The molecular cues for the biological effects of ionizing radiation dose and post-irradiation time on human breast cancer SKBR3 cell line: A Raman spectroscopy study

Screening of BindingDB database ligands against EGFR, HER2, Estrogen, Progesterone and NF-κB receptors based on machine learning and molecular docking

Breast cancer, the second most prevalent cancer among women worldwide, necessitates the exploration of novel therapeutic approaches. To target the four subgroups of breast cancer "hormone receptor-positive and HER2-negative, hormone receptor-positive and HER2-positive, hormone receptor-negative and HER2-positive, and hormone receptor-negative and HER2-negative" it is crucial to inhibit specific targets such as EGFR, HER2, ER, NF-κB, and PR. In this study, we evaluated various methods for binary and multiclass classification. Among them, the GA-SVM-SVM:GA-SVM-SVM model was selected with an accuracy of 0.74, an F1-score of 0.73, and an AUC of 0.92 for virtual screening of ligands from the BindingDB database. This model successfully identified 4454, 803, 438, and 378 ligands with over 90% precision in both active/inactive and target prediction for the classes of EGFR+HER2, ER, NF-κB, and PR, respectively, from the BindingDB database. Based on to the selected ligands, we created a dendrogram that categorizes different ligands based on their targets. This dendrogram aims to facilitate the exploration of chemical space for various therapeutic targets. Ligands that surpassed a 90% threshold in the product of activity probability and correct target selection probability were chosen for further investigation using molecular docking. The binding energy range for these ligands against their respective targets was calculated to be between -15 and -5 kcal/mol. Finally, based on general and common rules in medicinal chemistry, we selected 2, 3, 3, and 8 new ligands with high priority for further studies in the EGFR+HER2, ER, NF-κB, and PR classes, respectively.

Personal history of irradiation and risk of breast cancer: A Mendelian randomisation study

Background

Studies on the relationship between personal history of irradiation and breast cancer have been reported for a long time. Still, epidemiological studies have not been conclusive, and the causal relationship is unclear. To address this issue, we employed Mendelian randomisation (MR) analysis to examine the association between individual radiation exposure history and breast cancer.

Methods

We used a series of quality control methods to select single nucleotide polymorphism (SNP) closely related to exposure. Meanwhile, several analysis methods were used to analyse the sample data to make the conclusion more reliable. To evaluate the horizontal pleiotropy, heterogeneity and stability of SNPs for breast cancer, the MR-Egger intercept test, Cochran's Q test and 'leave one' sensitivity analysis were used. Finally, the outlier variation determined by the Mendelian Randomisation Pleiotropy RESidual Sum and Outlier test is gradually eliminated to reduce the influence of heterogeneity and horizontal pleiotropy.

Results

After implementing rigorous quality control procedures, we carefully chose 102 qualified instrumental variables closely associated with the selected exposure for sensitivity analysis. This was conducted to evaluate the heterogeneity, level multiplicity, and stability of SNPs in the context of personal radiation history and its correlation with breast cancer. The results of the inverse variance weighted method analysis revealed a positive correlation between personal radiation and a heightened risk of breast cancer (odds ratio (OR) = 1.52; 95% confidence interval (CI) = 1.30-1.77). We also validated on another data set; the results were similar (OR = 1.51; 95% CI = 1.27-1.81). Furthermore, the findings from the sensitivity analysis were consistent. At the genetic level, our research demonstrated that personal radiation exposure is associated with an elevated risk of breast cancer.

Conclusions

Using genetic data provides evidence and strengthens the causal link that personal radiation causes breast cancer.

Raman spectroscopy for classification of neoplastic and non-neoplastic CAM colon tumors

This paper demonstrates the potential of Raman spectroscopy for differentiating neoplastic from non-neoplastic colon tumors, obtained with the CAM (chicken chorioallantoic membrane) model. For the CAM model two human cell lines were used to generate two types of tumors, the RKO cell line for neoplastic colon tumors and the NCM460 cell line for non-neoplastic colon tumors. The Raman spectra were acquired with a 785 nm excitation laser. The measured Raman spectra from the CAM samples (n = 14) were processed with several methods for baseline correction and to remove artifacts. The corrected spectra were analyzed with PCA (principal component analysis). Additionally, machine learning based algorithms were used to create a model capable of classifying neoplastic and non-neoplastic tumors. The principal component scores showed a clear differentiation between neoplastic and non-neoplastic colon tumors. The classification model had an accuracy of 93 %. Thus, a complete methodology to process and analyze Raman spectra was validated, using a rapid, accessible, and well-established tumor model that mimics the human tumor pathology with minor ethical concerns.

