A novel prognostic signature related to programmed cell death in osteosarcoma.

Background: Osteosarcoma primarily affects children and adolescents, with current clinical treatments often resulting in poor prognosis. There has been growing evidence linking programmed cell death (PCD) to the occurrence and progression of tumors. This study aims to enhance the accuracy of OS prognosis assessment by identifying PCD-related prognostic risk genes, constructing a PCD-based OS prognostic risk model, and characterizing the function of genes within this model. Method: We retrieved osteosarcoma patient samples from TARGET and GEO databases, and manually curated literature to summarize 15 forms of programmed cell death. We collated 1621 PCD genes from literature sources as well as databases such as KEGG and GSEA. To construct our model, we integrated ten machine learning methods including Enet, Ridge, RSF, CoxBoost, plsRcox, survivalSVM, Lasso, SuperPC, StepCox, and GBM. The optimal model was chosen based on the average C-index, and named Osteosarcoma Programmed Cell Death Score (OS-PCDS). To validate the predictive performance of our model across different datasets, we employed three independent GEO validation sets. Moreover, we assessed mRNA and protein expression levels of the genes included in our model, and investigated their impact on proliferation, migration, and apoptosis of osteosarcoma cells by gene knockdown experiments. Result: In our extensive analysis, we identified 30 prognostic risk genes associated with programmed cell death (PCD) in osteosarcoma (OS). To assess the predictive power of these genes, we computed the C-index for various combinations. The model that employed the random survival forest (RSF) algorithm demonstrated superior predictive performance, significantly outperforming traditional approaches. This optimal model included five key genes: MTM1, MLH1, CLTCL1, EDIL3, and SQLE. To validate the relevance of these genes, we analyzed their mRNA and protein expression levels, revealing significant disparities between osteosarcoma cells and normal tissue cells. Specifically, the expression levels of these genes were markedly altered in OS cells, suggesting their critical role in tumor progression. Further functional validation was performed through gene knockdown experiments in U2OS cells. Knockdown of three of these genes-CLTCL1, EDIL3, and SQLE-resulted in substantial changes in proliferation rate, migration capacity, and apoptosis rate of osteosarcoma cells. These findings underscore the pivotal roles of these genes in the pathophysiology of osteosarcoma and highlight their potential as therapeutic targets. Conclusion: The five genes constituting the OS-PCDS model-CLTCL1, MTM1, MLH1, EDIL3, and SQLE-were found to significantly impact the proliferation, migration, and apoptosis of osteosarcoma cells, highlighting their potential as key prognostic markers and therapeutic targets. OS-PCDS enables accurate evaluation of the prognosis in patients with osteosarcoma.

Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction. METHOD: We retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic models. RESULT: Our integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis. CONCLUSION: The current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC.

RBMS2 Chemosensitizes Breast Cancer Cells to Doxorubicin by Regulating BMF Expression.

Chemoresistance is closely related to the therapeutic effect and prognosis in breast cancer patients. Increasing evidences demonstrated that RNA binding proteins (RBPs) have notable roles in regulating cancer cell proliferation, metastasis and chemotherapeutic sensitivity. RNA binding motif single stranded interacting protein 2 (RBMS2), an RBP, has been considered to be a tumor suppressor in several cancers. However, its role of doxorubicin sensitivity in breast cancer patients has not yet been fully revealed. Here, we performed doxorubicin cytotoxicity assay, flow cytometry and mouse xenograft model to examine the influence of RBMS2 on doxorubicin sensitization in vitro and in vivo. RIP assay and dual-luciferase reporter assay were performed to explore the relationship between RBMS2 and BMF. Our data demonstrated that upregulation of RBMS2 in breast cancer cells could enhance sensitivity to doxorubicin and promote apoptosis in the presence of doxorubicin, while inhibition of RBMS2 showed an opposite trend. Moreover, this chemosensitizing effect of RBMS2 could be reversed by the inhibition of Bcl-2 modifying factor (BMF). RBMS2 positively regulated BMF expression and increased BMF-induced expression of (cleaved) caspase 3, (cleaved) caspase 9 and poly (ADP-Ribose) polymerase (PARP). These results uncovered a novel mechanism for RBMS2 in the sensibilization of doxorubicin, suggesting that RBMS2 may act as a potential therapeutic target for drug-resistant breast cancer.

A novel cuproptosis-related prognostic 2-lncRNAs signature in breast cancer.

Background: Cuproptosis, a newly defined regulated form of cell death, is mediated by the accumulation of copper ions in cells and related to protein lipoacylation. Seven genes have been reported as key genes of cuproptosis phenotype. Cuproptosis may be developed by subsequent research as a target to treat cancer, such as breast cancer. Long-noncoding RNA (lncRNA) has been proved to play a vital role in regulating the biological process of breast cancer. However, the role of lncRNAs in cuproptosis is poorly studied. Methods: Based on TCGA (The Cancer Genome Atlas) database and integrated several R packages, we screened out 153 cuproptosis-related lncRNAs and constructed a novel cuproptosis-related prognostic 2-lncRNAs signature (BCCuS) in breast cancer and then verified. By using pRRophetic package and machine learning, 72 anticancer drugs, significantly related to the model, were screened out. qPCR was used to detect the differentially expression of two model lncRNAs and seven cuproptosis genes between 10 pairs of breast cancer tissue samples and adjacent samples. Results: We constructed a novel cuproptosis-related prognostic 2-lncRNAs (USP2-AS1, NIFK-AS1) signature (BCCuS) in breast cancer. Univariate COX analysis (p < .001) and multivariate COX analysis (p < .001) validated that BCCuS was an independent prognostic factor for breast cancer. Overall survival Kaplan Meier-plotter, ROC curve and Risk Plot validated the prognostic value of BCCuS both in test set and verification set. Nomogram and C-index proved that BCCuS has strong correlation with clinical decision-making. BCCuS still maintain inspection efficiency when patients were splitting into Stage I-II (p = .024) and Stage III-IV (p = .003) breast cancer. BCCuS-high group and BCCuS-low group showed significant differences in gene mutation frequency, immune function, TIDE (tumor immune dysfunction and exclusion) score and other phenotypes. TMB (tumor mutation burden)-high along with BCCuS-high group had the lowest Survival probability (p = .005). 36 anticancer drugs whose sensitivity (IC50) was significantly related to the model were screened out using pRRophetic package. qPCR results showed that two model lncRNAs (USP2-AS1, NIFK-AS1) and three Cuproptosis genes (FDX1, PDHA1, DLAT) expressed differently between 10 pairs of breast cancer tissue samples and adjacent samples. Conclusion: The current study reveals that cuproptosis-related prognostic 2-lncRNAs signature (BCCuS) may be useful in predicting the prognosis, biological characteristics, and appropriate treatment of breast cancer patients.