Development of an Immune-Related Prognostic Signature in Breast Cancer

Prognostic implication of novel immune-related signature in breast cancer

Checkpoint inhibitor therapy has become increasingly important and has been endorsed as a treatment regimen in breast cancer. But benefits were limited to a small proportion of patients. We aimed to develop an improved signature on the basis of immune genes for detection of potential benefit from immunotherapy. Gene expression data of patients with breast cancer initially extracted from The Cancer Genome Atlas were analyzed. Ten genes were selected from the interaction of differentially expressed genes as well as immune-related genes to develop a survival signature. We compared the high-risk and low-risk groups by gene set enrichment analysis, immune infiltration, checkpoint molecule expression and immunophenoscore. Ten genes were extracted from interactions of differentially expressed and immune-related genes. The immune risk score was determined on the basis of the Cox regression coefficient of hub genes and validated with the GSE96058 dataset. Immune cell infiltrates, including CD8 + T cells, plasma cells, follicular helper T cells, CD4 + memory T cells, M1 macrophages, regulatory T cells and resting NK cells, were more highly infiltrated in the high-risk group as compared to the low-risk group. Checkpoint molecules, including CTLA-4, PD-L1, TIM-3, VISTA, ICOS, PD-1, and PD-L2, were expressed at markedly lower levels in the high-risk group as compared to the low-risk group. Immunophenoscores, as a surrogate of response to immune checkpoint therapy, was observed significant lower in the high-risk group. The 10-gene prognostic signature could identify patients' survival and was correlated with the biomarkers of immune checkpoint inhibitor therapy, which may guide precise therapeutic decisions in clinical practice.

Immune gene signatures as prognostic criteria for cancer patients

Recently, the possibility of using immune gene signatures (IGSs) has been considered as a novel prognostic tool for numerous cancer types. State-of-the-art methods of genomic, transcriptomic, and protein analysis have allowed the identification of a number of immune signatures correlated to disease outcome. The major adaptive and innate immune components are the T lymphocytes and macrophages, respectively. Herein, we collected essential data on IGSs consisting of subsets of T cells and tumor-associated macrophages and indicating cancer patient outcomes. We discuss factors that can introduce errors in the recognition of immune cell types and explain why the significance of immune signatures can be interpreted with uncertainty. The unidirectional functions of cell types should be entirely addressed in the signatures constructed by the combination of innate and adaptive immune cells. The state of the antitumor immune response is the key basis for IGSs and should be considered in gene signature construction. We also analyzed immune signatures for the prediction of immunotherapy response. Finally, we attempted to explain the present-day limitations in the use of immune signatures as robust criteria for prognosis.

New Biomarkers Based on Dendritic Cells for Breast Cancer Treatment and Prognosis Diagnosis

Dendritic cells(DCs) play a protective role in the antitumor immunity of most cancers, which can be divided into conventional dendritic cells (cDCs) and plasmacytoid dendritic cells (pDCs). Most current studies are only based on either cDCs or pDCs for the study of the relationship between DCs and breast cancer prognosis, without combining the two together. We aimed to select new biomarkers from pDCs and cDCs. In this paper, the xCell algorithm was first used to calculate the cellular abundance of 64 types of immune cells and stromal cells in tumor samples from the TCGA database, and the high-abundance pDC group and cDC group were divided according to the results of a survival analysis. Then, we looked for the co-expressed gene module of highly infiltrating pDC and cDC patients with a weighted correlation network analysis (WGCNA) and screened out the hub genes, including RBBP5, HNRNPU, PEX19, TPR, and BCL9. Finally, we analyzed the biological functions of the hub genes, and the results showed that RBBP5, TPR, and BCL9 were significantly related to the immune cells and prognosis of patients, and RBBP5 and BCL9 were involved in responding to TCF-related instructions of the Wnt pathway. In addition, we also evaluated the response of pDCs and cDCs with different abundances to chemotherapy, and the results showed that the higher the abundance of pDCs and cDCs, the higher their sensitivity to drugs. This paper revealed new biomarkers related to DCs-among them, BCL9, TPR, and RBBP5 were proven to be closely related to dendritic cells in cancer. For the first time, this paper puts forward that HNRNPU and PEX19 are related to the prognosis of dendritic cells in cancer, which also provides new possibilities for finding new targets for breast cancer immunotherapy.

