A senescence-associated signature refines the classification of different modification patterns and characterization of tumor immune microenvironment infiltration in triple-negative breast cancer

Molecular signatures of cellular senescence in cancer: a critical review of prognostic implications and therapeutic opportunities

Cellular senescence is a state of permanent loss of proliferative capacity. Therefore, cells that reach a senescent state prevent tumor initiation, acting as an anti-tumor mechanism. However, despite not being proliferative, senescent cells have high secretory activity, constituting the Senescence-Associated Secretory Phenotype (SASP). SASP includes thousands of soluble molecules and extracellular vesicles, through which senescent cells can affect other cells and the extracellular matrix. In advanced tumors, the enrichment of senescent cells can have anti- or pro-tumor effects depending on features like SASP composition, tumor microenvironment (TME) composition, the anatomic site, histopathologic characteristics of malignancy, and tumor molecular background. We reviewed the studies assessing the impact of the senescence status, measured by mRNA or lncRNA molecular signatures, in the prognosis and other clinically relevant information in cancer, including anti-tumor immunity and response to therapy. We discussed the pros and cons of different strategies to define those molecular signatures and the main limitations of the studies. Finally, we also raised clinical challenges regarding the crossroad between cellular senescence and cancer prognosis, including some therapeutic opportunities in the field.

A Clinically Feasible Diagnostic Electrochemical Micronano Motors Biosensor Built on Miniature Swimmer for Multiplex Detection and Grading of Breast Cancer Biomarkers

Estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2), and Ki67 are four crucial biomarkers used in the clinical diagnosis of breast cancer. Accurate detection of these biomarkers is essential for an effective diagnosis and treatment. MOF-based micronano motors (MOFtors) are promising for various applications, including environmental remediation, targeted nanosurgery, and biomarker detection. This paper presents a clinically feasible diagnostic electrochemical micronano motor biosensor, built on a miniature swimmer, for the multiplex detection and grading of breast cancer biomarkers. We designed a biosensor, named MOFtor-MSEM, incorporating aptamers and antibodies functionalized on SiO2@Co-Fe-MOF, which acts as a miniature swimmer in solution. The SiO2@Co-Fe-MOF serves as the body, while complementary double-chain-linked antibodies function as paddles. In a homogeneous solution, when a positive voltage is applied to the working electrode, the electrostatic interaction between the neutral SiO2@Co-Fe-MOF and the negatively charged complementary double-linked antibody causes the antibody to move toward the electrode and then regress due to water resistance. This back-and-forth motion propels the miniature swimmer, enabling it to move the target analyte through the solution. The sensor features an automatic "sample-amplifying signal-output" process, achieving simultaneous signal amplification and output of four electrochemical signals on a single nanomaterial, a significant challenge in electrochemical sensing. The biosensor boasts a short detection time of 40 min, compared to approximately 1 week for current clinical tissue testing. Additionally, the bioplatform selectively detects HER2, ER, Ki67, and PR in the range of 0-1500 pg/mL, with detection limits of 0.01420, 0.03201, 0.01430, and 0.01229 pg/mL, respectively.

Constructing a novel prognostic model for triple-negative breast cancer based on genes associated with vasculogenic mimicry

BACKGROUND

Research has shown a connection between vasculogenic mimicry (VM) and cancer progression. However, the functions of genes related to VM in the emergence and progression of TNBC have not been completely elucidated.

METHODS

A survival risk model was constructed by screening biomarkers using DESeq2 and WGCNA based on public TNBC transcriptome data. Furthermore, gene set enrichment analysis was performed, and tumor microenvironment and drug sensitivity were analyzed. The selected biomarkers were validated via quantitative PCR detection, immunohistochemical staining, and protein detection in breast cancer cell lines. Biomarkers related to the proliferation and migration of TNBC cells were validated via in vitro experiments.

RESULTS

The findings revealed that 235 target genes were connected to the complement and coagulation cascade pathways. The risk score was constructed using KCND2, NRP1, and VSTM4. The prognosis model using the risk score and pathological T stage yielded good validation results. The clinical risk of TNBC was associated with the angiogenesis signaling pathway, and the low-risk group exhibited better sensitivity to immunotherapy. Quantitative PCR and immunohistochemistry indicated that the expression levels of KCND2 in TNBC tissues were higher than those in adjacent nontumor tissues. In the TNBC cell line, the protein expression of KCND2 was increased. Knockdown of KCND2 and VSTM4 inhibited the proliferation and migration of TNBC cells in vitro.

CONCLUSIONS

In this study, three VM-related biomarkers were identified, including KCND2, NRP1, and VSTM4. These findings are likely to aid in deepening our understanding of the regulatory mechanism of VM in TNBC.

An exosome-based specific transcriptomic signature for profiling regulation patterns and modifying tumor immune microenvironment infiltration in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a highly heterogeneous tumor that lacks effective treatment and has a poor prognosis. Exosomes carry abundant genomic information and have a significant role in tumorigenesis, metastasis, and drug resistance. However, further exploration is needed to investigate the relationship between exosome-related genes and the heterogeneity and tumor immune microenvironment of TNBC. Based on the exosome-related gene sets, multiple machine learning algorithms, such as Cox boost, were used to screen the risk score model with the highest C-index. A 9-gene risk score model was constructed, and the TNBC population was divided into high- and low-risk groups. The effectiveness of this model was verified in multiple datasets. Compared with the low-risk group, the high-risk group exhibited a poorer prognosis, which may be related to lower levels of immune infiltration and immune response rates. The gene mutation profiles and drug sensitivity of the two groups were also compared. By screening for genes with the most prognostic value, the hub gene, CLDN7, was identified, and thus, its potential role in predicting prognosis, as well as providing ideas for the clinical diagnosis, treatment, and risk assessment of TNBC, was also discussed. This study demonstrates that exosome-related genes can be used for risk stratification in TNBC, identifying patients with a worse prognosis. The high-risk group exhibited a poorer prognosis and required more aggressive treatment strategies. Analysis of the genomic information in patient exosomes may help to develop personalized treatment decisions and improve their prognosis. CLDN7 has potential value in prognostic prediction in the TNBC population.

Biomarkers derived from CmP signal network in triple negative breast cancers

Breast cancer is the most commonly diagnosed cancer and the second leading cause of cancer-related death in women, accounting for approximately 30% of all new cancer cases. The prognosis of breast cancer heavily depends on the stage of diagnosis, with early detection resulting in higher survival rates. Various risk factors, including family history, alcohol consumption and hormone exposure, contribute to breast cancer development. Triple-negative breast cancer (TNBC), characterized by the absence of certain receptors, is particularly aggressive and heterogeneous. Cerebral cavernous malformations (CCMs), abnormal dilations of small blood vessels in the brain, is contributed by mutated genes like CCM1, CCM2, and CCM3 through the perturbed formation of the CCM signaling complex (CSC). The CSC-non-classic membrane progesterone receptors (mPRs)-progesterone (PRG) (CmP)/CSC-mPRs-PRG-classic nuclear progesterone receptors (nPRs) (CmPn) signaling network, which integrates the CSC with mPRs and nPRs, plays a role in breast cancer tumorigenesis. Understanding these pathways can provide insights into potential treatments. This paper focuses on the emerging field of CmPn/CmP signal networks, which involve PRG, its receptors (nPRs and mPRs), and the CSC. These networks play a role in tumorigenesis, particularly in TNBCs. Aims to deliver a thorough examination of the CmP/CmPn pathways concerning TNBCs, this paper provides a comprehensive overview of these pathways, explores their applications and highlights their significance in the context of TNBCs.