte infiltration, macrophage regulation, interferon‐gamma (IFN‐γ) response, and transforming growth factor‐β (TGF‐β) response which was representative immune‐expression signatures, all had higher signature scores in the TERT mutation group. Activated CD4 T cell, naïve B cell, activated dendritic cell, M0 macrophage, M1 macrophage, neutrophil, resting NK cell, and plasma cells all had relatively higher immune scores in the TERT mutation group, whereas Th series cells, memory B cell, resting mast cells, monocytes, and activated NK cells had lower immune scores. Notably, in the subgroup analysis of monotherapy and combination ICI treatment, only in the anti‐cytotoxic‐T‐lymphocyte‐associated antigen 4 (anti‐CTLA4) group, patients with TERT mutations had a better prognosis, especially for melanoma. Therefore, TERT mutations were closely related to a higher TMB value and unique tumor microenvironment, which may be the reason that TERT mutations may be a potential biomarker for anti‐CTLA4 treatment.

Weighted correlation gene network analysis reveals a new stemness index-related survival model for prognostic prediction in hepatocellular carcinoma

In this study, we constructed a new survival model using mRNA expression-based stemness index (mRNAsi) for prognostic prediction in hepatocellular carcinoma (HCC). Weighted correlation network analysis (WGCNA) of HCC transcriptome data (374 HCC and 50 normal liver tissue samples) from the TCGA database revealed 7498 differentially expressed genes (DEGs) that clustered into seven gene modules. LASSO regression analysis of the top two gene modules identified ANGPT2, EMCN, GLDN, USHBP1 and ZNF532 as the top five mRNAsi-related genes. We constructed our survival model with these five genes and tested its performance using 243 HCC and 202 normal liver samples from the ICGC database. Kaplan-Meier survival curve and receive operating characteristic curve analyses showed that the survival model accurately predicted the prognosis and survival of high- and low-risk HCC patients with high sensitivity and specificity. The expression of these five genes was significantly higher in the HCC tissues from the TCGA, ICGC, and GEO datasets (GSE25097 and GSE14520) than in normal liver tissues. These findings demonstrate that a new survival model derived from five strongly correlating mRNAsi-related genes provides highly accurate prognoses for HCC patients.