Prostate cancer (PCa) is the second most frequently diagnosed malignancy and a leading cause of cancer-related mortality among men worldwide. According to the Global Cancer Observatory (GLOBOCAN) 2020, prostate cancer accounts for approximately 1.4 million new cases and over 375,000 deaths annually. ^[1] These statistics underscore the pressing need for better understanding of the factors that contribute to prostate cancer progression and its precursor states, including prostatic intraepithelial neoplasia (PIN), which is widely recognized as a precancerous condition. Research efforts have increasingly focused on elucidating the complex interplay of metabolic, hormonal, and inflammatory factors in prostate cancer pathogenesis. ^[2]

Metabolic dysregulation is a hallmark of cancer, including prostate cancer, and plays a pivotal role in tumor initiation, progression, and resistance to therapy. Key metabolic parameters such as glucose metabolism, lipid metabolism, and energy homeostasis are often altered in prostate cancer. ^[3] Hyperglycemia and insulin resistance, features commonly associated with metabolic syndrome, have been linked to an increased risk of prostate cancer development and progression. Elevated levels of circulating insulin, a major driver of metabolic syndrome, can stimulate insulin-like growth factor-1 (IGF-1) signaling, which is implicated in promoting cellular proliferation and inhibiting apoptosis in prostate epithelial cells. ^[4]

Cytokines, small protein mediators of inflammation and immune responses, play a critical role in the tumor microenvironment of prostate cancer. ^[5] Chronic inflammation has been established as a key contributor to both prostate cancer initiation and progression. Pro-inflammatory cytokines such as interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and interleukin-1 beta (IL-1β) promote tumor growth by enhancing angiogenesis, suppressing anti-tumor immunity, and modulating the tumor stroma. ^[6] Moreover, elevated levels of anti-inflammatory cytokines such as interleukin-10 (IL-10) can impair immune surveillance, further facilitating tumor progression. Thus, profiling the cytokine landscape provides valuable insights into the inflammatory milieu of prostate cancer. ^[7]

Androgens, primarily testosterone and its more potent derivative dihydrotestosterone (DHT), are critical regulators of prostate physiology and pathology. The androgen receptor (AR) signaling axis is central to prostate cancer biology, driving tumor cell proliferation and survival. Dysregulation of androgen levels and AR signaling is a hallmark of prostate cancer, especially in the transition to castration-resistant prostate cancer (CRPC), a lethal stage of the disease. ^[8]

The insulin-IGF axis is another critical pathway implicated in prostate cancer. Elevated insulin levels, often associated with obesity and type 2 diabetes, can enhance the bioavailability of IGF-1 by reducing levels of its binding proteins. IGF-1, in turn, activates downstream signaling cascades such as the PI3K-AKT and MAPK pathways, which promote cell survival, proliferation, and migration. ^[10]

Emerging evidence suggests that hyperinsulinemia may also exacerbate prostate cancer risk and progression by modulating androgen biosynthesis and AR activity. Furthermore, insulin resistance, a hallmark of metabolic syndrome, has been associated with aggressive prostate cancer phenotypes. These findings highlight the importance of targeting insulin signaling and metabolic dysfunction in prostate cancer prevention and management. ^[11]

This study is a cross-sectional analysis designed to evaluate the association of metabolic parameters, lipid and cytokine profiles, androgen levels, and insulin signaling with prostate cancer and its precursor lesions among Department of Biochemistry, Index Medical College.

The study recruited participants from urology outpatient clinics, including patients with confirmed prostate cancer, those with precancerous conditions such as prostatic intraepithelial neoplasia (PIN), and healthy controls.

The study included a total of 210 participants divided into three groups: (1) confirmed prostate cancer patients, (2) patients with precancerous lesions (prostatic intraepithelial neoplasia), and (3) healthy controls.

Inclusion and Exclusion Criteria

Inclusion Criteria:

1. Male participants aged 40–80 years.
2. Histopathological diagnosis of prostate cancer or PIN.
3. Availability of complete clinical and biochemical data.

Exclusion Criteria:

1. History of other malignancies.
2. Ongoing hormonal or metabolic therapy.
3. Severe systemic illness or acute infections.

Sample Collection

Venous blood samples will be collected from participants after an overnight fast. Serum and plasma will be separated by centrifugation and stored at −80°C until analysis. Prostate tissue samples will be obtained via biopsy or prostatectomy and preserved in formalin for histopathological evaluation.

Sample Size Calculation

The sample size for this study was calculated based on the primary outcome measure: the association between metabolic parameters, cytokine profiles, and prostate cancer status.

Assuming a moderate effect size (Cohen's f = 0.25), a power of 80%, and a significance level of 0.05, the required sample size was determined using G*Power software.

For a three-group comparison (prostate cancer, PIN, and healthy controls) with equal group sizes, the minimum required sample size was 159 participants (53 per group). To account for potential dropouts and missing data, the sample size was increased by 20%, resulting in a total of 192 participants.

Sample Size

The total sample size of 210 was determined based on a power calculation with a significance level of 0.05 and a power of 80%, ensuring adequate representation across the three study groups:

• Prostate cancer group: 70 participants
• Precancer group: 70 participants
• Healthy control group: 70 participants

Ethical Approval

The study protocol was approved by the institutional ethics committee, and written informed consent was obtained from all participants before enrollment.

