Renal Cancer Research Articles

Calcium Channel Blocker Versus Renin-Angiotensin System Inhibitor in Risk of Kidney Cancer Among Patients With Hypertension: A Propensity Score-Matched Cohort Study.

Use of antihypertensive medications could be associated with an increased risk of kidney cancer. Despite their various mechanisms of action, whether this association differs between different classes of medications remains unclear. The objective of this study is to compare the risk of kidney cancer between first-line treatment options of antihypertensive medications in a hypertensive population. In this retrospective cohort study, we used the MarketScan Databases (2007-2021). We included individuals older than 30 years of age with a diagnosis of hypertension who received first-line medications for hypertension, which included three classes: angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers (ARB), and dihydropyridine calcium channel blockers (dCCB). We applied a propensity score matching method and created three separate cohorts: (1) ARB versus ACEI, (2) dCCB versus ACEI, and (3) dCCB versus ACEI. For non-dCCB, we repeated the analyses. The primary outcome was kidney cancer incidence. To assess kidney cancer risk, we applied multivariable conditional Cox proportional hazards models. In the first cohort, ARB use was associated with an increased risk of kidney cancer compared to ACEI use (hazard ratio [HR] 1.10, 95% confidence interval [CI] 1.02-1.18). In the second cohort, dCCB use was associated with an increased risk of kidney cancer compared to ACEI use (HR 1.29, 95% CI 1.18-1.40). In the third cohort, dCCB use was associated with a higher risk of kidney cancer compared to ARB use (HR 1.17, 95% CI 1.08-1.28). Null association was shown when comparing non-dCCB with ACEI or ARB use. Use of dCCB showed a higher risk of kidney cancer compared to ACEI or ARB use in patients with hypertension.

A machine learning-based analysis for the definition of an optimal renal biopsy for kidney cancer

ObjectiveRenal Tumor biopsy (RTB) can assist clinicians in determining the most suitable approach for treatment of renal cancer. However, RTB's limitations in accurately determining histology and grading have hindered its broader adoption and data on the concordance rate between RTB results and final pathology after surgery are unavailable. Therefore, we aimed to develop a machine learning algorithm to optimize RTB technique and to investigate the degree of concordance between RTB and surgical pathology reports. Materials and methodsWithin a prospectively maintained database, patients with indeterminate renal masses who underwent RTB at a single tertiary center were identified. We recorded and analyzed the approach (US vs. CT), the number of biopsy cores (NoC), and total core tissue length (LoC) to evaluate their impact on diagnostic outcomes. The K-Nearest Neighbors (KNN), a non-parametric supervised machine learning model, predicted the probability of obtaining pathological characterization and grading. In surgical patients, final pathology reports were compared with RTB results. ResultsOverall, 197 patients underwent RTB. Overall, 89.8% (n=177) and 44.7% (n=88) of biopsies were informative in terms of histology and grading, respectively. The discrepancy rate between the pathology results from renal tissue biopsy (RTB) and the final pathology report following surgery was 3.6% (n=7) for histology and 5.0% (n=10) for grading. According to the machine learning model, a minimum of 2 cores providing at least 0.8 cm of total tissue should be obtained to achieve the best accuracy in characterizing the cancer. Alternatively, in cases of RTB with more than two cores, no specific minimum tissue threshold is required. ConclusionsThe discordance rates between RTB pathology and final surgical pathology are notably minimal. We defined an optimal renal biopsy strategy based on at least 2 cores and at least 0.8 cm of tissue or at least 3 cores and no minimum tissue threshold. Patients summaryRTB is a useful test for kidney cancer, but it's not always perfect. Our study shows that it usually matches up well with what doctors find during surgery. Using machine learning can make RTB even better by helping doctors know how many samples to take. This helps doctors treat kidney cancer more accurately.

Application of Multi-Slice Spiral CT Renal Angiography Combined with Intraoperative Ultrasound in Laparoscopic Partial Nephrectomy.

