Vol 54, No 2 (2026)

Background: Survival in cervical cancer largely depends on the presence of morphological risk factors, among which lymphovascular space invasion (LVSI) plays a key role in the spread of tumor cells through the circulatory and/or lymphatic system. Dysfunction of the p53 tumor suppressor may be considered a determining factor in the development of LVSI during tumor progression. Studies have shown an association of the TP53 Pro72Arg polymorphism with cervical cancer risk. However, the association of Pro72Arg with LVSI formation has not been investigated.

Aim: To determine the association of the TР53 Pro72Arg (rs1042522) polymorphism with LVSI and survival in cancer depending on the type of adjuvant radiotherapy.

Methods: We retrospectively evaluated 5-year overall survival (OS) and progression-free survival (PFS) in patients with stage I–II cervical cancer who underwent radical Wertheim hysterectomy followed by adjuvant radiotherapy or adjuvant chemoradiotherapy between 2014 and 2020. Radiotherapy was initiated 3–4 weeks after surgery and included external beam radiotherapy to the primary tumor bed and regional lymph nodes (single fraction dose 2 Gy, total dose 40–45 Gy) and intracavitary gamma-therapy using brachytherapy devices (vaginal stump irradiation at a single fraction dose of 5 Gy twice weekly to a total dose of 20–25 Gy) in 2–4 sessions; chemoradiotherapy included polychemotherapy 3 weeks after surgery followed by external beam radiotherapy and brachytherapy. Polychemotherapy was administered in 2–3 cycles according to the following regimen: paclitaxel 175 mg/m² intravenously on day 1 + cisplatin 75 mg/m² intravenously on day 1 every 3 weeks. The TP53 Pro72Arg (rs1042522) polymorphism was determined by polymerase chain reaction in all patients. The presence of tumor emboli in lymphatic/blood vessels (LVSI status) was assessed histologically in tumor and/or peritumoral tissues.

Results. The study included 90 patients treated for stage I–II cervical cancer (median age 49 (47; 64) years) with LVSI (LVSI+ group, n = 60) and without LVSI (LVSI- group, n = 30). In the LVSI+ group, 30 patients received adjuvant radiotherapy (follow-up period 39.89 ± 14.81 months), and 30 patients received adjuvant chemoradiotherapy (follow-up period 51 ± 9.59 months); all patients in the LVSI- group (n = 30) received adjuvant radiotherapy (follow-up period 48.72 ± 10.16 months). No differences were found between patient groups depending on the therapy received (adjuvant radiotherapy vs chemoradiotherapy in LVSI+ status), as well as depending on the presence of LVSI (LVSI+ vs LVSI- on adjuvant radiotherapy) for age (p = 0.792 and p = 1.00), TNM stage (p = 0.605 and p = 0.796), histological type (p = 0.488 and p = 0.739), tumor grade (p = 0.811 and p = 0.481), or number of surviving and deceased patients (p = 0.085 and p = 0.228). In LVSI+ patients, chemoradiotherapy compared with radiotherapy was associated with a reduction in the number of recurrences: 3.33% (1/30) vs 23.33% (7/30), p = 0.023. Radiotherapy in LVSI+ vs LVSI- did not affect the recurrence rate: 23.33% (7/30) vs 6.67% (2/30), p = 0.071. Five-year OS did not differ significantly depending on LVSI status (among patients receiving radiotherapy, 5-year OS was 83.33% in LVSI+ and 93.33% in LVSI-, p = 0.161), nor depending on therapy type (in LVSI+ patients, 5-year OS was 83.33% with radiotherapy and 96.67% with chemoradiotherapy, p = 0.063). However, 5-year PFS was statistically significant – 76.67% vs 93.33% (p = 0.048) and 76.67% vs 96.67% (p = 0.016), respectively.

An association of LVSI in cervical cancer with the TP53 Pro72Arg (rs1042522) genetic variant was established in multiplicative (χ² = 5.18; p = 0.024) and dominant (χ² = 4.36; p = 0.038) models. In Cox regression analysis in a univariate model, the minor Arg/Arg (G/G) genotype of the Pro72Arg variant was a significant risk factor associated with worse 5-year OS (hazard ratio 8.425; 95% confidence interval 1.05–67.43; p = 0.045) and 5-year PFS (hazard ratio 8.46; 95% confidence interval 1.058–67.67; p = 0.044).

