An Immune-Related Gene Prognostic Index for Triple-Negative Breast Cancer Integrates Multiple Aspects of Tumor-Immune Microenvironment

Clinical significance of circulating biomarkers of immune-checkpoint molecules with atezolizumab plus bevacizumab therapy in unresectable hepatocellular carcinoma

BACKGROUND

The aims of this study were to identify clinically significant biomarkers of a response to atezolizumab plus bevacizumab (ATZ + BV) therapy and to develop target strategies against unresectable hepatocellular carcinoma (u-HCC).

METHOD

We first investigated the potential of circulating tumor DNA (ctDNA) to serve as a biomarker for predicting the therapeutic outcome in 24 u-HCC patients treated with ATZ + BV therapy. Next, we analyzed levels of immune-related cytokines in blood samples from 134 u-HCC patients who received ATZ + BV. For this, serum immune-related molecules or cancer-immune cycle-related molecules that have been reported in HCC patient sera, namely CD274, LAG-3, CCL2, 4, 5, CXCL1, 9, 10, 12, 13, CX3CL1, CCR5, IFNγ and IL-6, 8 were measured using enzyme-linked immunosorbent assay.

RESULTS

More than 1% of variant read frequency (VRF) mutations were found in TP53, APC, PIK3CA and VHL, although with no correlation with treatment response. Among the 15 cytokines evaluated, CXCL9 and LAG-3 levels were significantly different between patients with objective response (OR), stable disease (SD), and progressive disease (PD) following ATZ + BV treatment. Receiver-operating characteristic curve analyses of CXCL9 (cut-off value: 419.1 pg/ml) and LAG-3 (cut-off value: 3736.3 pg/ml) indicated areas of 0.779 and 0.697, respectively, for differentiating PD from non-PD and OR from non-OR. In multivariate analysis of progression-free survival (PFS) and overall survival (OS), high serum CXCL9 (hazard ratio (HR) and 95% confidence interval (CI): 0.412 (0.251-0.677) (p = 0.0005) for PFS and 0.252 (0.125-0.508) (p = 0.0001) for OS), and low serum LAG-3 (HR and 95% CI 0.419 (0.249-0.705) (p = 0.0011) for PFS and 0.294 (0.140-0.617) (p = 0.0012) for OS) were independent positive predictive factors.

CONCLUSION

Although, as far as we examined, no ctDNA mutations in blood were found to be related to ATZ + BV treatment efficacy, serum CXCL9 and LAG-3 levels, which are related to the cancer-immune cycle, were associated with treatment efficacy and could be predictive markers of the efficacy of ATZ + BV treatment in HCC patients.

Mapping the immunological battlefield in gastric cancer: prognostic implications of an immune gene expression signature

BACKGROUND

Gastric cancer (GC) is a heterogeneous malignancy with variable clinical outcomes. The immune system has been implicated in GC development and progression, highlighting the importance of immune-related gene expression patterns and their prognostic significance.

OBJECTIVE

This study aimed to identify differentially expressed immune-related genes (DEIRGs) and establish a prognostic index for GC patients using comprehensive bioinformatic analyses.

METHODS

We integrated RNA sequencing data from multiple databases and identified DEIRGs by overlapping differentially expressed genes with immune-related genes. Functional enrichment analysis was performed to uncover the biological processes and signaling pathways associated with DEIRGs. We conducted a Weighted Gene Co-expression Network Analysis (WGCNA) to identify key gene modules related to with GC. Cox regression analysis was conducted to determine independent prognostic DEIRGs for overall survival prediction. Based on these findings, we developed an immune-related gene prognostic index (IRGPI) based on these findings. The prognostic value of the IRGPI was validated using survival analysis and an independent validation cohort. Functional enrichment analysis, gene mutation analysis, and immune cell profiling were performed to gain insights into the biological functions and immune characteristics associated with the IRGPI-based subgroups.

