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

CENPA knockdown restrains cell progression and tumor growth in breast cancer by reducing PLA2R1 promoter methylation and modulating PLA2R1/HHEX axis

BACKGROUND

Breast cancer is a lethal malignancy affecting females worldwide. It has been reported that upregulated centromere protein A (CENPA) expression might indicate unfortunate prognosis and can function as a prognostic biomarker in breast cancer. This study aimed to investigate the accurate roles and downstream mechanisms of CENPA in breast cancer progression.

METHODS

CENPA protein levels in breast cancer tissues and cell lines were analyzed by Western blot and immunohistochemistry assays. We used gain/loss-of-function experiments to determine the potential effects of CENPA and phospholipase A2 receptor (PLA2R1) on breast cancer cell proliferation, migration, and apoptosis. Co-IP assay was employed to validate the possible interaction between CENPA and DNA methyltransferase 1 (DNMT1), as well as PLA2R1 and hematopoietically expressed homeobox (HHEX). PLA2R1 promoter methylation was determined using methylation-specific PCR assay. The biological capabilities of CENPA/PLA2R1/HHEX axis in breast cancer cells was determined by rescue experiments. In addition, CENPA-silenced MCF-7 cells were injected into mice, followed by measurement of tumor growth.

RESULTS

CENPA level was prominently elevated in breast cancer tissues and cell lines. Interestingly, CENPA knockdown and PLA2R1 overexpression both restrained breast cancer cell proliferation and migration, and enhanced apoptosis. On the contrary, CENPA overexpression displayed the opposite results. Moreover, CENPA reduced PLA2R1 expression through promoting DNMT1-mediated PLA2R1 promoter methylation. PLA2R1 overexpression could effectively abrogate CENPA overexpression-mediated augment of breast cancer cell progression. Furthermore, PLA2R1 interacted with HHEX and promoted HHEX expression. PLA2R1 knockdown increased the rate of breast cancer cell proliferation and migration but restrained apoptosis, which was abrogated by HHEX overexpression. In addition, CENPA silencing suppressed tumor growth in vivo.

CONCLUSION

CENPA knockdown restrained breast cancer cell proliferation and migration and attenuated tumor growth in vivo through reducing PLA2R1 promoter methylation and increasing PLA2R1 and HHEX expression. We may provide a promising prognostic biomarker and novel therapeutic target for breast cancer.

A new prognostic model for glioblastoma multiforme based on coagulation-related genes

Background

Glioblastoma multiforme (GBM) is the most aggressive, common, and lethal type of primary brain tumor. Multiple cancers have been associated with abnormalities in the coagulation system that facilitate tumor invasion and metastasis. In GBM, the prognostic value and underlying mechanism of coagulation-related genes (CRGs) have not been explored.

Methods

RNA sequencing (RNA-seq) and clinical information on GBM were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), respectively. Following the identification of differentially expressed CRGs (DECRGs) between GBM and control samples, the survival-related DECRGs were selected via univariate and multivariate Cox regression analyses to establish a prognostic signature. The prognostic performance and clinical utility of the prognostic signature were assessed by the Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curve analysis, and a nomogram was constructed. The signature genes-related underlying mechanisms were analyzed according to gene set enrichment analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-cell analysis. Finally, the difference in immune cell infiltration, stromal score, immune score, and Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) score were compared between different risk groups.

Results

A 5-gene prognostic signature (PLAUR, GP6, C5AR1, SERPINA5, F2RL2) was established for overall survival (OS) prediction of GBM patients. The predicted efficiency of the prognostic signature was confirmed in TGGA-GBM dataset and validated in the CGGA-GBM dataset, revealing that it could differentiate GBM patients from controls well, and high risk score was accompanied with poor prognosis. Moreover, biological process (BP) and signaling pathway analyses showed that signature genes were mainly enriched in the functions of blood coagulation and tumor invasion and metastasis. Moreover, high-risk patients exhibited higher levels of immune cell infiltration, stromal score, immune score, and ESTIMATE score than that of low-risk patients.

Conclusions

An analysis of coagulation-related prognostic signatures was conducted in this study, as well as how signature genes may affect GBM progress, providing information that might provide new ideas for the development of GBM-related molecular targeted therapies.

