Androgen receptor expression and clinical characteristics in breast cancer.

OBJECTIVE: To investigate the relationship between the expression of androgen receptor (AR) and clinical characteristics in breast cancer. PATIENTS AND METHODS: The clinical records of all 432 patients tested for AR in our institution between January 2020 and May 2023 were reviewed. Clinical characteristics, age, menopausal status, tumor node metastasis (TNM) stage, distant metastasis, pathological complete response (pCR), histopathological features histological grade, estrogen receptor (ER), progesterone receptor, Her-2, Ki-67, and molecular subtype were registered for all patients. RESULTS: About 377 (87.27%) of the 432 patients had AR expression. No significant difference in AR expression was found with age, menopausal status, TNM stage of primary tumor, or pCR. AR was positively and significantly associated with the histological grade, and recurrence. The AR expression was significantly related with molecular subtypes, including ER, PR Her-2, Ki67 and molecular subtype. ER (OR = 10.489, 95%CI: 5.470-21.569), PR (OR = 7.690, 95%CI: 3.974-16.129, Her-2 (OR = 10.489, 95%CI: 2.779-23.490 and tumor recurrence (OR = 0.110, 95%CI: 0.031-0.377 were significant independent risk factors affecting AR expression. CONCLUSIONS: AR expression can serve as a reliable basis for judging the clinical molecular types and poor prognosis for breast cancer. AR may be a novel biomarker and target in AR-positive breast cancer depending on significant difference in AR expression among different molecular types of breast cancer.

Eleven inflammation-related genes risk signature model predicts prognosis of patients with breast cancer.

Background: Changes in gene expression are associated with malignancy. Analysis of gene expression data could be used to reveal cancer subtypes, key molecular drivers, and prognostic characteristics and to predict cancer susceptibility, treatment response, and mortality. It has been reported that inflammation plays an important role in the occurrence and development of tumors. Our aim was to establish a risk signature model of breast cancer with inflammation-related genes (IRGs) to evaluate their survival prognosis. Methods: We downloaded 200 IRGs from the Molecular Signatures Database (MSigDB). The data of breast cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differential gene expression analysis, the least absolute shrinkage and selection operator (LASSO), Cox regression analysis, and overall survival (OS) analysis were used to construct a multiple-IRG risk signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out to annotate functions of the differentially expressed IRGs (DEIRGs) The predictive accuracy of the prognostic model was evaluated by time-dependent receiver operating characteristic (ROC) curves. Subsequently, nomograms were constructed to guide clinical application according to the univariate and multivariate Cox proportional hazards regression analyses. Eventually, we applied gene set variation analysis (GSVA), mutation analysis, immune infiltration analysis, and drug response analysis to compare the differences between high- and low-risk patients. Results: Totally, 65 DEIRGs were obtained after comparing 1,092 breast cancer tissues with 113 paracancerous tissues in TCGA. Among them, 11 IRGs (IL18, IL12B, RASGRP1, HPN, CLEC5A, SCARF1, TACR3, VIP, CCL2, CALCRL, ABCA1) were screened with nonzero coefficient by LASSO regression analysis to construct the prognostic model, which was validated in GSE96058.The 11-gene IRGs risk signature model stratified patients into high- or low-risk groups, with those in the low-risk group having longer survival time and less deaths. Multivariate Cox analysis manifested that risk score, age, and stage were the three independent prognostic factors for breast cancer patients. There were 12 pathways with higher activities and 24 pathways with lower activities in the high-risk group compared with the low-risk group, yet no difference of gene mutation load was observed between the two groups. In immune infiltration analysis, we noted that the proportion of T cells showed a decreased trend according to the increase of risk score and most of the immune cells were enriched in the low-risk group. Inversely, macrophages M2 were more highly distributed in the high-risk group. We identified 67 approved drugs that showed a different effect between the high- and low-risk patients and the top 2 gene-drug pairs were IL12B-sunitinib and SCARF1-ruxolitinib. Conclusions: The 11-IRG risk signature model is a promising tool to predict the survival of breast cancer patients and the expressions of IL12B and SCARF1 may serve as potential targets for therapy of breast cancer.