RESULTS

We identified 493 DEIRGs significantly enriched in immune-related biological processes and signaling pathways associated with GC. WGCNA analysis revealed a significant module (turquoise module) associated with GC, revealing potential therapeutic targets. Cox regression analysis identified RNASE2, CGB5, CTLA4, and DUSP1 as independent prognostic DEIRGs. The IRGPI, incorporating the expression levels of these genes, demonstrated significant prognostic value in predicting overall survival. The IRGPI-based subgroups exhibited distinct biological functions, genetic alterations, and immune cell compositions.

CONCLUSION

Our study identified DEIRGs and established a prognostic index (IRGPI) for GC patients. The IRGPI exhibited promising prognostic potential and provided insights into GC tumor biology and immune characteristics. These findings have implications for guiding therapeutic strategies.

BPIFB1 promotes metastasis of hormone receptor-positive breast cancer via inducing macrophage M2-like polarization

Metastasis is an important factor affecting the prognosis of hormone receptor-positive breast cancer (BC). However, the molecular basis for migration and invasion of tumor cells remains poorly understood. Here, we identify that bactericidal/permeability-increasing-fold-containing family B member 1 (BPIFB1), which plays an important role in innate immunity, is significantly elevated in breast cancer and associated with lymph node metastasis. High expression of BPIFB1 and its coding mRNA are significantly associated with poor prognosis of hormone receptor-positive BC. Using enrichment analysis and constructing immune infiltration evaluation, we predict the potential ability of BPIFB1 to promote macrophage M2 polarization. Finally, we demonstrate that BPIFB1 promotes the metastasis of hormone receptor-positive BC by stimulating the M2-like polarization of macrophages via the establishment of BC tumor cells/THP1 co-culture system, qPCR, Transwell assay, and animal experiments. To our knowledge, this is the first report on the role of BPIFB1 as a tumor promoter by activating the macrophage M2 polarization in hormone receptor-positive breast carcinoma. Together, these results provide novel insights into the mechanism of BPIFB1 in BC.

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.

An EMT-Related Gene Signature to Predict the Prognosis of Triple-Negative Breast Cancer

INTRODUCTION

Epithelial-mesenchymal transition (EMT) is an important biological process in tumor invasion and metastasis, and thus a potential indicator of the progression and drug resistance of breast cancer. This study comprehensively analyzed EMT-related genes in triple-negative breast cancer (TNBC) to develop an EMT-related prognostic gene signature.

METHODS

With the application of The Cancer Genome Atlas (TCGA) database, Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and the Genotype-Tissue Expression (GTEx) database, we identified EMT-related signature genes (EMGs) by Cox univariate regression and LASSO regression analysis. Risk scores were calculated and used to divide patients with TNBC into high-risk group and low-risk groups by the median value. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curve analyses were applied for model validation. Independent prognostic predictors were used to develop nomograms. Then, we assessed the risk model in terms of the immune microenvironment, genetic alteration and DNA methylation effects on prognosis, the probability of response to immunotherapy and chemotherapy, and small molecule drugs predicted by The Connectivity Map (Cmap) database.

RESULTS

Thirteen EMT-related genes with independent prognostic value were identified and used to stratify the patients with TNBC into high- and low-risk groups. The survival analysis revealed that patients in the high-risk group had significantly poorer overall survival than patients in the low-risk group. Populations of immune cells, including CD4 memory resting T cells, CD4 memory activated T cells, and activated dendritic cells, significantly differed between the high- and low-risk groups. Moreover, some therapeutic drugs to which the high-risk group might show sensitivity were identified.

CONCLUSIONS

Our research identified the significant impact of EMGs on prognosis in TNBC, providing new strategies for personalizing TNBC treatment and improving clinical outcomes.

Targeting myeloid-derived suppressor cells in combination with tumor cell vaccination predicts anti-tumor immunity and breast cancer dormancy: an in silico experiment

