Immune and Stroma Related Genes in Breast Cancer: A Comprehensive Analysis of Tumor Microenvironment Based on the Cancer Genome Atlas (TCGA) Database

Identification of the Expression of TIE1 and Its Mediated Immunosuppression in Gastric Cancer

Background: Recently, various evidence has confirmed that Tyrosine Kinase with Immunoglobulin-like and EGF-like domains 1 (TIE1) promotes tumor growth in many cancers. However, the precise mechanism underlying TIE1's involvement in Gastric Cancer (GC) remains elusive. This research aimed to investigate the biological function of TIE1 in regulating GC progression. Methods: The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), GEPIA2.0, Sangerbox3.0 and TIMER databases were used to analyze the TIE1 expression. Immunohistochemistry (IHC) was used to demonstrate the expression of TIE1. TCGA, GEPIA2.0 and Kaplan-Meier were utilized for survival analysis and to explore the association of TIE1 with clinicopathological features. Protein-Protein Interaction (PPI) networks were constructed using Cytoscape. The potential molecular mechanism of TIE1 was investigated by Gene Ontology (GO), Kyoto Encyclopedia of Gene Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). We studied the relationships between TIE1 and mutations, immune checkpoints (ICs), tumor mutational burden (TMB), as well as microsatellite instability (MSI) to explore the underlying mechanism of immunity in GC. Results: Compared with normal tissue, TIE1 was significantly overexpressed in GC tissues (p = 0.0072) and was associated with poor survival (P < 0.05). According to GO and KEGG enrichment analyses, TIE1 was enriched in signal pathways related to the occurrence, invasion, and migration of malignant tumors (i.e., PI3K-Akt signaling pathway, Calcium signaling pathway, etc.). Immune infiltration analysis suggested that TIE1 is positively correlated with macrophages M2 and negatively correlated with Mast cells, naive B cells and Follicular helper T cells (TFH), which may be a contributing factor to tumor progression. Furthermore, the research on the tumor microenvironment (TME) and tumor purity also proved that TIE1 may be an oncogene. Mutation analysis showed that the high expression group of TIE1 had a higher frequency of mutations in TP53 and ARID1, while the TMB score was lower. Conclusion: TIE1 might be an oncogene via regulating dysregulated immune infiltration to cause immunosuppression in GC and could be identified as a biomarker for prognosis and a therapeutic target for GC.

Identifying the tumor immune microenvironment-associated prognostic genes for prostate cancer

PURPOSE

This study aimed to explore novel tumor immune microenvironment (TIME)-associated biomarkers in prostate adenocarcinoma (PRAD).

METHODS

PRAD RNA-sequencing data were obtained from UCSC Xena database as the training dataset. The ESTIMATE package was used to evaluate stromal, immune, and tumor purity scores. Differentially expressed genes (DEGs) related to TIME were screened using the immune and stromal scores. Gene functions were analyzed using DAVID. The LASSO method was performed to screen prognostic TIME-related genes. Kaplan-Meier curves were used to evaluate the prognosis of samples. The correlation between the screened genes and immune cell infiltration was explored using Tumor IMmune Estimation Resource. The GSE70768 dataset from the Gene Expression Omnibus was used to validate the expression of the screened genes.

RESULTS

The ESTIMATE results revealed that high immune, stromal, and ESTIMATE scores and low tumor purity had better prognoses. Function analysis indicated that DEGs are involved in the cytokine-cytokine receptor interaction signaling pathway. In TIME-related DEGs, METTL7B, HOXB8, and TREM1 were closely related to the prognosis. Samples with low expression levels of METTL7B, HOXB8, and TREM1 had better survival times. Similarly, both the validation dataset and qRT-PCR suggested that METTL7B, HOXB8, and TREM1 were significantly decreased. The three genes showed a positive correlation with immune infiltration.

CONCLUSIONS

This study identified three TIME-related genes, namely, METTL7B, HOXB8, and TREM1, which correlated with the prognosis of patients with PRAD. Targeting the TIME-related genes might have important clinical implications when making decisions for immunotherapy in PRAD.

The categorizations of vasculogenic mimicry in clear cell renal cell carcinoma unveil inherent connections with clinical and immune features

Background: Clear cell renal cell carcinoma (ccRCC) stands as the prevailing variant kidney cancer in humans. Unfortunately, patients with disseminated RCC at diagnosis often have a diminished prognosis. Rapid tumor growth necessitates efficient blood supply for oxygen and nutrients, involving the circulation of blood from vessels to tumor tissues, facilitating tumor cell entry into the extracellular matrix. Vasculogenic mimicry (VM) significantly contributes to tumor growth and metastasis. Within this investigation, we identified vasculogenic mimicry-related genes (VMRGs) by analyzing data from 607 cases of kidney renal clear cell carcinoma (KIRC) in The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). These findings offer insights into ccRCC progression and metastasis. Method: We identified VMRGs-related subtypes using consistent clustering methods. The signature of the VMRGs was created using univariate Cox regression and LASSO Cox regression analyses. To evaluate differences in immune cell infiltration, we employed ssGSEA. Afterwards, we created an innovative risk assessment model, known as the VM index, along with a nomogram to forecast the prognosis of ccRCC. Additionally, we verified the expression of an important gene related to VM, peroxiredoxin 2 (PRDX2), in tissue samples. Furthermore, we assessed the sensitivity to drugs in various groups by utilizing the pRRophetic R package. Results: Significant predictors of survival rates in both high- and low-risk groups of KIRC patients were identified as VMRGs. The independent prognostic factors for RCC were confirmed by both univariate and multivariate Cox regression analyses, validating VMRG risk signatures. Differences were observed in drug sensitivity, immune checkpoint expression, and responses to immune therapy between patients classified into high- and low-VMRG-risk groups. Our nomograms consistently demonstrated precise predictive capabilities. Finally, we experimentally verified PRDX2 expression levels and their impact on prognosis. Conclusion: The signature predicts patient prognosis and therapy response, laying the groundwork for future clinical strategies in treating ccRCC patients.

P2RY13 is a prognostic biomarker and associated with immune infiltrates in renal clear cell carcinoma: A comprehensive bioinformatic study

Background and Aims

Clear cell renal cell carcinoma (ccRCC) is a common and aggressive form of cancer with a high incidence globally. This study aimed to investigate the role of P2RY13 in the progression of ccRCC and elucidate its mechanism of action.

Methods

Gene Expression Omnibus and The Cancer Genome Atlas databases were used to extract gene expression profiles of ccRCC. These profiles were annotated and visualized by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses, as well as Gene Set Enrichment Analysis (GSEA). The STRING database was used to establish a protein-protein interaction network and to analyze the functional similarity. The GEPIA2 database was used to predict survival associated with hub genes. Meanwhile, the TIMER2.0 database was used to assess immune cell infiltration and its link with the hub genes. Immunohistochemistry (IHC) was used to determine the difference between ccRCC and adjacent normal tissue.

Results

We identified 272 differentially expressed genes (DEGs). GO and KEGG analyses suggested that DEGs were primarily involved in lymphocyte activation, inflammatory response, immunological effector mechanism pathways. By cytohubba, the 20 highest-scoring hub genes were screened to identify critical genes in the protein-protein interaction network linked with ccRCC. Resting dendritic cells, CD8 T cells, and activated mast cells all showed a significant positive correlation with these hub genes. Moreover, a higher immune score was associated with increased prognostic risk scores, which in turn correlated with a poorer prognosis. IHC revealed that P2RY13 was expressed at higher levels in ccRCC compared to para-cancer tissues.

Conclusion

Identifying the DEGs will aid in the understanding of the causes and molecular mechanisms involved in ccRCC. P2RY13 may play a pivotal role in the progression and prognosis of ccRCC, potentially driving carcinogenesis though immune system mechanisms.

