Physical Cues in the Microenvironment Regulate Stemness-Dependent Homing of Breast Cancer Cells

Premetastatic niche mechanics and organotropism in breast cancer

Numerous physical and mechanical changes occur in the premetastatic niche. Here, we review the mechanics of the premetastatic niche and how the altered extracellular matrix and cancer cell mechanics may play a role in organotropism in breast cancer. Future research into premetastatic niche development and organotropic cell behavior should address physical alterations and biomechanical effects to the same rigor that biochemical alterations are studied.

Tumor matrix stiffness provides fertile soil for cancer stem cells

Matrix stiffness is a mechanical characteristic of the extracellular matrix (ECM) that increases from the tumor core to the tumor periphery in a gradient pattern in a variety of solid tumors and can promote proliferation, invasion, metastasis, drug resistance, and recurrence. Cancer stem cells (CSCs) are a rare subpopulation of tumor cells with self-renewal, asymmetric cell division, and differentiation capabilities. CSCs are thought to be responsible for metastasis, tumor recurrence, chemotherapy resistance, and consequently poor clinical outcomes. Evidence suggests that matrix stiffness can activate receptors and mechanosensor/mechanoregulator proteins such as integrin, FAK, and YAP, modulating the characteristics of tumor cells as well as CSCs through different molecular signaling pathways. A deeper understanding of the effect of matrix stiffness on CSCs characteristics could lead to development of innovative cancer therapies. In this review, we discuss how the stiffness of the ECM is sensed by the cells and how the cells respond to this environmental change as well as the effect of matrix stiffness on CSCs characteristics and also the key malignant processes such as proliferation and EMT. Then, we specifically focus on how increased matrix stiffness affects CSCs in breast, lung, liver, pancreatic, and colorectal cancers. We also discuss how the molecules responsible for increased matrix stiffness and the signaling pathways activated by the enhanced stiffness can be manipulated as a therapeutic strategy for cancer.

Multi compartmental 3D breast cancer disease model–recapitulating tumor complexity in in-vitro

Breast cancer is the most common ailment among women. In 2020, it had the highest incidence of any type of cancer. Many Phase II and III anti-cancer drugs fail due to efficacy, durability, and side effects. Thus, accelerated drug screening models must be accurate. In-vivo models have been used for a long time, but delays, inconsistent results, and a greater sense of responsibility among scientists toward wildlife have led to the search for in-vitro alternatives. Stromal components support breast cancer growth and survival. Multi-compartment Transwell models may be handy instruments. Co-culturing breast cancer cells with endothelium and fibroblasts improves modelling. The extracellular matrix (ECM) supports native 3D hydrogels in natural and polymeric forms. 3D Transwell cultured tumor spheroids mimicked in-vivo pathological conditions. Tumor invasion, migration, Trans-endothelial migration, angiogenesis, and spread are studied using comprehensive models. Transwell models can create a cancer niche and conduct high-throughput drug screening, promising future applications. Our comprehensive shows how 3D in-vitro multi compartmental models may be useful in producing breast cancer stroma in Transwell culture.

Nanoscale Prognosis of Colorectal Cancer Metastasis from AFM Image Processing of Histological Sections

Early ascertainment of metastatic tumour phases is crucial to improve cancer survival, formulate an accurate prognostic report of disease advancement, and, most importantly, quantify the metastatic progression and malignancy state of primary cancer cells with a universal numerical indexing system. This work proposes an early improvement to metastatic cancer detection with 97.7 nm spatial resolution by indexing the metastatic cancer phases from the analysis of atomic force microscopy images of human colorectal cancer histological sections. The procedure applies variograms of residuals of Gaussian filtering and theta statistics of colorectal cancer tissue image settings. This methodology elucidates the early metastatic progression at the nanoscale level by setting metastatic indexes and critical thresholds based on relatively large histological sections and categorising the malignancy state of a few suspicious cells not identified with optical image analysis. In addition, we sought to detect early tiny morphological differentiations indicating potential cell transition from epithelial cell phenotypes of low metastatic potential to those of high metastatic potential. This metastatic differentiation, which is also identified in higher moments of variograms, sets different hierarchical levels for metastatic progression dynamics.

HOXA-AS2 may be a potential prognostic biomarker in human cancers: A meta-analysis and bioinformatics analysis

Background: Dysregulation of long non-coding (lncRNA) has been reported in various solid tumors. HOXA cluster antisense RNA 2 (HOXA-AS2) is a newly identified lncRNA with abnormal expression in several human malignancies. However, its prognostic value remains controversial. This meta-analysis synthesized available data to clarify the association between HOXA-AS2 expression levels and clinical prognosis in multiple cancers. Methods: Four public databases (Embase, PubMed, Web of Science, The Cochrane Library) were used to identify eligible studies. Hazard ratios (HRs) and odds ratios (ORs) with their 95% confidence intervals (CIs) were combined to assess the correlation of HOXA-AS2 expression with survival outcomes and clinicopathological features of cancer patients. Publication bias was measured using Begg's funnel plot and Egger's regression test, and the stability of the combined results was measured using sensitivity analysis. Additionally, multiple public databases were screened and extracted to validate the results of this meta-analysis. Results: The study included 20 studies, containing 1331 patients. The meta-analysis showed that the overexpression of HOXA-AS2 was associated with poor overall survival (HR = 2.06, 95% CI 1.58-2.69, p < 0.001). In addition, the high expression of HOXA-AS2 could forecast advanced tumor stage (OR = 3.89, 95% CI 2.90-5.21, p < 0.001), earlier lymph node metastasis (OR = 3.48, 95% CI 2.29-5.29, p < 0.001), larger tumor size (OR = 2.36, 95% CI 1.52-3.66, p < 0.001) and earlier distant metastasis (OR = 3.54, 95% CI 2.00-6.28, p < 0.001). However, other clinicopathological features, including age (OR = 1.09, 95% CI 0.86-1.38, p = 0.467), gender (OR = 0.92, 95% CI 0.72-1.18, p = 0.496), depth of invasion (OR = 2.13, 95% CI 0.77-5.90, p = 0.146) and differentiation (OR = 1.02, 95% CI 0.65-1.59, p = 0.945) were not significantly different from HOXA-AS2 expression. Conclusion: Our study showed that the overexpression of HOXA-AS2 was related to poor overall survival and clinicopathological features. HOXA-AS2 may serve as a potential prognostic indicator and therapeutic target for tumor treatment.