Among the different breast cancer subsets, triple-negative breast cancer (TNBC) has the worst prognosis and limited options for targeted therapies. Immunotherapies are emerging as novel treatment opportunities for TNBC. However, the surging immune response elicited by immunotherapies to eradicate cancer cells can select resistant cancer cells, which may result in immune escape and tumor evolution and progression. Alternatively, maintaining the equilibrium phase of the immune response may be advantageous for keeping a long-term immune response in the presence of a small-size residual tumor. Myeloid-derived suppressor cells (MDSCs) are activated, expanded, and recruited to the tumor microenvironment by tumor-derived signals and can shape a pro-tumorigenic micro-environment by suppressing the innate and adaptive anti-tumor immune responses. We recently proposed a model describing immune-mediated breast cancer dormancy instigated by a vaccine consisting of dormant, immunogenic breast cancer cells derived from the murine 4T1 TNBC-like cell line. Strikingly, these 4T1-derived dormant cells recruited fewer MDSCs compared to aggressive 4T1 cells. Recent experimental studies demonstrated that inactivating MDSCs has a profound impact on reconstituting immune surveillance against the tumor. Here, we developed a deterministic mathematical model for simulating MDSCs depletion from mice bearing aggressive 4T1 tumors resulting in immunomodulation. Our computational simulations indicate that a vaccination strategy with a small number of tumor cells in combination with MDSC depletion can elicit an effective immune response suppressing the growth of a subsequent challenge with aggressive tumor cells, resulting in sustained tumor dormancy. The results predict a novel therapeutic opportunity based on the induction of effective anti-tumor immunity and tumor dormancy.

Genetic Heterogeneity, Tumor Microenvironment and Immunotherapy in Triple-Negative Breast Cancer

Heterogeneity of triple-negative breast cancer is well known at clinical, histopathological, and molecular levels. Genomic instability and greater mutation rates, which may result in the creation of neoantigens and enhanced immunogenicity, are additional characteristics of this breast cancer type. Clinical outcome is poor due to early age of onset, high metastatic potential, and increased likelihood of distant recurrence. Consequently, efforts to elucidate molecular mechanisms of breast cancer development, progression, and metastatic spread have been initiated to improve treatment options and improve outcomes for these patients. The extremely complex and heterogeneous tumor immune microenvironment is made up of several cell types and commonly possesses disorganized gene expression. Altered signaling pathways are mainly associated with mutated genes including p53, PIK3CA, and MAPK, and which are positively correlated with genes regulating immune response. Of note, particular immunity-associated genes could be used in prognostic indexes to assess the most effective management. Recent findings highlight the fact that long non-coding RNAs also play an important role in shaping tumor microenvironment formation, and can mediate tumor immune evasion. Identification of molecular signatures, through the use of multi-omics approaches, and effector pathways that drive early stages of the carcinogenic process are important steps in developing new strategies for targeted cancer treatment and prevention. Advances in immunotherapy by remodeling the host immune system to eradicate tumor cells have great promise to lead to novel therapeutic strategies. Current research is focused on combining immune checkpoint inhibition with chemotherapy, PARP inhibitors, cancer vaccines, or natural killer cell therapy. Targeted therapies may improve therapeutic response, eliminate therapeutic resistance, and improve overall patient survival. In the future, these evolving advancements should be implemented for personalized medicine and state-of-art management of cancer patients.

Identification of immune-related signature for the prognosis and benefit of immunotherapy in triple-negative breast cancer

Background: There is a lack of biomarkers for predicting the efficacy of immunotherapy in triple-negative breast cancer (TNBC). Hence, we constructed an immune risk score (IRS) model to predict the prognosis of patients with TNBC and evaluate those who are sensitive to immunotherapy. Methods: The ribonucleic acid (RNA) sequencing data, mutation data, and clinical information of TNBC patients were obtained from The Cancer Genome Atlas database. Data of immune-related genes were obtained from the Import and InnateDB databases. The IRS model was constructed using univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, and the predictive ability of the prognostic model was evaluated. Further external validation was performed using the Gene Expression Omnibus (GEO) databases GSE58812 and GSE135565. Data on the clinical characteristics, immune landscape, and immune checkpoint inhibitors used in different risk groups were analyzed. Finally, the drug sensitivity of the patients in the high- and low-risk groups was predicted. Results: The prognostic risk score model comprised six genes: HSPA6, LCN1, ARTN, IL36G, BCL2A1, and CASP12. The area under the curve values at 1 year, 3 years, and 5 years were 0.835, 0.852, and 0.843, respectively, indicating that the model has a good potential for predicting the long-term survival of TNBC patients, which is consistent with the results of the GEO cohort. Compared with the high-risk group, the low-risk group had a better prognosis; more abundant immune-activated cell infiltrates, such as CD8+ T cells and CD4 memory-activated T cells, and a higher enrichment of immune-related signaling pathways, such as the cytokine receptor interaction, nucleotide oligomerization domain-like receptor signal pathway, T-cell receptor signal pathway, and B-cell receptor signaling pathway, were observed. In addition, the immune checkpoint encoding genes, such as CD274, CTLA4, PDCD1, and PDCD1LG2 were highly expressed in the low-risk group, which showed that this group was more likely to benefit from immunotherapy. Conclusion: A new IRS gene feature was established to predict the patients' prognosis and guide immunotherapy. Moreover, it was revealed that several potential therapeutic drugs can be used in high-risk patients who are unresponsive to immunotherapy.