A novel model associated with tumor microenvironment on predicting prognosis and immunotherapy in triple negative breast cancer

Triple negative breast cancer (TNBC) is the most aggressive and malignant subtype in breast cancer. Immunotherapy is a currently promising and effective treatment for TNBC, while not all patients are responsive. Therefore, it is necessary to explore novel biomarkers to screen sensitive populations for immunotherapy. All mRNA expression profiles of TNBC from The Cancer Genome Atlas (TCGA) database were clustered into two subgroups by analyzing tumor immune microenvironment (TIME) with single sample gene set enrichment analysis (ssGSEA). A risk score model was constructed based on differently expressed genes (DEGs) identified from two subgroups using Cox and Least Absolute Shrinkage and Selector Operation (LASSO) regression model. And it was validated by Kaplan-Meier analysis and Receiver Operating Characteristic (ROC) analysis in Gene Expression Omnibus (GEO) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases. Multiplex immunofluorescence (mIF) and Immunohistochemical (IHC) staining were performed on clinical TNBC tissue samples. The relationship between risk score and immune checkpoint blockades (ICB) related signatures was further investigated, as well as the biological processes were performed by gene set enrichment analysis (GSEA). We obtained three DEGs positively related to prognosis and infiltrating immune cells in TNBC. Our risk score model could be an independent prognostic factor and the low risk group exhibited a prolonged overall survival (OS). Patients in low risk group were more likely to present a higher immune infiltration and stronger response to immunotherapy. GSEA revealed the model was associated with immune-related pathways. We constructed and validated a novel model based on three prognostic genes related to TIME in TNBC. The model contributed a robust signature that could predict the prognosis in TNBC, especially for the efficacy of immunotherapy.

Bifidobacterium infantis-Mediated Herpes Simplex Virus-TK/Ganciclovir Treatment Inhibits Cancer Metastasis in Mouse Model

Previous studies have found that Bifidobacterium infantis-mediated herpes simplex virus-TK/ganciclovir (BF-TK/GCV) reduces the expression of VEGF and CD146, implying tumor metastasis inhibition. However, the mechanism by which BF-TK/GCV inhibits tumor metastasis is not fully studied. Here, we comprehensively identified and quantified protein expression profiling for the first time in gastric cancer (GC) cells MKN-45 upon BF-TK/GCV treatment using quantitative proteomics. A total of 159 and 72 differential expression proteins (DEPs) were significantly changed in the BF-TK/GCV/BF-TK and BF-TK/GCV/BF/GCV comparative analysis. Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis enriched some metastasis-related pathways such as gap junction and cell adhesion molecules pathways. Moreover, the transwell assay proved that BF-TK/GCV inhibited the invasion and migration of tumor cells. Furthermore, immunohistochemistry (IHC) demonstrated that BF-TK/GCV reduced the expression of HIF-1α, mTOR, NF-κB1-p105, VCAM1, MMP13, CXCL12, ATG16, and CEBPB, which were associated with tumor metastasis. In summary, BF-TK/GCV inhibited tumor metastasis, which deepened and expanded the understanding of the antitumor mechanism of BF-TK/GCV.

The coagulation-related genes for prognosis and tumor microenvironment in pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a malignancy characterized by challenging early diagnosis and poor prognosis. It is believed that coagulation has an impact on the tumor microenvironment of PDAC. The aim of this study is to further distinguish coagulation-related genes and investigate immune infiltration in PDAC.

METHODS

We gathered two subtypes of coagulation-related genes from the KEGG database, and acquired transcriptome sequencing data and clinical information on PDAC from The Cancer Genome Atlas (TCGA) database. Using an unsupervised clustering method, we categorized patients into distinct clusters. We investigated the mutation frequency to explore genomic features and performed enrichment analysis, utilizing Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) to explore pathways. CIBERSORT was used to analyze the relationship between tumor immune infiltration and the two clusters. A prognostic model was created for risk stratification, and a nomogram was established to assist in determining the risk score. The response to immunotherapy was assessed using the IMvigor210 cohort. Finally, PDAC patients were recruited, and experimental samples were collected to validate the infiltration of neutrophils using immunohistochemistry. In addition, and identify the ITGA2 expression and function were identified by analyzing single cell sequencing data.

RESULTS

Two coagulation-related clusters were established based on the coagulation pathways present in PDAC patients. Functional enrichment analysis revealed different pathways in the two clusters. Approximately 49.4% of PDAC patients experienced DNA mutation in coagulation-related genes. Patients in the two clusters displayed significant differences in terms of immune cell infiltration, immune checkpoint, tumor microenvironment and TMB. We developed a 4-gene prognostic stratified model through LASSO analysis. Based on the risk score, the nomogram can accurately predict the prognosis in PDAC patients. We identified ITGA2 as a hub gene, which linked to poor overall survival (OS) and short disease-free survival (DFS). Single-cell sequencing analysis demonstrated that ITGA2 was expressed by ductal cells in PDAC.

CONCLUSIONS

Our study demonstrated the correlation between coagulation-related genes and the tumor immune microenvironment. The stratified model can predict the prognosis and calculate the benefits of drug therapy, thus providing the recommendations for clinical personalized treatment.

CCL21/CCR7 axis as a therapeutic target for autoimmune diseases

Chemokine receptor 7 (CCR7) is a G protein-coupled receptor containing 7 transmembrane domains that is expressed on various cells, such as naive T/B cells, central memory T cells, regulatory T cells, immature/mature dendritic cells (DCs), natural killer cells, and a minority of tumor cells. Chemokine ligand 21 (CCL21) is the known high-affinity ligand that binds to CCR7 and drives cell migration in tissues. CCL21 is mainly produced by stromal cells and lymphatic endothelial cells, and its expression is significantly increased under inflammatory conditions. Genome-wide association studies (GWAS) have shown a strong association between CCL21/CCR7 axis and disease severity in patients with rheumatoid arthritis, sjogren's syndrome, systemic lupus erythematosus, polymyositis, ankylosing spondylitis, and asthma. Disrupting CCL21/CCR7 interaction with antibodies or inhibitors prevents the migration of CCR7-expressing immune and non-immune cells at the site of inflammation and reduces disease severity. This review emphasizes the importance of the CCL21 /CCR7 axis in autoimmune diseases and evaluates its potential as a novel therapeutic target for these conditions.

NNMT Is an Immune-Related Prognostic Biomarker That Modulates the Tumor Microenvironment in Pan-Cancer

Emerging evidence has revealed the significant roles of nicotinamide n-methyltransferase (NNMT) in cancer initiation, development, and progression; however, a pan-cancer analysis of NNMT has not been conducted. In this study, we first thoroughly investigated the expression and prognostic significance of NNMT and the relationship between NNMT and the tumor microenvironment using bioinformatic analysis. NNMT was significantly increased and associated with poor prognosis in many common cancers. NNMT expression correlated with the infiltration levels of cancer-associated fibroblasts and macrophages in pan-cancer. Function enrichment analysis discovered that NNMT related to cancer-promoting and immune pathways in various common cancers, such as colon adenocarcinoma, head and neck squamous cell carcinoma, ovarian serous cystadenocarcinoma, and stomach adenocarcinoma. NNMT expression was positively correlated with tumor-associated macrophages (TAMs), especially M2-like TAMs. The results suggest that NNMT might be a new biomarker for immune infiltration and poor prognosis in cancers, providing new direction on therapeutics of cancers.

WGS Data Collections: How Do Genomic Databases Transform Medicine?

As a scientific community we assumed that exome sequencing will elucidate the basis of most heritable diseases. However, it turned out it was not the case; therefore, attention has been increasingly focused on the non-coding sequences that encompass 98% of the genome and may play an important regulatory function. The first WGS-based datasets have already been released including underrepresented populations. Although many databases contain pooled data from several cohorts, recently the importance of local databases has been highlighted. Genomic databases are not only collecting data but may also contribute to better diagnostics and therapies. They may find applications in population studies, rare diseases, oncology, pharmacogenetics, and infectious and inflammatory diseases. Further data may be analysed with Al technologies and in the context of other omics data. To exemplify their utility, we put a highlight on the Polish genome database and its practical application.

