MOLECULAR BIOLOGY OF CANCER AND NEW TOOLS IN ONCOLOGY

Abstract

Background: Cancer continues to be among the most prevalent illnesses as well as the primary cause of death in the modern world. Understanding cancer biology and improvements in oncology have led to the formulation of more precise and potent therapies. This research explores the biological processes that control cancer development and analyses the effects of new oncology practices, including targeted treatment, immunotherapy, and genomic medicine, on patient outcomes and overall health satisfaction. 

Objective: The primary focus of this research was to determine how effective the new oncology implementable tools and strategies were in achieving favorable patient outcomes. More specifically, this research sought to relate some of the molecular changes occurring in the cancer cells relative to the treatment received and patient-reported outcomes. 

Methods: This quantitative study employed a cross-sectional research design. Data was obtained from experimental studies and administered surveys. The lab work included the assessment of genetic alterations in cancer cells using Next Generation Sequencing (NGS) and Polymerase Chain Reaction (PCR) techniques. In the survey stage, 250 respondents were recruited which included 150 cancer patients and 100 other healthcare professionals. The survey tested their satisfaction and perceived effectiveness of various oncology tools, employing a multiple-choice format along with Likert scale items. The data was analyzed using descriptive statistics, Pearson correlation, Cronbach’s Alpha, normality tests (Shapiro-Wilk and Kolmogorov-Smirnov), and ANOVA.

Results: The analysis showed a considerable increase in health ratings after the intervention as compared to before, along with a moderate positive correlation (r = 0.6485) between pre-intervention and post-intervention health scores. The degree of satisfaction with the intervention was different among participants, which led to a notable lack of normal distribution in the satisfaction data. Satisfaction and effectiveness scales demonstrated satisfactory internal consistency as per Cronbach’s Alpha (α = 0.7564). ANOVA did not indicate any considerable differences in satisfaction among different age groups. Normality tests indicated that all variables, except for satisfaction with the intervention, were normally distributed.

Conclusion: The study supports the argument that innovative tools in oncology, such as targeted therapies and genomic ones, positively influence health outcomes in patients, particularly those with more severe conditions. While individual satisfaction may vary, the tools seem effective across different age demographics. The results emphasize the role of treatment personalization in oncology, while also pointing to the lack of appropriate tools designed to evaluate patients’ experiences with cancer care as an oncological measurement gap. The findings also indicate the need to further examine the reasons shaping satisfaction among patients as well as the sustained effectiveness of these interventions.