Prognostic Capability of TNBC 3-Gene Score among Triple-Negative Breast Cancer Subtypes

Background: Triple-negative breast cancer (TNBC) is a complex and molecularly heterogeneous entity, with the poorest outcome compared with other breast cancer subtypes. Previously, we developed a TNBC 3-gene score with a significant prognostic capability. This study aims to test the 3-gene score in the different TNBC subtypes. Methods: Data from 204 TNBC patients treated with neoadjuvant chemotherapy were retrieved from public datasets and pooled (GSE25066, GSE58812, and GSE16446). After removing batch effects, cases were classified into Lehman’s TNBC subtypes and then the TNBC 3-gene score was used to evaluate the risk of distant recurrence in each subgroup. In addition, the association with tumor-infiltrating lymphocyte (TILs) levels was evaluated in a retrospective group of 72 TNBC cases. Results: The TNBC 3-gene score was able to discriminate patients with different risks within the pooled cohort (HR = 2.41 for high vs. low risk; 95%CI: 1.50−3.86). The score showed predictive capability in the immunomodulatory subtype (HR = 4.16; 95%CI: 1.63−10.60) and in the mesenchymal stem-like subtype (HR = 18.76; 95%CI: 1.68−208.97). In the basal-like 1, basal-like-2, and mesenchymal subtypes, the observed differential risk patterns showed no statistical significance. The score had poor predictive capability in the luminal androgen receptor subtype (p = 0.765). In addition, a low TNBC 3-gene score was related to a high level of TIL infiltration (p < 0.001). Conclusions: The TNBC 3-gene score is able to predict the risk of distant recurrence in TNBC patients, specifically in the immunomodulatory and mesenchymal stem-like subtype. Despite a small sample size in each subgroup, an improved prognostic capability was seen in TNBC subtypes with tumor-infiltrating components.

A Novel Metabolism-Related Gene Signature for Predicting the Prognosis of HBV-Infected Hepatocellular Carcinoma

Metabolic reprogramming is one of the crucial hallmarks of cancer. Hepatocellular carcinoma (HCC) resulting from hepatitis B has various altered metabolic features. However, the impact of such alterations on the tumor microenvironment (TME) and immunotherapy efficacy is still unclear. Here, a prognostic signature of metabolism-related gene (MRG) composition was constructed, and the immune profile of different subgroups and potential response to immunotherapy were described. Based on the HCC gene dataset, we used weighted gene coexpression network analysis for identifying MRGs linked to hepatitis B. An MRG prognostic index (MRGPI) with two genes, ATIC and KIF2C, was constructed using Cox regression analysis, an independent prognostic factor. In addition, the model was validated using the GEO dataset. The immune profile and prediction of HCC response to immunotherapy in different subgroups were analyzed using CIBERSORT and TIDE. Based on the outcomes, the distributions of memory B cells, monocytes, resting mast cells, and M0 macrophages in TME were different with a greater benefit of immunotherapy in the low MRGPI risk group. In addition, the MRGPI risk groups showed substantial differences in sensitivity to conventional drug therapy. This study concludes that MRGPI is an effective biomarker for predicting the prognoses of patients with HCC resulting from hepatitis B virus infections and determining the efficacy of immunotherapy and conventional medical therapy.