Exploration of Immunogenic Cell Death-Associated Genes for Predicting Prognosis and Immunological Characteristics in Cervical Squamous Cell Carcinoma

Background. The tumor microenvironment (TME) has gradually entered the vision of researchers and is becoming a vital part of the occurrence of cervical squamous cell carcinoma (CSCC). However, understanding the specific composition of TME still confront enormous challenges, particularly immune and stromal components. Methods. In this study, we performed an unsupervised cluster analysis to determine the immunogenic cell death-associated subtype of CSCC patients. The differences in immune status, genomic alteration, and clinical outcomes between each subtype were compared. Subsequently, we screened vital prognostic factors. The HPA database was employed to verify the protein localization and the expression level between cancer and adjacent tissues. Results. CSCC patients were divided into three subtypes according to the expression of immunogenic cell death-associated genes. Cluster C has the highest survival rate because of the lower activation of tumor-related pathways. The immune score and stromal score of patients with Cluster B were the highest, so it may be considered that stromal tissue inhibits the anti-tumor effect of immunocytes. In addition, we constructed a risk score based on immunogenic cell death-associated genes to screen for vital markers. We systematically revealed the genomic alteration of vital markers. Conclusions. We have established a novel immunogenic cell death-associated risk scoring system in CSCC, and the expression of immunogenic cell death-associated genes may be a valuable biomarker for immunotherapy strategies. Our work may contribute to the development of new immunomodulators and develop new precision immunotherapies for CSCC.

Construction and characterization of a cuproptosis- and immune checkpoint-based LncRNAs signature for breast cancer risk stratification

BACKGROUND

Cuproptosis is the most recently identified form of cell death, and copper homeostasis is an important cancer therapeutic target. However, the therapeutic benefits of cuproptosis-targeted treatment in BRCA remain undetermined. This study utilized LncRNAs linked to cuproptosis genes and immune checkpoint genes to generate a BRCA predictive signature.

METHODS

We screened a population of LncRNAs that correlated with both cuproptosis genes and immune checkpoint genes and used ten of these LncRNAs to construct a prognosis-predictive signature. We then validated and proved the efficacy of the signature in predicting the prognosis of BRCA patients. We also unraveled the relationship between the signature and the immunological milieu, immune function, and susceptibility to chemotherapy.

RESULTS

The signature derived from the ten cuproptosis- and immune-related prognostic LncRNAs (CuImP-LncRNAs) can be implied to categorize patients into two groups, including the high- and low-risk groups. The value of the signature was validated, and the risk score was verified as an independent prognostic indicator. The TIME and TMB distribution patterns and chemosensitivity were depicted in the high- and low-risk groups, respectively. Patients of the high-risk group with a suppressive immunological intratumor context were more sensitive to a broad range of antitumor agents. In contrast, low-risk individuals with active immune function responded more favorably to immunotherapy.

CONCLUSION

Our findings provided a novel and effective model for predicting BRCA prognosis and the propensity to different treatment modalities, thus contributing to the optimization of personalized BRCA therapy in the future.

Rutaecarpine prevents the malignant biological properties of breast cancer cells by the miR-149-3p/S100A4 axis

Background

Breast cancer (BC) is a frequent malignancy that endangers women's health, and its fatality rate ranks 1st among female malignancies. Research has shown that rutaecarpine (RUT), which is a Chinese herbal medicine, blocks the proliferation of cancer cells by a variety of molecular mechanisms. However, the possible effects and mechanism of RUT in the autophagy and angiogenesis of BC cells has not been clearly articulated.

Methods

MiR-149-3p and S100A4 expression levels were assessed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and the optimal concentration and time of RUT was confirmed by Cell Counting Kit-8 (CCK-8) assays of the BC cells. After treatment, changes in cell proliferation and the cell cycle were evaluated by CCK-8 assays, clone formation assays, and flow cytometry, and the levels of apoptosis, autophagy, and angiogenesis-related proteins were identified by Western blot. The targeted regulation of miR-149-3p on S100A4 was also examined by luciferase reporter assays.

Results

We found that RUT inhibited cell growth and upregulated miR-149-3p in MDA-MB-231 cells. In relation to the biological function activity, RUT attenuated proliferation and angiogenesis, and induced cell-cycle arrest and autophagy by miR-149-3p in the MDA-MB-231 cells. Additionally, miR-149-3p downregulated S100A4 by targeting binding to S100A4, and S100A4 was required for miR-149-3p to play a role in BC progression. We also discovered that an autophagy agonist (rapamycin) or an angiogenesis inhibitor (TNP-470) changed BC progression mediated by the RUT/miR-149-3p/S100A4 axis.

Conclusions

RUT blocks the malignant behaviors of BC cells through the miR-149-3p/S100A4 axis and thus alters autophagy and angiogenesis. Thus, the RUT-mediated miR-149-3p/S100A4 axis might be an underlying therapeutic agent and target for BC.

N1-Methyladenosine-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Response in Uterine Corpus Endometrial Carcinoma

Uterine corpus endometrial carcinoma (UCEC) is a malignant disease that, at present, has no well-characterised prognostic biomarker. In this study, two clusters were identified based on 28 N1-methyladenosine- (m1A-) related long noncoding RNAs (lncRNAs), of which cluster 1 was related to immune pathways according to the results of an enrichment analysis. We further observed better prognosis in patients with higher levels of immune cell infiltration, tumor mutation burden, microsatellite instability, and immune checkpoint gene expression. In addition, through Cox regression analysis and least absolute shrinkage and selection operator regression analysis, 10 m1A-related lncRNAs (mRLs) were employed to build a prognosis model. We found that people in higher risk categories had a poorer survival probability than those in lower risk. Low-risk samples were enriched with immune-related pathways, while the high-risk group was similar to the definition of the “immune desert” phenotype, which was associated with decreased immune infiltration, T cell failure, and decreased tumor mutation burden, while also being insensitive to immunotherapy and chemotherapy. This mRL-based model has the ability to accurately predict the prognosis of UCEC patients, and the mRLs could become promising therapeutic targets in enhancing the response of immunotherapy.

Expression characteristics of long non-coding RNA in colon adenocarcinoma and its potential value for judging the survival and prognosis of patients: bioinformatics analysis based on The Cancer Genome Atlas database

Background

To investigate the expression characteristics of long non-coding RNA (lncRNA) in colon adenocarcinoma (COAD) and its potential value in predicting the prognosis of patient survival.

Methods

We downloaded COAD-related RNA sequencing (RNA-seq) data and patient survival data from The Cancer Genome Atlas (TCGA). The data were analyzed for lncRNA expression differences, subjected to Cox regression analysis for survival rate, and Kaplan-Meier (KM) survival curves were plotted to analyze the role of the key genes related to prognostic survival by pathway enrichment analysis.

Results

The data of 494 COAD clinical samples from TCGA were analyzed; 204 lncRNAs were differentially expressed, 156 were up-regulated, and 48 were down-regulated. The 10 genes with the most significant expression differences were Linc02418, Blacat1, ELFN1-AS1, CRNDE, AC002384.1, AL353801.1, LINC01645, AC073283.2, AC087379.1, and LINC00484. Cox regression analysis of 204 lncRNA genes showed that 23 lncRNA genes with significant effects on the prognosis and survival rate of COAD patients were obtained when P<0.05 was used as the threshold. With P≤0.001 as the threshold, the KM curves of 4 genes (Linc02257, Linc02474, Ac010789.1, Ac083967.1) were statistically significant (P<0.05). The gene Linc02257 was selected for Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and it was revealed that the inheritance of Linc02257-regulated gene expression was closely related to tumor development, such as collagen-containing extracellular matrix, organogenesis, activity of membrane protein receptors, and ion channel activity. The signaling pathways regulated by Linc02257 were also closely related to tumors, such as neuroactive ligand-receptor interaction, the PI3K-AKT signaling pathway, calcium signaling pathway, and protein digestion and absorption.

