Exploration the Significance of a Novel Immune-Related Gene Signature in Prognosis and Immune Microenvironment of Breast Cancer

Bioinformatics Identification and Experimental Validation of a Prognostic Model for the Survival of Lung Squamous Cell Carcinoma Patients

Lung squamous cell carcinoma (LUSC) kills more than four million people yearly. Creating more trustworthy tumor molecular markers for LUSC early detection, diagnosis, prognosis, and customized treatment is essential. Cuproptosis, a novel form of cell death, opened up a new field of study for searching for trustworthy tumor indicators. Our goal was to build a risk model to assess drug sensitivity, monitor immune function, and predict prognosis in LUSC patients. The 19 cuproptosis-related genes were found in the literature, and patient genomic and clinical information was collected using the Cancer Genomic Atlas (TCGA) database. The LUSC patients were grouped using unsupervised clustering techniques, and 7626 differentially expressed genes were identified. Using univariate COX analysis, LASSO regression analysis, and multivariate COX analysis, a prognostic model for LUSC patients was developed. The tumor immune escape was evaluated using the Tumor Immune Dysfunction and Exclusion (TIDE) method. The R packages 'pRRophetic,' 'ggpubr,' and 'ggplot2' were utilized to examine drug sensitivity. For modeling, a 6-cuproptosis-based gene signature was found. Patients with high-risk LUSC had significantly worse survival rates than those with low-risk conditions. The possibility of tumor immunological escape was increased in patients with higher risk scores due to more immune cell inactivation. For patients with high-risk LUSC, we discovered seven potent potential drugs (AZD6482, CHIR.99021, CMK, Embelin, FTI.277, Imatinib, and Pazopanib). In conclusion, the cuproptosis-based genes predictive risk model can be utilized to predict outcomes, track immune function, and evaluate medication sensitivity in LUSC patients.

Biomarkers of ulcerative colitis disease activity CXCL1, CYP2R1, LPCAT1, and NEU4 and their relationship to immune infiltrates

The diagnosis and assessment of ulcerative colitis (UC) poses significant challenges, which may result in inadequate treatment and a poor prognosis for patients. This study aims to identify potential activity biomarkers for UC and investigate the role of infiltrating immune cells in the disease. To perform gene set enrichment analysis, we utilized the cluster profiler and ggplot2 packages. Kyoto encyclopedia of genes and genomes was used to analyze degenerate enrichment genes. Significant gene set enrichment was determined using the cluster profiler and ggplot2 packages. Additionally, quantitative PCR (qRT-PCR) was employed to validate the expression of each marker in the ulcerative colitis model. We identified 651 differentially expressed genes (DEGs) and further investigated potential UC activity biomarkers. Our analysis revealed that CXCL1 (AUC = 0.710), CYP2R1 (AUC = 0.863), LPCAT1 (AUC = 0.783), and NEU4 (AUC = 0.833) were promising activity markers for the diagnosis of UC. Using rat DSS model, we validated these markers through qRT-PCR, which showed statistically significant differences between UC and normal colon mucosa. Infiltrating immune cell analysis indicated that M1 macrophages, M2 macrophages, activated dendritic cells (DCs), and neutrophils played crucial roles in the occurrence and progression of UC. Moreover, the activity markers exhibited varying degrees of correlation with activated memory CD4 T cells, M0 macrophages, T follicular helper cells, memory B cells, and activated DCs. The potential diagnostic genes for UC activity, such as CXCL1, CYP2R1, LPCAT1, and NEU4, as well as the infiltration of immune cells, may contribute to the pathogenesis and progression of UC.

A novel immune score model predicting the prognosis and immunotherapy response of breast cancer

Breast cancer (BC) is one of the most common malignancies. However, the existing pathological grading system cannot accurately and effectively predict the survival rate and immune checkpoint treatment response of BC patients. In this study, based on The Cancer Genome Atlas (TCGA) database, a total of 7 immune-related genes (IRGs) were screened out to construct a prognostic model. Subsequently, the clinical prognosis, pathological characteristics, cancer-immunity cycle, tumour immune dysfunction and exclusion (TIDE) score, and immune checkpoint inhibitor (ICI) response were compared between the high- and low-risk groups. In addition, we determined the potential regulatory effect of NPR3 on BC cell proliferation, migration, and apoptosis. The model consisting of 7 IRGs was an independent prognostic factor. Patients with lower risk scores exhibited longer survival times. Moreover, the expression of NPR3 was increased but the expression of PD-1, PD-L1, and CTLA-4 was decreased in the high-risk group compared to the low-risk group. In addition, compared with si-NC, si-NPR3 suppressed proliferation and migration but promoted apoptosis in both MDA-MB-231 and MCF-7 cells. This study presents a model for predicting survival outcomes and provides a strategy to guide effective personalized immunotherapy in BC patients.