An iron metabolism and immune related gene signature for the prediction of clinical outcome and molecular characteristics of triple-negative breast cancer

BACKGROUND

An imbalance of intracellular iron metabolism can lead to the occurrence of ferroptosis. Ferroptosis can be a factor in the remodeling of the immune microenvironment and can affect the efficacy of cancer immunotherapy. How to combine ferroptosis-promoting modalities with immunotherapy to suppress triple-negative breast cancer (TNBC) has become an issue of great interest in cancer therapy. However, potential biomarkers related to iron metabolism and immune regulation in TNBC remain poorly understand.

METHODS

We constructed an optimal prognostic TNBC-IMRGs (iron metabolism and immune-related genes) signature using least absolute shrinkage and selection operator (LASSO) cox regression. Survival analysis and ROC curves were analyzed to identify the predictive value in a training cohort and external validation cohorts. The correlations of gene signature with ferroptosis regulators and immune infiltration are also discussed. Finally, we combined the gene signature with the clinical model to construct a combined model, which was further evaluated using a calibration curve and decision curve analysis (DCA).

RESULTS

Compared with the high-risk group, TNBC patients with low-risk scores had a remarkably better prognosis in both the training set and external validation sets. Both the IMRGs signature and combined model had a high predictive capacity, 1/3/5- year AUC: 0.866, 0.869, 0.754, and 1/3/5-yaer AUC: 0.942, 0.934, 0.846, respectively. The calibration curve and DCA also indicate a good predictive performance of the combined model. Gene set enrichment analysis (GSEA) suggests that the high-risk group is mainly enriched in metabolic processes, while the low-risk group is mostly clustered in immune related pathways. Multiple algorithms and single sample GSEA further show that the low-risk score is associated with a high tumor immune infiltration level. Differences in expression of ferroptosis regulators are also observed among different risk groups.

CONCLUSIONS

The IMRGs signature based on a combination of iron metabolism and immune factors may contribute to evaluating prognosis, understanding molecular characteristics and selecting treatment options in TNBC.

Development of a Novel Immune-Related Gene Signature to Predict Prognosis and Immunotherapeutic Efficiency in Gastric Cancer

Background: Gastric cancer (GC) is the fifth most common malignancy and the third leading cause of tumor-related deaths globally. Herein, we attempted to build a novel immune-related gene (IRG) signature that could predict the prognosis and immunotherapeutic efficiency for GC patients.

Methods: The mRNA transcription data and corresponding clinical data of GC were downloaded from The Cancer Genome Atlas (TCGA) database as the training group and the GSE84437 data set as the testing cohort, followed by acquisition of IRGs from the InnateDB resource and ImmPort database. Using the univariate Cox regression analysis, an IRG signature was developed. Several immunogenomic analyses were performed to illustrate the associations between the immune risk score and tumor mutational burden, immune cell infiltrations, function of immune infiltration, clinical characteristics, immune subtype, and immunotherapeutic response.

Results: The analysis of 343 GC samples and 30 normal samples from the TCGA database gave rise to 8,713 differentially expressed genes (DEGs) and 513 differentially expressed immune-related genes (DEIRGs) were extracted. The novel IRG signature contained eight DEIRGs (FABP4, PI15, RNASE2, CGB5, INHBE, RLN2, DUSP1, and CD36) and was found to serve as an independent predictive and prognostic factor for GC. Then, the GC patients were separated into the high- and low-risk groups based on the median risk score, wherein the low-risk group presented a better prognosis and was more sensitive to immunotherapy than did the high-risk group. According to the time-dependent ROC curves and AUCs, the immunotherapeutic value of the signature was better than the Tumor Immune Dysfunction and Exclusion (TIDE) and T-cell inflammatory signature (TIS) scores. In addition, the AUCs of the risk score for predicting 1-, 2-, and 3-year OS were 0.675, 0.682, and 0.710, respectively, which indicated that the signature had great predictive power.

Conclusion: This study presents a novel IRG signature based on the tumor immune microenvironment, which could improve the prediction of the prognosis and immunotherapeutic efficiency for GC patients. The powerful signature may serve as novel biomarkers and provide therapeutic targets for precision oncology in clinical practice.