Conclusions

In COAD, lncRNA is differentially expressed and plays an important role in the disease regulation. It has potential application value in the diagnosis, targeted therapy, and prognosis of COAD patients.

A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD

Screening for early-stage lung cancer with low-dose computed tomography is recommended for high-risk populations; consequently, the incidence of pure ground-glass opacity (pGGO) is increasing. Ground-glass opacity (GGO) is considered the appearance of early lung cancer, and there remains an unmet clinical need to understand the pathology of small GGO (&lt;1 cm in diameter). The objective of this study was to use the transcriptome profiling of pGGO specimens &lt;1 cm in diameter to construct a pGGO-related gene risk signature to predict the prognosis of early-stage lung adenocarcinoma (LUAD) and explore the immune microenvironment of GGO. pGGO-related differentially expressed genes (DEGs) were screened to identify prognostic marker genes with two machine learning algorithms. A 15-gene risk signature was constructed from the DEGs that were shared between the algorithms. Risk scores were calculated using the regression coefficients for the pGGO-related DEGs. Patients with Stage I/II LUAD or Stage IA LUAD and high-risk scores had a worse prognosis than patients with low-risk scores. The prognosis of high-risk patients with Stage IA LUAD was almost identical to that of patients with Stage II LUAD, suggesting that treatment strategies for patients with Stage II LUAD may be beneficial in high-risk patients with Stage IA LUAD. pGGO-related DEGs were mainly enriched in immune-related pathways. Patients with high-risk scores and high tumor mutation burden had a worse prognosis and may benefit from immunotherapy. A nomogram was constructed to facilitate the clinical application of the 15-gene risk signature. Receiver operating characteristic curves and decision curve analysis validated the predictive ability of the nomogram in patients with Stage I LUAD in the TCGA-LUAD cohort and GEO datasets.

Identification of CD4+ Conventional T Cells-Related lncRNA Signature to Improve the Prediction of Prognosis and Immunotherapy Response in Breast Cancer

Background

Breast cancer (BC) is one of the most common malignancies in women, and long non-coding RNAs (lncRNAs) are key regulators of its development. T cells can recognize and kill cancer cells, and CD4⁺ T conventional (Tconv) cells are the main orchestrators of cancer immune function. However, research on CD4⁺ Tconv-related lncRNAs (CD4TLAs) prognostic signature in patients with BC is still lacking.

Method

A TCGA database and a GEO database were used to collect the BC patients. Through LASSO Cox regression analysis CD4TLAs-related prognostic models were further constructed, and risk scores (RS) were generated and developed a nomogram based on CD4TLAs. The accuracy of this model was validated in randomized cohorts and different clinical subgroups. Gene set enrichment analysis (GSEA) was used to explore potential signature-based functions. The role of RS has been further explored in the tumor microenvironment (TME), immunotherapy, and chemotherapy.

Result

A prognostic model based on 16 CD4TLAs was identified. High-RS was significantly associated with a poorer prognosis. RS was shown to be an independent prognostic indicator in BC patients. The low-RS group had a significant expression of immune infiltrating cells and significantly enriched immune-related functional pathways. In addition, the results of immunotherapy prediction indicated that patients with low-RS were more sensitive to immunotherapy.

Conclusions

Our signature has potential predictive value for BC prognosis and immunotherapy response. The findings of this work have greatly increased our understanding of CD4TLA in BC.

Feature screening for survival trait with application to TCGA high-dimensional genomic data

Background

In high-dimensional survival genomic data, identifying cancer-related genes is a challenging and important subject in the field of bioinformatics. In recent years, many feature screening approaches for survival outcomes with high-dimensional survival genomic data have been developed; however, few studies have systematically compared these methods. The primary purpose of this article is to conduct a series of simulation studies for systematic comparison; the second purpose of this article is to use these feature screening methods to further establish a more accurate prediction model for patient survival based on the survival genomic datasets of The Cancer Genome Atlas (TCGA).

Results

Simulation studies prove that network-adjusted feature screening measurement performs well and outperforms existing popular univariate independent feature screening methods. In the application of real data, we show that the proposed network-adjusted feature screening approach leads to more accurate survival prediction than alternative methods that do not account for gene-gene dependency information. We also use TCGA clinical survival genetic data to identify biomarkers associated with clinical survival outcomes in patients with various cancers including esophageal, pancreatic, head and neck squamous cell, lung, and breast invasive carcinomas.

Conclusions

These applications reveal advantages of the new proposed network-adjusted feature selection method over alternative methods that do not consider gene-gene dependency information. We also identify cancer-related genes that are almost detected in the literature. As a result, the network-based screening method is reliable and credible.

Identify potential prognostic indicators and tumor-infiltrating immune cells in pancreatic adenocarcinoma

Background: Pancreatic adenocarcinoma (PAAD) is a kind of highly malignant tumor and lacks early diagnosis method and effective treatment. Tumor microenvironment (TME) is of great importance for the occurrence and development of PAAD. Thus, a comprehensive overview of genes and tumor-infiltrating immune cells (TICs) related to TME dynamic changes conduce to develop novel therapeutic targets and prognostic indicators.

Methods: We used MAlignant Tumors using Expression data (ESTIMATE) algorithm to analyze the transcriptome RNA-seq data of 182 PAAD cases on The Cancer Genome Atlas (TCGA) platform. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein–protein interaction (PPI) network, COX regression analysis and gene set enrichment analysis (GSEA) were carried out to get the hub genes related to the prognosis of PAAD patients. These core genes were validated in GEPIA. CXCL10 expression as a poor prognostic indicator was validated in GEO database. Finally, CIBERSORT algorithm was applied to understand the status of TICs.

Results: A total of 715 up-regulated differential expression genes (DEGs) and 57 down-regulated DEGs were found simultaneously in stromal and immune groups. These DEGs were mainly enriched in immune recognition, activation and response processes. CD4, CXCL12, CXCL10, CCL5 and CXCL9 were the top five core genes. Then, the validation of these genes showed that CD4, CXCL10, CXCL5, CXCL9 were up-regulated in PAAD. Among the core genes, CXCL10 had a negative correlation with the survival time of PAAD patients. CD8+ T cells, CD4+ T cells memory activated, macrophages M1 had positive correlation of CXCL10 expression, whereas regulatory T cells (Tregs), macrophages M0 and B cells memory had negative correlation.

Conclusion: We generated a series of genes related to TME with prognostic implications and TICs in PAAD, which have the potential to be novel immunotherapy targets and prognostic markers. The data showed that CXCL10 was favorable as a poor prognostic indicator in PAAD patients.

Identification of HCC Subtypes With Different Prognosis and Metabolic Patterns Based on Mitophagy

Background: Mitophagy is correlated with tumor initiation and development of malignancy. However, HCC heterogeneity with reference to mitophagy has yet not been systematically explored.

Materials and Methods: Mitophagy-related, glycolysis-related, and cholesterol biosynthesis-related gene sets were obtained from the Reactome database. Mitophagy-related and metabolism-related subtypes were identified using the ConsensusClusterPlus algorithm. Univariate Cox regression was analysis was performed to identify prognosis-related mitophagy regulators. Principal component analysis (PCA) was used to create composite measures of the prognosis-related mitophagy regulators (mitophagyscore). Individuals with a mitophagyscore higher or lower than the median value were classified in high- or low-risk groups. Kaplan-Meier survival and ROC curve analyses were utilized to evaluate the prognostic value of the mitophagyscore. The nomogram and calibration curves were plotted using the“rms” R package. The package “limma” was used for differential gene expression analysis. Differentially expressed genes (DEGs) between high- and low-risk groups were used as queries in the CMap database. R package “pRRophetic” and Genomics of Drug Sensitivity in Cancer (GDSC) database were used to determine the sensitivity of 21 previously reported anti-HCC drugs.

