In addition, obesity, characterized by excessive adipose tissue, has been associated with adverse prostate cancer outcomes. The metabolic alterations in obesity, including dyslipidemia, hyperinsulinemia, and chronic inflammation, create a pro-tumorigenic microenvironment. [1] 

For instance, elevated levels of triglycerides, low-density lipoprotein (LDL) cholesterol, and free fatty acids contribute to enhanced lipid biosynthesis, a metabolic reprogramming hallmark in cancer cells known as the Warburg effect. [2]

Lipid metabolism is intricately linked to prostate cancer pathogenesis. Alterations in lipid profiles, such as increased synthesis of cholesterol and fatty acids, are commonly observed in prostate cancer cells. [3] These changes support membrane biogenesis, energy production, and the generation of signaling molecules essential for tumor growth and survival. [5] Dysregulation of lipid metabolism also contributes to the development of drug resistance, highlighting its clinical significance. [6]

Cytokines, small protein mediators of inflammation and immune responses, play a critical role in the tumor microenvironment of prostate cancer. 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. 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]

Precancerous conditions such as PIN share several molecular and cellular features with prostate cancer, including alterations in metabolic and hormonal pathways. The transition from precancer to invasive prostate cancer involves complex interactions between genetic and environmental factors. Chronic inflammation, oxidative stress, and metabolic reprogramming are critical drivers of this transition. Lipid accumulation, cytokine-mediated inflammation, and dysregulated androgen signaling contribute to the precancerous microenvironment, facilitating malignant transformation. [8]

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

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.

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. Lipid Profile: Enzymatic colorimetric methods were employed to quantify cholesterol, triglycerides, LDL, and HDL. Enzymes catalyze reactions producing color changes measured spectrophotometrically.
2. Cytokine Profile: Multiplex bead-based immunoassays on a Luminex platform detect multiple cytokines simultaneously using antibody-coated magnetic beads.
3. C-reactive Protein (CRP): Nephelometric methods detect CRP levels based on light scatter by antigen-antibody complexes.

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.

Graph 1: Baseline Characteristics of Study Participants

Graph 1 to reflect a total of 210 samples divided into 3 groups: Baseline characteristics, including age, BMI, and metabolic parameters, are summarized in Graph 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.

Graph 2. Distribution of Triglycerides

Healthy Control group exhibits a concentration of triglyceride levels around 150–175 mg/dL, which falls within or close to the borderline-high range for triglycerides in clinical guidelines. Precancer and Prostate Cancer groups show a similar distribution pattern, with peaks slightly lower around 140–160 mg/dL.

Variability and Overlap:

There is a significant overlap in triglyceride levels across the three groups, making it difficult to distinguish between them solely based on this parameter. Healthy Control tends to show a broader and slightly higher range, while Prostate Cancer and Precancer groups appear slightly compressed toward the lower end.

Graph 3: Distribution of LDL and HDL Participants

Graph 4: TNF-alpha by Group

TNF-alpha levels are highest in the prostate cancer group (mean = 5.48 pg/mL) and lowest in the precancer group (mean = 4.99 pg/mL). Healthy controls have an intermediate mean TNF-alpha level (mean = 5.34 pg/mL). The lowest recorded TNF-alpha level is in the precancer group (1.36 pg/mL), while the highest level is in the prostate cancer group (8.79 pg/mL). TNF-alpha is a key pro-inflammatory cytokine. Elevated levels in the prostate cancer group suggest a potential role of systemic inflammation in cancer progression.

Healthy Control group exhibits a concentration of triglyceride levels around 150–175 mg/dL, which falls within or close to the borderline-high range for triglycerides in clinical guidelines. Precancer and Prostate Cancer groups show a similar distribution pattern, with peaks slightly lower around 140–160 mg/dL.

The data demonstrate significant alterations in lipid profiles among the groups. Elevated LDL cholesterol and reduced HDL cholesterol levels in the prostate cancer group highlight the role of dyslipidemia in carcinogenesis. [9] LDL cholesterol is known to promote oxidative stress and inflammation, both of which are critical drivers of tumor progression. [10] Conversely, HDL cholesterol exhibits anti-inflammatory and antioxidant properties, potentially mitigating cancer risk. These findings emphasize the need for lipid-lowering strategies, such as statins, as potential adjuncts in prostate cancer prevention and treatment. [11]

Furthermore, the lipid profile disparities observed between groups underscore the dual role of cholesterol as a cellular building block and a contributor to oxidative stress. [12] Elevated LDL cholesterol levels in prostate cancer patients highlight a pressing need for therapeutic lipid-lowering approaches. [13] The potential repurposing of lipid-regulating drugs such as statins warrants further investigation, particularly in high-risk populations. [14]

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.

TNF-alpha levels are highest in the prostate cancer group (mean = 5.48 pg/mL) and lowest in the precancer group (mean = 4.99 pg/mL). Healthy controls have an intermediate mean TNF-alpha level (mean = 5.34 pg/mL). The precancer group shows the highest variability (std dev = 1.48 pg/mL), indicating a wider spread of TNF-alpha levels in this group. Healthy controls exhibit slightly less variability (std dev = 1.43 pg/mL).

The data provide compelling evidence of elevated systemic inflammation in prostate cancer patients. IL-6, a pro-inflammatory cytokine, was marginally higher in the prostate cancer group, while TNF-α levels were significantly elevated. [15] Chronic inflammation is a well-established contributor to cancer progression, facilitating genomic instability, angiogenesis, and immune evasion. [16] The observed differences in cytokine levels between the groups underscore the importance of targeting inflammatory pathways in prostate cancer management. [17]

Inflammatory mediators like IL-6 and TNF-α serve as pivotal links between obesity, metabolic syndrome, and cancer progression. By amplifying oxidative stress and promoting a pro-inflammatory microenvironment, these cytokines facilitate tumor development. [18] This study reinforces the therapeutic promise of targeting systemic inflammation through agents that neutralize cytokine activity or inhibit downstream signaling pathways. [19]

The interplay between androgen signaling and metabolic dysregulation further complicates the prostate cancer landscape. While androgen deprivation remains a cornerstone of treatment for advanced disease, its metabolic sequelae highlight a critical need for integrated care models. [20] Strategies that combine androgen suppression with metabolic optimization may enhance treatment efficacy while mitigating adverse effects. [21]

In conclusion, this study emphasizes the importance of a multifaceted approach to prostate cancer prevention and treatment. By addressing the interconnected pathways of metabolic dysregulation, lipid imbalances, and systemic inflammation, it is possible to develop comprehensive strategies that not only mitigate cancer risk but also improve overall health outcomes. Future research should prioritize the identification of biomarkers that capture these complex interactions, enabling personalized therapeutic interventions tailored to individual metabolic and inflammatory profiles. By advancing our understanding of these pathways, we can significantly reduce the global burden of prostate cancer and enhance the quality of life for affected individuals.

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