Biochemical and Hormonal Analysis

1. Metabolic Parameters: Serum glucose and insulin were measured using enzymatic and immunoassay techniques.
2. Hormonal Levels: Serum testosterone, dihydrotestosterone (DHT), and IGF-1 were quantified using chemiluminescent immunoassays.

1. Cytokine Profiling: Levels of IL-6, TNF-α, IL-1β, and IL-10 will be measured using multiplex cytokine assays.

Data Collection

Clinical and Demographic Data

Baseline demographic data, including age, body mass index (BMI), and medical history, were recorded using a structured questionnaire. Information on smoking, alcohol consumption, and family history of prostate cancer was also collected.

Blood Sample Collection

Fasting blood samples (8-12 hours fasting) were collected from all participants. Samples were processed within two hours of collection and stored at -80°C until analysis.

Laboratory Analysis

1. Insulin Levels:
   • Principle: Serum insulin concentrations were measured using a sandwich enzyme-linked immunosorbent assay (ELISA) technique. The method relies on the specific binding of insulin to antibodies and subsequent detection using an enzyme-conjugated secondary antibody.
2. Androgen Levels: Chemiluminescent immunoassays (CLIA) were used for total testosterone and dihydrotestosterone (DHT) quantification. The method involves antigen-antibody reaction emitting light for measurement.
3. Glucose Metabolism Markers: Principle: Fasting glucose was measured using a hexokinase-based method, while HbA1c was analyzed using high-performance liquid chromatography (HPLC).
4. Prostate-Specific Antigen (PSA): Principle: Electrochemiluminescence immunoassay (ECLIA) detects PSA with high sensitivity by measuring light emission from an electrochemical reaction.

Statistical Analysis

Data were analyzed using SPSS software (version 29.0). Continuous variables were expressed as mean ± standard deviation, and categorical variables were expressed as frequencies and percentages. Between-group comparisons were conducted using ANOVA for continuous variables and chi-square tests for categorical variables. Correlation analyses were performed to assess the relationship between insulin levels and other metabolic and hormonal parameters. Multivariate regression models were used to control for potential confounders.

Table 1: Baseline Characteristics of Study Participants

Table 1 to reflect a total of 210 samples divided into 3 groups: Baseline characteristics, including age, BMI, and metabolic parameters, are summarized in Table 1. The study included a total of 210 participants, evenly divided into three groups: 70 with confirmed prostate cancer, 70 with prostatic intraepithelial neoplasia (PIN), and 70 healthy controls. Age: The prostate cancer group is significantly older than the other two groups, indicating age as a potential risk factor for prostate cancer. BMI: Higher BMI values were observed in the prostate cancer group compared to PIN and healthy controls, suggesting a possible link between obesity and prostate cancer.

Table 2. Distribution of Insulin Levels

Healthy Control shows a distinct peak around 15–16 units, indicating that most individuals in this group have insulin levels concentrated near this value. Precancer and Prostate Cancer groups have a slightly broader and flatter distribution compared to Healthy Controls, indicating more variability in insulin levels. Healthy Control includes individuals with higher insulin levels (up to 35 units), while Prostate Cancer and Precancer rarely exceed 25 units.

Table 3. Distribution of Testosterone Levels

Healthy Control group shows the highest concentration of testosterone levels around 450–500 ng/dL, which aligns with normal physiological levels in healthy men. Precancer and Prostate Cancer groups have distributions shifted slightly lower, with peaks around 400–450 ng/dL, indicating reduced testosterone levels in these groups. There is significant overlap in testosterone levels across the groups, but the Healthy Control group has a distinct higher peak compared to the other two groups. The Prostate Cancer group exhibits a broader range, extending down to lower levels (as low as 100–200 ng/dL) compared to the other groups.

Table 4. Distribution of IL-6

The Prostate Cancer group shows a sharp peak around 7 pg/mL, suggesting a concentration of IL-6 levels near this value. The Precancer group has a similar but slightly broader distribution around 7–8 pg/mL. The Healthy Control group exhibits a more evenly spread distribution, peaking around 8 pg/mL, indicating higher variability in IL-6 levels.

IL-6 levels overlap across all three groups, but the Healthy Control group shows slightly higher values in the range of 8–10 pg/mL compared to the other groups. The Prostate Cancer group has a narrower and slightly lower distribution, suggesting reduced IL-6 variability in this group.

Table 5: Fasting Glucose by Group

Fasting glucose levels are slightly higher in the prostate cancer group (mean = 101.31 mg/dL) compared to the healthy control group (mean = 100.31 mg/dL). The precancer group shows a similar mean fasting glucose level (101.02 mg/dL). The precancer group exhibits the highest standard deviation (16.70 mg/dL), indicating greater variability in fasting glucose levels within this group. The healthy control group has the lowest variability (14.96 mg/dL).