Results: Three distinct HCC subtypes with different mitophagic accumulation (low, high, and intermediate mitophagy subtypes) were identified. High mitophagy subtype had the worst outcome and highest glycolysis level. The lowest degree of hypoxia and highest cholesterol biosynthesis was observed in the low mitophagy subtype; oncogenic dedifferentiation level in the intermediate mitophagy subtype was the lowest. Mitophagyscore could serve as a novel prognostic indicator for HCC.High-risk patients had a poorer prognosis (log-rank test, p < 0.001). The area under the ROC curve for mitophagyscore in 1-year survival was 0.77 in the TCGA cohort and 0.75 in the ICGC cohort. Nine candidate small molecules which were potential drugs for HCC treatment were identified from the CMap database. A decline in the sensitivity towards 21 anti-HCC drugs was observed in low-risk patients by GDSC database. We also identified a novel key gene, SPP1, which was highly associated with different mitophagic subtypes.

Conclusion: Based on bioinformatic analyses, we systematically examined the HCC heterogeneity with reference to mitophagy and observed three distinct HCC subtypes having different prognoses and metabolic patterns.

Immune landscape of advanced gastric cancer tumor microenvironment identifies immunotherapeutic relevant gene signature

Background

Advanced gastric cancer (AGC) is a disease with poor prognosis due to the current lack of effective therapeutic strategies. Immune checkpoint blockade treatments have shown effective responses in patient subgroups but biomarkers remain challenging. Traditional classification of gastric cancer (GC) is based on genomic profiling and molecular features. Therefore, it is critical to identify the immune-related subtypes and predictive markers by immuno-genomic profiling.

Methods

Single-sample gene-set enrichment analysis (ssGSEA) and ESTIMATE algorithm were used to identify the immue-related subtypes of AGC in two independent GEO datasets. Weighted gene co-expression network analysis (WGCNA) and Molecular Complex Detection (MCODE) algorithm were applied to identify hub-network of immune-related subtypes. Hub genes were confirmed by prognostic data of KMplotter and GEO datasets. The value of hub-gene in predicting immunotherapeutic response was analyzed by IMvigor210 datasets. MTT assay, Transwell migration assay and Western blotting were performed to confirm the cellular function of hub gene in vitro.

Results

Three immune-related subtypes (Immunity_H, Immunity_M and Immunity_L) of AGC were identified in two independent GEO datasets. Compared to Immunity_L, the Immuntiy_H subtype showed higher immune cell infiltration and immune activities with favorable prognosis. A weighted gene co-expression network was constructed based on GSE62254 dataset and identified one gene module which was significantly correlated with the Immunity_H subtype. A Hub-network which represented high immune activities was extracted based on topological features and Molecular Complex Detection (MCODE) algorithm. Furthermore, ADAM like decysin 1 (ADAMDEC1) was identified as a seed gene among hub-network genes which is highly associated with favorable prognosis in both GSE62254 and external validation datasets. In addition, high expression of ADAMDEC1 correlated with immunotherapeutic response in IMvigor210 datasets. In vitro, ADAMDEC1 was confirmed as a potential protein in regulating proliferation and migration of gastric cancer cell. Deficiency of ADAMDEC1 of gastric cancer cell also associated with high expression of PD-L1 and Jurkat T cell apoptosis.

Conclusions

We identified immune-related subtypes and key tumor microenvironment marker in AGC which might facilitate the development of novel immune therapeutic targets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09065-z.

Identification of the GTPase IMAP family as an immune-related prognostic biomarker in the breast cancer tumor microenvironment

INTRODUCTION

Breast cancer is the most common malignancy threatening women's health worldwide. The GTPase IMAP family genes are proteins belonging to the immune-associated nucleotide subfamily of the GTP-binding superfamily and nucleotide-binding proteins. However, little is known about the role of different GTPase IMAP family genes in breast cancer.

METHODS

We obtained differential genes from the GEPIA and UALCAN databases and then used the Kaplan-Meier plotter, The Human Protein Atlas, NetworkAnalyst, STRING, and TIMER to analyze the prognostic value, protein expression, and immune cell infiltration of the GTPase IMAP family in patients with breast cancer.

RESULTS

Among the GIMAP family genes, the expression levels of GIMAP1, GIMAP5, GIMAP6, GIMAP7, and GIMAP8 were significantly low in breast tumor tissues. In the overall population, patients with high expression of all genes of the GIMAP family had a significantly higher overall survival (OS), with the most significant increase correlated with the GIMAP2 gene (hazard ratio [HR] = 0.45, 95% confidence interval [CI], 0.34-0.59, P = 3.1e-09). However, patients with high expression of the GIMAP family genes in triple-negative breast cancer compared to those with low expression had a significant OS benefit, with the most pronounced benefit correlated with the GIMAP2 gene (HR = 0.37, 95% CI, 0.23-0.59, P = 1.4e-05). GIMAP7 and GIMAP8 were significantly upregulated in breast tumor tissues. The expression of genes in different GIMAP families was positively correlated with the infiltration and expression of six immune cell types (B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells).

CONCLUSION

This study may provide novel insights into the selection of GIMAP family prognostic biomarkers for breast cancer.

Screening of a novel autophagy-related prognostic signature and therapeutic targets in hepatocellular carcinoma

BACKGROUND

Many studies have indicated that autophagy plays an important role in multiple cancers, including hepatocellular carcinoma (HCC). This study aimed to establish a prognostic signature for HCC based on autophagy-related genes (ARGs) to predict the prognosis of patients.

METHODS

The list of ARGs was derived from screening National Center for Biotechnology Information (NCBI)-Gene and Molecular Signatures Database (MSigDB) datasets. Differential analysis was conducted via the R limma package in HCC patients based on The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analysis were conducted to identify key prognostic ARGs via the survival package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by clusterProfiler package. The Estimation of Stromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm was used to conduct immune analysis. Finally, the correlation between the prognostic model and clinical characteristics was also assessed, including age, tumor-node-metastasis (TNM) stages, and tumor grades.

RESULTS

Firstly, 106 differential ARGs were identified and 10 candidates were further confirmed via Cox regression analysis, including BAMBI, HIF1A, SERPINE1, EZH2, SLC9A3R1, IGFBP3, HSPB8, DAB2, CXCL1 and PRNP. The receiver operating characteristic (ROC) curve analysis revealed that the ARGs risk model had a well diagnostic positive rate with 1-year area under the curve (AUC) =0.688 and 3-year AUC =0.674. Correlation analysis indicated that only advanced tumor stages were positively associated with high ARGs scores with P=0.0227. There were also significant differences in tumor purity (P=6.71e-05), infiltrating cell analysis (P=7.77e-05), immune analysis (P=7.9e-05), and stromal cells analysis (P=0.0015) in high- and low-risk ARGs samples. The genes HIF1A, IGFBP3, and DAB2 were found to have high frequent missense mutations in samples with high-risk ARGs scores. Lastly, we also established a nomogram to predict overall survival (OS) of HCC by integrating ARGs scores and other clinical parameters.

CONCLUSIONS

Our study established an autophagy-related signature for predicting the prognosis of HCC patients, providing a thorough understanding of the underlying mechanisms of autophagy in HCC.

Immune and Stromal Related Genes in Colon Cancer: Analysis of Tumor Microenvironment Based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) Databases

BACKGROUND

The incidence of colon cancer is among the top three in the world. The tumor microenvironment plays an important role in the occurrence and development of colon cancer. Stromal cells and immune cells are the main components of the tumor microenvironment.

METHODS

Our study detected genes which affected the infiltration of stromal, immune cells and the way they affected the prognosis of colon cancer patients.

