Development of a Novel Immune-Related Gene Prognostic Index for Breast Cancer

Male breast cancer differs from female breast cancer in molecular features that affect prognoses and drug responses

BACKGROUND

Male breast cancer (MBC) is a rare malignancy with a worse prognosis than female breast cancer (FBC). Current MBC treatment strategies are based on those for FBC. However, molecular differences between MBC and FBC with respect to prognosis and drug responses remain unclear.

METHODS

After controlling for confounding factors with propensity score matching (PSM), differences between MBC and FBC were comprehensively analyzed using many types of data: survival, immune microenvironments, sex hormone responses, drug sensitivity, transcriptomes, genomes, epigenomes, and proteomes.

RESULTS

Overall survival (OS) and cancer-specific survival (CSS) were both worse for MBC than for FBC. Differentially expressed mRNAs were enriched in numerous cancer-related functions and pathways, with SPAG16 and STOX1 being as the most important prognosis-related mRNAs for MBC. Competing endogenous RNA (ceRNA) and transcription factor (TF)-mRNA regulatory networks contain potential prognostic genes. Nine genes had higher mutation frequencies in MBC than in FBC. MBC shows a comparatively poor response to immunotherapy, with five proteins that promote breast cancer progression being highly expressed in MBC. MBC may be more responsive than FBC to estrogen. We detected six United States Food and Drug Administration (FDA)-approved therapeutic target genes as being differentially expressed between MBC and FBC.

CONCLUSION

The poor prognosis of MBC compared to FBC is due to numerous molecular differences and resulting drug responses.

Investigating gene signatures associated with immunity in colon adenocarcinoma to predict the immunotherapy effectiveness using NFM and WGCNA algorithms

Colon adenocarcinoma (COAD), a frequently encountered and highly lethal malignancy of the digestive system, has been the focus of intensive research regarding its prognosis. The intricate immune microenvironment plays a pivotal role in the pathological progression of COAD; nevertheless, the underlying molecular mechanisms remain incompletely understood. This study aims to explore the immune gene expression patterns in COAD, construct a robust prognostic model, and delve into the molecular mechanisms and potential therapeutic targets for COAD liver metastasis, thereby providing critical support for individualized treatment strategies and prognostic evaluation. Initially, we curated a comprehensive dataset by screening 2600 immune-related genes (IRGs) from the ImmPort and InnateDB databases, successfully obtaining a rich data resource. Subsequently, the COAD patient cohort was classified using the non-negative matrix factorization (NMF) algorithm, enabling accurate categorization. Continuing on, utilizing the weighted gene co-expression network analysis (WGCNA) method, we analyzed the top 5000 genes with the smallest p-values among the differentially expressed genes (DEGs) between immune subtypes. Through this rigorous screening process, we identified the gene modules with the strongest correlation to the COAD subpopulation, and the intersection of genes in these modules with DEGs (COAD vs COAD vs Normal colon tissue) is referred to as Differentially Expressed Immune Genes Associated with COAD (DEIGRC). Employing diverse bioinformatics methodologies, we successfully developed a prognostic model (DPM) consisting of six genes derived from the DEIGRC, which was further validated across multiple independent datasets. Not only does this predictive model accurately forecast the prognosis of COAD patients, but it also provides valuable insights for formulating personalized treatment regimens. Within the constructed DPM, we observed a downregulation of CALB2 expression levels in COAD tissues, whereas NOXA1, KDF1, LARS2, GSR, and TIMP1 exhibited upregulated expression levels. These genes likely play indispensable roles in the initiation and progression of COAD and thus represent potential therapeutic targets for patient management. Furthermore, our investigation into the molecular mechanisms and therapeutic targets for COAD liver metastasis revealed associations with relevant processes such as fat digestion and absorption, cancer gene protein polysaccharides, and nitrogen metabolism. Consequently, genes including CAV1, ANXA1, CPS1, EDNRA, and GC emerge as promising candidates as therapeutic targets for COAD liver metastasis, thereby providing crucial insights for future clinical practices and drug development. In summary, this study uncovers the immune gene expression patterns in COAD, establishes a robust prognostic model, and elucidates the molecular mechanisms and potential therapeutic targets for COAD liver metastasis, thereby possessing significant theoretical and clinical implications. These findings are anticipated to offer substantial support for both the treatment and prognosis management of COAD patients.