The prognostic value and response to immunotherapy of immunogenic cell death-associated genes in breast cancer

Breast cancer (BRCA) remains the most prevalent cancer worldwide and the tumor microenvironment (TME) has been discovered to exert a wide influence on the overall survival and therapeutic response. Numerous lines of evidence reported that the effects of immunotherapy of BRCA were manipulated by TME. Immunogenic cell death (ICD) is a form of regulated cell death (RCD) that is capable of fueling adaptive immune responses and aberrant expression of ICD-related genes (ICDRGs) can govern the TME system by emitting danger signals or damage-associated molecular patterns (DAMPs). In the current study, we obtained 34 key ICDRGs in BRCA. Subsequently, using the transcriptome data of BRCA from the TCGA database, we constructed a risk signature based on 6 vital ICDRGs, which had a good performance in predicting the overall survival of BRCA patients. We also examined the efficacy of our risk signature in the validation dataset (GSE20711) in the GEO database and it performed excellently. According to the risk model, patients with BRCA were divided into high-risk and low-risk groups. Also, the unique immune characteristics and TME between the two subgroups and 10 promising small molecule drugs targeting BRCA patients with different ICDRGs risk have been investigated. The low-risk group had good immunity indicated by T cell infiltration and high immune checkpoint expression. Moreover, the BRCA samples could be divided into three immune subtypes according to immune response severity (ISA, ISB, and ISC). ISA and ISB predominated in the low-risk group and patients in the low-risk group exhibited a more vigorous immune response. In conclusion, we developed an ICDRGs-based risk signature that can predict the prognosis of BRCA patients and offer a novel therapeutic strategy for immunotherapy, which would be of great significance in the BRCA clinical setting.

FabNet: A Features Agglomeration-Based Convolutional Neural Network for Multiscale Breast Cancer Histopathology Images Classification

The definitive diagnosis of histology specimen images is largely based on the radiologist's comprehensive experience; however, due to the fine to the coarse visual appearance of such images, experts often disagree with their assessments. Sophisticated deep learning approaches can help to automate the diagnosis process of the images and reduce the analysis duration. More efficient and accurate automated systems can also increase the diagnostic impartiality by reducing the difference between the operators. We propose a FabNet model that can learn the fine-to-coarse structural and textural features of multi-scale histopathological images by using accretive network architecture that agglomerate hierarchical feature maps to acquire significant classification accuracy. We expand on a contemporary design by incorporating deep and close integration to finely combine features across layers. Our deep layer accretive model structure combines the feature hierarchy in an iterative and hierarchically manner that infers higher accuracy and fewer parameters. The FabNet can identify malignant tumors from images and patches from histopathology images. We assessed the efficiency of our suggested model standard cancer datasets, which included breast cancer as well as colon cancer histopathology images. Our proposed avant garde model significantly outperforms existing state-of-the-art models in respect of the accuracy, F1 score, precision, and sensitivity, with fewer parameters.

A signature of immune-related genes correlating with clinical prognosis and immune microenvironment in sepsis

BACKGROUND

Immune-related genes (IRGs) remain poorly understood in their function in the onset and progression of sepsis.

METHODS

GSE65682 was obtained from the Gene Expression Omnibus database. The IRGs associated with survival were screened for subsequent modeling using univariate Cox regression analysis and least absolute shrinkage and selection operator in the training cohort. Then, we assessed the reliability of the 7 IRGs signature's independent predictive value in the training and validation cohorts following the creation of a signature applying multivariable Cox regression analysis. After that, we utilized the E-MTAB-4451 external dataset in order to do an independent validation of the prognostic signature. Finally, the CIBERSORT algorithm and single-sample gene set enrichment analysis was utilized to investigate and characterize the properties of the immune microenvironment.

RESULTS

Based on 7 IRGs signature, patients could be separated into low-risk and high-risk groups. Patients in the low-risk group had a remarkably increased 28-day survival compared to those in the high-risk group (P < 0.001). In multivariable Cox regression analyses, the risk score calculated by this signature was an independent predictor of 28-day survival (P < 0.001). The signature's predictive ability was confirmed by receiver operating characteristic curve analysis with the area under the curve reaching 0.876 (95% confidence interval 0.793-0.946). Moreover, both the validation set and the external dataset demonstrated that the signature had strong clinical prediction performance. In addition, patients in the high-risk group were characterized by a decreased neutrophil count and by reduced inflammation-promoting function.

CONCLUSION

We developed a 7 IRGs signature as a novel prognostic marker for predicting sepsis patients' 28-day survival, indicating possibilities for individualized reasonable resource distribution of intensive care unit.