EIF4G1 Is a Potential Prognostic Biomarker of Breast Cancer

BACKGROUND

Breast cancer (BRCA) is one of the most common cancers in women worldwide and a leading cause of death from malignancy. This study was designed to identify a novel biomarker for prognosticating the survival of BRCA patients.

METHODS

The prognostic potential of eukaryotic translation initiation factor 4 gamma 1 (EIF4G1) was assessed using RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) as training cohort and validation set, respectively. The functional enrichment analysis of differentially expressed genes (DEGs) was performed. The relationship between EIF4G1 and tumor microenvironment (TME) was analyzed. Immunotherapy responses were explored by the immunophenoscores (IPS) and tumor immune dysfunction and exclusion (TIDE) score. The Connectivity Map (CMap) was used to discover potentially effective therapeutic molecules against BRCA. Immunohistochemistry (IHC) was applied to compare the protein levels of EIF4G1 in normal and cancer tissues and to verify the prognostic value of EIF4G1.

RESULTS

BRCA patients with increased expression of EIF4G1 had a shorter overall survival (OS) in all cohorts and results from IHC. EIF4G1-related genes were mainly involved in DNA replication, BRCA metastasis, and the MAPK signaling pathway. Infiltration levels of CD4⁺-activated memory T cells, macrophages M0, macrophages M1, and neutrophils were higher in the EIF4G1 high-expression group than those in the EIF4G1 low-expression group. EIF4G1 was positively correlated with T cell exhaustion. Lower IPS was revealed in high EIF4G1 expression patients. Five potential groups of drugs against BRCA were identified.

CONCLUSION

EIF4G1 might regulate the TME and affect BRCA metastasis, and it is a potential prognostic biomarker and therapeutic target for BRCA.

CXCL12, a potential modulator of tumor immune microenvironment (TIME) of bladder cancer: From a comprehensive analysis of TCGA database

BACKGROUND

Tumor immune microenvironment (TIME) plays a significant role in the initiation and progression of bladder urothelial carcinoma (BLCA). However, there are only a few researches regarding the association between immune-related genes and tumor-infiltrating immune cells (TICs) in TIME of BLCA.

METHODS

We calculated the proportion of immune/stromal component and TICs of 414 BLCA samples and 19 normal samples downloaded from TCGA database with the help of ESTIMATE and CIBERSORT algorithms. Differentially expressed genes (DEGs) were obtained from the comparison between Stromal and Immune Score and further analyzed by GO and KEGG enrichment analysis, as well as PPI network and COX regression analysis. CXCL12 was overlapping among the above analyses. Single gene analysis of CXCL12 was carried out through difference analysis, paired analysis and GSEA. The association between CXCL12 and TICs was assessed by difference analysis and correlation analysis.

RESULTS

Immune and stromal component in TIME of BLCA were associated with patients' clinicopathological characteristics. 284 DEGs were primarily enriched in immune-associated activities, among which CXCL12 was the most significant gene sharing the leading nodes in PPI network and being closely related with patients' survival. Single gene analysis and immunohistochemistry revealed that CXCL12 was down-regulated in BLCA samples and significantly related with the clinicopathological characteristics of patients. Further analysis suggested that CXCL12 was involved in the immune-associated activities probably through its close cross-talk with TICs.

CONCLUSIONS

CXCL12 down-regulation could be a potential biomarker to predict the unbalanced immune status of TIME of BLCA, which might provide an extra insight for the immunotherapy of BLCA.

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

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

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

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

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

A Differentiation-Related Gene Prognostic Index Contributes to Prognosis and Immunotherapy Evaluation in Patients with Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common gastrointestinal tumor with a poor prognosis, which is associated with poor differentiation of tumor cells. However, the potential value of cell differentiation-related molecules in predicting the benefit and prognosis of immune checkpoint inhibitors (ICI) therapy remains unknown. Herein, to investigate the differentiation trajectory of HCC cells and their clinical significance, a differentiation-related gene prognostic index (DRGPI) based on HCC differentiation-related genes (HDRGs) was constructed to elucidate the immune characteristics and therapeutic benefits of ICI in the HCC subgroup defined by DRGPI. Single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from four HCC samples were integrated for bioinformatics analysis. Then, PON1, ADH4, SQSTM1, HSP90AA1, and STMN1 were screened out to construct a DRGPI. More intriguingly, RT-qPCR validation of the expression of these genes yielded consistent results with the TCGA database. Next, the risk scoring (RS) constructed based on DRGPI suggested that the overall survival (OS) of the DRGPI-high patients was significantly worse than that of the DRGPI-low patients. A nomogram was constructed based on DRGPI-RS and clinical characteristics, which showed strong predictive performance and high accuracy. The comprehensive results indicated that a low DRGPI score was associated with low TP53 mutation rates, high CD8 T cell infiltration, and more benefit from ICI therapy. Homoplastically, the high DRGPI score reflected the opposite results. Taken together, our study highlights the significance of HCC cell differentiation in predicting prognosis, indicating immune characteristics, and understanding the therapeutic benefits of ICI, and suggests that DRGPI is a valuable prognostic biomarker for HCC.