Conclusion. An association of the TP53 Pro72Arg (rs1042522) polymorphism with LVSI in cervical cancer was identified. Carriage of the minor Arg/Arg (G/G) genotype of the Pro72Arg polymorphism is associated with an unfavorable prognosis during radiotherapy and chemoradiotherapy, representing a risk factor for reduced survival.

Background: The combination of chronic obstructive pulmonary disease (COPD) and tuberculosis forms a specific clinical and pathogenetic phenotype. Studying the cytokine profile in comorbid pathology is necessary to understand the mechanisms of mutual aggravation of disease course.

Aim: To assess the strength of association between serum levels of key cytokines and clinically significant manifestations of comorbid COPD and tuberculosis, with a primary focus on the presence of bacterial shedding.

Methods: A single-center observational comparative cross-sectional study was conducted. From December 2024 to March 2026, clinical and laboratory parameters, serum cytokine levels, and the presence of Mycobacterium tuberculosis in sputum were evaluated in men aged 45 years and older followed up in pulmonology and phthisiology departments for COPD, pulmonary tuberculosis, or their combination, as well as in healthy volunteers in the control group. Serum levels of interleukin (IL)-1β, IL-6, IL-8, IL-10, and tumor necrosis factor-α (TNF-α) were measured by enzyme-linked immunosorbent assay. Mycobacterium tuberculosis (MTB) isolation was assessed by sputum smear microscopy, fluorescent microscopy and/or sputum culture. Patients with a positive result were classified as MTB+, while those without confirmed bacterial shedding were classified as MTB-.

Results: Data from 209 men were analyzed: 42 healthy volunteers (control group); 63 patients with COPD without active tuberculosis (COPD group); 51 patients with active pulmonary tuberculosis without comorbid COPD (TB group); and 53 patients with both COPD and active pulmonary tuberculosis (COPD + TB group). Median age across groups ranged from 64 to 69 years (p = 0.137). The groups differed significantly in smoking history, forced expiratory volume in 1 second (FEV₁), oxygen saturation (SpO₂), and markers of systemic inflammation. The comorbid group (COPD + TB) had the highest smoking history (median 31 pack-years), lowest FEV₁ (41.3% of predicted), highest C-reactive protein (32.7 mg/L), and lowest SpO₂ (90%) (all p < 0.001). Levels of all five cytokines increased from the control group to the comorbid group (COPD + TB): median IL-6 from 6.3 to 51.7 pg/mL, IL-1β from 3.1 to 27.1 pg/mL, IL-8 from 18.4 to 66.0 pg/mL (all p < 0.001). In bacterial shedders (MTB+, n = 59), cytokine levels were higher than in patients without bacterial shedding (MTB-, n = 45): IL-1β, 32.9 vs 13.2 pg/mL; IL-10, 29.1 vs 13.4 pg/mL; TNF-α, 30.0 vs 15.3 pg/mL (all p < 0.001). The best discriminative ability for bacterial shedding was shown by IL-10 (AUC = 0.924) and TNF-α (AUC = 0.923). A multivariate logistic model including IL-1β, IL-6, TNF-α and the IL-6/IL-10 ratio demonstrated an in-sample AUC of 0.991 (95% confidence interval 0.979–1.000), and 0.974 (SD 0.009) after cross-validation.

Conclusion: Serum cytokine levels are strongly associated with bacterial shedding. The cytokine profile may be considered as a research tool for stratifying the inflammatory phenotype in combined COPD and pulmonary tuberculosis.

Reproducibility of scientific research has become a major concern in recent years. Inappropriate application of statistical analysis methods by researchers is considered one of the causes of the reproducibility crisis. Drawing on the concept of "statistical myths" – persistent, recurring distortions of statistical concepts – it is argued that these myths are systematic rather than accidental, serve specific functions, and reflect a deep methodological gap between the research domain and the practice of statistical analysis. The relationship between scientific and statistical inference is shown in the form of a biomedical research framework consisting of seven stages: conceptualization and formulation of the research question; operationalization; selection of the estimand; statistical model building and application; interpretation of the estimate accounting for uncertainty; clinical interpretation; and answering the research question. The framework is illustrated using examples from three real-world studies across various medical fields. Based on their functions, the authors classify statistical myths into four categories: epistemological, fundamental, instrumental, and terminological. The defining characteristics and examples of each type are provided, alongside an analysis of several widespread myths. The proposed approach allows statistical myths to be viewed not merely as a pedagogical issue, but as indicators of deeper disruptions in research logic. From a methodological perspective, rather than memorizing yet another list of "correct" and "incorrect" statistical practices, researchers are encouraged to proceed systematically from the research question at hand to its statistical formalization.