CircABHD2 Inhibits Malignant Progression of Endometrial Cancer by Regulating NAD+/NAMPT Metabolism Axis.

Circular RNAs (circRNAs) perform important functions in the regulation of diverse physiological and pathological processes. CircABHD2 exhibits down-regulation in both endometrial cancer (EC) cells and tissues, but the biological roles and mechanisms of action in EC are still unclear. This study aims to provide a theoretical basis for the role of circABHD2 in EC and potential targets for individualized precision therapy. Dysregulated circRNAs were identified using RNA sequencing (RNA-Seq) from EC tissues and validated using RT-qPCR. CCK-8, colony formation assay, wound healing assay, transwell assay, cell cycle, and apoptosis assay were used to evaluate the effects of circABHD2 on EC cells. Metabolomics assay and western blot analyses were used to investigate the potential mechanisms of circABHD2. From sequencing of RNA (RNA-Seq) analysis of EC tissues, we obtained 19 dysregulated circRNAs, including 8 upregulated ones and 11 downregulated ones. Using RT-qPCR on 32 EC tissues and 19 normal endometrial tissues, we confirmed that circABHD2 was downregulated in EC tissues. The expression levels of circABHD2 were closely relevant to the International Federation of Gynecology and Obstetrics (FIGO) stage and differentiation degree of EC. Functional experiments demonstrated that overexpression of circABHD2 decreased proliferation, migration, invasion, and promoted cell apoptosis. Un-targeted metabolomic assay revealed 31 differential metabolites in EC cells overexpressing circABHD2. KEGG analysis of differential metabolites indicated that NAD+ is the core metabolite regulated by circABHD2. NAMPT is one key enzyme involved in the synthetic pathway responsible for NAD+. Subsequent experiments confirmed that by inhibiting NAMPT protein expression in EC cells, cirABHD2 can inhibit NAD+ level, suggesting that circABHD2 may inhibit EC by regulating the metabolic axis of NAD+/NAMPT. CircABHD2, a downregulated circRNA in EC cells and tissues, inhibits the malignant progression of EC via the NAD+/NAMPT metabolic axis. This discovery presents a promising diagnostic biomarker and potential therapeutic target for EC.

Circular RNA HSDL2 promotes breast cancer progression via miR-7978 ZNF704 axis and regulating hippo signaling pathway.

Circular RNAs (circRNAs) are a new group of endogenous RNAs recently found to be involved in the development of various diseases, including their confirmed involvement in the progression of several types of cancers. Unluckily, the abnormal expression and functions of circRNAs in breast cancer shall be further investigated. This work aims to elucidate the action and molecular mechanism of circHSDL2 in the malignant progression of breast cancer. Differential expression profiles of circRNAs in breast cancer tissues relative to normal breast tissues and in the exosomes of breast cancer patients compared to healthy women were analyzed from databases to identify potentially functional circRNAs. CircHSDL2 was selected for further investigation. Cell proliferation, migration and invasion assays were done to assess the effect of circHSDL2 overexpression on breast cancer cells. Bioinformatics test and dual-luciferase reporter experiments were done to explore the interaction between circHSDL2 and miRNA. Downstream target genes were further investigated through proteomics analysis and Western blotting. The influence of circHSDL2 on breast cancer in vivo was evaluated through xenograft experiments in nude mice. Functional analysis demonstrated circHSDL2 overexpression promoted the division, movement, and invasion of breast cancer cells both in vivo and in vitro. Mechanistically, circHSDL2 acted as a sponge for miR-7978 to affect ZNF704 expression and thereby regulate the Hippo pathway in breast cancer cells. In conclusion, circHSDL2 regulates the Hippo pathway through the miR-7978/ZNF704 axis to facilitate the malignancy of breast cancer. This may be a potential biomarker and treatment target.

Single-cell RNA-seq reveals MIF-(CD74 + CXCR4) dependent inhibition of macrophages in metastatic papillary thyroid carcinoma.