Prognostic Characteristics of Immune-Related Genes and the Related Regulatory Axis in Patients With Stage N+M0 Breast Cancer

Breast cancer (BRCA) has the highest incidence rate among female tumours. The function of the immune system affects treatment efficacy and prognosis in patients with BRCA. However, the exact role of immune-related genes (IRGs) in stage N+M0 BRCA is unknown. We constructed a predictive risk scoring model with five IRGs (CDH1, FGFR3, INHBA, S100B, and SCG2) based on the clinical, mutation, and RNA sequencing data of individuals with stage N+M0 BRCA sourced from The Cancer Genome Atlas. Results from the Shandong Cancer Hospital and Institute validation cohort suggested that regardless of clinical stage, tumour size, or the number of lymph node metastases, this model was able to reliably discriminate low-risk patients from high-risk ones and assess the prognosis of patients with stage N+M0 BRCA, and low-risk patients could benefit more from immunotherapy than high-risk patients. In addition, significant inter-group variations in immunocyte infiltration and the tumour microenvironment were observed. Moreover, risk score and age were found to be independent factors in multivariate COX regression analysis, which influenced the outcome of patients with stage N+M0 BRCA. Based on the above findings, we plotted a prognostic nomogram. Finally, we constructed a lncRNA KCNQ1OT1-LINC00665-TUG1/miR-9-5p/CDH1 regulatory axis of the ceRNA network to explore the mechanism of BRCA progression. In summary, we conducted a systemic and extensive bioinformatics investigation and established an IRG-based prognostic scoring model. Finally, we constructed a ceRNA regulatory axis that might play a significant role in BRCA development. More research is required to confirm this result. Scoring system-based patient grouping can help predict the outcome of patients with stage N+M0 BRCA more effectively and determine their sensitivity to immunotherapies, which will aid the development of personalised therapeutic strategies and inspire the research and development of novel medications.

Integrated Analysis of ECT2 and COL17A1 as Potential Biomarkers for Pancreatic Cancer

Background

Pancreatic cancer (PC) is a malignant tumor of the digestive tract. It presents with atypical clinical symptoms and lacks specific diagnostic indicators. This study is aimed at exploring the potential biomarkers of PC.

Methods

TCGA database pancreatic cancer dataset was normalized and used to identify differentially expressed genes (DEGs). Survival, independent prognostic, and clinical correlation analyses were performed on DEGs to screen for key genes. DNA methylation, mutation, and copy number variation (CNV) analyses were used to analyze genetic variants in key genes. GSEA was performed to explore the functional enrichment of the key genes. Based on the expression of key genes, construction of a competing endogenous RNA (ceRNA) network, analysis of the tumor microenvironment (TME), and prediction of chemotherapeutic drug sensitivity were performed. Furthermore, the GEO database was used to validate the reliability of key genes.

Results

Two key genes (ECT2 and COL17A1) were identified, which were highly expressed in PC. The mRNA expression of ECT2 and COL17A1 was associated with DNA methylation and CNV. The cell cycle, proteasome, and pathways in cancer were enriched in the high-COL17A1 and ECT2 groups. The TME results showed that immune scores were decreased in the high-ECT2 group. CeRNA network results showed that there were eleven miRNAs were involved in the regulation of ECT2 and COL17A1. Moreover, pRRophetic analysis showed that 20 chemotherapeutic drugs were associated with ECT2 and COL17A1 expression.

Conclusions

Collectively, ECT2 and COL17A1 may be potential biomarkers for PC, providing a new direction for clinical diagnosis and treatment.

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

Background

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

Method

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

Result

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

Conclusion

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

Essential amino acids deprivation is a potential strategy for breast cancer treatment

Aims

The study aimed to search novel, simple and practical index reflecting the level of essential amino acids (EAAs) metabolism in breast cancer (BC), as well as to explore the effect of enhanced EAAs metabolism on the prognosis and immune microenvironment of BC, thus providing new evidence for the application of EAAs deprivation in the BC treatment.