Identification and validation of a novel 16-gene prognostic signature for patients with breast cancer

Despite increased early diagnosis and improved treatment in breast cancer (BRCA) patients, prognosis prediction is still a challenging task due to the disease heterogeneity. This study was to identify a novel gene signature that can accurately evaluate BRCA patient survival. The gene expression and clinical data of BRCA patients were collected from The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of BRCA International Consortium (METABRIC) databases. Genes associated with prognosis were determined by Kaplan–Meier survival analysis and multivariate Cox regression analysis. A prognostic 16-gene score was established with linear combination of 16 genes. The prognostic value of the signature was validated in the METABRIC and GSE202203 datasets. Gene expression analysis was performed to investigate the diagnostic values of 16 genes. The 16-gene score was associated with shortened overall survival in BRCA patients independently of clinicopathological characteristics. The signalling pathways of cell cycle, oocyte meiosis, RNA degradation, progesterone mediated oocyte maturation and DNA replication were the top five most enriched pathways in the high 16-gene score group. The 16-gene nomogram incorporating the survival‐related clinical factors showed improved prediction accuracies for 1-year, 3-year and 5‐year survival (area under curve [AUC] = 0.91, 0.79 and 0.77 respectively). MORN3, IGJ, DERL1 exhibited high accuracy in differentiating BRCA tissues from normal breast tissues (AUC > 0.80 for all cases). The 16-gene profile provides novel insights into the identification of BRCA with a high risk of death, which eventually guides treatment decision making.

A CD8+ T Cell-Related Genes Expression Signature Predicts Prognosis and the Efficacy of Immunotherapy in Breast Cancer

Immunotherapy has been applied to patients with breast cancer. However, only part of patients benefits from the current immunotherapy. Accurate prediction of individual response to immunotherapy can be beneficial for breast cancer management. CD8+ T cells are the main force of anti-tumor immunity. This study aimed to establish a CD8+ T cell-related gene expression signature for prediction of breast cancer prognostic and immunotherapy efficacy. RNA-seq transcriptomic data was the basics of this research. Weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis established the prognostic signature. We identified 290 CD8+ T cell-related genes in the training set and established a risk-score model based on 8-genes panel (SOCS1, IL10, CAMK4, CXCL13, KIR2DS4, TESPA1, CD70 and ICAM4). Subsequently, univariate Cox regression analysis suggested that high risk-score was a risk factor for breast cancer (HR = 3.1, 95%CI 2.0-4.8, P < 0.001). In tumor microenvironment, high-risk tumors present decreased tumor infiltrating CD8+ T cells and increased M2 macrophages. The low-risk patients may benefit more from immune checkpoint blockade immunotherapy than the high-risk patients. Moreover, breast tumors which sensitive to immune checkpoint inhibitor (ICI) showed higher IL10 expression.

A Tumor Suppressor Gene-Based Prognostic Classifier Predicts Prognosis, Tumor Immune Infiltration, and Small Molecule Compounds in Breast Cancer

Objective: Tumor suppressor genes (TSGs) play critical roles in the cell cycle checkpoints and in modulating genomic stability. Here, we aimed to develop a TSG-based prognostic classifier for breast cancer.

Methods: Gene expression profiles and clinical information of breast cancer were curated from TCGA (discovery set) and Gene Expression Omnibus (GEO) repository (GSE12093 and GSE17705 datasets as testing sets). Univariate cox regression analysis and random forest machine learning method were presented for screening characteristic TSGs. After multivariate cox regression analyses, a TSG-based prognostic classifier was constructed. The predictive efficacy was verified by C-index and receiver operating characteristic (ROC) curves. Meanwhile, the predictive independency was assessed through uni- and multivariate cox regression analyses and stratified analyses. Tumor immune infiltration was estimated via ESTIMATE and CIBERSORT algorithms. Small molecule agents were predicted through CMap method. Molecular subtypes were clustered based on the top 100 TSGs with the most variance.

Results: A prognostic classifier including nine TSGs was established. High-risk patients were predictive of undesirable prognosis. C-index and ROC curves demonstrated its excellent predictive performance in prognosis. Also, this prognostic classifier was independent of conventional clinicopathological parameters. Low-risk patients exhibited increased infiltration levels of immune cells like T cells CD8. Totally, 48 small molecule compounds were predicted to potentially treat breast cancer. Five TSG-based molecular subtypes were finally constructed, with distinct prognosis and clinicopathological features.

Conclusion: Collectively, this study provided a TSG-based prognostic classifier with the potential to predict clinical outcomes and immune infiltration in breast cancer and identified potential small molecule agents against breast cancer.