Development and verification of a manganese metabolism- and immune-related genes signature for prediction of prognosis and immune landscape in gastric cancer

Background

Gastric cancer (GC) poses a global health challenge due to its widespread prevalence and unfavorable prognosis. Although immunotherapy has shown promise in clinical settings, its efficacy remains limited to a minority of GC patients. Manganese, recognized for its role in the body's anti-tumor immune response, has the potential to enhance the effectiveness of tumor treatment when combined with immune checkpoint inhibitors.

Methods

Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases was utilized to obtain transcriptome information and clinical data for GC. Unsupervised clustering was employed to stratify samples into distinct subtypes. Manganese metabolism- and immune-related genes (MIRGs) were identified in GC by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis. We conducted gene set variation analysis, and assessed the immune landscape, drug sensitivity, immunotherapy efficacy, and somatic mutations. The underlying role of NPR3 in GC was further analyzed in the single-cell RNA sequencing data and cellular experiments.

Results

GC patients were classified into four subtypes characterized by significantly different prognoses and tumor microenvironments. Thirteen genes were identified and established as MIRGs, demonstrating exceptional predictive effectiveness in GC patients. Distinct enrichment patterns of molecular functions and pathways were observed among various risk subgroups. Immune infiltration analysis revealed a significantly greater abundance of macrophages and monocytes in the high-risk group. Drug sensitivity analysis identified effective drugs for patients, while patients in the low-risk group could potentially benefit from immunotherapy. NPR3 expression was significantly downregulated in GC tissues. Single-cell RNA sequencing analysis indicated that the expression of NPR3 was distributed in endothelial cells. Cellular experiments demonstrated that NPR3 facilitated the proliferation of GC cells.

Conclusion

This is the first study to utilize manganese metabolism- and immune-related genes to identify the prognostic MIRGs for GC. The MIRGs not only reliably predicted the clinical outcome of GC patients but also hold the potential to guide future immunotherapy interventions for these patients.

Development of machine learning-based malignant pericardial effusion-related model in breast cancer: Implications for clinical significance, tumor immune and drug-therapy

Background

Malignant pericardial effusion (MPE) is a common complication of advanced breast cancer (BRCA) and plays an important role in BRCA. This study is aims to construct a prognostic model based on MPE-related genes for predicting the prognosis of breast cancer.

Methods

The BRCA samples are analyzed based on the expression of MPE-related genes by using an unsupervised cluster analysis method. This study processes the data by least absolute shrinkage and selection operator and multivariate Cox analysis, and uses machine learning algorithms to construct BRCA prognostic model and develop web tool.

Results

BRCA patients are classified into three clusters and a BRCA prognostic model is constructed containing 9 MPE-related genes. There are significant differences in signature pathways, immune infiltration, immunotherapy response and drug sensitivity testing between the high and low-risk groups. Of note, a web-based tool (http://wys.helyly.top/cox.html) is developed to predict overall survival as well as drug-therapy response of BRCA patients quickly and conveniently, which can provide a basis for clinicians to formulate individualized treatment plans.

Conclusion

The MPE-related prognostic model developed in this study can be used as an effective tool for predicting the prognosis of BRCA and provides new insights for the diagnosis and treatment of BRCA patients.