Methods

The study includes the analysis of multi-omics and clinical data of 13 BC cell lines and 2898 BC patients in the public database. Further validation was performed using multi-omics and immunohistochemistry data from 83 BC tissue samples collected at our hospital.

Results

According to the multi-omics data, the SLC7A5 to SLC7A8 Ratio (SSR) score was found to be significantly correlated with the EAAs level and EAAs-metabolic activity of BC, suggesting that the SSR score might be used as a biomarker to assess the degree of EAAs metabolism in BC. Besides, BC patients with high EAAs metabolism had shorter overall survival (OS) time, higher PD-L1 expression, and higher T regulatory cells (Tregs) infiltration, indicating that a high EAAs metabolism was related to a poor prognosis and immune suppression in BC. Additionally, MYC amplification is a critical molecular process in the metabolic reprogramming of EAAs in BC.

Conclusion

EAAs may be a possible therapeutic target for BC treatment.

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A novel biomarker to assess the EAAs metabolism in breast cancer.

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High EAAs metabolism is related to a poor prognosis in breast cancer.

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High EAAs metabolism is also related to immune suppression in breast cancer.

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MYC amplification drives the metabolic reprogramming of EAAs in breast cancer.

Identification of differentially expressed genes-related prognostic risk model for survival prediction in breast carcinoma patients

Since the imbalance of gene expression has been demonstrated to tightly related to breast cancer (BRCA) genesis and growth, common genes expressed of BRCA were screened to explore the essence in-between. In current work, most common differentially expressed genes (DEGs) in various subtypes of BRCA were identified. Functional enrichment analysis illustrated the driving factor of deactivation of the cell cycle and the oocyte meiosis, which critically triggers the development of BRCA. Herein, we constructed a 12-gene prognostic risk model relative to differential gene expression. Subsequently, the K-M curves, analysis on time-ROC curve and Cox regression were performed to assess this risk model by determining the respective prognostic value, and the prediction performance were ascertained for both training and validation cohorts. In addition, multivariate Cox regression was analysed to reveal the independence between risk score and prognostic stage, and the accuracy and sensitivity of prognosis are particularly improved after clinical indicators are included into the analysis. In summary, this study offers novel insights into the imbalance of gene expression within BRCA, and highlights 12 selected genes associated with patient prognosis. The risk model can help individualize treatment for patients at different risks, and propose precise strategies and treatments for BRCA therapy.

Establishment of a Novel Risk Score System of Immune Genes Associated With Prognosis in Esophageal Carcinoma

Background

Few studies have addressed the role of immune-related genes in the survival and prognosis of different esophageal cancer (EC) sub-types. We established two new prognostic model indexes by bioinformatics analysis to select patients with esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) who may benefit from immunotherapy.

Methods

Based on TCGA and ImmPort data sets, we screened immune genes differentially expressed between tumor and normal tissues in ESCC and EAC and analyzed the relationship between these genes and patient survival outcomes. We established the risk score models of immune-related genes in ESCC and EAC by multivariate COX regression analysis.

Results

We identified 12 and 11 immune-related differentially expressed genes associated with the clinical prognosis of ESCC and EAC respectively, based on which two prognostic risk score models of the two EC sub-types were constructed. It was found that the survival probability of patients with high scores was significantly lower than that of patients with low scores (p < 0.001). BMP1, EGFR, S100A12, HLA-B, TNFSF18, IL1B, MAPT and OXTR were significantly related to sex, TNM stage or survival outcomes of ESCC or EAC patients (p < 0.05). In addition, the risk score of ESCC was significantly correlated with the level of B cell infiltration in immune cells (p < 0.05).

Conclusions

The prognosis-related immune gene model indexes described herein prove to be useful prognostic biomarkers of the two EC sub-types in that they may provide a reference direction for looking for the beneficiaries of immunotherapy for EC patients.

Identification of a Novel Glycolysis-Related Gene Signature for Predicting Breast Cancer Survival

To identify a glycolysis-related gene signature for the evaluation of prognosis in patients with breast cancer, we analyzed the data of a training set from TCGA database and four validation cohorts from the GEO and ICGC databases which included 1,632 patients with breast cancer. We conducted GSEA, univariate Cox regression, LASSO, and multiple Cox regression analysis. Finally, an 11-gene signature related to glycolysis for predicting survival in patients with breast cancer was developed. And Kaplan–Meier analysis and ROC analyses suggested that the signature showed a good prognostic ability for BC in the TCGA, ICGC, and GEO datasets. The analyses of univariate Cox regression and multivariate Cox regression revealed that it’s an important prognostic factor independent of multiple clinical features. Moreover, a prognostic nomogram, combining the gene signature and clinical characteristics of patients, was constructed. These findings provide insights into the identification of breast cancer patients with a poor prognosis.