This study aimed to evaluate the clinical significance of multi-slice spiral CT renal angiography combined with intraoperative ultrasound in laparoscopic partial nephrectomy. Eighty patients were seen at the Affiliated Hospital of Hebei University from January 2021 to December 2022. The patients were divided into two groups, the experimental group and the control group, with 40 cases in each group. The experimental group received laparoscopic partial nephrectomy combined with intraoperative ultrasound, while the control group received only conventional laparoscopic partial nephrectomy. The experimental group had significantly shorter operative time and intraoperative thermal ischaemia time (p <0.05) and had significant advantage in the detection of microscopic cancer foci (p = 0.04). The experimental group also had significantly lower of positive rate of postoperative incisal margin (p = 0.01). The experimental group had significantly lower of recurrence rate (p = 0.03). The study concluded that multi-slice spiral CT renal angiography combined with intraoperative ultrasound boasts various benefits in the treatment of patients with renal cell carcinoma, it is safe and effective with no significant impact on renal function. Key Words: CT renal angiography, Intraoperative ultrasound, Laparoscopic partial nephrectomy, Renal cancer.

Application of Multi-Slice Spiral CT Renal Angiography Combined with Intraoperative Ultrasound in Laparoscopic Partial Nephrectomy.

This study aimed to evaluate the clinical significance of multi-slice spiral CT renal angiography combined with intraoperative ultrasound in laparoscopic partial nephrectomy. Eighty patients were seen at the Affiliated Hospital of Hebei University from January 2021 to December 2022. The patients were divided into two groups, the experimental group and the control group, with 40 cases in each group. The experimental group received laparoscopic partial nephrectomy combined with intraoperative ultrasound, while the control group received only conventional laparoscopic partial nephrectomy. The experimental group had significantly shorter operative time and intraoperative thermal ischaemia time (p <0.05) and had significant advantage in the detection of microscopic cancer foci (p = 0.04). The experimental group also had significantly lower of positive rate of postoperative incisal margin (p = 0.01). The experimental group had significantly lower of recurrence rate (p = 0.03). The study concluded that multi-slice spiral CT renal angiography combined with intraoperative ultrasound boasts various benefits in the treatment of patients with renal cell carcinoma, it is safe and effective with no significant impact on renal function. Key Words: CT renal angiography, Intraoperative ultrasound, Laparoscopic partial nephrectomy, Renal cancer.

Comparative prognostic value of tumor volume in IOIO and IOTKI treatment for metastatic renal cancer

ObjectivesWe aimed to investigate the prognostic significance of tumor size in metastatic renal cell carcinoma (mRCC) by comparing the effectiveness of dual immune checkpoint inhibitor (IOIO) and immune checkpoint inhibitor combined with tyrosine kinase inhibitor (IOTKI) therapies. MethodsThis retrospective observational study included patients with mRCC diagnosed between October 2014 and February 2024 who received IOIO or IOTKI treatment at Kobe University Hospital and 5 affiliated hospitals. Clinical and imaging data were collected, and target lesions were measured according to RECIST v.1.1 criteria. Time-dependent ROC curve analysis was performed to evaluate the prognostic value of tumor size, nephrectomy status, and IMDC risk criteria for progression-free survival (PFS) and overall survival (OS). ResultsThe study included 180 mRCC patients, consisting of 99 receiving IOIO therapy and 81 receiving IOTKI therapy. Time-dependent AUC analysis showed that tumor size had a higher predictive ability for PFS and OS in the IOIO group than the IOTKI group. In multivariate analysis, tumor size was a significant independent prognostic factor for PFS (HR: 1.010, 95% CI: 1.004–1.016, P < 0.001) in the IOIO group. Moreover, the AUC for tumor size was consistently superior in predicting outcomes compared to nephrectomy status and IMDC risk classification in the IOIO group. Kaplan-Meier curves indicated that tumor size effectively stratified PFS in both nephrectomized and non-nephrectomized cases. ConclusionTumor size significantly impacts the prognosis of mRCC patients treated with IOIO therapy, demonstrating greater predictive ability than nephrectomy status and IMDC risk classification. These findings suggest that tumor volume should be considered a critical factor in treatment decision-making for renal cancer, particularly in patients undergoing IOIO therapy.