Machine learning identification of cuproptosis and necroptosis-associated molecular subtypes to aid in prognosis assessment and immunotherapy response prediction in low-grade glioma

Both cuproptosis and necroptosis are typical cell death processes that serve essential regulatory roles in the onset and progression of malignancies, including low-grade glioma (LGG). Nonetheless, there remains a paucity of research on cuproptosis and necroptosis-related gene (CNRG) prognostic signature in patients with LGG. We acquired patient data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) and captured CNRGs from the well-recognized literature. Firstly, we comprehensively summarized the pan-cancer landscape of CNRGs from the perspective of expression traits, prognostic values, mutation profiles, and pathway regulation. Then, we devised a technique for predicting the clinical efficacy of immunotherapy for LGG patients. Non-negative matrix factorization (NMF) defined by CNRGs with prognostic values was performed to generate molecular subtypes (i.e., C1 and C2). C1 subtype is characterized by poor prognosis in terms of disease-specific survival (DSS), progression-free survival (PFS), and overall survival (OS), more patients with G3 and tumour recurrence, high abundance of immunocyte infiltration, high expression of immune checkpoints, and poor response to immunotherapy. LASSO-SVM-random Forest analysis was performed to aid in developing a novel and robust CNRG-based prognostic signature. LGG patients in the TCGA and GEO databases were categorized into the training and test cohorts, respectively. A five-gene signature, including SQSTM1, ZBP1, PLK1, CFLAR, and FADD, for predicting OS of LGG patients was constructed and its predictive reliability was confirmed in both training and test cohorts. In both the training and the test datasets (cohorts), higher risk scores were linked to a lower OS rate. The time-dependent ROC curve proved that the risk score had outstanding prediction efficiency for LGG patients in the training and test cohorts. Univariate and multivariate Cox regression analyses showed the CNRG-based prognostic signature independently functioned as a risk factor for OS in LGG patients. Furthermore, we developed a highly reliable nomogram to facilitate the clinical practice of the CNRG-based prognostic signature (AUC > 0.9). Collectively, our results gave a promising understanding of cuproptosis and necroptosis in LGG, as well as a tailored prediction tool for prognosis and immunotherapeutic responses in patients.

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.

Identification of Key Genes and Key Pathways in Breast Cancer Based on Machine Learning

BACKGROUND Breast cancer is one of the most common malignant tumors among women worldwide. This study aimed to screen key genes and pathways for breast cancer diagnosis and treatment. MATERIAL AND METHODS We obtained public data from the NCBI GEO database. The data were divided into a control group (normal breast tissue) and a treatment group (breast cancer tissue). We screened 32 differentially expressed genes (DEGs) between normal breast and cancerous tissues and used GO analysis and GSEA to identify the key pathways. We then combined LASSO and SVM-RFE analyses to screen key genes, and used CIBERSORT to obtain the proportion of 22 types of immune cells. The relationships between key genes and immune-infiltrating cells were further explored. RESULTS We screened 32 DEGs from the 2 groups, including 27 downregulated genes and 5 upregulated genes. GO analysis indicated that the DEGs were mainly correlated with collagen-containing extracellular matrix (ECM), Wnt signaling pathway, and glycosaminoglycan binding. GSEA indicated that the treatment group was correlated with chromosome segregation and cell cycle while the control group was correlated with cornification, intermediate filament, and nuclear transcription. Through machine learning, SYNM, TGFBR3, and COL10A1 were screened as key genes. Numbers of CD8 T cells, gamma delta T cells, and M1 macrophages were significantly higher, while monocytes and follicular helper-T cells were significantly lower in the treatment group. The downregulated genes, SYNM and TGFBR3, were positively correlated with CD8 T cells and monocytes, but were negatively correlated with gamma delta T cells and M1 macrophages. The upregulated gene, COL10A1, was positively correlated with gamma delta T cells and M1 macrophages, and was negatively correlated with CD8 T cells, monocytes, and follicular helper-T cells. CONCLUSIONS SYNM, TGFBR3, and COL10A1 are diagnostic genes of breast cancer. They affect breast cancer cells by modulating immune-infiltrating cells.

Polygenic and Network-Based Studies in Risk Identification and Demystification of cancer

INTRODUCTION

Diseases were initially thought to be the consequence of a single gene mutation. Advances in DNA sequencing tools and our understanding of gene behavior have revealed that complex diseases, such as cancer, are the product of genes cooperating with each other and with their environment in orchestrated communication networks. Seeing that the function of individual genes is still used to analyze cancer, the shift to using functionally interacting groups of genes as a new unit of study holds promise for demystifying cancer.

AREAS COVERED

The literature search focused on three types of cancer, namely breast, lung, and prostate, but arguments from other cancers were also included. The aim was to prove that multigene analyses can accurately predict and prognosticate cancer risk, subtype cancer for more personalized and effective treatments, and discover anti-cancer therapies. Computational intelligence is being harnessed to analyze this type of data and is proving indispensable to scientific progress.

EXPERT OPINION

In the future, comprehensive profiling of all kinds of patient data (e.g., serum molecules, environmental exposures) can be used to build universal networks that should help us elucidate the molecular mechanisms underlying diseases and provide appropriate preventive measures, ensuring lifelong health and longevity.