Analysis of the molecular alterations in cancer cells following nanotechnology-assisted targeted radiotherapy using Raman spectroscopy

The study unveiled an innovative strategy for precise radiation targeting in cancer treatment, along with the monitoring of molecular changes induced by this therapeutic approach. In this research, we explored the impact of administering anti-HER2-AgNPs nanoconjugates either individually or in conjunction with gamma irradiation on the viability of SKBR3 breast cancer cells. The utilization of nanoconjugates resulted in an enhancement of cellular sensitivity toward radiation. The viability of the cells exhibited a decline as the dose of irradiation increased, and this decrease was further exacerbated by the passage of time following irradiation. The analysis of RS revealed distinct cellular responses in varying conditions. The observed increase in SERS intensity, resulting from the increment in dose from 0 to 2 Gy, can be attributed to the probable upregulation of HER2 expression induced by irradiation. The observed decrease in SERS intensity at doses of 4 and 6 Gy can be attributed to the likely reduction in HER2 expression. It was illustrated that the analysis of Raman spectroscopy data can aid in the identification of radiation-induced biochemical alterations in cancer cells during the application of nanoconjugates-based radiotherapy. The findings revealed that nanoconjugates have the potential to enhance cellular sensitivity to radiation along with facilitating the detection of radiation-induced biochemical alterations within cancer cells.

Investigation of the Dose-Enhancement Effects of Spherical and Rod-Shaped Gold Nanoparticles on the HeLa Cell Line

Background:

Cervical cancer cells are known as radioresistant cells. Current treatment methods have not improved the patients’ survival efficiently; thus, new therapeutic strategies are needed to enhance the efficacy of radiotherapy. Gold nanomaterials with different shapes and sizes have been explored as radiosensitizers. The present study compared the radiosensitizing effects of gold nanorods (AuNRs) with spherical gold nanoparticles (AuNPs) on the HeLa cell line irradiated with megavoltage X-rays.

Materials and Methods:

The cytotoxicity of AuNRs and AuNPs on HeLa cells in the presence and absence of 6-MV X-ray was investigated using the MTT assay. For this aim, HeLa cells were incubated with and AuNPs and AuNRs at various concentrations (5, 10, and 15 µg/mL) for 6 hours. Afterward, HeLa cells were irradiated with 6-MV X-ray at a single dose of 2 Gy.

Results:

The results showed that the addition of AuNRs and AuNPs could enhance the radiosensitivity of HeLa cells. Both AuNRs and AuNPs showed low toxicity on HeLa cells, while AuNRs were more toxic than AuNPs at the examined concentrations. Moreover, it was found that AuNRs could enhance the radiosensitivity of HeLa cells more than spherical-shaped AuNPs.

Conclusion:

This study revealed that the shape of nanoparticles is an effective factor when they are used as radiosensitizing agents during radiotherapy.

SERS-based differential diagnosis between multiple solid malignancies: breast, colorectal, lung, ovarian and oral cancer

PURPOSE

Surface-enhanced Raman scattering (SERS) spectroscopy on serum and other biofluids for cancer diagnosis represents an emerging field, which has shown promising preliminary results in several types of malignancies. The purpose of this study was to demonstrate that SERS spectroscopy on serum can be employed for the differential diagnosis between five of the leading malignancies, ie, breast, colorectal, lung, ovarian and oral cancer.

PATIENTS AND METHODS

Serum samples were acquired from healthy volunteers (n=39) and from patients diagnosed with breast (n=42), colorectal (n=109), lung (n=33), oral (n=17), and ovarian cancer (n=13), comprising n=253 samples in total. SERS spectra were acquired using a 532 nm laser line as excitation source, while the SERS substrates were represented by Ag nanoparticles synthesized by reduction with hydroxylamine. The classification accuracy yielded by SERS was assessed by principal component analysis-linear discriminant analysis (PCA-LDA).

RESULTS

The sensitivity and specificity in discriminating between cancer patients and controls was 98% and 91%, respectively. Cancer samples were correctly assigned to their corresponding cancer types with an accuracy of 88% for oral cancer, 86% for colorectal cancer, 80% for ovarian cancer, 76% for breast cancer and 59% for lung cancer.

CONCLUSION

SERS on serum represents a promising strategy of diagnosing cancer which can discriminate between cancer patients and controls, as well as between cancer types such as breast, colorectal, lung ovarian and oral cancer.