Currently, breast cancer (BRCA) has become the most common cancer in the world, whose pathological mechanism is complex. Among its subtypes, triple-negative breast cancer (TNBC) has the worst prognosis. With the increasing number of diagnosed TNBC patients, the urgent need of novel biomarkers is also rising. Cyclin-dependent kinase inhibitor 2A (CDKN2A) has recently emerged as a key regulator associated with ferroptosis and cuproptosis (FAC) and has exhibited a significant effect on BRCA, but its detailed mechanism remains elusive. Herein, we conducted the first converge comprehensive landscape analysis of FAC-related gene CDKN2A in BRCA and disclosed its prognostic value in BRCA. Then, an unsupervised cluster analysis based on CDKN2A-correlated genes unveiled three subtypes, namely cold-immune subtype, IFN-γ activated subtype and FTL-dominant subtype. Subsequent analyses depicting hallmarks of tumor microenvironment (TME) among three subtypes suggested strong association between TNBC and CDKN2A. Given the fact that the most clinically heterogeneous TNBC always displayed the most severe outcomes and lacked relevant drug targets, we further explored the potential of immunotherapy for TNBC by interfering CDKN2A and constructed the CDKN2A-derived prognostic model for TNBC patients by Lasso-Cox. The 21-gene–based prognostic model showed high accuracy and was verified in external independent validation cohort. Moreover, we proposed three drugs for TNBC patients based on our model via targeting epidermal growth factor receptor. In summary, our study indicated the potential of CDKN2A as a pioneering prognostic predictor for TNBC and provided a rationale of immunotherapy for TNBC, and offered fresh perspectives and orientations for cancer treatment via inducing ferroptosis and cuproptosis to develop novel anti-cancer treatment strategies.

Breast cancer (BRCA) is the most common cancer in the world, of which incidence rate and mortality are the highest in women. Being responsible for the remodeling and degradation of extracellular matrix proteins, matrix metalloproteinases (MMPs) have been regarded as one of the most important protease family related to tumorigenesis. It has been demonstrated that MMPs play crucial roles in some tumor invasion and metastasis. However, the potential roles of MMPs in tumorigenesis and progression of BRCA and its subtype remain elusive. Herein, we conducted a systematic study on MMPs via a series of database-based retrospective analysis, including TCGA, R Studio, GEPIA, Kaplan-Meier Plotter, cBioPortal, STRING, GeneMANIA and TIMER. As a result, many MMP family members were differentially expressed in patients with BRCA, e.g., the expressions of MMP1, MMP9, MMP11 and MMP13 were up-regulated, whereas the expression levels of MMP19 and MMP28 were down-regulated. MMP9, MMP12, MMP15 and MMP27 were significantly correlated with the clinical stages of BRCA, implying their important roles in the occurrence and development of BRCA. In addition, the survival analysis indicated that different expression pattern of MMPs exhibited distinct outcomes in patient with BRCA, e.g., patients with high expression of MMP2, MMP8, MMP16, MMP17, MMP19, MMP20, MMP21, MMP24, MMP25, MMP26 and MMP27 had a prolonged survival time, while the others (MMP1, MMP7, MMP9, MMP12 and MMP15) exhibited poor prognosis. Subsequent functional and network analysis revealed MMPs were mainly correlated with parathyroid hormone synthesis and secretion pathway, collagen metabolism, and their effect on the activities of serine hydrolase, serine peptidase and aminopeptidase. Notably, our analysis showed that the expression of MMPs was significantly correlated with the infiltration of various immune cells in BRCA, including CD8+T cells, CD4+T cells, macrophages, neutrophils, B cells, and dendritic cells, suggesting the close correlations between MMPs and immune functions. In short, our study disclosed MMPs play multiple biological roles in the development of BRCA, MMP1 and MMP9 might be used as independent prognostic markers and potential therapeutic targets for diagnosis and treatment for patients with BRCA.

