Bone marrow mesenchymal stem cells-derived exosomal microRNA-16-5p restrains epithelial-mesenchymal transition in breast cancer cells via EPHA1/NF-κB signaling axis.

OBJECTIVE: This study intends to conquer the mystery of microRNA-16-5p/erythropoietin-producing hepatocellular A1/nuclear factor-κB signaling (miR-16-5p/EPHA1/NF-κB signaling) in breast cancer. METHODS: Expression of miR-16-5p, EPHA1 and NF-κB signaling-related proteins were detected. Gene overexpression or silencing was used to examine the biological roles of bone marrow mesenchymal stem cells (BMSCs)-derived exo-miR-16-5p in breast cancer. The effect of exo-miR-16-5p on tumorigenesis of breast cancer was confirmed by the xenograft nude mouse model. RESULTS: Low miR-16-5p and high EPHA1 expression were examined in breast cancer. BMSCs-derived exosomes, up-regulated miR-16-5p or down-regulated EPHA1 restrained epithelial-mesenchymal transition (EMT) of breast cancer cells and tumor growth in nude mice. Down-regulated miR-16-5p or up-regulated EPHA1 activated NF-κB signaling. Knockdown of EPHA1 or inhibition of NF-κB signaling reversed the effects of down-regulated miR-16-5p on breast cancer cells. CONCLUSION: BMSCs-derived exosomal miR-16-5p hinders breast cancer cells progression via EPHA1/NF-κB signaling axis.

Novel classification and risk model based on ferroptosis-related lncRNAs to predict oncologic outcomes for gastric cancer patients.

Gastric cancer (GC) is a highly heterogeneous malignancy, characterized by high mortality and poor prognosis. Ferroptosis is a newly defined nonapoptotic programmed cell death mechanism that has been implicated in the development of various pathological conditions. We aimed to identify ferroptosis-related long noncoding RNA (lncRNAs) that might be used to predict GC prognosis. The data were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus database. Two subtypes, C1 and C2, were identified, which had significant variations in prognosis and immune cell infiltrations. Differentially expressed genes between the subtypes were found to be involved in multiple tumor-associated pathways. Subsequently, a training dataset and a testing dataset were created from the TCGA dataset. A predictive model for GC patients based on six ferroptosis-related lncRNAs (including STX18-AS1, MIR99AHG, LINC01197, LINC00968, LINC00865, and LEF1-AS1) was developed. The model could stratify patients into a high- and low-risk group, showing good predictive performance. The testing dataset, entire TCGA dataset, and GSE62254 cohort both confirmed the predictive value of the model. Compared to the clinical parameters (including gender, age, and grade), the risk model was an independent risk factor for GC patients. Moreover, a nomogram (containing our risk score model and clinical parameters) was constructed, which might provide great potential to improve prediction accuracy. Moreover, the single-sample gene set enrichment analysis revealed that the high-risk group was linked to various signaling pathways involved in the regulation of GC progression. Conclusively, a novel classification and risk model based on ferroptosis-related lncRNAs that can predict oncologic outcomes for GC patients has been developed.

Classification of colorectal carcinoma subtypes based on ferroptosis-associated molecular markers.

BACKGROUND: Ferroptosis is associated with the development of many cancers; the molecular features of colorectal carcinoma (CRC) based on ferroptosis-related genes (FRGs) remain unknown. Herein, we aimed to identify ferroptosis-associated molecular subtypes of CRC based on the expression profiles of FRGs. METHODS: To explore ferroptosis-associated subtypes of CRC, the gene expression data and clinical information of 682 patients were extracted from The Cancer Genome Atlas and Gene Expression Omnibus databases. We performed consensus clustering to identify robust clusters of patients. Then the distribution of the subtypes in terms of prognosis significance, transcriptome features, immune microenvironment, drug sensitivity, gene mutations, and copy number alternations (CNAs) were evaluated respectively. In addition, we analyzed the correlation of these ferroptosis-associated molecular subtypes with the distribution of conventional clinical indicators in CRC. RESULTS: Four subtypes of CRC (C1, C2, C3, and C4) were identified in which the prognosis, immune cell infiltration, immune score, stromal score, and tumor purity were significantly different between the four subtypes. The C3 subtype had a higher infiltration of B cells, M2 macrophages, resting mast cells, monocytes, natural killer cells, plasma cells, and CD8 T cells. The C3 subtype had the highest immune and stromal scores and the lowest tumor purity. In contrast, the C4 subtype demonstrated the lowest immune and stromal scores and the highest tumor purity. Programmed cell death ligand 1 (PD-L1), an immune checkpoint protein, was differentially expressed in the four subtypes (P < 2e-16) and was significantly correlated with the expression of several FRGs in all subtypes. Significant differences in stem cell indices (P < 0.01) and drug sensitivity (P < 0.01) were observed in the four subtypes. Additionally, gene mutations analysis showed that FRGs such as TP53 had a high frequency of mutation in the four subtypes (49%, 62%, 61%, and 71%, respectively), and the CNAs showed significant difference among all subtypes (P < 0.001). CONCLUSION: In summary, the ferroptosis-associated subtypes could serve as an independent biomarker for estimating oncological outcomes in patients with CRC. Our results demonstrated that the high level of heterogeneity in the expression of FRGs might be useful for the stratification of patients with CRC and the implementation of individualized therapeutic strategies.

