Role of the BMP6 protein in breast cancer and other types of cancer

Identification of a 9-gene signature to enhance biochemical recurrence prediction in primary prostate cancer: A benchmarking study using ten machine learning methods and twelve patient cohorts

Prostate cancer (PCa) is a prevalent malignancy among men worldwide, and biochemical recurrence (BCR) after radical prostatectomy (RP) is a critical turning point commonly used to guide the development of treatment strategies for primary PCa. However, the clinical parameters currently in use are inadequate for precise risk stratification and informing treatment choice. To address this issue, we conducted a study that collected transcriptomic data and clinical information from 1662 primary PCa patients across 12 multicenter cohorts globally. We leveraged 101 algorithm combinations that consisted of 10 machine learning methods to develop and validate a 9-gene signature, named BCR SCR, for predicting the risk of BCR after RP. Our results demonstrated that BCR SCR generally outperformed 102 published prognostic signatures. We further established the clinical significance of these nine genes in PCa progression at the protein level through immunohistochemistry on Tissue Microarray (TMA). Moreover, our data showed that patients with higher BCR SCR tended to have higher rates of BCR and distant metastasis after radical radiotherapy. Through drug target prediction analysis, we identified nine potential therapeutic agents for patients with high BCR SCR. In conclusion, the newly developed BCR SCR has significant translational potential in accurately stratifying the risk of patients who undergo RP, monitoring treatment courses, and developing new therapies for the disease.

Tricetin suppresses the cell migration and BMP-6 expression through p38 signaling pathways in human retinal pigment epithelium cells

Proliferative vitreoretinopathy (PVR) is a visual-threatening disease, which cause from the migration of retinal pigment epithelium (RPE). Tricetin, a family of flavonoids, can inhibit the metastasis of several cancers. Herein, we aim to evaluate the possible effect of tricetin on inhibiting ARPE-19 cells migration. The Boyden chamber assay, wound healing assay, RNA sequencing, and Western blot analysis were applied in our experiment. The results revealed that tricetin inhibited the cell migration abilities of ARPE-19 cells. Moreover, using RNA sequencing technology, we revealed that tricetin repressed bone morphogenetic protein-6 (BMP-6) gene expressions in ARPE-19 cells. Overexpression of BMP-6 resulted in significant restoration of cell migration capabilities of tricetin-treated ARPE-19 cells. Furthermore, tricetin suppressed the phosphorylation of the p38 signaling pathway. Moreover, blocking the p38 pathway also inhibits BMP-6 expression and migration in the ARPE-19 cells. In conclusion, this study revealed that tricetin inhibits the ARPE-19 cell migration mainly via the suppression of BMP-6 expression and p38 signaling pathway.

TGF-β2-induced alterations of m6A methylation in hTERT RPE-1 cells

N6-methyladenosine (m6A) is a major type of RNA modification implicated in various pathophysiological processes. Transforming growth factor β2 (TGF-β2) induces epithelial-mesenchymal transition (EMT) in retinal pigmental epithelial (RPE) cells and promotes the progression of proliferative vitreoretinopathy (PVR). However, the role of m6A methylation in the EMT of human telomerase reverse transcriptase (hTERT) retinal pigmental epithelium (RPE)-1 cells has not been clarified. Here, we extracted RNA from RPE cells subjected to 0 or 20 ng/mL TGF-β2 for 72 h and identified differentially methylated genes (DMGs) by m6A-Seq and differentially expressed genes (DEGs) by RNA-Seq. We selected the genes related to EMT by conjoint m6A-Seq/RNA-Seq analysis and verified them by qRT-PCR. We then confirmed the function of m6A methylation in the EMT of RPE cells by knocking down the methyltransferase METTL3 and the m6A reading protein YTHDF1. Sequencing yielded 5814 DMGs and 1607 DEGs. Conjoint analysis selected 467 genes altered at the m6A and RNA levels that are closely associated with the EMT-related TGF-β, AGE-RAGE, PI3K-Akt, P53, and Wnt signaling pathways. We also identified ten core EMT genes ACTG2, BMP6, CDH2, LOXL2, SNAIL1, SPARC, BMP4, EMP3, FOXM1, and MYC. Their RNA levels were evaluated by qRT-PCR and were consistent with the sequencing results. We observed that METTL3 knockdown enhanced RPE cell migration and significantly upregulated the EMT markers N-cadherin (encoded by CDH2), fibronectin (FN), Snail family transcription repressor (SLUG), and vimentin. However, YTHDF1 knockdown had the opposite effects and decreased both cell migration and the N-cadherin, FN, and SLUG expression levels. The present study clarified TGF-β2-induced m6A- and RNA-level differences in RPE cells, indicated that m6A methylation might regulate EMT marker expression, and showed that m6A could regulate TGF-β2-induced EMT.