BACKGROUND: The mechanism promoting papillary thyroid carcinoma (PTC) metastasis remains unclear. We aimed to investigate the potential metastatic mechanisms at a single-cell resolution. METHODS: We performed single-cell RNA-seq (scRNA-seq) profiling of thyroid tumour (TT), adjacent normal thyroid (NT) and lymph node metastasized tumour (LN) from a young female with PTC. Validation of our results was conducted in 31 tumours with metastasis and 30 without metastasis. RESULTS: ScRNA-seq analysis generated data on 38,215 genes and 0.14 billion transcripts from 28,839 cells, classified into 18 clusters, each annotated to represent 10 cell types. PTC cells were found to originate from epithelial cells. Epithelial cells and macrophages emerged as the strongest signal emitters and receivers, respectively. After reclustering epithelial cells and macrophages, our analysis, incorporating gene set variation analysis (GSVA), SCENIC analysis, and pseudotime trajectory analysis, indicated that subcluster 0 of epithelial cells (EP_0) showed a more malignant phenotype, and subclusters 3 and 4 of macrophages (M_3 and M_4) demonstrated heightened activity. Further analysis suggested that EP_0 may suppress the activity of M_3 and M_4 via MIF - (CD74 + CXCR4) in the MIF pathway. After analysing the expression of the 4 genes in the MIF pathway in both the TCGA cohort and our cohort (n = 61), CD74 was identified as significantly overexpressed in PTC tumours particularly those with lymph node metastasis. CONCLUSION: Our study revealed that PTC may facilitate lymph node metastasis by inhibiting macrophages via MIF signalling. It is suggested that malignant PTC cells may suppress the immune activity of macrophages by consistently releasing signals to them via MIF-(CD74 + CXCR4).

circNFIB decreases synthesis of arachidonic acid and inhibits breast tumor growth and metastasis.

We identified circNFIB (hsa_circ_0086376) as a down-regulated circRNA in breast cancer but its effect is unclear. We aimed to explore the roles of circNFIB in breast cancer. The expression levels of circNFIB in breast cancer tissues and cells were detected. Both in vitro and in vivo experiments were used to assess the effects and mechanisms of circNFIB. circNFIB was down-regulated in 29 breast cancer tissues compared to adjacent normal tissues. circNFIB is a highly conserved circRNA and mainly located in cytoplasm of breast cancer cells. In vitro experiments showed that overexpression of circNFIB inhibited proliferation and invasion of breast cancer cells, whereas knockdown of circNFIB induced proliferation and invasion. Animal experiments indicated that circNFIB inhibited tumor growth and metastasis in vivo. Bioinformatics analysis showed that circNFIB contained an open reading frame (ORF) spanning its spliced junction, an internal ribosome entry site (IRES) and a N6-methyladenosine (m6A) site, suggesting circNFIB had the potential to encode a 56 amino acid (aa) protein, which was then confirmed by experiments. Metabonomics analysis results indicated that circNFIB may inhibit synthesis of arachidonic acid (AA) by regulating phospholipase. EIF4A3 and U2AF65 may regulate circNFIB expression by binding to the flanking sequence of circNFIB. In conclusion, circNFIB is a down-regulated circRNA in breast cancer tissues and encodes a 56 aa protein. circNFIB down-regulates AA in breast cancer cells, thus decreasing AA metabolites. Based on reported evidences of AA metabolites on cancer, we speculated that circNFIB may inhibit breast tumor growth and metastasis partly by inhibiting AA.

Cuproptosis-related risk score predicts prognosis and characterizes the tumor microenvironment in colon adenocarcinoma.

