Prediction of Late Recurrence and Distant Metastasis in Early-stage Breast Cancer: Overview of Current and Emerging Biomarkers

Analysis of differentially expressed proteins in lymph fluids related to lymphatic metastasis in a breast cancer rabbit model guided by contrast‑enhanced ultrasound

The aim of the present study was to identify differentially expressed proteins in the lymph fluid of rabbits with breast cancer lymphatic metastasis compared with healthy rabbits and to analyze and verify these proteins using proteomics technologies. In the process of breast cancer metastasis, the composition of the lymph fluid will also change. Rabbits with breast cancer lymph node metastasis and normal rabbits were selected for analysis. Lymph fluid was extracted under the guidance of percutaneous contrast-enhanced ultrasound. Label-free quantitative proteomics was used to detect and compare differences between the rabbit cancer model and healthy rabbits and differential protein expression results were obtained. Bioinformatics analysis was performed using Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analysis software, selecting the most significantly differentially expressed proteins. Finally, parallel reaction monitoring technology was applied for validation. A total of 547 significantly differentially expressed proteins were found in the present study, which included 371 upregulated proteins and 176 downregulated proteins. The aforementioned genes were mainly involved in various cellular and metabolic pathways, including upregulated proteins, such as biliverdin reductase A and isocitrate dehydrogenase 2 and downregulated proteins, such as pyridoxal kinase. The upregulated proteins protein disulfide-isomerase 3, protein kinase cAMP-dependent type I regulatory subunit α and ATP-binding cassette sub-family C member 4 participated in immune regulation, endocrine regulation and anti-tumor drug resistance regulation, respectively. Compared with healthy rabbits, rabbits with breast cancer metastasis differentially expressed of a number of different proteins in their lymph, which participate in the pathophysiological process of tumor occurrence and metastasis. Through further research, these differential proteins can be used as predictive indicators of breast cancer metastasis and new therapeutic targets.

Clinicomics-guided distant metastasis prediction in breast cancer via artificial intelligence

BACKGROUND

Breast cancer has become the most common malignant tumour worldwide. Distant metastasis is one of the leading causes of breast cancer-related death. To verify the performance of clinicomics-guided distant metastasis risk prediction for breast cancer via artificial intelligence and to investigate the accuracy of the created prediction models for metachronous distant metastasis, bone metastasis and visceral metastasis.

METHODS

We retrospectively enrolled 6703 breast cancer patients from 2011 to 2016 in our hospital. The figures of magnetic resonance imaging scanning and ultrasound were collected, and the figures features of distant metastasis in breast cancer were detected. Clinicomics-guided nomogram was proven to be with significant better ability on distant metastasis prediction than the nomogram constructed by only clinical or radiographic data.

RESULTS

Three clinicomics-guided prediction nomograms on distant metastasis, bone metastasis and visceral metastasis were created and validated. These models can potentially guide metachronous distant metastasis screening and lead to the implementation of individualized prophylactic therapy for breast cancer patients.

CONCLUSION

Our study is the first study to make cliniomics a reality. Such cliniomics strategy possesses the development potential in artificial intelligence medicine.

Multi-omics analysis reveals a molecular landscape of the early recurrence and early metastasis in pan-cancer

Cancer remains a formidable challenge in medicine due to its propensity for recurrence and metastasis, which can result in unfavorable treatment outcomes. This challenge is particularly acute for early-stage patients, who may experience recurrence and metastasis without timely detection. Here, we first analyzed the differences in clinical characteristics among the primary tumor, recurrent tumor, and metastatic tumor in different stages of cancer, which may be caused by the molecular level. Moreover, the importance of predicting early cancer recurrence and metastasis is emphasized by survival analyses. Next, we used a multi-omics approach to identify key molecular changes associated with early cancer recurrence and metastasis and discovered that early metastasis in cancer demonstrated a high degree of genomic and cellular heterogeneity. We performed statistical comparisons for each level of omics data including gene expression, mutation, copy number variation, immune cell infiltration, and cell status. Then, various analytical techniques, such as proportional hazard model and Fisher's exact test, were used to identify specific genes or immune characteristics associated with early cancer recurrence and metastasis. For example, we observed that the overexpression of BPIFB1 and high initial B-cell infiltration levels are linked to early cancer recurrence, while the overexpression or amplification of ANKRD22 and LIPM, mutation of IGHA1 and MUC16, high fibroblast infiltration level, M1 polarization of macrophages, cellular status of DNA repair are all linked to early cancer metastasis. These findings have led us to construct classifiers, and the average area under the curve (AUC) of these classifiers was greater than 0.75 in The Cancer Genome Atlas (TCGA) cancer patients, confirming that the features we identified could be biomarkers for predicting recurrence and metastasis of early cancer. Finally, we identified specific early sensitive targets for targeted therapy and immune checkpoint inhibitor therapy. Once the biomarkers we identified changed, treatment-sensitive targets can be treated accordingly. Our study has comprehensively characterized the multi-omics characteristics and identified a panel of biomarkers of early cancer recurrence and metastasis. Overall, it provides a valuable resource for cancer recurrence and metastasis research and improves our understanding of the underlying mechanisms driving early cancer recurrence and metastasis.