Topological embedding and directional feature importance in ensemble classifiers for multi-class classification

Cancer is the second leading cause of disease-related death worldwide, and machine learning-based identification of novel biomarkers is crucial for improving early detection and treatment of various cancers. A key challenge in applying machine learning to high-dimensional data is deriving important features in an interpretable manner to provide meaningful insights into the underlying biological mechanismsWe developed a class-based directional feature importance (CLIFI) metric for decision tree methods and demonstrated its use for the The Cancer Genome Atlas proteomics data. The CLIFI metric was incorporated into four algorithms, Random Forest (RF), LAtent VAriable Stochastic Ensemble of Trees (LAVASET), and Gradient Boosted Decision Trees (GBDTs), and a new extension incorporating the LAVA step into GBDTs (LAVABOOST). Both LAVA methods incorporate topological information from protein interactions into the decision function.The different models' performance in classifying 28 cancers resulted in F1-scores of 92.6% (RF), 92.0% (LAVASET), 89.3% (LAVABOOST) and 85.7% (GBDT), with no method outperforming all others for individual cancer type prediction. The CLIFI metric enables visualisation of the model's decision-making functions. The resulting CLIFI value distributions indicated heterogeneity in the expression of several proteins (MYH11, ERα, BCL2) across different cancer types (including brain glioma, breast, kidney, thyroid and prostate cancer) aligning with the original raw expression data.In conclusion, we have developed an integrated, directional feature importance metric for multi-class decision tree-based classification models that facilitates interpretable feature importance assessment. The CLIFI metric can be combined with incorporating topological information into the decision functions of models to introduce inductive bias, enhancing interpretability.

Risk factors affecting all-cause mortality in cats hospitalized by a referral soft tissue service.

The objective of this study was to describe the all-cause mortality rate in cats hospitalized by the soft tissue surgery service of an academic referral hospital over a 5-year period and to identify specific risk factors for mortality. The hypotheses were that the all-cause mortality rate during hospitalization would be low, and cats undergoing emergency surgery and those with an American Society of Anesthesiologists (ASA) status of 3 or more would be at increased risk for mortality. The case log of cats hospitalized by the soft tissue surgery service at the University of Georgia was searched retrospectively to identify all cats hospitalized in the years 2015-2020. Data collected about each cat included age, sex and neuter status, weight, body condition score (1-9), pre-existing heart disease, chronic kidney disease, concurrent infection or cancer, emergency status, time of surgery (daytime vs after hours, which was defined as after 4 pm), if the surgery was performed on a weekday or weekend, and general type of surgery. Univariable logistic regressions were implemented to test and estimate odds ratios for the effects of risk factors on in-hospital mortality. A multivariable logistic regression was developed that initially included all risk factors with P <0.05 on univariable analysis. Log-likelihood ratio test P values and profile-likelihood confidence intervals were reported. The all-cause mortality rate was 6.1%. Analysis was limited because of low mortality, but multivariable analysis identified increasing ASA status and emergency surgery as significant risk factors for increased mortality while hospitalized. The findings of this study confirmed that increasing ASA status and emergency procedures are significant risk factors for mortality in cats. Clinicians should be aware of these risk factors and consider how to best monitor and manage these feline patients.

Non-nucleophilic base promoted synthesis of azo-linked oxazolone-pyrazole hybrids: Antimicrobial, antitubercular, anticancer evaluations and in-silico modeling insights

Ten new non-nucleophilic base (DBU) catalyzed oxazolone-bearing pyrazole derivatives were created to treat infectious diseases caused by bacterial, mycobacterial strains, and cancerous cell lines. Compounds 7b and 7c showed excellent antibacterial and antifungal activity. Compound 7b was highly effective against M. tuberculosis H37Rv, with a MIC of 0.06 μg/mL and significant binding affinities to AcrB of E. coli (−12 Kcal/mol), TriABC of P. aeruginosa (−12.5 Kcal/mol), and MepR of S. aureus (−10.6 Kcal/mol). Based on the findings, compounds 7b, 7c, and 7e exhibit proficient binding affinity with two essential targets, namely Enoyl-[acyl-carrier-protein] reductase and Topoisomerase I, of M. tuberculosis. Compound 7b showed superior cytotoxic activity against all cancer types except leukemia with GI50 values of 1.26–1.83 µM and LC50 values of 5.36–7.88 µM. Compound 7a demonstrated remarkable anticancer activity against colon, melanoma, ovarian, renal, and breast cancer cell lines with GI50 values of 1.60–2.55 µM.