A novel immune checkpoint-related signature for prognosis and immune analysis in breast cancer

Breast cancer is one of the most prevailing forms of cancer globally. Immunotherapy has demonstrated efficacy in improving the overall survival of breast cancer. The aim of us was to formulate a novel signature predicated on immune checkpoint-related genes (ICGs) that could anticipate the prognosis and further analyze the immune status of patients with breast cancer. After acquiring data, we pinpointed the definitive ICGs for constructing the prognostic model of breast cancer. We constructed a novel prognostic model and created a fresh risk score called Immune Checkpoint-related Risk Score in breast cancer (ICRSBC). The nomogram was constructed to evaluate the accuracy of the model, and the new web-based tool was created to be more intuitive for predicting prognosis. We also investigated immunotherapy responsiveness and analyzed the tumor mutational burden (TMB) in ICRSBC subgroups. The ICRSBC was found to have significant correlations with the immune environment, immunotherapy responsiveness, and TMB. The expression levels of the 9 ICGs that construct the prognostic model and their promoter methylation levels are significantly different between breast cancer and normal tissues. Furthermore, the mutation profiles, the copy number alterations, and the levels of protein expression also exhibit marked disparities among the 9 ICGs. We have identified and validated a novel signature related to ICGs that is strongly associated with breast cancer progression. This signature enables us to create a risk score for prognosticating the survival and assessing the immune status of individuals affected by breast cancer.

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.

Analysis of a new therapeutic target and construction of a prognostic model for breast cancer based on ferroptosis genes

Breast cancer, which is the most common malignant tumor among women worldwide and an important cause of death in women. The existing prognostic model for patients with breast cancer is not accurate as breast cancer is resistant to commonly used antitumor drugs. Ferroptosis is a novel mechanism of programmed cell death that depends on iron accumulation and lipid peroxidation. Various studies have confirmed the role of ferroptosis in tumor regulation and ferroptosis is now considered to play an important role in breast cancer development. At present, the association between breast cancer prognosis and ferroptosis-related gene expression remains unclear. Further exploration of this research area may optimize the evaluation and prediction of prognosis of patients with breast cancer and finding of new therapeutic targets. In this study, clinical factors and the expression of multiple genes were evaluated in breast cancer samples from the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database database. Eleven prognostication-related genes (TP63, IFNG, MT3, ANO6, FLT3, PTGS2, SLC1A4, JUN, SLC7A5, CHAC1, and TF) were identified from differentially expressed genes to construct a survival prediction model, which showed a good prediction ability. KEGG pathway analysis revealed that immune-related pathways were the primary pathways. ssGSEA analysis showed significant differences in the distribution of certain immune-related cell subsets, such as CD8+T cells and B cells, and in the expression of multiple immune genes, including type II IFN response and APC coinhibition. In addition, 10 immune targets related to ferroptosis in breast cancer were found: CD276, CD80, HHLA2, LILRA2, NCR3LG1, NECTIN3, PVR, SLAMF9,TNFSF4, and BTN1A1. Using TCGA, new ferroptosis genes related to breast cancer prognosis were identified, a new reliable and accurate prognosis model was developed, and 10 new potential therapeutic targets different from the traditional targeted drugs were identified to provide a reference for improving the poor prognosis of patients with breast cancer.

An immune-related gene prognostic index for predicting prognosis in patients with colorectal cancer

Background

Colorectal cancer (CRC) is one of the most common solid malignant burdens worldwide. Cancer immunology and immunotherapy have become fundamental areas in CRC research and treatment. Currently, the method of generating Immune-Related Gene Prognostic Indices (IRGPIs) has been found to predict patient prognosis as an immune-related prognostic biomarker in a variety of tumors. However, their role in patients with CRC remains mostly unknown. Therefore, we aimed to establish an IRGPI for prognosis evaluation in CRC.

Methods

RNA-sequencing data and clinical information of CRC patients were retrieved from The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) databases as training and validation sets, respectively. Immune-related gene data was obtained from the ImmPort and InnateDB databases. The weighted gene co-expression network analysis (WGCNA) was used to identify hub immune-related genes. An IRGPI was then constructed using Cox regression methods. Based on the median risk score of IRGPI, patients could be divided into high-risk and low-risk groups. To further investigate the immunologic differences, Gene set variation analysis (GSVA) studies were conducted. In addition, immune cell infiltration and related functional analysis were used to identify the differential immune cell subsets and related functional pathways.