Establishment and Analysis of an Individualized Immune-Related Gene Signature for the Prognosis of Gastric Cancer

A growing number of studies have shown that immunity plays an important clinical role in the process of gastric cancer (GC). The purpose of this study was to explore the function of differentially expressed immune-related genes (DEIRGs) of GC, and construct a gene signature to predict the overall survival (OS) of patients. Gene expression profiles and clinical data of GC patients were downloaded from TCGA and GEO databases. Combined with immune-related genes (IRGs) downloaded from the ImmPort database, 357 DEIRGs in GC tissues and adjacent tissues were identified. Based on the analysis of Lasso and Cox in the training set, a prognostic risk scoring model consisting of 9 (RBP7, DES, CCR1, PNOC, SPP1, VIP, TNFRSF12A, TUBB3, PRKCG) DEIRGs was obtained. Functional analysis revealed that model genes may participate in the formation and development of tumor cells by affecting the function of cell gap junction intercellular communication (GJJC). According to the model score, the samples were divided into high-risk and low-risk groups. In multivariate Cox regression analysis, the risk score was an independent prognostic factor (HR = 1.674, 95% CI = 1.470–1.907, P &lt; 0.001). Survival analysis showed that the OS of high-risk GC patients was significantly lower than that of low-risk GC patients (P &lt; 0.001). The area under the receiver operating characteristic curve (ROC) of the model was greater than other clinical indicators when verified in various data sets, confirming that the prediction model has a reliable accuracy. In conclusion, this study has explored the biological functions of DEIRGs in GC and discovered novel gene targets for the treatment of GC. The constructed prognostic gene signature is helpful for clinicians to determine the prognosis of GC patients and formulate personalized treatment plans.

Construction and Identification of a Novel 5-Gene Signature for Predicting the Prognosis in Breast Cancer

Background: Breast cancer is one of the most common malignancies in women worldwide. The purpose of this study was to identify the hub genes and construct prognostic signature that could predict the survival of patients with breast cancer (BC).

Methods: We identified differentially expressed genes between the responder group and non-responder group based on the GEO cohort. Drug-resistance hub genes were identified by weighted gene co-expression network analysis, and a multigene risk model was constructed by univariate and multivariate Cox regression analysis based on the TCGA cohort. Immune cell infiltration and mutation characteristics were analyzed.

Results: A 5-gene signature (GP6, MAK, DCTN2, TMEM156, and FKBP14) was constructed as a prognostic risk model. The 5-gene signature demonstrated favorable prediction performance in different cohorts, and it has been confirmed that the signature was an independent risk indicater. The nomogram comprising 5-gene signature showed better performance compared with other clinical features, Further, in the high-risk group, high M2 macrophage scores were related with bad prognosis, and the frequency of TP53 mutations was greater in the high-risk group than in the low-risk group. In the low-risk group, high CD8+ T cell scores were associated with a good prognosis, and the frequency of CDH1 mutations was greater in the low-risk group than that in the high-risk group. At the same time, patients in the low risk group have a good response to immunotherapy in terms of immunotherapy. The results of immunohistochemistry showed that MAK, GP6, and TEMEM156 were significantly highly expressed in tumor tissues, and DCTN2 was highly expressed in normal tissues.

Conclusions: Our study may find potential new targets against breast cancer, and provide new insight into the underlying mechanisms.

Xanthine dehydrogenase as a prognostic biomarker related to tumor immunology in hepatocellular carcinoma

Background

Xanthine dehydrogenase (XDH) is a critical enzyme involved in the oxidative metabolism of purines, pterin and aldehydes and a central component of the innate immune system. However, the prognostic value of XDH in predicting tumor-infiltrating lymphocyte abundance, the immune response, and survival in different cancers, including hepatocellular carcinoma (HCC), is still unclear.

Methods

XDH expression was analyzed in multiple databases, including Oncomine, the Tumor Immune Estimation Resource (TIMER), the Kaplan–Meier plotter database, the Gene Expression Profiling Interactive Analysis (GEPIA) database, and The Cancer Genome Atlas (TCGA). XDH-associated transcriptional profiles were detected with an mRNA array, and the levels of infiltrating immune cells were validated by immunohistochemistry (IHC) of HCC tissues. A predictive signature containing multiple XDH-associated immune genes was established using the Cox regression model.

