ATP2C2 Has Potential to Define Tumor Microenvironment in Breast Cancer

LINC01116 affects patient survival differently and is dissimilarly expressed in ER+ and ER- breast cancer samples

BACKGROUND

Breast cancer is the most commonly detected cancer and one of the leading causes of cancer mortality. Emerging evidence supports that aberrant expression of lncRNAs is correlated with tumor progression and various aspects of tumor development.

AIM

This study aimed to evaluate the expression pattern of LINC01116 in breast cancer tissues and investigate the impact of LINC01116 on patients' survival.

METHODS AND RESULTS

Microarray and qRT-PCR data analysis were performed, and the KM-plotter database was used in this study. In addition, the gain of function approach was performed to examine the effect of LINC01116 on breast cancer cells in-vitro. The results exhibited that LINC01116 is meaningfully upregulated in the ER+ tumor specimens compared to the ER- ones. Also, relative to normal tissues, the expression of LINC01116 in ER+ and ER- tumor tissues significantly increased and decreased, respectively. ROC curve analysis revealed the power of LINC01116 in distinguishing ER+ from ER- samples. Additionally, the Kaplan-Meier survival analysis showed that the LINC01116 expression positively correlates with survival probability in all as well as ER+ patients. However, this correlation was negative in ER- patients. Furthermore, our results showed that the overexpression of LINC01116 induces TGF-β signaling in ER- cells (MDA-MB-231), and microarray data analysis revealed that LINC01116 is significantly upregulated in 17β-Estradiol treated MCF7 cells.

CONCLUSION

In conclusion, our results suggest that LINC01116 can be a potential biomarker in distinguishing ER+ and ER- tissues and has different effects on patients' survival based on ER status by affecting TGF-β and ER signaling.

ATP2C2 as a novel immune-related marker that defines the tumor microenvironment in triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) is an aggressive cancer that affects about 13/100,000 women yearly. Patients with TNBC are often resistant to endocrine and molecular targeted therapy, making clinical treatment challenging. Researches indicate that tumor microenvironment (TME) is related to prognosis in many cancers. Therefore, we aim to identify TME immune-related biomarkers to enhance the prognosis and immunotherapy efficacy in patients with TNBC.

Methods

The bulk mRNA transcriptome data and clinical information of the (GSE58812) and (GSE25055) datasets were downloaded from the Gene Expression Omnibus (GEO) database, and the ESTIMATE algorithm was used to calculate the ImmuneScore, StromalScore, and ESTIMATEScore. Patients were divided into low and high groups according to the quartiles of ImmuneScore, StromalScore, and the median of ESTIMATEScore to filter differential expression genes (DEGs), respectively. The DEGs were then evaluated using univariate and multivariate Cox regression to identify TME-related genes and its association with survival rate for the construction of a TMErisk model with three biomarkers. Then Gene Expression Profiling Interactive Analysis (GEPIA) and The Cancer Genome Atlas (TCGA) data were used to compare the gene expression in cancer and normal tissues. xCell analysis calculated the proportion of tumor-infiltrating immune cells in low and high expression of ATPase Secretory Pathway Ca2+ Transporting 2 (ATP2C2). In addition, samples from 20 TNBC patients admitted to our institution were used for immunohistochemical (IHC) examination.

Results

Three immune-related DEGs were identified, including prolyl 3-hydroxylase 2 (P3H2), sodium voltage-gated channel beta subunit 3 (SCN3B), and ATP2C2 and a TMErisk model was constructed and validated. However, only ATP2C2 was selected for further analysis. ATP2C2 mRNA level of TNBC patients was higher than that of normal breast tissue. Survival analysis showed that patients with high expression of ATP2C2 had a bad prognosis. xCell analysis demonstrated that the expression of ATP2C2 was associated with 16 kinds of tumor-infiltrating immune cells. Protein expression of ATP2C2 in TNBC tissues was higher compared to paired normal tissues in IHC.

Conclusions

This study constructed and validated a TMErisk model that can effectively predict 3- and 5-year survival rate for TNBC patients. TNBC patients with lower expression of ATP2C2 had a good prognosis.