Health care costs in the United States by demographic characteristics and comorbidity status

Current real-world health care cost information is needed to project future expenditures and inform policy. We estimated adults' 2019 health care costs in the United States (US) by age, sex, race/ethnicity, geographic region, and comorbidity. We aggregated and summarized health care costs in 2021 US dollars using claims data derived from Optum's de-identified Clinformatics® Data Mart database, which includes inpatient, outpatient, and prescription claims for commercial and Medicare Advantage beneficiaries nationwide. A total of 9,227,901 adults were included in the analysis. The largest group represented was 71-75 years old (13%), female (53%), White (68%), received care in the South (41%), and had commercial health insurance (56%). There was a positive relationship between health care cost and age. Females had a 1.3-fold multiplicative increase in costs compared to males (95% CI 1.33-1.34). There were 92.5% of individuals who had health claims in the Northeast, 89.6% in the Midwest, 88.9% in the South, 77.1% in the West, and 12.7% with unknown geographic region. Patients with severe renal failure, heart failure, or metastatic cancer incurred the highest mean yearly costs ($139,844, $113,031, and $85,299, respectively). Metastatic cancer and severe renal failure were associated with a 5.3-fold multiplicative increase in costs compared with not having these conditions, after adjusting for potential confounders (95% CI, 5.26-5.41 and 4.98-5.16, respectively). We identified patient characteristics and medical conditions that are associated with high health care cost burden and could benefit from tailored interventions. We provided detailed cost estimates to aid health care modeling, cost projection, and cost-minimizing interventions.

P202 Real-life data on nivolumab as second-line treatment in metastatic kidney cancer

P202 Real-life data on nivolumab as second-line treatment in metastatic kidney cancer

P190 Stereotactic body radiotherapy for localized kidney cancer, a comparative evaluation with standard surgery within a Young Academic Urologists Working group database

P190 Stereotactic body radiotherapy for localized kidney cancer, a comparative evaluation with standard surgery within a Young Academic Urologists Working group database

P199 Real-world outcomes on Spanish patients with metastatic renal cancer treated with first-line immunotherapy combinations

P199 Real-world outcomes on Spanish patients with metastatic renal cancer treated with first-line immunotherapy combinations

The Regulation and Function of the Amino Acid Transporters LAT1, ASCT2, xCT in Urological Cancers

Amino acid transporters play pivotal roles in cancer biology, including in urological cancers. Among them, L-type amino acid transporter 1 (LAT1), alanine-serine-cysteine transporter 2 (ASCT2), and cystine-glutamate transporter (xCT) have garnered significant attention due to their involvement in various aspects of tumor progression and response to therapy. This review focuses on elucidating the regulation and functions of these amino acid transporters in urological cancers, including prostate, bladder, and renal cancers. Understanding the intricate regulatory mechanisms governing these amino acid transporters is essential for developing effective therapeutic strategies. Furthermore, exploring their interactions with signaling pathways and microenvironmental cues in the context of urological cancers may uncover novel therapeutic vulnerabilities. This comprehensive overview highlights the importance of amino acid transporters, particularly LAT1, ASCT2, and xCT, in urological cancers and underscores the potential of their inhibitors as therapeutic targets for improving patient outcomes.

Surgery or Comorbidities: What Is the Primum Movens of Kidney Dysfunction After Nephrectomy? A Multicenter Study in Living Donors and Cancer Patients.

Background and Hypothesis: Acute Kidney Injury (AKI) and Chronic Kidney Disease (CKD) are significant risks for kidney cancer (KC) patients undergoing partial (PN) or radical nephrectomy (RN) and for living kidney donors (LKD). This study compares AKI and CKD incidence in these groups with a pre-operative glomerular filtration rate (GFR) over 60 mL/min/1.73 m2. Methods: This study included 465 KC patients with cT1-2N0M0 kidney mass and 256 LKD who underwent nephrectomy at four Italian institutions from 2014 to 2021. Data on demographics, comorbidities, and therapies were analyzed. Serum creatinine and estimated GFR (eGFR) were measured before and after surgery. Outcomes were AKI (per KDIGO guidelines) and CKD stage progression. Analyses included descriptive statistics, ANOVA, logistic regression, and Kaplan-Meier survival. Results: Among 721 patients, significant age and gender differences were noted. Hypertension (41%) and diabetes (7.1%) were prevalent in RN and PN groups. Post-surgery AKI was more common in donors (84%), while CKD stage progression varied by surgery type (CKD stage G3 after 60 months: RN 48.91%, PN 18.22%, LKD 26.56%). Age, pre-surgery CKD, and surgery type predicted CKD progression. Limitations include retrospective design and bias. Conclusions: Both LKD and KC patients face similar AKI and CKD risks. Surgery type significantly influences AKI and CKD incidence, highlighting the importance of approach.