Results

Decreased XDH mRNA expression was detected in human cancers originating from the liver, bladder, breast, colon, bile duct, kidney, and hematolymphoid system. The prognostic potential of XDH mRNA expression was also significant in certain other cancers, including HCC, breast cancer, kidney or bladder carcinoma, gastric cancer, mesothelioma, lung cancer, and ovarian cancer. In HCC, a low XDH mRNA level predicted poorer overall survival, disease-specific survival, disease-free survival, and progression-free survival. The prognostic value of XDH was independent of the clinical features of HCC patients. Indeed, XDH expression in HCC activated several immune-related pathways, including the T cell receptor, PI3K-AKT, and MAPK signaling pathways, which induced a cytotoxic immune response. Importantly, the microenvironment of XDH^high HCC tumors contained abundant infiltrating CD8 + T cells but not exhausted T cells. A risk prediction signature based on multiple XDH-associated immune genes was revealed as an independent predictor in the TCGA liver cancer cohort.

Conclusion

These findings suggest that XDH is a valuable prognostic biomarker in HCC and other cancers and indicate that it may function in tumor immunology. Loss of XDH expression may be an immune evasion mechanism for HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02173-7.

Identification and Validation of a Five-Gene Signature Associated With Overall Survival in Breast Cancer Patients

BACKGROUND

Recent years, the global prevalence of breast cancer (BC) was still high and the underlying molecular mechanisms remained largely unknown. The investigation of prognosis-related biomarkers had become an urgent demand.

RESULTS

In this study, gene expression profiles and clinical information of breast cancer patients were downloaded from the TCGA database. The differentially expressed genes (DEGs) were estimated by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A risk score formula involving five novel prognostic associated biomarkers (EDN2, CLEC3B, SV2C, WT1, and MUC2) were then constructed by LASSO. The prognostic value of the risk model was further confirmed in the TCGA entire cohort and an independent external validation cohort. To explore the biological functions of the selected genes, in vitro assays were performed, indicating that these novel biomarkers could markedly influence breast cancer progression.

CONCLUSIONS

We established a predictive five-gene signature, which could be helpful for a personalized management in breast cancer patients.

Establishment of Immune-related Gene Pair Signature to Predict Lung Adenocarcinoma Prognosis

Tumor microenvironment (TME) has critical impacts on the pathogenesis of lung adenocarcinoma (LUAD). However, the molecular mechanism of TME effects on the prognosis of LUAD patients remains unclear. Our study aimed to establish an immune-related gene pair (IRGP) model for prognosis prediction and internal mechanism investigation. Based on 702 TME-related differentially expressed genes (DEGs) extracted from The Cancer Genome Atlas (TCGA) training cohort using the ESTIMATE algorithm, a 10-IRGP signature was established to predict LUAD patient prognosis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that DEGs were significantly associated with tumor immune response. In both TCGA training and Gene Expression Omnibus validation datasets, the risk score was an independent prognostic factor for LUAD patients using Lasso-Cox analysis, and patients in the high-risk group had poorer prognosis than those in the low-risk one. In the high-risk group, M2 macrophage and neutrophil infiltrations were higher, while the levels of T cell follicular helpers were significantly lower. The gene set enrichment analysis results showed that DNA repair signaling pathways were involved. In summary, we established an IRGP signature as a potential biomarker to predict the prognosis of LUAD patients.

Novel immune-related genes in the tumor microenvironment with prognostic value in breast cancer

Background

Breast cancer is one of the most frequently diagnosed cancers among women worldwide. Alterations in the tumor microenvironment (TME) have been increasingly recognized as key in the development and progression of breast cancer in recent years. To deeply comprehend the gene expression profiling of the TME and identify immunological targets, as well as determine the relationship between gene expression and different prognoses is highly critical.

Methods

The stromal/immune scores of breast cancer patients from The Cancer Genome Atlas (TCGA) were employed to comprehensively evaluate the TME. Then, TME characteristics were assessed, overlapping genes of the top 3 Gene Ontology (GO) terms and upregulated differentially expressed genes (DEGs) were analyzed. Finally, through combined analyses of overall survival, time-dependent receiver operating characteristic (ROC), and protein-protein interaction (PPI) network, novel immune related genes with good prognosis were screened and validated in both TCGA and GEO database.

Results

Although the TME did not correlate with the stages of breast cancer, it was closely associated with the subtypes of breast cancer and gene mutations (CDH1, TP53 and PTEN), and had immunological characteristics. Based on GO functional enrichment analysis, the upregulated genes from the high vs low immune score groups were mainly involved in T cell activation, the external side of the plasma membrane, and receptor ligand activity. The top GO terms of the upregulated DEGs from the high vs low immune score groups exhibited better prognosis in breast cancer; 15 of them were related to good prognosis in breast cancer, especially CD226 and KLRC4-KLRK1.

Conclusions

High CD226 and KLRC4-KLRK1 expression levels were identified and validated to correlate with better overall survival in specific stages or subtypes of breast cancer. CD226, KLRC4-KLRK1 and other new targets seem to be promising avenues for promoting antitumor targeted immunotherapy in breast cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07837-1.