RESULTS

We found that the colon's immune system had a special way to affect the tumor microenvironment. Moderate infiltration of stromal and immune cells were proved to be important protective factors for colon cancer patients, which has not been found in other tumors. C3, C5, CXCL12, GNAI1, LPAR1, PENK, PYY, SAA1 and SST were the differential expression hub genes of moderate stromal and immune score group. They had a more significant correlation with tumor purity and infiltration of B cells, CD8⁺ T cells, CD4⁺ T cells, macrophage, neutrophil, democratic cells. The proteins encoded by C3, C5, CXCL12, GNAI1, PENK, PYY, SST were detected in colon cancer cells.

CONCLUSION

These genes had the potential to become markers to predict the prognosis of patients with colon cancer.

Biological Function and Clinical Value of VPS13A in Pan-Cancer Based on Bioinformatics Analysis

Purpose

Vacuolar protein sorting-associated protein 13A (VPS13A) has been shown to be associated with rhabdomyosarcoma, gastric cancer and ovarian cancer, but the pan cancer analysis of VPS13A is still lacking, and the bioinformatics function of VPS13A has not been studied yet.

Methods

We used TCGA and GEO databases to investigate the distribution, expression and prognosis of VPS13A in 33 tumors for the first time. We used TIMER2, ULCAN databases to obtain the expression differences of VPS13A in tumor tissues and corresponding normal tissues, and further obtain the gene expression in different pathological stages of tumors from the GEPIA database. Mutation types and survival analysis of VPS13A were obtained from cBioPortal database. The relationship between VPS13A and immune infiltration was explored using TIMER2. We used the String website to obtain VPS13A binding proteins and draw the protein–protein interaction network map. JVENN was used for cross analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were used for gene enrichment analysis.

Results

VPS13A is highly expressed in most tumors, and gene expression is associated with prognosis in patients with tumors. The expression level of VPS13A was correlated with the infiltration depth of CD8+T cells in DLBC, LUAD, SKCM and TGCT, and was correlated with carcinoma-associated fibroblasts in BRCA, CESC, LIHC and THYM. Compared with normal tissue, VPS13A methylation levels were higher in some primary tumors. KEGG gene enrichment indicates that VPS13A is involved in RNA degradation, autophagy, cell senescence, cell cycle, apoptosis and other pathways.

Conclusion

VPS13A is closely related to the occurrence and progression of tumors and can be used as a biomarker for tumor screening and diagnosis. The level of VPS13A expression and the presence of mutations affect the prognosis of patients with certain cancers, which can be determined by early genetic testing.

Comprehensive Integrative Analysis Reveals the Association of KLF4 with Macrophage Infiltration and Polarization in Lung Cancer Microenvironment

Macrophage polarization and infiltration to the tumor microenvironment (TME) is a critical determining factor for tumor progression. Macrophages are polarized into two states—M1 (pro-inflammatory, anti-tumorigenic and stimulated by LPS or IFN-γ) and M2 (anti-inflammatory pro-tumorigenic and stimulated by IL-4) phenotypes. Specifically, M2 macrophages enhance tumor cell growth and survival. Recent evidences suggest the pivotal role of microRNAs in macrophage polarization during the development of Non-small cell lung cancer (NSCLC), thus proposing a new therapeutic option to target lung cancer. In silico analysis determined cogent upregulation of KLF4, downregulation of IL-1β and miR-34a-5p in NSCLC tissues, consequently worsening the overall survival of NSCLC patients. We observed a significant association of KLF4 with macrophage infiltration and polarization in NSCLC. We found that KLF4 is critically implicated in M2 polarization of macrophages, which, in turn, promotes tumorigenesis. KLF4 expression correlated with miR-34a-5p and IL-1β in a feed-forward loop (FFL), both of which are implicated in immune regulation. Mechanistic overexpression of miR-34a-5p in macrophages (IL-4 stimulated) inhibits KLF4, along with downregulation of ARG1, REL-1MB (M2 macrophage specific markers), and upregulation of IL-1β, IL-6, (M1 macrophage specific markers), demonstrating macrophage polarization switch from M2 to M1 phenotype. Moreover, co-culture of these macrophages with NSCLC cells reduces their proliferation, wound healing, clonogenic capacity and enhanced NO-mediated apoptosis. Further, transfection of miR-34a-5p in NSCLC cells, also degrades KLF4, but enhances the expression of KLF4 regulated genes—IL-1β, IL-6 (pro-inflammatory mediators), which is further enhanced upon co-culture with IL-4 stimulated macrophages. Additionally, we observed a significant increase in i-NOS/NO content upon co-culture, suggesting polarization reversion of macrophages from M2 to M1, and eventually leading to anti-tumor effects. Our findings thus show a significant role of KLF4 in tumorigenesis and TAM polarization of NSCLC. However, miR-34a-5p mediated targeting of these molecular networks will provide a better therapeutic intervention for NSCLC.

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.

Development and validation of a nomogram based on stromal score to predict progression-free survival of patients with papillary thyroid carcinoma

Background

Growing evidence has proved that stromal cells, as the critical component of tumor microenvironment (TME), are closely associated with tumor's progression. However, the model based on stromal score to predict progression‐free survival (PFS) in papillary thyroid carcinoma (PTC) has not been developed. The study aimed at exploring the relation between stromal score and prognosis, then establishing a nomogram to predict PFS of patients with PTC.

Method

We obtained the stromal score and clinicopathological characteristics of PTC patients from The Cancer Genome Atlas (TCGA) database. Cox regression analysis assisted in selecting prognosis‐related factors. A stromal score‐based nomogram was built and verified in the training and validation cohorts, respectively. The calibration curve, concordance index (C‐index), decision curve analysis (DCA) as well as receiver operating characteristic (ROC) curve assisted in measuring the performance exhibited by the nomogram.

Results

We divided 381 PTC patients into the training cohort (n = 269) and the validation cohort (n = 112) randomly. Compared with patients who had a low stromal score, patients with a high stromal score appeared with significantly better PFS [Hazard ratio (HR) and 95% confidence interval (CI): 0.294, 0.130–0.664]. The C‐index of the PFS nomogram was 0.764 (0.662–0.866) in the training cohort and 0.717 (0.603–0.831) in the validation cohort. The calibration curves for PFS prediction in the nomogram were remarkably consistent with the actual observation. DCA indicated superior performance of the nomogram to predict PFS than the American Joint Committee on Cancer (AJCC) Tumor Node Metastasis (TNM) staging system. The ROC curves showed the favorable sensitivity and specificity of the novel nomogram.

Conclusion

High stromal score was significantly associated with improved PFS in patients with PTC. The nomogram based on the stromal score and clinicopathological patterns yielded a reliable performance to predict the prognosis of PTC.

Characterization of ferroptosis signature to evaluate the predict prognosis and immunotherapy in glioblastoma

BACKGROUND

Glioblastoma (GBM) is the most common type of brain cancer with poor survival outcomes and unsatisfactory response to current therapeutic strategies. Recent studies have demonstrated that ferroptosis-related genes (FRGs) are linked with the occurrence and development of GBM and may become promising biological indicators in GBM therapy.

METHODS

We systematically assessed the relationship between FRGs expression profiles and prognosis in glioma patients based on the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets to establish a risk score model according to the gene signature of multiple survival-associated DEGs. Further, the differences between the tumor microenvironment score, immune cell infiltration, immune checkpoint expression levels, and drug sensitivity in the high- and low-risk group are analyzed through a variety of algorithms in R software.

RESULTS

GBM patients were divided into two subgroups (high- and low-risk) according to the established risk score model. Patients in the high-risk group showed significantly reduced overall survival compared with those in the low-risk group. Also, we found that the high-risk group showed higher ImmuneScore and StromalScore, while different subgroups have significant differences in immune cell infiltration, immune checkpoint expression levels, and drug sensitivity. In summary, we developed and validated an FRGs risk model, which served as an independent prognostic indicator for GBM. Besides, the two subgroups divided by the model have significant differences, which provides novel insights for further studies as well as the personalized treatment of patients.