Introduction: Cuproptosis is a novel copper-dependent regulatory cell death (RCD), which is closely related to the occurrence and development of multiple cancers. However, the potential role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of colon adenocarcinoma (COAD) remains unclear. Methods: Transcriptome, somatic mutation, somatic copy number alteration and the corresponding clinicopathological data of COAD were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO). Difference, survival and correlation analyses were conducted to evaluate the characteristics of CRGs in COAD patients. Consensus unsupervised clustering analysis of CRGs expression profile was used to classify patients into different cuproptosis molecular and gene subtypes. TME characteristics of different molecular subtypes were investigated by using Gene set variation analysis (GSVA) and single sample gene set enrichment analysis (ssGSEA). Next, CRG Risk scoring system was constructed by applying logistic least absolute shrinkage and selection operator (LASSO) cox regression analysis and multivariate cox analysis. Real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) were used to exam the expression of key Risk scoring genes. Results: Our study indicated that CRGs had relatively common genetic and transcriptional variations in COAD tissues. We identified three cuproptosis molecular subtypes and three gene subtypes based on CRGs expression profile and prognostic differentially expressed genes (DEGs) expression profile, and found that changes in multilayer CRGs were closely related to the clinical characteristics, overall survival (OS), different signaling pathways, and immune cell infiltration of TME. CRG Risk scoring system was constructed according to the expression of 7 key cuproptosis-related risk genes (GLS, NOX1, HOXC6, TNNT1, GLS, HOXC6 and PLA2G12B). RT-qPCR and IHC indicated that the expression of GLS, NOX1, HOXC6, TNNT1 and PLA2G12B were up-regulated in tumor tissues, compared with those in normal tissues, and all of GLS, HOXC6, NOX1 and PLA2G12B were closely related with patient survival. In addition, high CRG risk scores were significantly associated with high microsatellite instability (MSI-H), tumor mutation burden (TMB), cancer stem cell (CSC) indices, stromal and immune scores in TME, drug susceptibility, as well as patient survival. Finally, a highly accurate nomogram was constructed to promote the clinical application of the CRG Risk scoring system. Discussion: Our comprehensive analysis showed that CRGs were greatly associated with TME, clinicopathological characteristics, and prognosis of patient with COAD. These findings may promote our understanding of CRGs in COAD, providing new insights for physicians to predict prognosis and develop more precise and individualized therapy strategies.

Integrating single-cell RNA-seq and spatial transcriptomics reveals MDK-NCL dependent immunosuppressive environment in endometrial carcinoma.

Objectives: The tumor microenvironment (TME) play important roles in progression of endometrial carcinoma (EC). We aimed to assess the cell populations in TME of EC. Methods: We downloaded datasets of single-cell RNA-seq (scRNA-seq) and spatial transcriptome (ST) for EC from GEO, and downloaded RNA-Seq (FPKM) and clinical data of TCGA-UCEC project from TCGA. The datasets were analyzed using R software. Results: We obtained 5 datasets of scRNA-seq, 1 of ST and 569 samples of RNA-seq. Totally, 0.2 billion transcripts and 33,408 genes were detected in 33,162 cells from scRNA-seq. The cells were classified into 9 clusters, and EC cells were originated from epithelial cells and ciliated cells. Gene set variation analysis (GSVA) indicated that the pathways enriched in the subclusters of epithelial cells and endothelial cells were significantly different, indicating great heterogeneity in EC. Cell-cell communication analyses showed that EC cells emitted the strongest signals, and endothelial cells received more signals than other cells. Further analysis found that subclusters of 1 and 2 of epithelial cells were showed a more malignant phenotype, which may confer malignant phenotype to subcluster of 0 of endothelial cells through MK pathway by MDL-NCL signal. We also analyzed communications between spatial neighbors with ST data and confirmed the findings on MDL-NCL in cell-cell communication. TCGA and GEO analyses indicated that the expression levels of NCL was inversely correlated with ImmuneScore. Conclusion: Our study revealed EC cells can confer malignant phenotype to endothelial cells by MDK-NCL signal, and NCL is associated with suppressed immune activity. EC cells may shape TME by inhibiting immune cells and "educating" stromal cells via MDK-NCL signal.

PHF20 is a Novel Prognostic Biomarker and Correlated with Immune Status in Breast Cancer.