Identification of an Individualized Immune-Related Prognostic Risk Score in Lung Squamous Cell Cancer

Background: Lung squamous cell carcinoma (LUSC) is one of the most common histological subtypes of non-small cell lung cancer (NSCLC), and its morbidity and mortality are steadily increasing. The purpose of this study was to study the relationship between the immune-related gene (IRGs) profile and the outcome of LUSC in patients by analyzing datasets from The Cancer Genome Atlas (TCGA). Methods: We obtained publicly available LUSC RNA expression data and clinical survival data from The Cancer Genome Atlas (TCGA), and filtered IRGs based on The ImmPort database. Then, we identified risk immune-related genes (r-IRGs) for model construction using Cox regression analysis and defined the risk score in this model as the immune gene risk index (IRI). Multivariate analysis was used to verify the independent prognostic value of IRI and its association with other clinicopathological features. Pearson correlation analysis was used to explore the molecular mechanism affecting the expression of IRGs and the correlation between IRI and immune cell infiltration. Results: We screened 15 r-IRGs for constructing the risk model. The median value of IRI stratified the patients and there were significant survival differences between the two groups (p = 4.271E-06). IRI was confirmed to be an independent prognostic factor (p < 0.001) and had a close correlation with the patients' age (p < 0.05). Interestingly, the infiltration of neutrophils or dendritic cells was strongly upregulated in the high-IRI groups (p < 0.05). Furthermore, by investigating differential transcription factors (TFs) and functional enrichment analysis, we explored potential mechanisms that may affect IRGs expression in tumor cells. Conclusion: In short, this study used 15 IRGs to build an effective risk prediction model, and demonstrated the significance of IRGs-based personalized immune scores in LUSC prognosis.

Analysis of immune subtypes based on immunogenomic profiling identifies prognostic signature for cutaneous melanoma

Skin cutaneous melanoma (SKCM) is the most invasive form of skin cancer with poor prognosis. Growing evidence has demonstrated that tumor immune microenvironment plays a key contributing role in tumorigenesis and predicting clinical outcomes. The aim of this study was to recognize immune classification and a reliable prognostic signature for patients with SKCM. By using single-sample gene set enrichment (ssGSEA) and hierarchical clustering analyses, we evaluated the immune infiltration landscape of SKCM afflicted patients from The Cancer Genome Atlas (TCGA) dataset and named two SKCM subtypes: Immunity-high and Immunity-low. The Immunity-high subgroup was characterized by up-regulation of immune response and favorable survival probability. Seven candidate small molecule drugs which potentially reverse SKCM immune status were identified by using Connectivity map (CMap) database. A prognostic five-immune-associated gene (IAG) signature consisting IFITM1, TNFSF13B, APOBEC3G, CCL8 and KLRK1 with high predictive value was established using the LASSO Cox regression analysis in training set, and validated in testing set as well as Gene Expression Omnibus (GEO) external validation cohort (P < 0.05). Lower tumor purity and active immune-related signaling pathways were obviously presented in low-risk group. Furthermore, a novel composite nomogram based upon the five-IAG signature and other clinical parameters was built with excellent calibration curves. Collectively, comprehensively characterizing the SKCM subtypes based upon immune microenvironment landscape and development of a survival-related IAG signature may provide a promising avenue for improving individualized treatment design and prognosis prediction for patients with SKCM.

Prognostic model of invasive ductal carcinoma of the breast based on differentially expressed glycolysis-related genes

Background

Invasive ductal carcinoma (IDC) is a common pathological type of breast cancer that is characterized by high malignancy and rapid progression. Upregulation of glycolysis is a hallmark of tumor growth, and correlates with the progression of breast cancer. We aimed to establish a model to predict the prognosis of patients with breast IDC based on differentially expressed glycolysis-related genes (DEGRGs).

Methods

Transcriptome data and clinical data of patients with breast IDC were from The Cancer Genome Atlas (TCGA). Glycolysis-related gene sets and pathways were from the Molecular Signatures Database (MSigDB). DEGRGs were identified by comparison of tumor tissues and adjacent normal tissues. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to screen for DEGRGs with prognostic value. A risk-scoring model based on DEGRGs related to prognosis was constructed. Receiver operating characteristic (ROC) analysis and calculation of the area under the curve (AUC) were used to evaluate the performance of the model. The model was verified in different clinical subgroups using an external dataset (GSE131769). A nomogram that included clinical indicators and risk scores was established. Gene function enrichment analysis was performed, and a protein-protein interaction network was developed.