Revisiting CCL-type chemokines in breast cancer and its milieu: prominent targetable chemokines, CCL8 and CCL21

The patterns of chemokine expression play a decisive role in both breast cancer prognosis and metastasis. In a recent article published in Bioscience Reports, ‘Bioinformatics identification of CCL8/21 as potential prognostic biomarkers in breast cancer microenvironment’, Chen et al. presented that expression of both CCL8 and CCL21 among CCL-type chemokines is prominent for prognosis of the breast cancer, metastasis and chemoresistance (Biosci Rep (2020) 40(11); DOI: 10.1042/BSR20202042). Identifying the sources of the CCL8 and CCL21 in the tumor microenvironment and developing targeting strategies for these chemokines to prevent tumor growth will improve both prognosis and therapeutic outcomes.

Highly heterogeneous-related genes of triple-negative breast cancer: potential diagnostic and prognostic biomarkers

Background

Triple-negative breast cancer (TNBC) is a highly heterogeneous subtype of breast cancer, showing aggressive clinical behaviors and poor outcomes. It urgently needs new therapeutic strategies to improve the prognosis of TNBC. Bioinformatics analyses have been widely used to identify potential biomarkers for facilitating TNBC diagnosis and management.

Methods

We identified potential biomarkers and analyzed their diagnostic and prognostic values using bioinformatics approaches. Including differential expression gene (DEG) analysis, Receiver Operating Characteristic (ROC) curve analysis, functional enrichment analysis, Protein-Protein Interaction (PPI) network construction, survival analysis, multivariate Cox regression analysis, and Non-negative Matrix Factorization (NMF).

Results

A total of 105 DEGs were identified between TNBC and other breast cancer subtypes, which were regarded as heterogeneous-related genes. Subsequently, the KEGG enrichment analysis showed that these genes were significantly enriched in ‘cell cycle’ and ‘oocyte meiosis’ related pathways. Four (FAM83B, KITLG, CFD and RBM24) of 105 genes were identified as prognostic signatures in the disease-free interval (DFI) of TNBC patients, as for progression-free interval (PFI), five genes (FAM83B, EXO1, S100B, TYMS and CFD) were obtained. Time-dependent ROC analysis indicated that the multivariate Cox regression models, which were constructed based on these genes, had great predictive performances. Finally, the survival analysis of TNBC subtypes (mesenchymal stem-like [MSL] and mesenchymal [MES]) suggested that FAM83B significantly affected the prognosis of patients.

Conclusions

The multivariate Cox regression models constructed from four heterogeneous-related genes (FAM83B, KITLG, RBM24 and S100B) showed great prediction performance for TNBC patients’ prognostic. Moreover, FAM83B was an important prognostic feature in several TNBC subtypes (MSL and MES). Our findings provided new biomarkers to facilitate the targeted therapies of TNBC and TNBC subtypes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08318-1.

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.

Mining immune-related genes with prognostic value in the tumor microenvironment of breast invasive ductal carcinoma

ABSTRACT

The tumor microenvironment (TME) plays an important role in the development of breast cancer. Due to limitations in experimental conditions, the molecular mechanism of TME in breast cancer has not yet been elucidated. With the development of bioinformatics, the study of TME has become convenient and reliable.Gene expression and clinical feature data were downloaded from The Cancer Genome Atlas database and the Molecular Taxonomy of Breast Cancer International Consortium database. Immune scores and stromal scores were calculated using the Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data algorithm. The interaction of genes was examined with protein-protein interaction and co-expression analysis. The function of genes was analyzed by gene ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes analysis and gene set enrichment analysis. The clinical significance of genes was assessed with Kaplan-Meier analysis and univariate/multivariate Cox regression analysis.Our results showed that the immune scores and stromal scores of breast invasive ductal carcinoma (IDC) were significantly lower than those of invasive lobular carcinoma. The immune scores were significantly related to overall survival of breast IDC patients and both the immune and stromal scores were significantly related to clinical features of these patients. According to the level of immune/stromal scores, 179 common differentially expressed genes and 5 hub genes with prognostic value were identified. In addition, the clinical significance of the hub genes was validated with data from the molecular taxonomy of breast cancer international consortium database, and gene set enrichment analysis analysis showed that these hub genes were mainly enriched in signaling pathways of the immune system and breast cancer.We identified five immune-related hub genes with prognostic value in the TME of breast IDC, which may partly determine the prognosis of breast cancer and provide some direction for development of targeted treatments in the future.

Tumor Microenvironmental Competitive Endogenous RNA Network and Immune Cells Act as Robust Prognostic Predictor of Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is malignant hematologic tumors with frequent recurrence and cause high mortality. Its fate is determined by abnormal intracellular competitive endogenous RNA (ceRNA) network and extracellular tumor microenvironment (TME). This study aims to build a ceRNA network related to AML TME to explore new prognostic and therapeutic targets. The RNA expression data of AML were obtained from The Cancer Genome Atlas (TCGA) database. First, we used the ESTIMATE algorithm to calculate the immune cells and stromal cells infiltration scores in the TME and found that all scores were highly correlated with AML’s prognostic characteristics. Subsequently, differentially expressed mRNAs and lncRNAs between high and low score groups were identified to construct a TME-related ceRNA network. Further, the Cox-lasso survival model was employed to screen out the hub prognostic ceRNA network composed of two mRNAs (EPB41L3, COL2A1), three miRNAs (hsa-mir-26a-5p, hsa-mir-148b-3p, hsa-mir-148a-3p), and two lncRNAs (CYP1B1-AS1, C9orf106), and construct nomograms. Finally, we used CIBERSORT algorithm and Kaplan-Meier survival analysis to identify the prognostic TME immune cells and found that naive B cells, M2-type macrophages, and helper follicular T cells were related to prognosis, and the hub ceRNAs were highly correlated with immune cell infiltration. This study provided a new perspective to elucidate how TME regulates AML process and put forward the new therapy strategies combining targeting tumor cells with disintegrating TME.

Development of a four-gene prognostic model for clear cell renal cell carcinoma based on transcriptome analysis

This study aimed to develop a prognostic model for clear cell renal cell carcinoma (ccRCC) based on transcriptome analysis. We screened Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database for gene expression data and clinical characteristics of ccRCC. After differentially expression analysis, we identified 533 key genes of the development of ccRCC. Next, a weighted gene co-expression network analysis (WGCNA) was executed to investigate the association between differentially expressed genes and clinical characteristics. Then, based on protein-protein interaction (PPI) network, least absolute shrinkage and selection operator (LASSO) regression and Cox regression, a four-gene (COL4A5, ABCB1, NR3C2 and PLG) prognostic model has been constructed in TCGA training cohort. Finally, we examined the predictive power of this model with survival analysis and receiver operating characteristic (ROC) curve both in training cohort and in validation cohorts. And we found this model was significantly correlated with infiltrating immune cells. The four-gene prognosis model could be a potential prediction tool with high accuracy and reliability for ccRCC patients.

Profiles of immune cell infiltration and immune-related genes in the tumor microenvironment of esophageal squamous cell carcinoma

BACKGROUND

As a complex system participating in tumor development and progression, the tumor microenvironment was poorly understood in esophageal cancer especially squamous cell carcinoma (ESCC).

METHODS

ESTIMATE algorithm is used to investigate tumor-infiltrating immune cells and prognostic genes which were associated with the tumor microenvironment in ESCC.

RESULTS

Based on the immune and stromal scores, ESCC samples were divided into high and low score groups and 299 overlapping differentially expressed genes were identified. Functional enrichment analysis showed that these genes were mainly involved in muscle-related function. Prognostic genes including COL9A3, GFRA2, and VSIG4 were used to establish a risk prediction model using Cox regression analyses. Then multivariate analysis showed that COL9A3 was an independent discriminator of a better prognosis. Kaplan-Meier survival analysis showed that the expression of COL9A3 was significantly correlated with the overall survival of ESCC patients. The area under the curve for the risk model in predicting 1- and 3- year survival rates were 0.660 and 0.942, respectively. The risk score was negatively correlated with plasma cells, while positively correlated with the proportions of activated CD4 memory T cells, M1 Macrophages and M2 Macrophages (p < 0.001 for each comparison). Gene set enrichment analysis suggested that both immune response and immune system process gene sets were significantly enriched in high-risk group.

