Collagen fiber features and COL1A1: are they associated with elastic parameters in breast lesions, and can COL1A1 predict axillary lymph node metastasis?

Changes in the expression of cell interaction-related pathways during brain metastasis in lung adenocarcinoma: Gene expression and immunohistochemical analysis

BACKGROUND

Brain metastasis (BM) is a prevalent prognostic event in the development of lung adenocarcinoma (LUAD) with a poor prognosis. Alterations in gene or protein expression during various phases of BM remain unclear.

METHODS

We performed gene expression and pathway analyses using a metastasis-related gene panel on 12 lung tissues from patients with confirmed BM, 12 lung tissues from patients without BM, and 12 matched brain tissues from patients with confirmed BM during follow-up after LUAD surgery. The results of the gene expression analysis were validated by immunohistochemistry.

RESULTS

Cell interaction-related pathways (such as focal adhesion, extracellular matrix-receptor interaction, and proteoglycans in cancer) showed the greatest differences among the three groups. Expression of the cell interaction-related pathway was highest in the lung sample of BM group and lowest in the matched brain tissue. Using a machine learning model, a signature of 20 genes from cell interaction-related pathways accurately predicted BM (area under the curve score of 0.792 and an accuracy rate of 0.875). Immunohistochemical analysis showed higher expression of proteins associated with cell interaction-related genes and a mesenchymal phenotype in the lung sample of BM group than in those without BM or matched brain tissue.

CONCLUSIONS

LUAD acquires the characteristics of the cell interaction-related pathway that leads to the development of BM, with a significant decrease in expression following brain colonization.

The value of microvascular Doppler ultrasound technique, qualitative or quantitative shear-wave elastography of breast lesions for predicting axillary nodal burden in patients with breast cancer

Background

The status of the axillary lymph node (ALN) in patients with breast cancer can critically inform clinical decision-making and prognosis. Preoperative evaluation of limited nodal burden (0-2 metastatic ALNs) and high nodal burden (≥3 metastatic ALNs) is vital for individual treatment in patients with breast cancer. Thus, this study aimed to evaluate the value of Angio-PLUS (AP; Aixplorer, SuperSonic Imagine) and the qualitative and quantitative shear-wave elastography (SWE) of breast lesions to predict limited or high axillary nodal burden and to develop a model for predicting limited or high axillary nodal burden.

Methods

From March 2020 to November 2022, a total of 232 consecutive patients with breast cancer comprising 232 breast lesions were enrolled retrospectively from Yueyang Central Hospital. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), accuracy, and area under the receiver operating characteristic curve (AUC) of AP, qualitative SWE, quantitative SWE, and the predictive model for evaluating limited or high axillary nodal burden were compared.

Results

There was no significant difference in AP patterns between the limited nodal burden group and high nodal burden group. The best cutoff values of Emin (the minimal value of the first Q-box), Emean (the mean value of the first Q-box), Emax (the maximum value of the first Q-box), Eratio (ratio of the first Q-Box and the second Q-Box) and standard deviation for predicting limited or high nodal burden were 80.85 KPa, 133.45 KPa, 153.40 KPa, 9.95, and 19.25 KPa, respectively. The Emax had the highest AUC, and its sensitivity, specificity, PPV, NPV, accuracy, and AUC were 71.64%, 56.36%, 40.00%, 83.04%, 60.78%, and 0.640 [95% confidence interval (CI): 0.575-0.702], respectively. The sensitivity, specificity, PPV, NPV, accuracy, and AUC of seven color patterns for qualitative SWE were 71.64%, 74.55%, 53.33%, 86.62%, 73.71%, and 0.731 (95% CI: 0.669-0.787), respectively, which was significantly higher than all the other quantitative SWE parameters. ALN evaluation in ultrasound and qualitative SWE were independent risk factors for predicting limited or high nodal burden according to a binary logistics regression analysis. The AUC of the predictive model based on independent risk factors was 0.820 (95% CI: 0.765-0.867), which was significantly higher than that of the other independent risk factors.

Conclusions

The seven color patterns in the qualitative SWE of breast lesions were valuable for predicting limited or high nodal burden for patients with breast cancer. Compared with quantitative SWE, qualitative SWE exhibited a better diagnostic performance. Breast lesions present no findings, vertical stripes, and spot patterns were important indicators for limited nodal burden. The predictive model developed in this study could be a simple, noninvasive, and convenient method for predicting limited or high nodal burden, which would be beneficial for clinical decision-making and individual treatment to improve prognosis.

