Corrigendum: Targeting p53 misfolding conundrum by stabilizing agents and their analogues in breast cancer therapy: a comprehensive computational analysis.

[This corrects the article DOI: 10.3389/fphar.2023.1333447.].

Targeting p53 misfolding conundrum by stabilizing agents and their analogs in breast cancer therapy: a comprehensive computational analysis.

Cancer continues to be a major global public health concern and one of the foremost causes of death. Delays in the diagnosis and cure may cause an increase in advanced stage disease and mortality. The most common cancer found in women currently is breast carcinoma. Breast carcinoma has surpassed lung carcinoma and currently represents the chief type of cancer diagnosed (2.3 million new cases, which amount to 11.7% of all cancer cases). In addition, by 2040, the incidence will increase by more than 46% as per the estimates of GLOBOCAN. Triple-negative breast cancer (TNBC) represents a highly aggressive and invasive subtype of breast cancer, characterized by rapid progression, short response time to the available treatment, and poor clinical results. Thus, it is very crucial to develop novel diagnostic tools and therapeutics with good efficacy. A majority of cancers display malfunction along the p53 pathway. Moreover, p53 not only loses its function but is also prone to misfolding and aggregation, leading to formation of amyloid aggregates as well. Research is being carried out to find ways to restore the normal action and expression of p53. Here, we have explored PhiKan-083 for its possible stabilizing effect on p53 in order to address the problem with its misfolding. Thus, examining the analogs of PhiKan-083 that have a role in p53 stability will help update our understanding of cancer progression and may expedite the progress of new anticancer treatments. We anticipate that the drug molecules and their analogs targeting p53 aggregation may be used in combination with other anticancer compounds to solve the problem with p53 aggregation. In this study, by employing ADMET analysis, the compounds were screened, and we further examined the chosen compounds with the help of molecular docking. By using databases like UALCAN, TIMER, GEPIA, and PredictProtein, we investigated TP53's expression pattern and prognostic relevance in various cancer settings.