Identification of an endoplasmic reticulum stress-related gene signature to predict prognosis and potential drugs of uterine corpus endometrial cancer

Uterine corpus endometrial cancer (UCEC) is the sixth most common female cancer worldwide, with an increasing incidence. Improving the prognosis of patients living with UCEC is a top priority. Endoplasmic reticulum (ER) stress has been reported to be involved in tumor malignant behaviors and therapy resistance, but its prognostic value in UCEC has been rarely investigated. The present study aimed to construct an ER stress-related gene signature for risk stratification and prognosis prediction in UCEC. The clinical and RNA sequencing data of 523 UCEC patients were extracted from TCGA database and were randomly assigned into a test group (n = 260) and training group (n = 263). An ER stress-related gene signature was established by LASSO and multivariate Cox regression in the training group and validated by Kaplan-Meier survival analysis, Receiver Operating Characteristic (ROC) curves and nomograms in the test group. Tumor immune microenvironment was analyzed by CIBERSORT algorithm and single-sample gene set enrichment analysis. R packages and the Connectivity Map database were used to screen the sensitive drugs. Four ERGs (ATP2C2, CIRBP, CRELD2 and DRD2) were selected to build the risk model. The high-risk group had significantly reduced overall survival (OS) (P < 0.05). The risk model had better prognostic accuracy than clinical factors. Tumor-infiltrating immune cells analysis depicted that CD8+ T cells and regulatory T cells were more abundant in the low-risk group, which may be related to better OS, while activated dendritic cells were active in the high-risk group and associated with unfavorable OS. Several kinds of drugs sensitive to the high-risk group were screened out. The present study constructed an ER stress-related gene signature, which has the potential to predict the prognosis of UCEC patients and have implications for UCEC treatment.

Integrated multi-omics approach revealed cellular senescence landscape

Cellular senescence is a complex multifactorial biological phenomenon that plays essential roles in aging, and aging-related diseases. During this process, the senescent cells undergo gene expression altering and chromatin structure remodeling. However, studies on the epigenetic landscape of senescence using integrated multi-omics approaches are limited. In this research, we performed ATAC-seq, RNA-seq and ChIP-seq on different senescent types to reveal the landscape of senescence and identify the prime regulatory elements. We also obtained 34 key genes and deduced that NAT1, PBX1 and RRM2, which interacted with each other, could be the potential markers of aging and aging-related diseases. In summary, our work provides the landscape to study accessibility dynamics and transcriptional regulations in cellular senescence. The application of this technique in different types of senescence allows us to identify the regulatory elements responsible for the substantial regulation of transcription, providing the insights into molecular mechanisms of senescence.

An Immune-Related Long Noncoding RNA Pair as a New Biomarker to Predict the Prognosis of Patients in Breast Cancer

Background: Immune-related long non-coding RNAs (irlncRNAs) might remodel the tumor immune microenvironment by changing the inherent properties of tumor cells and the expression of immune genes, which have been used to predict the efficacy of immunotherapy and the prognosis of various tumors. However, the value of irlncRNAs in breast cancer (BRCA) remains unclear.

Materials and Methods: Initially, transcriptome data and immune-related gene sets were downloaded from The Cancer Genome Atlas (TCGA) database. The irlncRNAs were extracted from the Immunology Database and Analysis Portal (ImmPort) database. Differently expressed irlncRNAs (DEirlncRNAs) were further identified by utilizing the limma R package. Then, univariate and multivariate Cox regression analyses were conducted to select the DEirlncRNAs associated with the prognosis of BRCA patients. In addition, the univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were performed to determine the DEirlncRNA pairs with the independent prediction capability of prognosis in BRCA patients. Finally, the chosen DEirlncRNA pair would be evaluated in terms of survival time, clinicopathological characteristics, tumor-infiltrating immune cells, immune checkpoints (ICs), signaling pathways, and potential small-molecule drugs.

Results: A total of 21 DEirlncRNA pairs were extracted, and among them, lncRNA MIR4435-2HG and lncRNA U62317.1 were chosen to establish a risk signature that served as an independent prognostic biomarker in BRCA patients. Patients in the high-risk group had a worse prognosis than those in the low-risk group, and they also had an abundance of infiltration of CD4+ T and CD8+ T cells to enhance the immune response to tumor cells. Furthermore, the risk signature showed a strong correlation with ICs, signaling pathways, and potential small-molecule drugs.

Conclusion: Our research revealed that the risk signature independent of specific DEirlncRNA pair expression was closely associated with the prognosis and tumor immune microenvironment in BRCA patients and had the potential to function as an independent prognostic biomarker and a predictor of immunotherapy for BRCA patients, which would provide new insights for BRCA accurate treatment.