Exploring the Role of Cuprotosis-Associated Genes in Cancer Progression and Therapy Resistance: A Comprehensive Analysis across Multiple Cancer Types

This review presents a comprehensive overview of the role of cuprotosis-associated genes in various cancer types, highlighting their significance in tumor progression and therapy resistance. In breast cancer and colorectal cancer (CRC), dysregulation of genes involved in mitochondrial function and copper metabolism, such as FDX1, LIAS, LIPT1, DLD, DLAT, and PDHA1/PDHB, promotes metabolic reprogramming and enhances cancer cell survival. Ovarian cancer exhibits unique dysregulations in genes like ATP7B, CCS, and COMMD1, influencing copper metabolism and redox signaling pathways, thereby contributing to chemoresistance and tumor growth. Head and neck cancer involves upregulation of MT1X, ATP7A, and CCS, potentially aiding cancer cell survival under oxidative stress conditions. Lung cancer is characterized by distinct dysregulation of genes like SLC31A1, ATOX1, and COMMD1, modulating copper homeostasis and redox signaling to support tumor proliferation. Liver cancer and kidney cancer each present unique sets of dysregulated cuprotosis-associated genes, such as SLC39A4, SCO2, and ATP7A, suggesting novel therapeutic targets specific to these cancer types. Pathway analysis reveals enrichment in mineral absorption pathways, emphasizing the importance of these genes in maintaining cellular mineral homeostasis. Understanding the intricate interplay between cuprotosis-associated genes and cancer biology offers insights into potential therapeutic strategies targeting copper metabolism for improved treatment outcomes across various cancer types.

Nephroblastoma in a Nigerian Newborn: A Case of a Common Kidney Tumour in an Uncommon Age Group

Nephroblastoma (Wilm’s Tumour) is the commonest paediatric renal cancer worldwide. It is uncommon in infants less than six months of age and very rarely seen in neonates. The uncommon presentation of nephroblastoma in the newborn period of this two month old, depicts the possibility of its sporadic occurrence in this age group. Due to the rarity of such occurrence, there is a risk of misdiagnosis and missed diagnosis. We therefore present this report to raise awareness of its possibility of occurence.

Avelumab reduces STAT3 expression with effects on IL-17RA and CD15.

Avelumab is a human antibody that targets the programmed cell death ligand-1 (PD-L1) protein in cancer cells. Novel anticancer therapies for renal cell carcinoma (RCC) consider cluster of differentiation 15 (CD15) and interleukin 17 receptor A (IL-17RA) as potential targets. Notably, the expression of PD-L1, CD15 and IL-17RA is dependent on signal transducer and activator of transcription 3 (STAT3). The aim of the study was to investigate whether targeting PD-L1 with avelumab alters the expression levels of CD15 and IL-17RA, and to assess the STAT3-mediated regulation of CD15 and IL-17RA. We applied immunocytochemistry (ICC) and confocal laser scanning (CLS) microscopy to assess the expression and localization of the immunotherapy targets in 3 renal cancer cell lines and 1 healthy renal cell line. After treatment with 20 ng/mL avelumab, renal cancer cells showed a reduction in STAT3 expression. The expression of CD15 increased in cancer cells that exhibited a high level of IL-17RA, and the membrane signal of CD15 was reduced. In other renal cancer cell lines, the expression of CD15 decreased. Conversely, the level of IL-17RA changed only in healthy renal cells after treatment with avelumab, with no impact on renal cancer cells. Our study suggests that the targeting of PD-L1 with avelumab alters the expression of CD15 and IL-17RA, which play an important prognostic and therapeutic role in novel anticancer therapy.

Growth of Renal Cancer Cell Lines Is Strongly Inhibited by Synergistic Activity of Low-Dosed Amygdalin and Sulforaphane.