Results

We identified 49 immune-related genes associated with the prognosis of CRC, 17 of which were selected for an IRGPI. The IRGPI model significantly differentiates the survival rates of CRC patients in the different groups. The IRGPI as an independent prognostic factor significantly correlates with clinico-pathological factors such as age and tumor stage. Furthermore, we developed a nomogram to improve the clinical utility of the IRGPI score. Immuno-correlation analysis in different IRGPI groups revealed distinct immune cell infiltration (CD4⁺ T cells resting memory) and associated pathways (macrophages, Type I IFNs responses, iDCs.), providing new insights into the tumor microenvironment. At last, drug sensitivity analysis revealed that the high-risk IRGPI group was sensitive to 11 and resistant to 15 drugs.

Conclusion

Our study established a promising immune-related risk model for predicting survival in CRC patients. This could help to better understand the correlation between immunity and the prognosis of CRC providing a new perspective for personalized treatment of CRC.

Value of negatively correlated miR-205-5p/HMGB3 and miR-96-5p/FOXO1 on the diagnosis of breast cancer and benign breast diseases

Background

MicroRNA (miRNA) and mRNA levels in matching specimens were used to identify miRNA-mRNA interactions. We aimed to integrate transcriptome, immunophenotype, methylation, mutation, and survival data analyses to examine the profiles of miRNAs and target mRNAs and their associations with breast cancer (BC) diagnosis.

Methods

Based on the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA), differentially expressed miRNAs and targeted mRNAs were screened from experimentally verified miRNA-target interaction databases using Pearson's correlation analysis. We used real-time quantitative reverse transcription polymerase chain reaction to verify BC and benign disease samples, and logistic regression analysis was used to establish a diagnostic model based on miRNAs and target mRNAs. Receiver operating characteristic curve analysis was performed to test the ability to recognize the miRNA-mRNA pairs. Next, we investigated the complex interactions between miRNA-mRNA regulatory pairs and phenotypic hallmarks.

Results

We identified 27 and 359 dysregulated miRNAs and mRNAs, respectively, based on the GEO and TCGA databases. Using Pearson's correlation analysis, 10 negative miRNA-mRNA regulatory pairs were identified after screening both databases, and the related miRNA and target mRNA levels were assessed in 40 BC tissues and 40 benign breast disease tissues. Two key regulatory pairs (miR-205-5p/High mobility group box 3 (HMGB3) and miR-96-5p/Forkhead Box O1 (FOXO1)) were selected to establish the diagnostic model. They also had utility in survival and clinical analyses.

Conclusions

A diagnostic model including two miRNAs and their respective target mRNAs was established to distinguish between BC and benign breast diseases. These markers play essential roles in BC pathogenesis.

Deciphering comprehensive features of tumor microenvironment controlled by chromatin regulators to predict prognosis and guide therapies in uterine corpus endometrial carcinoma

Background

Dysregulation of chromatin regulators (CRs) can perturb the tumor immune microenvironment, but the underlying mechanism remains unclear. We focused on uterine corpus endometrial carcinoma (UCEC) and used gene expression data from TCGA-UCEC to investigate this mechanism.

Methods

We used weighted gene co-expression network analysis (WGCNA) and consensus clustering algorithm to classify UCEC patients into Cluster_L and Cluster_H. TME-associated CRs were identified using WGCNA and differential gene expression analysis. A CR risk score (CRRS) was constructed using univariate Cox and LASSO-Cox regression analyses. A nomogram was developed based on CRRS and clinicopathologic factors to predict patients' prognosis.