Functional loss of TAGLN inhibits tumor growth and increases chemosensitivity of non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is the leading cause of tumor mortality worldwide. However, the mechanisms underlying NSCLC tumorigenesis are incompletely understood. TAGLN, also named SM22, as a member of the calponin family, is highly expressed in many types of tumors. Nevertheless, its effects on NSCLC progression remain unclear. In this study, we found that TAGLN was over-expressed in tumor tissues of NSCLC patients and cell lines. Additionally, NSCLC patients with high expression showed worse overall survival rate. Then, gene silencing results indicated that TAGLN knockdown markedly inhibited proliferation and induced apoptosis in NSCLC cells, while rescue study exhibited opposite results. Moreover, suppressing TAGLN significantly reduced migration and invasion of NSCLC cells, and its over-expression promoted the migratory and invasive activities of NSCLC cells. The in vivo studies confirmed the oncogenic roles of TAGLN in NSCLC, along with clearly elevated metastasis. Notably, these effects were abrogated in mice with TAGLN deletion. Finally, we found that TAGLN knockdown could improve the sensitivity of NSCLC cells to sorafenib (SFB) and 5-FU treatment, further suppressing the proliferation, migration and invasion of NSCLC cells. Consistently, TAGLN deletion attenuated tumor xenografts growth and metastasis of NSCLC in mouse models by enhancing the anti-cancer effects of SFB and 5-FU. Altogether, these findings demonstrated that TAGLN functioned as an oncogene as well as a chemotherapeutic regulator during NSCLC development, which suggested a potential therapeutic strategy for NSCLC treatment mainly through repressing TAGLN expression.

Comprehensive Molecular Analyses of an M2-Like Tumor-Associated Macrophage for Predicting the Prognosis and Immunotherapy in Breast Cancer.

The involvement of M2-like tumor-associated macrophages (TAMs) in the advancement and treatment of cancer has been widely documented. This study aimed to develop a new signature associated with M2-like TAMs to predict the prognosis and treatment response in individuals diagnosed with breast cancer (BC). Weighted gene co-expression network analysis (WGCNA) was used to identity for M2-like TAM-related modular genes. The M2-like TAM-related modular subtype was identified using unsupervised clustering. WGCNA identified 722 M2-like TAM genes, 204 of which were associated with recurrence-free survival (RFS). Patients in cluster 1 exhibited upregulated cancer-related pathways, a higher proportion of triple-negative breast cancer (TNBC) subtypes, lower expression of immune checkpoints, and worse prognosis. Cluster 2 was characterized by upregulated immune-related pathways, a higher proportion of luminal A subtypes, and higher expression of immune checkpoints. A prognostic signature was created and confirmed using an independent dataset. A well-built nomogram can accurately forecast the survival outcomes for every individual. Furthermore, patients classified as low-risk exhibited a more favorable outlook, elevated tumor microenvironment (TME) score, and superior reaction to immunotherapy. In conclusion, we discovered 2 different types of M2-like TAMs and developed a prognostic signature revealing the diversity of M2-like TAMs in BC and their correlation with immune status and prognosis. This feature can predict the prognosis and immunotherapeutic effects of BC and offer novel concepts and approaches for tailoring BC treatment.

Identification of Five N6-Methylandenosine-Related ncRNA Signatures to Predict the Overall Survival of Patients with Gastric Cancer.