Results

We analyzed data from 772 tumors and 88 adjacent normal tissues from the TCGA database and identified 286 glycolysis-related genes from the MSigDB. There were 185 DEGRGs. Univariate Cox regression and LASSO regression indicated that 13 of these genes were related to prognosis. A risk-scoring model based on these 13 DEGRGs allowed classification of patients as high-risk or low-risk according to median score. The duration of overall survival (OS) was longer in the low-risk group (P < 0.001), and the AUC was 0.755 for 3-year OS and 0.726 for 5-year OS. The results were similar when using the GEO data set for external validation (AUC for 3-year OS: 0.731, AUC for 5-year OS: 0.728). Subgroup analysis showed there were significant differences in OS among high-risk and low-risk patients in different subgroups (T1-2, T3-4, N0, N1-3, M0, TNBC, non-TNBC; all P < 0.01). The C-index was 0.824, and the AUC was 0.842 for 3-year OS and 0.808 for 5-year OS from the nomogram. Functional enrichment analysis demonstrated the DEGRGs were mainly involved in regulating biological functions.

Conclusions

Our prognostic model, based on 13 DEGRGs, had excellent performance in predicting the survival of patients with IDC of the breast. These DEGRGs appear to have important biological functions in the progression of this cancer.

Weighted gene correlation network analysis identifies microenvironment-related genes signature as prognostic candidate for Grade II/III glioma

Glioma is the most common malignant tumor in the central nervous system. Evidence shows that clinical efficacy of immunotherapy is closely related to the tumor microenvironment. This study aims to establish a microenvironment-related genes (MRGs) model to predict the prognosis of patients with Grade II/III gliomas. Gene expression profile and clinical data of 459 patients with Grade II/III gliomas were extracted from The Cancer Genome Atlas. Then according to the immune/stromal scores generated by the ESTIMATE algorithm, the patients were scored one by one. Weighted gene co-expression network analysis (WGCNA) was used to construct a gene co-expression network to identify potential biomarkers for predicting the prognosis of patients. When adjusting clinical features including age, histology, grading, IDH status, we found that these features were independently associated with survival. The predicted value of the prognostic model was then verified in 440 samples in CGGA part B dataset and 182 samples in CGGA part C dataset by univariate and multivariate cox analysis. The clinical samples of 10 patients further confirmed our signature. Our findings suggested the eight-MRGs signature identified in this study are valuable prognostic predictors for patients with Grade II/III glioma.

Construction and validation of an immunity-related prognostic signature for breast cancer

Breast cancer is one of the most lethal malignancies among women, and understanding the effects of host immunity on disease progression offers the potential to improve immunotherapies against it. Here, we constructed an immunity-related gene (IRG)-based prognostic signature to stratify breast cancer patients and predict their survival. We identified differentially-expressed genes by analyzing the breast cancer transcriptome data from The Cancer Genome Atlas. Univariate Cox regression revealed 179 survival-correlated IRGs, 12 of which we used to construct an immunity-based prognostic signature that stratified breast cancer patients into high- and low-risk groups. The signature was an independent predictor for survival and was validated in an independent dataset. We also investigated the correlations between our prognostic signature and immune infiltrates and found that signature-derived risk scores correlated negatively with infiltration of B cells, CD4+ T cells, CD8+ T cells, neutrophils and dendritic cells. Our results show that the proposed prognostic signature reflects the tumor immune microenvironment, which makes it a potential indicator for survival that warrants further research to assess its clinical utility.

Characterization of somatic mutation-associated microenvironment signatures in acute myeloid leukemia patients based on TCGA analysis

Recurrent genetic mutations occur in acute myeloid leukemia (AML) and have been incorporated into risk stratification to predict the prognoses of AML patients. The bone marrow microenvironment plays a critical role in the development and progression of AML. However, the characteristics of the genetic mutation-associated microenvironment have not been comprehensively identified to date. In this study, we obtained the gene expression profiles of 173 AML patients from The Cancer Genome Atlas (TCGA) database and calculated their immune and stromal scores by applying the ESTIMATE algorithm. Immune scores were significantly associated with OS and cytogenetic risk. Next, we categorized the intermediate and poor cytogenetic risk patients into individual-mutation and wild-type groups according to RUNX1, ASXL1, TP53, FLT3-ITD, NPM1 and biallelic CEBPA mutation status. The relationships between the immune microenvironment and each genetic mutation were investigated by identifying differentially expressed genes (DEGs) and conducting functional enrichment analyses of them. Significant immune- and stromal-relevant DEGs associated with each mutation were identified, and most of the DEGs (from the FLT3-ITD, NPM1 and biallelic CEBPA mutation groups) were validated in the GSE14468 cohort downloaded from the Gene Expression Omnibus (GEO) database. In summary, we identified key immune- and stromal-relevant gene signatures associated with genetic mutations in AML, which may provide new biomarkers for risk stratification and personalized immunotherapy.