Results

Lower CRRS was associated with lower grade, more benign molecular subtypes, and improved survival. Patients with low CRRS showed abundant immune infiltration, a higher mutation burden, fewer CNVs, and better response to immunotherapy. Moreover, low CRRS patients were more sensitive to 24 chemotherapeutic agents.

Conclusion

A comprehensive assessment of CRRS could identify immune activation and improve the efficacy of UCEC treatments.

A composite score based on immune-related gene prognostic index and m6A risk score of head and neck squamous cell carcinoma

Background: Immunotherapy has been demonstrated favorable in head and neck squamous cell carcinoma (HNSCC). Studies indicated that immune-related gene prognostic index (IRGPI) was a robust signature, and N⁶-methyladenosine (m⁶A) methylation had a significant impact on the tumor immune microenvironment (TIME) and immunotherapy of head and neck squamous cell carcinoma. Thus, combining indicated that immune-related gene prognostic index with m⁶A status should offer a better predictive power for immune responses. Methods: Head and neck squamous cell carcinoma samples from the cancer genome atlas (TCGA, n = 498) and gene expression omnibus database (GSE65858, n = 270) were used in this study. Cox regression analysis was used to construct the indicated that immune-related gene prognostic index through immune-related hub genes which were identified by weighted gene co-expression network analysis (WGCNA). The m⁶A risk score was constructed by least absolute shrinkage and selection operator (LASSO) regression analysis. Principal component analysis was used to construct a composite score, and systematically correlate subgroups according to tumor immune microenvironment cell-infiltrating characteristics. Results: A composite score was determined based on indicated that immune-related gene prognostic index and m⁶A risk score. Head and neck squamous cell carcinoma patients in the cancer genome atlas were divided into four subgroups: A (IRGPI-High&m⁶A-risk-High, n = 127), B (IRGPI-High&m⁶A-risk-Low, n = 99), C (IRGPI-Low&m⁶A-risk-High, n = 99), and D (IRGPI-Low&m⁶A-risk-Low, n = 128), and overall survival (OS) was significantly different between subgroups (p < 0.001). The characteristics of tumor immune microenvironment cell infiltration in the four subgroups were significantly different in subgroups (p < 0.05). The receiver operating characteristic (ROC) curves show the predictive value of composite score for overall survival was superior to other scores. Conclusion: The composite score is a promising prognostic signature which might distinguish immune and molecular characteristics, predict prognosis, and guide more effective immunotherapeutic strategies for head and neck squamous cell carcinoma.

One Step Nucleic Acid Amplification (OSNA) Lysate Samples Are Suitable to Establish a Transcriptional Metastatic Signature in Patients with Early Stage Hormone Receptors-Positive Breast Cancer

The One Step Nucleic Acid Amplification (OSNA) is being adopted worldwide for sentinel lymph nodes (SLNs) staging in breast cancer (BC). As major disadvantage, OSNA precludes prognostic information based on structural evaluation of SLNs. Our aim is to identify biomarkers related to tumor-microenvironment interplay exploring gene expression data from the OSNA remaining lysate. This study included 32 patients with early stage hormone receptors-positive BC. Remaining OSNA lysates were prepared for targeted RNA-sequencing analysis. Identification of differentially expressed genes (DEGs) was performed by DESeq2 in R and data analysis in STATA. The results show that, in metastatic SLNs, several genes were upregulated: KRT7, VTCN1, CD44, GATA3, ALOX15B, RORC, NECTIN2, LRG1, CD276, FOXM1 and IGF1R. Hierarchical clustering analysis revealed three different clusters. The identified DEGs codify proteins mainly involved in cancer aggressiveness and with impact in immune response. The overexpression of the immune suppressive genes VTCN1 and CD276 may explain that no direct evidence of activation of immune response in metastatic SLNs was found. We show that OSNA results may be improved incorporating microenvironment-related biomarkers that may be useful in the future for prognosis stratification and immunotherapy selection. As OSNA assay is being implemented for SLNs staging in other cancers, this approach could also have a wider utility.