Increasing evidence has demonstrated that enhanced PHF20 expression plays a crucial role in cancer development and progression. However, little is known about the prognostic value of PHF20 expression in breast cancer (BRCA). In this study, we attempted to explore the expression pattern of PHF20 and its associations with prognosis and immune status in BRCA. The gene expression data and clinical information of 1109 BRCA samples were downloaded from TCGA database. The expression level of PHF20 protein was determined using UALCAN and HPA database. Kaplan-Meier method and CIBERSORT algorithm were used to analyze the associations of PHF20 expression with overall survival (OS) and immune microenvironment, respectively. Besides, GSEA analysis was conducted to explore potential biological functions and molecular mechanisms of PHF20 in BRCA. Moreover, starBase database was applied to construct a ceRNA nework. PHF20 was highly expressed in BRCA samples both at the transcriptional and protein level, and was strongly correlated with the OS and immune status. Univariable and multivariate Cox regression analyses identified PHF20 as an independent prognostic factor. Additionally, GSEA analysis showed that high PHF20 expression was closely associated with TGF-ß signaling pathway, Wnt signaling pathway, and adherens junction. Furthermore, three ceRNA networks (AC037198.1/hsa-miR-223-3p/PHF20, CBR3-AS1/hsa-miR-223-3p/PHF20, and ZNF561-AS1/hsa-miR-223-3p/PHF20) were identified by starBase analysis. Functional experiments validated that PHF20 knockdown inhibited the cell viability and progression in BRCA cells. PHF20 overexpression was significantly associated with poor prognosis and immune status in BRCA, and could act as a potential novel prognostic biomarker for BRCA.

Identification of cuproptosis-related subtypes, construction of a prognosis model, and tumor microenvironment landscape in gastric cancer.

Introduction: Cuproptosis is a novel identified regulated cell death (RCD), which is correlated with the development, treatment response and prognosis of cancer. However, the potential role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of gastric cancer (GC) remains unknown. Methods: Transcriptome profiling, somatic mutation, somatic copy number alteration and clinical data of GC samples were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database to describe the alterations of CRGs from genetic and transcriptional fields. Differential, survival and univariate cox regression analyses of CRGs were carried out to investigate the role of CRGs in GC. Cuproptosis molecular subtypes were identified by using consensus unsupervised clustering analysis based on the expression profiles of CRGs, and further analyzed by GO and KEGG gene set variation analyses (GSVA). Genes in distinct molecular subtypes were also analyzed by GO and KEGG gene enrichment analyses (GSEA). Differentially expressed genes (DEGs) were screened out from distinct molecular subtypes and further analyzed by GO enrichment analysis and univariate cox regression analysis. Consensus clustering analysis of prognostic DEGs was performed to identify genomic subtypes. Next, patients were randomly categorized into the training and testing group at a ratio of 1:1. CRG Risk scoring system was constructed through logistic least absolute shrinkage and selection operator (LASSO) cox regression analysis, univariate and multivariate cox analyses in the training group and validated in the testing and combined groups. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to evaluate the expression of key Risk scoring genes. Sensitivity and specificity of Risk scoring system were examined by using receiver operating characteristic (ROC) curves. pRRophetic package in R was used to investigate the therapeutic effects of drugs in high- and low- risk score group. Finally, the nomogram scoring system was developed to predict patients' survival through incorporating the clinicopathological features and CRG Risk score. Results: Most CRGs were up-regulated in tumor tissues and showed a relatively high mutation frequency. Survival and univariate cox regression analysis revealed that LIAS and FDX1 were significantly associated with GC patients' survival. After consensus unsupervised clustering analysis, GC patients were classified into two cuproptosis molecular subtypes, which were significantly associated with clinical features (gender, age, grade and TNM stage), prognosis, metabolic related pathways and immune cell infiltration in TME of GC. GO enrichment analyses of 84 DEGs, obtained from distinct molecular subtypes, revealed that DEGs primarily enriched in the regulation of metabolism and intracellular/extracellular structure in GC. Univariate cox regression analysis of 84 DEGs further screened out 32 prognostic DEGs. According to the expression profiles of 32 prognostic DEGs, patients were re-classified into two gene subtypes, which were significantly associated with patients' age, grade, T and N stage, and survival of patients. Nest, the Risk score system was constructed with moderate sensitivity and specificity. A high CRG Risk score, characterized by decreased microsatellite instability-high (MSI-H), tumor mutation burden (TMB) and cancer stem cell (CSC) index, and high stromal and immune score in TME, indicated poor survival. Four of five key Risk scoring genes expression were dysregulated in tumor compared with normal samples. Moreover, CRG Risk score was greatly related with sensitivity of multiple drugs. Finally, we established a highly accurate nomogram for promoting the clinical applicability of the CRG Risk scoring system. Discussion: Our comprehensive analysis of CRGs in GC demonstrated their potential roles in TME, clinicopathological features, and prognosis. These findings may improve our understanding of CRGs in GC and provide new perceptions for doctors to predict prognosis and develop more effective and personalized therapy strategies.