Background: Plant derived isolated compounds or extracts enjoy great popularity among cancer patients, although knowledge about their mode of action is unclear. The present study investigated whether the combination of two herbal drugs, the cyanogenic diglucoside amygdalin and the isothiocyanate sulforaphane (SFN), influences growth and proliferation of renal cell carcinoma (RCC) cell lines. Methods: A498, Caki-1, and KTCTL-26 cells were exposed to low-dosed amygdalin (1 or 5 mg/mL), or SFN (5 µM) or to combined SFN-amygdalin. Tumor growth and proliferation were analyzed by MTT, BrdU incorporation, and clone formation assays. Cell cycle phases and cell cycle-regulating proteins were analyzed by flow cytometry and Western blotting, respectively. The effectiveness of the amygdalin-SFN combination was determined using the Bliss independence model. Results: 1 mg/mL amygdalin or 5 µM SFN, given separately, did not suppress RCC cell growth, and 5 mg/mL amygdalin only slightly diminished A498 (but not Caki-1 and KTCTL-26) cell growth. However, already 1 mg/mL amygdalin potently inhibited growth of all tumor cell lines when combined with SFN. Accordingly, 1 mg/mL amygdalin suppressed BrdU incorporation only when given together with SFN. Clonogenic growth was also drastically reduced by the drug combination, whereas only minor effects were seen under single drug treatment. Superior efficacy of co-treatment, compared to monodrug exposure, was also seen for cell cycling, with an enhanced G0/G1 and diminished G2/M phase in A498 cells. Cell cycle regulating proteins were altered differently, depending on the applied drug schedule (single versus dual application) and the RCC cell line, excepting phosphorylated Akt which was considerably diminished in all three cell lines with maximum effects induced by the drug combination. The Bliss independence analysis verified synergistic interactions between amygdalin and SFN. Conclusions: These results point to synergistic effects of amygdalin and SFN on RCC cell growth and clone formation and Akt might be a relevant target protein. The combined use of low-dosed amygdalin and SFN could, therefore, be beneficial as a complementary option to treat RCC. To evaluate clinical feasibility, the in vitro protocol must be applied to an in vivo model.

AI predictive modeling of survival outcomes for renal cancer patients undergoing targeted therapy.

Renal clear cell cancer (RCC) is a complex disease that is challenging to predict patient outcomes. Despite improvements with targeted therapy, personalized treatment planning is still needed. Artificial intelligence (AI) can help address this challenge by developing predictive models that accurately forecast patient survival periods. With AI-powered decision support, clinicians can provide patients with tailored treatment plans, enhancing treatment efficacy and quality of life. The study analyzed 267 patients with renal clear cell carcinoma, focusing on 26 who received targeted drug therapy. The data was refined by excluding 8 patients without enhanced CT scans. The research team categorized patients into two groups based on their expected lifespan: Group 1 (over 3 years) and Group 2 (under 3 years). The UPerNet algorithm was used to extract features from CT tumor markers, validating their effectiveness. These features were then used to develop an AI-based predictive model trained on the dataset. The developed AI model demonstrated remarkable accuracy, achieving a rate of 93.66% in Group 1 and 94.14% in Group 2. In conclusion, our study demonstrates the potential of AI technology in predicting the survival time of RCC patients undergoing targeted drug therapy. The established prediction model exhibits high predictive accuracy and stability, serving as a valuable tool for clinicians to facilitate the development of more personalized treatment plans for patients. This study highlights the importance of integrating AI technology in clinical decision-making, enabling patients to receive more effective and targeted treatment plans that enhance their overall quality of life.

Abiraterone and Galeterone, Powerful Tools Against Prostate Cancer: Present and Perspective

Due to the high prostate cancer incidence worldwide, the development of different methods of treatment continues to be a hot research topic. Since its first clinical application at the beginning of the 2010s, abiraterone in the form of prodrug abiraterone acetate continues to be the most used hormone derivative in the treatment of castration-resistant prostate cancer. This is the reason behind the publication of many scientific results regarding its synthesis, biological activity, metabolism, novel designed steroid derivatives based on its structure, etc. A similar steroid compound with a heterocycle in the C17 position, called galeterone, also designed to treat prostate cancer, continues to be in clinical studies, which provides further proof of the importance of these steroid derivatives. Besides prostate cancer treatment, abiraterone showed indications for possible clinical application in the treatment of breast, ovarian, lung, kidney, salivary gland, and adrenocortical cancer, congenital adrenal hyperplasia, Cushing’s syndrome, and COVID-19, while galeterone is investigated for its use against prostate, pancreatic, and breast cancer. Herein, we report a review comprising methods of synthesis, possible clinical applications, and mechanisms of action, as well as structures and bioactivities of derivatives of these two important steroids.