It has been shown in multiple experimental and biological investigations that kaempferol, an edible flavonoid generated from plants, may be used as an anti-cancer drug and has been shown to have anti-cancer properties. Many signaling pathways are altered in cancer cells, resulting in cell growth inhibition and death in various tumor types. Cancer is a multifaceted illness coordinated by multiple external and internal mechanisms. Natural extracts with the fewest side effects have piqued the attention of researchers in recent years, attempting to create cancer medicines based on them. An extensive array of natural product-derived anti-cancer agents have been examined to find a successful method. Numerous fruits and vegetables have high levels of naturally occurring flavonoid kaempferol, and its pharmacological and biological effects have been studied extensively. Certain forms of cancer are sensitive to kaempferol-mediated anti-cancer activity, although complete research is needed. We have endeavored to concentrate our review on controlling carcinogenic pathways by kaempferol in different malignancies. Aside from its extraordinary ability to modify cell processes, we have also discussed how kaempferol has the potential to be an effective therapy for numerous tumors.

Paclitaxel is a natural anticancer compound with minimal toxicity, the capacity to stabilize microtubules, and high efficiency that has remained the standard of treatment alongside platinum-based therapy as a remedy for a variety of different malignancies. In contrast, polyphenols such as flavonoids are also efficient antioxidant and anti-inflammatory and have now been shown to possess potent anticancer properties. Therefore, the synergistic effects of paclitaxel and flavonoids against cancer will be of interest. In this review, we use a Boolean query to comprehensively search the well-known Scopus database for literature research taking the advantage of paclitaxel and flavonoids simultaneously while treating various types of cancer. After retrieving and reviewing the intended investigations based on the input keywords, the anticancer mechanisms of flavonoids and paclitaxel and their synergistic effects on different targets raging from cell lines to animal models are discussed in terms of the corresponding involved signaling transduction. Most studies demonstrated that these signaling pathways will induce apoptotic / pro-apoptotic proteins, which in turn may activate several caspases leading to apoptosis. Finally, it can be concluded that the results of this review may be beneficial in serving as a theoretical foundation and reference for future studies of paclitaxel synthesis, anticancer processes, and clinical applications involving different clinical trials.

Introduction There is an academic debate over surgical treatments of liver hydatid cyst disease. In this study, a systematic review and meta-analysis were carried out in order to evaluate the pros and cons of both PAIR (Puncture, Aspiration, Injection, Respiration) and laparoscopic techniques...

Breast cancer (BC), as one of the leading causes of death among women, comprises several subtypes with controversial and poor prognosis. Considering the TNM (tumor, lymph node, metastasis) based classification for staging of breast cancer, it is essential to diagnose the disease at early stages. The present study aims to take advantage of the systems biology approach on genome wide gene expression profiling datasets to identify the potential biomarkers involved at stage I, stage II, stage III, and stage IV as well as in the integrated group. Three HER2-negative breast cancer microarray datasets were retrieved from the GEO database, including normal, stage I, stage II, stage III, and stage IV samples. Additionally, one dataset was also extracted to test the developed predictive models trained on the three datasets. The analysis of gene expression profiles to identify differentially expressed genes (DEGs) was performed after preprocessing and normalization of data. Then, statistically significant prioritized DEGs were used to construct protein–protein interaction networks for the stages for module analysis and biomarker identification. Furthermore, the prioritized DEGs were used to determine the involved GO enrichment and KEGG signaling pathways at various stages of the breast cancer. The recurrence survival rate analysis of the identified gene biomarkers was conducted based on Kaplan–Meier methodology. Furthermore, the identified genes were validated not only by using several classification models but also through screening the experimental literature reports on the target genes. Fourteen (21 genes), nine (17 genes), eight (10 genes), four (7 genes), and six (8 genes) gene modules (total of 53 unique genes out of 63 genes with involving those with the same connectivity degree) were identified for stage I, stage II, stage III, stage IV, and the integrated group. Moreover, SMC4, FN1, FOS, JUN, and KIF11 and RACGAP1 genes with the highest connectivity degrees were in module 1 for abovementioned stages, respectively. The biological processes, cellular components, and molecular functions were demonstrated for outcomes of GO analysis and KEGG pathway assessment. Additionally, the Kaplan–Meier analysis revealed that 33 genes were found to be significant while considering the recurrence-free survival rate as an alternative to overall survival rate. Furthermore, the machine learning calcification models show good performance on the determined biomarkers. Moreover, the literature reports have confirmed all of the identified gene biomarkers for breast cancer. According to the literature evidence, the identified hub genes are highly correlated with HER2-negative breast cancer. The 53-mRNA signature might be a potential gene set for TNM based stages as well as possible therapeutics with potentially good performance in predicting and managing recurrence-free survival rates at stages I, II, III, and IV as well as in the integrated group. Moreover, the identified genes for the TNM-based stages can also be used as mRNA profile signatures to determine the current stage of the breast cancer.