Disulfidptosis, a newly identified regulated cell death, is linked to tumor progression, particularly in cancers with elevated SLC7A11 expression. This study investigates SLC7A11 expression in liver hepatocellular carcinoma (LIHC) and evaluates the therapeutic potential of ICG@C3F8-KL nanobubbles (NBs) combined with sonodynamic therapy (SDT) for inducing disulfidptosis. Bioinformatics analysis of TCGA datasets revealed upregulation of SLC7A11 in LIHC tissues. The synthesized ICG@C3F8-KL NBs exhibited a mean diameter of 156.46 nm and stable properties, with high encapsulation efficiencies of 51.32 % ± 0.7 % for KL and 80.15 % ± 0.21 % for ICG. In vitro, ICG@C3F8-KL NBs, under ultrasound, generated reactive oxygen species (ROS), enhancing cytotoxicity in HepG2 cells with an IC50 lower than KL alone. These NBs also inhibited cell migration and colony formation, suggesting disulfidptosis induction via altered glucose uptake and NADP+/NADPH ratio, as well as F-actin contraction. In vivo, ICG@C3F8-KL NBs accumulated in tumor tissues and suppressed growth without significant toxicity. Unsupervised clustering of disulfidptosis-related genes in TCGA LIHC cohort identified subtypes with distinct prognoses, and a predictive model based on five key genes was developed. In conclusion, ICG@C3F8-KL NBs, combined with ultrasound, effectively induce disulfidptosis, offering a promising strategy for LIHC treatment, with the potential for personalized therapy informed by disulfide-associated gene signatures.

Purpose: Sarcoma is the second most common solid tumor type in children and adolescents. The high level of tumor heterogeneity as well as aggressive behavior of sarcomas brings serious difficulties to developing effective therapeutic strategies for clinical application. Therefore, it is of great importance to identify accurate biomarkers for early detection and prognostic prediction of sarcomas. Experimental Design: In this study, we characterized three subtypes of sarcomas based on tumor immune infiltration levels (TIIL), and constructed a prognosis-related competing endogenous RNA (ceRNA) network to investigate molecular regulations in the sarcoma tumor microenvironment (TME). We further built a subnetwork consisting of mRNAs and lncRNAs that are targets of key miRNAs and strongly correlated with each other in the ceRNA network. After validation using public data and experiments in vivo and in vitro, we deeply dug the biological role of the miRNAs and lncRNAs in a subnetwork and their impact on TME. Results: Altogether, 5 miRNAs (hsa-mir-125b-2, hsa-mir-135a-1, hsa-mir92a-2, hsa-mir-181a-2, and hsa-mir-214), 3 lncRNAs (LINC00641, LINC01146, and LINC00892), and 10 mRNAs (AGO2, CXCL10, CD86, CASP1, IKZF1, CD27, CD247, CD69, CCR2, and CSF2RB) in the subnetwork were identified as vital regulators to shape the TME. On the basis of the systematic network, we identified that trichostatin A, a pan-HDAC inhibitor, could potentially regulate the TME of sarcoma, thereby inhibiting the tumor growth. Conclusions: Our study identifies a ceRNA network as a promising biomarker for sarcoma. This system provides a more comprehensive understanding and a novel perspective of how ceRNAs are involved in shaping sarcoma TME.

Common genetic variants and susceptibility loci associated with Alzheimer’s disease (AD) have been discovered through large-scale genome-wide association studies (GWAS), GWAS by proxy (GWAX) and meta-analysis of GWAS and GWAX (GWAS+GWAX). However, due to the very low repeatability of AD susceptibility loci and the low heritability of AD, these AD genetic findings have been questioned. We summarize AD genetic findings from the past 10 years and provide a new interpretation of these findings in the context of statistical heterogeneity. We discovered that only 17% of AD risk loci demonstrated reproducibility with a genome-wide significance of P < 5.00E-08 across all AD GWAS and GWAS+GWAX datasets. We highlighted that the AD GWAS+GWAX with the largest sample size failed to identify the most significant signals, the maximum number of genome-wide significant genetic variants or maximum heritability. Additionally, we identified widespread statistical heterogeneity in AD GWAS+GWAX datasets, but not in AD GWAS datasets. We consider that statistical heterogeneity may have attenuated the statistical power in AD GWAS+GWAX and may contribute to explaining the low repeatability (17%) of genome-wide significant AD susceptibility loci and the decreased AD heritability (40–2%) as the sample size increased. Importantly, evidence supports the idea that a decrease in statistical heterogeneity facilitates the identification of genome-wide significant genetic loci and contributes to an increase in AD heritability. Collectively, current AD GWAX and GWAS+GWAX findings should be meticulously assessed and warrant additional investigation, and AD GWAS+GWAX should employ multiple meta-analysis methods, such as random-effects inverse variance-weighted meta-analysis, which is designed specifically for statistical heterogeneity.