Identification of a 9-gene prognostic signature for breast cancer

Breast cancer (BRCA) is the most common cancer among women and is the second leading cause of cancer death in women. In this study, we developed a 9‐gene prognostic signature to predict the prognosis of patients with BRCA. GSE20685, GSE42568, GSE20711, and GSE88770 were used as training sets. The Kaplan–Meier plot was constructed to assess survival differences and log‐rank test was performed to evaluate the statistical significance. The overall survival (OS) of patients in the low‐risk group was significantly higher than that in the high‐risk group. ROC analysis indicated that this 9‐gene signature shows good diagnostic efficiency both in OS and disease‐free survival (DFS). The 9‐gene signature was further validated through GSE16446, GSE7390, and TCGA‐BRCA datasets. We also established a nomogram that integrates clinicopathological features and 9‐gene signature. The analysis of the calibration plot showed that the nomogram has good prognostic performance. More convincingly, real‐time reverse transcription‐polymerase chain reaction (RT‐PCR) results indicated that the protective prognostic factors in BRCA patients were downregulated, whereas the dangerous prognostic factors were upregulated. The innovation of this article is not only constructing a prognostic gene signature, but also combining with clinical information to further establish a nomogram to better predict the survival probability of patients. It is worth mentioning that this signature also does not depend on other clinical factors or variables.

Three Genes Predict Prognosis in Microenvironment of Ovarian Cancer

Ovarian cancer (OC) is the deadliest gynecological cancer in women. Immune cell infiltration has a critical role in regulating carcinogenesis and prognosis in OC. To identify prognostic genes relevant to the tumor microenvironment in OC, we investigated the association between OC and gene expression profiles. Results obtained with the ESTIMATE R tool showed that immune score and stromal score were correlated with lymphatic invasion, and high immune score predicted a favorable prognosis. A total of 342 common differentially expressed genes were identified according to the two scores; these genes were mainly involved in immune response, extracellular region, and serine-type endopeptidase activity. Three immune-related prognostic genes were selected by univariate and multivariate Cox regression analysis. We further established a prognostic model and validated the prognostic value of three hub genes in different databases; our results showed that this model could accurately predict survival and evaluate prognosis independent of clinical characteristics. Three hub genes have prognostic value in OC. TIMER analysis revealed that the three genes were correlated with different immune cells. Low levels of macrophage infiltration and high levels of CD4+ T cell infiltration were associated with favorable survival outcomes. Arm-level gain of GYPC was correlated with neutrophils and dendritic cells. These findings indicate that CXCR4, GYPC, and MMP12 modulate prognosis via effects on the infiltration of immune cells. Thus, these genes represent potential targets for immune therapy in OC.

An immune-related prognostic signature for predicting breast cancer recurrence

Breast cancer (BC) is the most common cancer among women worldwide and is the second leading cause of cancer‐related deaths in women. Increasing evidence has validated the vital role of the immune system in BC development and recurrence. In this study, we identified an immune‐related prognostic signature of BRCA that could help delineate risk scores of poor outcome for each patient. This prognostic signature comprised information on five danger genes—TSLP, BIRC5, S100B, MDK, and S100P—and three protect genes RARRES3, BLNK, and ACO1. Kaplan‐Meier survival curve showed that patients classified as low‐risk according to optimum cut‐off risk score had better prognosis than those identified within the high‐risk group. ROC analysis indicated that the identified prognostic signature had excellent diagnostic efficiency for predicting 3‐ and 5‐years relapse‐free survival (RFS). Multivariate Cox regression analysis proved that the prognostic signature is independent of other clinical parameters. Stratification analysis demonstrated that the prognostic signature can be used to predict the RFS of BC patients within the same clinical subgroup. We also developed a nomogram to predict the RFS of patients. The calibration plots exhibited outstanding performance. The validation sets (GSE21653, GSE20711, and GSE88770) were used to external validation. More convincingly, the real time RT‐PCR results of clinical samples demonstrated that danger genes were significantly upregulated in BC samples, whereas protect genes were downregulated. In conclusion, we developed and validated an immune‐related prognostic signature, which exhibited excellent diagnostic efficiency in predicting the recurrence of BC, and will help to make personalized treatment decisions for patients at different risk score.

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.