FEATURES OF COL1A1 EXPRESSION IN BREAST CANCER TISSUE OF YOUNG PATIENTS

BACKGROUND

In the last decades, the incidence of breast cancer (BCa) in young women has been increasing steadily. The quantitative indicators of expression of collagen, which play important role in stromal microenvironment, and their association with the age and survival rates of BCa patients have not been yet definitively clarified.

AIM

To investigate the relationship between the COL1A1 gene expression at the mRNA and protein levels in BCa tissue and the clinicopatological features and survival rates of BCa patients of different age groups.

MATERIALS AND METHODS

The study was conducted on the clinical material of 50 patients with stage I-III BCa. COL1A1 gene expression at the mRNA and protein levels in BCa tissue were studied using the real-time PCR and immunohistochemical methods, as well as the bioinformatic analysis (UALCAN and Kaplan - Meier Plotter databases).

RESULTS

The bioinformatic analysis showed that BCa tissue is characterized by 6.0 times (p < 0.05) higher level of COL1A1 mRNA compared to normal breast tissue. The correlation of COL1A1 expression at the mRNA and protein levels with the molecular subtype of neoplasms was demonstrated. According to Kaplan - Meier Plotter database, a low level of expression of COL1A1 protein level in BCa tissue is associated with lower rates of relapse-free survival of patients. The ex vivo study of the clinical material revealed a decrease in COL1A1 protein expression in tumor tissue of young patients with BCa of T3 category (p < 0.0374), low differentiation grade (p < 0.0163) and basal molecular subtype (p < 0.0001). A correlation between the expression of COL1A1 at the mRNA and protein levels and the expression status of estrogen receptors (p < 0.0001) and progesterone receptors (p < 0.0040) was established. The relapse-free 3-year survival rate of young BCa patients is significantly lower in the presence of a low COL1A1 optical density index in the tumor tissue.

CONCLUSIONS

The identified relationship between COL1A1 expression and such indicators of BCa malignancy as tumor size, differentiation grade, molecular subtype, receptor status, and the recurrencefree survival of patients indicates the prospects of its use to predict the aggressiveness of the BCa course in young patients.

Collagen code in tumor microenvironment: Functions, molecular mechanisms, and therapeutic implications

The tumor microenvironment (TME) is crucial in cancer progression, and the extracellular matrix (ECM) is an important TME component. Collagen is a major ECM component that contributes to tumor cell infiltration, expansion, and distant metastasis during cancer progression. Recent studies reported that collagen is deposited in the TME to form a collagen wall along which tumor cells can infiltrate and prevent drugs from working on the tumor cells. Collagen-tumor cell interaction is complex and requires the activation of multiple signaling pathways for biochemical and mechanical signaling interventions. In this review, we examine the effect of collagen deposition in the TME on tumor progression and discuss the interaction between collagen and tumor cells. This review aims to illustrate the functions and mechanisms of collagen in tumor progression in the TME and its role in tumor therapy. The findings indicated collagen in the TME appears to be a better target for cancer therapy.

Exploring the Potential Role of Circulating microRNAs as Biomarkers for Predicting Clinical Response to Neoadjuvant Therapy in Breast Cancer

Breast cancer (BC) is a leading cause of cancer-related deaths among women worldwide. Neoadjuvant therapy (NAT) is increasingly being used to reduce tumor burden prior to surgical resection. However, current techniques for assessing tumor response have significant limitations. Additionally, drug resistance is commonly observed, raising a need to identify biomarkers that can predict treatment sensitivity and survival outcomes. Circulating microRNAs (miRNAs) are small non-coding RNAs that regulate gene expression and have been shown to play a significant role in cancer progression as tumor inducers or suppressors. The expression of circulating miRNAs has been found to be significantly altered in breast cancer patients. Moreover, recent studies have suggested that circulating miRNAs can serve as non-invasive biomarkers for predicting response to NAT. Therefore, this review provides a brief overview of recent studies that have demonstrated the potential of circulating miRNAs as biomarkers for predicting the clinical response to NAT in BC patients. The findings of this review will strengthen future research on developing miRNA-based biomarkers and their translation into medical practice, which could significantly improve the clinical management of BC patients undergoing NAT.