Unveiling Anoikis-related genes: A breakthrough in the prognosis of bladder cancer

BACKGROUND

Bladder cancer (BLCA) is a prevalent malignancy worldwide. Anoikis remains a new form of cell death. It is necessary to explore Anoikis-related genes in the prognosis of BLCA.

METHODS

We obtained RNA expression profiles from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases for dimensionality reduction analysis and isolated epithelial cells, T cells and fibroblasts for copy number variation analysis, pseudotime analysis and transcription factor analysis based on R package. We integrated machine-learning algorithms to develop the artificial intelligence-derived prognostic signature (AIDPS).

RESULTS

The performance of AIDPS with clinical indicators was stable and robust in predicting BLCA and showed better performance in every validation dataset compared to other models. Mendelian randomization analysis was conducted. Single nucleotide polymorphism (SNP) sites of rs3100578 (HK2) and rs66467677 (HSP90B1) exhibited significant correlation of bladder problem (not cancer) and bladder cancer, whereasSNP sites of rs3100578 (HK2) and rs947939 (BAD) had correlation between bladder stone and bladder cancer. The immune infiltration analysis of the TCGA-BLCA cohort was calculated via the ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) algorithm which contains stromal, immune and estimate scores. We also found significant differences in the IC50 values of Bortezomib_1191, Docetaxel_1007, Staurosporine_1034 and Rapamycin_1084 among the high- and low-risk groups.

CONCLUSIONS

In conclusion, these findings indicated Anoikis-related prognostic genes in BLCA and constructed an innovative machine-learning model of AIDPS with high prognostic value for BLCA.

Construction and Validation of a Necroptosis-Related Signature Associated With the Immune Microenvironment in Liver Hepatocellular Carcinoma

Background: Liver hepatocellular carcinoma (LIHC) is a widespread and often deadly neoplasm. There is increasing evidence that necroptosis mediates numerous tumor-associated behaviors, as well as the regulation of the tumor microenvironment, suggesting its use as a biomarker for tumor prognosis.

Methods: Data on mRNA expression and necroptosis regulators were acquired from the TCGA and KEGG databases, respectively. Clinical liver hepatocellular carcinoma (LIHC) patient data and information on the expression of necroptosis regulators were processed by unsupervised cluster analysis was performed on LIHC patients together with necroptotic regulator expression and, differentially expressed necroptosis-related genes (DENRGs) were identified by comparing the two clusters. A signature based on eight DENRGs was constructed and verified through independent data sets, and its relationship with the tumor microenvironment was investigated.

Results: Unsupervised cluster analysis demonstrated inherent immune differences among LIHC patients. In all, 1,516 DENRGs were obtained by comparison between the two clusters. In the training set, the final eight genes obtained by univariate, LASSO, and multivariate Cox regression were utilized for constructing the signature. The survival and receiver operating characteristic (ROC) curve achieved satisfactory results in both sets. The high-risk group was characterized by greater immune infiltration and poor prognosis. The results of survival analysis based on the expression of eight DENRGs further confirmed the signature.

Conclusion: We established and validated a risk signature based on eight DERNGs related to the tumor microenvironment. This provides a possible explanation for the different clinical effects of immunotherapy and provides a novel perspective for predicting tumor prognosis in LIHC.