Noncoding ribonucleic acids (ncRNAs) are involved in various functions in the formation and progression of different tumors. However, the association between N6-methyladenosine-related ncRNAs (m6A-related ncRNAs) and gastric cancer (GC) prognosis remains elusive. As such, this research was aimed at identifying m6A-related ncRNAs (lncRNAs and miRNAs) in GC and developing prognostic models of relevant m6A-related ncRNAs and identifying potential biomarkers regulated by m6A. In this study, the m6A2Target database, Starbase database, and The Cancer Genome Atlas (TCGA) were used to screen m6A-related ncRNAs. And then, we performed integrated bioinformatics analyses to determine prognosis-associated ncRNAs and to develop the m6A-related ncRNA prognostic signature (m6A-NPS) for GC patients. Finally, five m6A-related ncRNAs (including lnc-ARHGAP12, lnc-HYPM-1, lnc-WDR7-11, LINC02266, and lnc-PRIM2-7) were identified to establish m6A-NPS. The predictive power of m6A-NPS was better in the receiver operating characteristic (ROC) curve analysis of the training set (area under the curve (AUC), >0.6). The m6A-NPS could be utilized to classify patients into high- and low-risk cohorts, and the Kaplan-Meier analysis indicated that participants in the high-risk cohort had a poorer prognosis. The entire TCGA dataset substantiated the predictive value of m6A-NPS. Significant differences in TCGA molecular GC subtypes were observed between high- and low-risk cohorts. The ROC curve analysis indicated that m6A-NPS had better predictive power than other clinical characteristics of GC prognosis. Uni- and multivariate regression analyses indicated m6A-NPS as an independent prognostic factor. Furthermore, the m6A status between the low-risk cohort and high-risk cohort was significantly different. Differential genes between them were enriched in multiple tumor-associated signaling pathways. In summary, five m6A-related ncRNA signatures that could forecast the overall survival of patients with GC were identified.

Necroptosis-related lncRNA signatures determine prognosis in breast cancer patients.

Necroptosis is a genetically regulated form of necrotic cell death that has emerged as an important pathway in cancers. Long non-coding RNAs (lncRNAs) are key regulators of breast cancer development. Nevertheless, few studies are reporting the effect of lncRNAs in necroptosis processes and the role of necroptosis-related lncRNAs (NRLs). The present study aimed to construct a prognostic model based on NRLs in breast cancer. NRLs were identified by combining expression profiling data from The Cancer Genome Atlas (TCGA) with necroptosis-related genes. The non-negative matrix factorization (NMF) clustering analysis was conducted to identify molecular subtypes of BC, and the clinical outcome and tumor-infiltrating immune cells (TIICs) in the different molecular subtypes were analyzed. Four molecular subtypes based on NRLs were identified, and these four molecular subtypes could predict clinical features, prognosis, and tumor-infiltrating immune cells (TIICs). A 4-NRLs signature and nomogram were established and validated its predictive capability of overall survival (OS) in breast cancer patients. Analyses of clinicopathological features, prognosis, TIICs, tumor microenvironment (TME), somatic mutations, and drug response revealed significant differences between the two risk groups. In addition, we found that low-risk patients exhibited higher levels of immune checkpoints and showed higher immunogenicity in immunophenoscore (IPS) analysis. In conclusion, we constructed a prognostic model based on the expression profile of NRLs, which may facilitate the assessment of patient prognosis, immunotherapeutic responses, and maybe a promising therapeutic target in clinical practice.

Relationship between Prognosis, Immune Infiltration Level, and Differential Expression of PARVG Gene in Uterine Corpus Endometrial Carcinoma.

Endometrial cancer (UCEC) is very common in gynecological diseases and ranks second in the death cause of gynecological cancer in developed countries. The connection between the overall survival of UCEC patients and immune invasion of the tumor microenvironment is positive. The PARVG gene has not been given notice in cancer, and its mechanism is unknown. The research utilized TCGA data to test the function of PARVG in UCEC. The manifestation of PARVG in UCEC was studied by GEPIA. By assessing the survival module, the authors learned the impact of PARVG on the survival of people with UCEC and then obtained UCEC information from TCGA. This study uses logistic regression to prove the possible relationship between PARVG expression and clinical information. From the research of Cox regression, clinicopathological characteristics of people with TCGA were connected with overall survival. Furthermore, the "correlation" module of GEPIA and CIBERSORT was used to study the association between cancer immune invasion and PARVG. Using univariate logistic regression analysis with PARVG expression as a categorical variable (median expression value of 2.5), the result suggested that raised PARVG expression was considerably connected with tumor status, pathological stage, and lymph nodes. Multiple factor studies have shown that upregulation of PARVG, distant metastasis, and negative pathological stage are absolute elements of excellent prognosis. In addition, CIBERSORT analysis was utilized to determine that raised PARVG expression has a positive connection with immune infiltration by T cells, mast cells, neutrophils, and B cells. This is recognized in GEPIA's "correlation" module. The above outcomes show us that the raised expression of PARVG is associated with a good prognosis and it raises the proportion of immune cells (such as T cells, mast cells, neutrophils, and B cells) in UCEC. These outcomes tell us that PARVG can be utilized as a possible biomarker to evaluate UCEC's immune infiltration levels and prognosis.