The Novel Methylation Biomarker NPY5R Sensitizes Breast Cancer Cells to Chemotherapy

Breast cancer (BC) is the most common tumor in women, and the molecular mechanism underlying its pathogenesis remains unclear. In this study, we aimed to investigate gene modules related to the phenotypes of BC, and identify representative candidate biomarkers for clinical prognosis of BC patients. Using weighted gene co-expression network analysis, we here identified NPY5R as a hub gene in BC. We further found that NPY5R was frequently downregulated in BC tissues compared with adjacent tumor-matched control tissues, due to its aberrant promoter CpG methylation which was confirmed by methylation analysis and treatment with demethylation agent. Higher expression of NPY5R was closely associated with better prognosis for BC patients. Gene set enrichment analysis showed that transcriptome signatures concerning apoptosis and cell cycle were critically enriched in specimens with elevated NPY5R. Ectopic expression of NPY5R significantly curbed breast tumor cell growth, induced cell apoptosis and G2/M arrest. Moreover, NPY5R also promoted the sensitivity of BC cells to doxorubicin. Mechanistically, we found that NPY5R restricted STAT3 signaling pathway activation through interacting with IL6, which may be responsible for the antitumor activity of NPY5R. Collectively, our findings indicate that NPY5R functions as a tumor suppressor but was frequently downregulated in BC.

Alterations in the Ca2+ toolkit in oesophageal adenocarcinoma

Aim:

To investigate alterations in transcription of genes, encoding Ca²⁺ toolkit proteins, in oesophageal adenocarcinoma (OAC) and to assess associations between gene expression, tumor grade, nodal-metastatic stage, and patient survival.

Methods:

The expression of 275 transcripts, encoding components of the Ca²⁺ toolkit, was analyzed in two OAC datasets: the Cancer Genome Atlas [via the University of Alabama Cancer (UALCAN) portal] and the oesophageal-cancer, clinical, and molecular stratification [Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS)] dataset. Effects of differential expression of these genes on patient survival were determined using Kaplan-Meier log-rank tests. OAC grade- and metastatic-stage status was investigated for a subset of genes. Adjustment for the multiplicity of testing was made throughout.

Results:

Of the 275 Ca²⁺-toolkit genes analyzed, 75 displayed consistent changes in expression between OAC and normal tissue in both datasets. The channel-encoding genes, N-methyl-D-aspartate receptor 2D (GRIN2D), transient receptor potential (TRP) ion channel classical or canonical 4 (TRPC4), and TRP ion channel melastatin 2 (TRPM2) demonstrated the greatest increase in expression in OAC in both datasets. Nine genes were consistently upregulated in both datasets and were also associated with improved survival outcomes. The 6 top-ranking genes for the weighted significance of altered expression and survival outcomes were selected for further analysis: voltage-gated Ca²⁺ channel subunit α 1D (CACNA1D), voltage-gated Ca²⁺ channel auxiliary subunit α2 δ4 (CACNA2D4), junctophilin 1 (JPH1), acid-sensing ion channel 4 (ACCN4), TRPM5, and secretory pathway Ca²⁺ ATPase 2 (ATP2C2). CACNA1D, JPH1, and ATP2C2 were also upregulated in advanced OAC tumor grades and nodal-metastatic stages in both datasets.

Conclusions:

This study has unveiled alterations of the Ca²⁺ toolkit in OAC, compared to normal tissue. Such Ca²⁺ signalling findings are consistent with those from studies on other cancers. Genes that were consistently upregulated in both datasets might represent useful markers for patient diagnosis. Genes that were consistently upregulated, and which were associated with improved survival, might be useful markers for patient outcome. These survival-associated genes may also represent targets for the development of novel chemotherapeutic agents.

CD40LG as a Prognostic Molecular Marker Regulates Tumor Microenvironment Through Immune Process in Breast Cancer

Purpose

Breast cancer (BRCA) is the second most common malignancy in the world and the most common in women. Here, we utilized publicly available BRCA dataset to investigate potential prognosis-related genes through integrated bioinformatics analysis.

Materials and Methods

BRCA dataset was obtained from the Cancer Genome Atlas (TCGA) database. The ESTIMATE algorithm was used to calculate the ImmuneScores and StromalScores of the samples and then divided them into high- and low-score groups based on the median score. Common differentially expressed genes (DEGs) were identified through differential expression analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. The core prognostic genes were the intersection of hub genes from PPI network and prognostic genes from univariate Cox proportional hazard regression analysis. Finally, the CIBERSORT algorithm was used to calculate proportions of 22 tumor-infiltrating immune cells (TICs) in BRCA samples.

Results

A total of 486 DEGs were identified. These genes were mainly enriched in immune-related pathways. Crossover genes between the hub genes and the prognostic genes were CD2 and CD40LG. CD40LG was further investigated in this study. CD40LG was downregulated in BRCA samples compared with normal samples, and a lower CD40LG expression was associated with advanced tumor stages and a poor prognosis. CD40LG was shown to be involved in immune-related pathways of BRCA by Gene Set Enrichment Analysis. Finally, 14 TICs were found to have a close relationship with CD40LG.

Conclusion

CD40LG was found to be a core prognostic gene related to tumor microenvironment and deeply involved in immune-related pathways in BRCA. Our findings may provide new insights for exploring the molecular mechanisms of BRCA and developing new immunotherapies for the disease.