Table 6: PSA Levels

The mean PSA level in the healthy control group is 6.51 ng/mL, which falls within the clinically acceptable range for men without prostate pathology. However, PSA values show variability, with some individuals in the control group having PSA levels as high as 11.20 ng/mL. These could be due to benign prostatic hyperplasia (BPH) or individual biological variability. The mean PSA level in individuals with precancerous conditions is slightly lower at 5.98 ng/mL. This may reflect early alterations in PSA production or secretion associated with prostatic intraepithelial neoplasia (PIN). The variability is higher in this group, with some individuals exhibiting PSA levels as low as 0.82 ng/mL, likely due to early-stage disease or sampling error.

Age has consistently been identified as a key risk factor in the development of prostate cancer. The data presented in Table 1 aligns with this understanding, showing that the prostate cancer group had a significantly higher mean age compared to the PIN and healthy control groups. This finding underscores the importance of age as a critical determinant of cancer progression, likely linked to cumulative genetic mutations and hormonal changes over time. ^[12] Additionally, the higher BMI in the prostate cancer group highlights the role of obesity in prostate carcinogenesis. Obesity is associated with systemic inflammation, insulin resistance, and altered lipid profiles, which collectively create a pro-tumorigenic environment. ^[13]

Healthy Control shows a distinct peak around 15–16 units, indicating that most individuals in this group have insulin levels concentrated near this value. Precancer and Prostate Cancer groups have a slightly broader and flatter distribution compared to Healthy Controls, indicating more variability in insulin levels. Healthy Control includes individuals with higher insulin levels (up to 35 units), while Prostate Cancer and Precancer rarely exceed 25 units.

Table 2 reveals subtle yet significant differences in insulin levels across the groups. While mean insulin levels were highest in healthy controls, the variability in the prostate cancer group suggests a complex interplay between insulin sensitivity and cancer progression. Hyperinsulinemia is a hallmark of metabolic syndrome, often leading to elevated levels of insulin-like growth factors (IGFs), which are potent mitogens implicated in tumor growth. Lower insulin levels in prostate cancer patients might reflect disease-induced metabolic dysregulation or advanced disease stages where metabolic exhaustion occurs. Further longitudinal studies are needed to elucidate these dynamics. ^[14]

The interaction between metabolic and systemic factors represents a crucial frontier in prostate cancer research. Insulin resistance, a hallmark of metabolic syndrome, is shown to not only predispose individuals to cancer but also exacerbate tumor growth through the upregulation of insulin-like growth factors (IGFs). The findings of this study align with broader epidemiological data, affirming the critical role of insulin signaling in the tumor microenvironment. ^[15] Efforts to improve metabolic health, including interventions such as weight management, dietary regulation, and increased physical activity, have the potential to significantly reduce the risk and progression of prostate cancer. ^[16]

Healthy Control group shows the highest concentration of testosterone levels around 450–500 ng/dL, which aligns with normal physiological levels in healthy men. Precancer and Prostate Cancer groups have distributions shifted slightly lower, with peaks around 400–450 ng/dL, indicating reduced testosterone levels in these groups. There is significant overlap in testosterone levels across the groups, but the Healthy Control group has a distinct higher peak compared to the other two groups. The Prostate Cancer group exhibits a broader range, extending down to lower levels (as low as 100–200 ng/dL) compared to the other groups.

Testosterone levels, as shown in Table, were slightly lower in the prostate cancer group compared to PIN and healthy controls. This finding aligns with the "androgen paradox," where androgens are necessary for prostate cancer initiation but their levels often decline in advanced disease stages. Androgen deprivation therapy (ADT), a cornerstone in prostate cancer management, may further contribute to these observed differences. The slight reduction in testosterone levels in precancerous states suggests that metabolic alterations begin early in the disease continuum, potentially offering a window for preventive interventions. ^[17]

Fasting glucose levels are slightly higher in the prostate cancer group (mean = 101.31 mg/dL) compared to the healthy control group (mean = 100.31 mg/dL). The precancer group shows a similar mean fasting glucose level (101.02 mg/dL). The precancer group exhibits the highest standard deviation (16.70 mg/dL), indicating greater variability in fasting glucose levels within this group. The healthy control group has the lowest variability (14.96 mg/dL).

Fasting glucose levels, summarized in Table, were comparable across the groups, with marginally higher values in the prostate cancer cohort. Hyperglycemia is a hallmark of insulin resistance and metabolic syndrome, both of which are linked to cancer risk. Elevated PSA levels, as shown in Table, are consistent with the known biology of prostate cancer, where PSA serves as a marker of disease activity. However, the overlap in PSA levels between precancerous and cancerous states highlights the need for complementary biomarkers to improve diagnostic accuracy. ^[18]

This study provides a comprehensive analysis of metabolic and systemic inflammatory profiles in prostate cancer and precancer conditions. The findings support the hypothesis that metabolic dysregulation and systemic inflammation are central to prostate cancer progression, offering potential targets for early intervention and therapeutic strategies. While PSA remains an essential biomarker, the overlap and variability in PSA levels across groups demonstrate its limitations as a standalone test. The findings suggest that PSA should be interpreted alongside other diagnostic modalities to improve sensitivity and specificity in detecting prostate cancer and distinguishing it from precancerous conditions.

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