Characterization of m6A Methylation Modification Patterns in Colorectal Cancer Determines Prognosis and Tumor Microenvironment Infiltration.

Cumulative studies have suggested that dysregulation of m6A regulators and immunity is highly linked to the prognosis of patients with cancer. However, the potential contribution of m6A modification patterns to the tumor microenvironment (TME) and the therapeutic efficacy of immunotherapy for colorectal cancer (CRC) remain elusive. A comprehensive analysis of the m6A modification profiles of 458 patients with CRC was performed by clustering 21 genes encoding m6A methylation regulators and linking the m6A modification pattern with TME characteristics. Using principal component analysis (PCA), a risk model was constructed to quantify individual m6A modification patterns in patients with CRC. The results indicated that the expression profiles and genetic mutations of 21 genes encoding m6A methylation regulators in CRC were characterized by a high degree of heterogeneity. Three m6A clusters had significant differences in prognosis, m6A modification patterns, and TME characteristics. Furthermore, a risk model, termed m6Ascore, was developed by PCA to quality m6A methylation patterns at an individual level. The m6Ascore could stratify patients into high- and low-m6Ascore groups. Further analyses demonstrated that the m6Ascore had a good predictive performance for overall survival and clinical efficacy of immunotherapy in patients with CRC. Finally, the predictive value of the model was validated by external cohorts. In conclusion, the comprehensive characterization of m6A methylation modification patterns might contribute to our understanding of the TME in CRC and the development of personalized antitumor immunotherapy in the future.

T-cell acute lymphoid leukemia resembling Burkitt leukemia cell morphology: A case report.

Biphenotypic acute leukemia (BAL) is an uncommon type of cancer, which accounts for <5% of all adult ALs. Based upon a previously described scoring system, the European Group for the Immunological Classification of Leukemias (EGIL) proposed a set of diagnostic criteria for BAL. This scoring system is based upon the number and degree of specificity of several markers for myeloid or T/B-lymphoid blasts. The present study describes a case of T-cell acute lymphoblastic leukemia (T-ALL) with Burkitt-like cytology, which according to the French-American-British classification, corresponded to a diagnosis of Burkitt type L3 ALL. Flow cytometry analysis demonstrated that the blasts were positive for T-lymphoid markers, cytoplasmic cluster of differentiation (CD)3, CD7 and CD56, and myeloid markers, CD13, CD33 and CD15. At first, a diagnosis of BAL was suggested by the EGIL score, however, according to the 2008 World Health Organization criteria, a case of T-ALL with aberrant myeloid markers was established. The study also reviewed the literature and discussed the limitations of the EGIL scoring system in clinical decision making, to aid in the selection of an appropriate therapeutic regimen.

MDR1 C3435T polymorphism and childhood acute lymphoblastic leukemia susceptibility: an updated meta-analysis.

BACKGROUND: Acute lymphoblastic leukemia (ALL) is the most common malignancy in children, though the etiology of the leukemia is poorly understood, both genetic and environmental factors appear to be involved. Previous studies investigating the association between MDR1 C3435T polymorphisms and risk of children with ALL reported controversial results. METHODS: We performed a comprehensive meta-analysis to clarify the effect of MDR1 C3435T polymorphisms on risk of childhood ALL. The strength of the association was measured by odds ratio (OR) with 95% confidence interval (CI). RESULTS: Nine studies were finally included, involving a total of 1462 cases and 1522 controls. There was no association between MDR1 C3435T polymorphism and children ALL risk in all of four models in overall populations (CT vs. CC: OR=0.86, 95% CI=0.65-1.15, P=0.310; TT vs. CC: OR=1.50, 95% CI=0.96-2.35, P=0.076; TT/CT vs. CC: OR=1.12, 95% CI=0.95-1.33, P=0.166; TT vs. CC/CT: OR=1.58, 95% CI=0.97-2.56, P=0.067). Subgroup analysis by race suggested that this association existed in Asians under the under the homozygote model and recessive model (TT vs. CC: OR=2.45, 95% CI=1.24-4.86, P=0.010; TT vs. CT/CC: OR=2.65, 95% CI=1.22-5.75, P=0.014). CONCLUSIONS: This meta-analysis suggests there was no association between MDR1 C3435T polymorphism and children ALL risk in overall populations, but significant association with an increased risk in Asians.