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.

CircPrimer 2.0: a software for annotating circRNAs and predicting translation potential of circRNAs.

BACKGROUND: Some circular RNAs (circRNAs) can be translated into functional peptides by small open reading frames (ORFs) in a cap-independent manner. Internal ribosomal entry site (IRES) and N6-methyladenosine (m6A) were reported to drive translation of circRNAs. Experimental methods confirming the presence of IRES and m6A site are time consuming and labor intensive. Lacking computational tools to predict ORFs, IRESs and m6A sites for circRNAs makes it harder. RESULTS: In this report, we present circPrimer 2.0, a Java based software for annotating circRNAs and predicting ORFs, IRESs, and m6A sites of circRNAs. circPrimer 2.0 has a graphical and a command-line interface that enables the tool to be embed into an analysis pipeline. CONCLUSIONS: circprimer 2.0 is an easy-to-use software for annotating circRNAs and predicting translation potential of circRNAs, and freely available at www.bio-inf.cn .

CircATRNL1 and circZNF608 Inhibit Ovarian Cancer by Sequestering miR-152-5p and Encoding Protein.

Background: CircRNAs have been found to be involved in the pathogenesis of various diseases. We aimed to explore the roles of circRNAs in ovarian cancer. Methods: The expression levels of circRNAs in ovarian cancer and normal ovarian tissues were analyzed using RNA sequencing. Fluorescent in situ hybridization (FISH), proliferation assays and transwell assays were used to assess the effects of circRNAs on ovarian cancer. Results: CircATRNL1 and circZNF608 were downregulated in 20 ovarian cancer tissues compared to normal tissues. CircATRNL1 and circZNF608 are mainly located in the cytoplasm of ovarian cancer cells, and circATRNL1 is a highly conserved circRNA. The overexpression of circATRNL1 and circZNF608 inhibits the proliferation and invasion of ovarian cancer cells. We predicted miRNA-circRNA interactions for circZNF608 and circATRNL1 and obtained 63 interactions. However, a luciferase reporter assay showed that only miR-152-5p was sequestered by circZNF608. Bioinformatics analysis and experiments indicated that circATRNL1 contains an internal ribosome entry site and an open reading frame encoding a 131 aa protein. Conclusion: In conclusion, circATRNL1 and circZNF608 are two downregulated circRNAs in ovarian cancer and work as tumor suppressors. CircZNF608 may exert antitumor activity in ovarian cancer by binding miR-152-5p, and circATRNL1 may encode a 131 aa protein.

tRF-20-S998LO9D inhibits endometrial carcinoma by upregulating SESN2.

Aim: To explore the roles of transfer RNA-derived small RNAs (tsRNAs) in endometrial carcinoma (EC). Materials & methods: tsRNA profiles for EC from TCGA were analyzed. The functions and mechanisms of tsRNA were explored using in vitro experiments. Results: 173 dysregulated tsRNAs were identified. After validating in EC tissues and serumal exosome samples from EC patients, a downregulated tsRNA in both EC tissues and serumal exosomes (i.e., tRF-20-S998LO9D) was observed. Exosomal tRF-20-S998LO9D had an area under the curve of 0.768. tRF-20-S998LO9D overexpression inhibited proliferation, migration and invasion and promoted apoptosis of EC cells and tRF-20-S998LO9D knockdown further confirmed its effects. Further analyses showed that tRF-20-S998LO9D upregulated SESN2 in protein levels. Conclusion: tRF-20-S998LO9D inhibits EC cells by upregulating SESN2.