CONCLUSIONS

Our study provided a comprehensive understanding of the TME in ESCC patients. The establishment of the risk model is valuable for the early identification of high-risk patients to facilitate individualized treatment and improve the possibility of immunotherapy response.

The aging-related risk signature in colorectal cancer

Background: Colorectal cancer (CRC) is the third most common cancer worldwide. The opening of the TCGA and GEO databases has promoted the progress of CRC prognostic assessment, while the aging-related risk signature has never been mentioned.

Methods: R software packages, GSEA software, Venn diagram, Metascape, STRING, Cytoscape, cBioPortal, TIMER and GeneMANIA website were used in this study.

Results: Aging-related gene sets, GO_AGING, GO_CELL_AGING and GO_CELLULAR_SENESCENCE, were activated significantly in CRC tissues. We constructed an aging-related risk signature using LASSO COX regression in training group TCGA and validated in testing group GSE39582. The risk score was significantly associated with the overall survival of CRC patients, whose stability was clarified by stratified survival analysis and accuracy was demonstrated using the ROC curve. The risk score was significantly increased in the advanced stage, T3-4, N1-3 and M1 and positively correlated with the richness of immune cell infiltration in the tumor microenvironment. We further investigated the molecular characteristics of 15 hub genes at the DNA and protein levels and performed GSEA between high- and low-risk groups.

Conclusions: The aging-related signature is a reliable prognostic analysis model and can predict the severity and immune cell infiltration of CRC patients.

Identification of Tumor Microenvironment-Related Prognostic Genes in Sarcoma

Aim

Immune cells that infiltrate the tumor microenvironment (TME) are associated with cancer prognosis. The aim of the current study was to identify TME related gene signatures related to the prognosis of sarcoma (SARC) by using the data from The Cancer Genome Atlas (TCGA).

Methods

Immune and stromal scores were calculated by estimation of stromal and immune cells in malignant tumor tissues using expression data algorithms. The least absolute shrinkage and selection operator (lasso) based cox model was then used to select hub survival genes. A risk score model and nomogram were used to predict the overall survival of patients with SARC.

Results

We selected 255 patients with SARC for our analysis. The Kaplan–Meier method found that higher immune (p = 0.0018) or stromal scores (p = 0.0022) were associated with better prognosis of SARC. The estimated levels of CD4+ (p = 0.0012) and CD8+ T cells (p = 0.017) via the tumor immune estimation resource were higher in patients with SARC with better overall survival. We identified 393 upregulated genes and 108 downregulated genes (p < 0.05, fold change >4) intersecting between the immune and stromal scores based on differentially expressed gene (DEG) analysis. The univariate Cox analysis of each intersecting DEG and subsequent lasso-based Cox model identified 11 hub survival genes (MYOC, NNAT, MEDAG, TNFSF14, MYH11, NRXN1, P2RY13, CXCR3, IGLV3-25, IGHV1-46, and IGLV2-8). Then, a hub survival gene-based risk score gene signature was constructed; higher risk scores predicted worse SARC prognosis (p < 0.0001). A nomogram including the risk scores, immune/stromal scores and clinical factors showed a good prediction value for SARC overall survival (C-index = 0.716). Finally, connectivity mapping analysis identified that the histone deacetylase inhibitors trichostatin A and vorinostat might have the potential to reverse the harmful TME for patients with SARC.

Conclusion

The current study provided new indications for the association between the TME and SARC. Lists of TME related survival genes and potential therapeutic drugs were identified for SARC.

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

The unprecedented proliferation of recent large-scale and multi-omics databases of cancers has given us many new insights into genomic and epigenomic deregulation in cancer discovery in general. However, we wonder whether or not there exists a systematic connection between copy number aberrations (CNA) and methylation (MET)? If so, what is the role of this connection in breast cancer (BRCA) tumorigenesis and progression? At the same time, the PAM50 intrinsic subtypes of BRCA have gained the most attention from BRCA experts. However, this classification system manifests its weaknesses including low accuracy as well as a possible lack of association with biological phenotypes, and even further investigations on their clinical utility were still needed. In this study, we performed an integrative analysis of three-omics profiles, CNA, MET, and mRNA expression, in two BRCA patient cohorts (one for discovery and another for validation) – to elucidate those complicated relationships. To this purpose, we first established a set of CNAcor and METcor genes, which had CNA and MET levels significantly correlated (and anti-correlated) with their corresponding expression levels, respectively. Next, to revisit the current classification of BRCA, we performed single and integrated clustering analyses using our clustering method PINSPlus. We then discovered two biologically distinct subgroups that could be an improved and refined classification system for breast cancer patients, which can be validated by a third-party data. Further studies were then performed and realized each-subgroup-specific genes and different interactions between each of the two identified subgroups with the age factor. These findings can show promise as diagnostic and prognostic values in BRCA, and a potential alternative to the PAM50 intrinsic subtypes in the future.

The nucleosome remodeling and deacetylase complex has prognostic significance and associates with immune microenvironment in skin cutaneous melanoma

PURPOSE

The Nucleosome Remodeling and Deacetylase (NuRD) complex is an important marker in multiple biological processes whose clinical significance has rarely previously been reported in cancers. In this study, we proposed to estimate the potential of NuRD complex as prognostic signature in skin cutaneous melanoma (SKCM) patients.

METHODS

SKCM samples were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Sample clustering was performed based on the mRNA levels of core subunits of NuRD complex. Survival analysis was carried out by using Kaplan-Meier method. SKCM samples were grouped into prognostically good and poor groups according to their overall survival (OS). Logistic regression analysis was adopted to construct a model based on the optimal subunits of NuRD complex to estimate prognosis of SKCM samples.

RESULTS

Samples from TCGA were grouped into four clusters which were then divided into good and poor prognostic groups. Significant differences existed in immune microenvironment and mutational rates of frequently mutated genes between good and poor prognostic groups. Besides, several immune-related pathways were significantly activated in good prognostic group. A logistic regression model was constructed by using patients' prognostic group and mRNA expressions of NuRD complex from TCGA as categorical responsive values and continuous predictive variables, respectively, which could independently distinguish prognostically different SKCM patients from another three independent GEO datasets.

CONCLUSION

In conclusion, we first reported the potential prognostic value and roles in immune microenvironment of NuRD complex in SKCM, which should be helpful for experimental and clinical research in SKCM.

Identification of Immune-Related Prognostic Biomarkers Based on the Tumor Microenvironment in 20 Malignant Tumor Types With Poor Prognosis

Cancer, especially malignant tumors with poor prognosis, has become a major hazard to human life and health. The tumor microenvironment is gaining increasing attention from researchers, as it offers a new focus for tumor diagnosis, therapy, and prognosis. The numbers of immune and stromal cells, which are major components of the tumor microenvironment, could be determined from RNA-seq data with the Estimation of STromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. To explore the effects of immune and stromal cells on tumor prognosis, we analyzed associations between overall survival and immune/stromal scores for 20 malignant tumor types based on The Cancer Genome Atlas (TCGA) data. For six of the 20 tumor types, we observed statistically significant associations. Furthermore, to better explain the predictive ability of these scores, differentially expressed genes (DEGs) were identified in groups of cases with high or low immune or stromal scores for each of these six malignant tumor types. In addition, a list of immune-related genes was screened to identify prognostic predictors for one or more tumor types. Thus, multi-database joint analysis can provide a new approach to the assessment of tumor prognosis and allow the identification of related genes that may be new biomarkers for tumor metastasis and prognosis.