Novel pH-sensitive nanoparticles based on prodrug strategy to delivery All-Trans Retinoic Acid for breast cancer

Developing chemotherapy with nanoparticle-based prodrugs provides promising strategies for improving the safety and delivery of anti-cancer drugs therapeutics and effective cancer treatment. Herein, we developed a pH-sensitive prodrug delivery system (All-Trans-Retinoic Acid (ATRA) grafted poly (β-amino esters) (PBAE) copolymers, ATRA-g-PBAE) for delivery of ATRA with some physicochemical and biological properties. The in vitro release of ATRA-g-PBAE prodrug nanoparticles (PNPs) was sustained-release and pH-sensitive. The cytotoxicity and uptake of different preparations in vitro were evaluated on MCF-7 cells at pH 7.4 and 5.5. The carrier PBAE had no cytotoxicity, and ATRA-g-PBAE PNPs could significantly inhibit cell growth at pH 5.5. MCF-7 cells treated with Cy5.5 grafted PBAE (Cy5.5-PBAE) showed stronger fluorescence signals at pH 5.5. Meanwhile, ATRA-g-PBAE PNPs entered the cell via a clathrin-mediated endocytic pathway. Subsequently, PBAE protonation facilitated the escape of PNPs from the lysosome and released the drug. ATRA-g-PBAE seems promising as a novel pH-sensitive prodrug to overcome the limitations of ATRA for breast cancer therapy.

Anlotinib inhibits the proliferation, migration and invasion, and induces apoptosis of breast cancer cells by downregulating TFAP2C

The vascular endothelial growth factor receptor (VEGFR) network contributes to breast cancer pathogenesis and progression. Anlotinib is a highly potent multi-target tyrosine kinase inhibitor that has been previously shown to exert antitumor effects in various types of cancer. The aim of the present study is to investigate the effect of Anlotinib against breast cancer cells in vitro and uncover the possible underlying mechanisms. The human breast cancer cell line MCF-7 was treated with different concentrations of Anlotinib, before cell proliferation, migration, invasion and apoptosis were assessed using colony formation, wound healing, Transwell and TUNEL staining assays. In addition, the expression of transcription factor AP-2γ (TFAP2C) following Anlotinib stimulation was measured using reverse transcription-quantitative PCR and western blot analysis. TFAP2C was overexpressed in MCF-7 using transfection with a pcDNA3.1 vector, before the aforementioned experiments were repeated. The results revealed that Anlotinib impaired cell viability and colony formation, reduced proliferating cell nuclear antigen, Ki-67, MMP2, MMP9 and Bcl-2 expression levels, and inhibited cell migration and invasion. By contrast, the expression levels of tissue inhibitor of metalloproteinase 1, the frequency of apoptotic cells, the expression of Bax and the cleaved caspase-3/caspase-3 ratio increased in a concentration-dependent manner. Additionally, the expression of TFAP2C decreased after Anlotinib treatment. However, TFAP2C overexpression partially blocked the effects of Anlotinib on the proliferation, migration, invasion and apoptosis of MCF-7 cells. To conclude, Anlotinib suppressed proliferation, migration and invasion, whilst inducing apoptosis of MCF-7 cells, which may be partially dependent on the inhibition of TFAP2C expression.