The m6A-related gene signature for predicting the prognosis of breast cancer.

N6-methyladenosine (m6A) modification has been shown to participate in tumorigenesis and metastasis of human cancers. The present study aimed to investigate the roles of m6A RNA methylation regulators in breast cancer. We used LASSO regression to identify m6A-related gene signature predicting breast cancer survival with the datasets downloaded from Gene Expression Omnibus and The Cancer Genome Atlas (TCGA). RNA-Seq data of 3409 breast cancer patients from GSE96058 and 1097 from TCGA were used in present study. A 10 m6A-related gene signature associated with prognosis was identified from 22 m6A RNA methylation regulators. The signature divided patients into low- and high-risk group. High-risk patients had a worse prognosis than the low-risk group. Further analyses indicated that IGF2BP1 may be a key m6A RNA methylation regulator in breast cancer. Survival analysis showed that IGF2BP1 is an independent prognostic factor of breast cancer, and higher expression level of IGF2BP1 is associated with shorter overall survival of breast cancer patients. In conclusion, we identified a 10 m6A-related gene signature associated with overall survival of breast cancer. IGF2BP1 may be a key m6A RNA methylation regulator in breast cancer.

Identification of hub genes with prognostic values in multiple myeloma by bioinformatics analysis.

PURPOSE: Multiple myeloma (MM) is a malignant disease with abnormal proliferation of clonal plasma cells. Hypoxia is an important factor in the pathogenesis and development of MM. However, the underlying mechanisms are not fully understood. MATERIAL & METHODS: To determine hub genes related to hypoxia in MM, this study took integrated bioinformatics analysis with two expression datasets (GSE80140 and GSE80545) downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were filtrated under the condition of both p-value < 0.05 and [log2FoldChange (log2FC)] > 1. Then, gene ontology (GO) and Kyoto encyclopedia of genes and genomes enrichment (KEGG) analysis, and protein-protein interaction (PPI) network construction were utilized to further explore these DEGs. PrognoScan evaluated all the candidate hub genes for survival analysis. RESULTS: In total, three hub genes, including FH, TSTA3, and POLR3G, were screened out to be related to hypoxia in MM. Patients with the lower expression level of FH, TSTA3, and POLR3G have statistically significantly longer disease- specific survival (Cox p < 0.05). CONCLUSION: We identified FH, TSTA3, and POLR3G as hub genes which can affect MM patients'outcome and new biomarkers for diagnosis and prognosis of MM. Further functional and mechanistic studies are need to develop in order to make them as potential target for clinical treatment.

Tumor-derived exosomal circPSMA1 facilitates the tumorigenesis, metastasis, and migration in triple-negative breast cancer (TNBC) through miR-637/Akt1/ß-catenin (cyclin D1) axis.

Circular RNAs (circRNAs) are increasingly gaining importance and attention due to their diverse potential functions and their value as diagnostic biomarkers (disease specific). This study aims to explore the novel mechanisms by which exosome-contained circRNAs promote tumor development and metastasis in TNBC. We identified increased circRNA circPSMA1 in TNBC cells, their exosomes, and serum exosomes samples from TNBC patients. The overexpression of circPSMA1 promoted TNBC cell proliferation, migration, and metastasis both in vitro and in vivo. Moreover, we investigated the tumor-infiltrating immune cells (TICs) or stromal components in immune microenvironment (IME), and identified the significant differences in the immune cells between TNBC and non-TNBC samples. Mechanistically, circPSMA1 acted as a "miRNAs sponge" to absorb miR-637; miR-637 inhibited TNBC cell migration and metastasis by directly targeted Akt1, which recognized as a key immune-related gene and affected downstream genes ß-catenin and cyclin D1. Subsequent co-culture experiments also demonstrated that exosomes from TNBC carrying large amounts of circPSMA1 could transmit migration and proliferation capacity to recipient cells. Kaplan-Meier plots showed that high expression of Akt1 and low expression of mir-637 are highly correlated with poor prognosis in patients with lymph node metastasis of TNBC. Collectively, all these results reveal that circPSMA1 functions as a tumor promoter through the circPSMA1/miR-637/Akt1-ß-catenin (cyclin D1) regulatory axis, which can facilitate the tumorigenesis, metastasis, and immunosuppression of TNBC. Our research proposes a fresh perspective on novel potential biomarkers and immune treatment strategies for TNBC.

Identification and validation of tumor microenvironment-related prognostic biomarkers in breast cancer.

BACKGROUND: Stromal cells and immune cells in tumor microenvironment (TME) have been reported to have significant value in the diagnosis and prognosis of cancers. We aimed to identify key biomarkers predicting survival in the TME of breast cancer. METHODS: Cell type enrichment analysis was performed to estimate cell scores using the xCell method with gene expression data from public database. Least absolute shrinkage and selection operator (LASSO) regression was used to identify key signature from the cell scores. RESULTS: Totally, 50 cells in TME had different scores between 1,078 breast cancer tissues and 112 adjacent normal tissues. We identified a 4-cell signature predicting breast cancer survival, including myocytes, natural killer T cell (NKT), conventional dendritic cell (cDC) and sebocytes, which was validated in the test set. Further analysis showed that cDC score was a key signature predicting prognosis of breast cancer. cDC score was significantly associated with molecular classification and stage of breast cancer, as well as expression level of Ki67. Spearman's correlation analysis found that cDC score was inversely correlated with the expression level of HER2. High cDC score may predicate better pathological complete response rate. Mechanism analysis indicated high cDC score was associated with elevated immune activity; IL-2 was a key gene associated with high cDC score; and Breast cancer patients with high IL-2 expression had a longer survival time. CONCLUSIONS: In conclusion, cDC score was a key signature predicting prognosis for breast cancer. cDCs may exert antitumor effects by upregulating IL-2.

The role of long non-coding RNAs in drug resistance of cancer.

Long non-coding RNAs (lncRNAs), a class of long RNAs, are longer than 200 nucleotides in length but lack protein-coding capacity. LncRNAs, as critical genomic regulators, are involved in genomic imprinting regulation, histone modification and gene expression regulation as well as tumor initiation and progression. However, it is also found that lncRNAs are associated with drug resistance in several types of cancer. Drug resistance is an important reason for clinical chemotherapy failure, and the molecular mechanism of tumor resistance is complex, which is a process of multi-cause, multi-gene and multi-signal transduction pathway interaction. Then comprehending the mechanisms of chemoresistance will help find ways to control the tumor progression effectively. Therefore, in this review, we will construct lncRNAs /drug resistance interaction network and shed light on the role of lncRNAs in drug resistance.

Identification and validation of prognostic signature for breast cancer based on genes potentially involved in autophagy.

We aimed to identify prognostic signature based on autophagy-related genes (ARGs) for breast cancer patients. The datasets of breast cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to construct multiple-ARG risk signature. In total, 32 ARGs were identified as differentially expressed between tumors and adjacent normal tissues based on TCGA. Six ARGs (IFNG, TP63, PPP1R15A, PTK6, EIF4EBP1 and NKX2-3) with non-zero coefficient were selected from the 32 ARGs using LASSO regression. The 6-ARG signature divided patients into high-and low-risk group. Survival analysis indicated that low-risk group had longer survival time than high-risk group. We further validated the 6-ARG signature using dataset from GEO and found similar results. We analyzed the associations between ARGs and breast cancer survival in TCGA and nine GEO datasets, and obtained 170 ARGs with significant associations. EIF4EBP1, FOS and FAS were the top three ARGs with highest numbers of significant associations. EIF4EBP1 may be a key ARG which had a higher expression level in patients with more malignant molecular subtypes and higher grade breast cancer. In conclusion, our 6-ARG signature was of significance in predicting of overall survival of patients with breast cancer. EIF4EBP1 may be a key ARG associated with breast cancer survival.