Cervical Cancer Metastasis Research Articles

PLAC1 augments the malignant phenotype of cervical cancer through the mTOR/HIF-1α/snail signaling pathway.

This study investigated the molecular mechanisms of placenta-specific protein 1 (PLAC1) in cervical cancer (CCa), aiming to elucidate its role in tumorigenesis through in vitro and in vivo experiments. CCa cell lines with overexpressed or silenced PLAC1 were established to evaluate its impact on cell cycle, apoptosis and the expression of key proteins in the PLAC1/mTOR/HIF-1α/Snail signaling pathways. Functional assays were conducted to assess the influence of the PLAC1/mTOR/HIF-1α/Snail regulatory pathway on cell proliferation, migration and invasion. The role of the mTOR signaling pathway in PLAC1-mediated modulation of CCa characteristics was validated using a mTOR activator (MHY1485) and a mTOR inhibitor (Rapamycin) respectively. HIF1A siRNA was introduced to confirm the role of HIF1A. Furthermore, an in vivo nude mouse model was constructed to confirm PLAC1's influence on tumorigenesis and metastasis in CCa. PLAC1 upregulated hypoxia-inducible factor (HIF)-1α and Snail, promoting CCa cell proliferation, migration, and invasion via the mTOR/HIF-1α/Snail pathway. Enrichment analysis of PLAC1-associated differentially expressed genes implicated their involvement in CCa and tumor promotion. In a xenograft mouse model, PLAC1 exhibited a pro-tumorigenic effect, which can be reversed by siRNA targeting HIF1A. This study enhances our understanding of PLAC1's role and molecular mechanisms in CCa progression, highlighting its potential as a diagnostic, prognostic, and therapeutic marker for the management of CCa.

Development and validation of MRI-based machine learning models for predicting lymph node metastasis in cervical cancer

Development and validation of MRI-based machine learning models for predicting lymph node metastasis in cervical cancer

Artificial Intelligence-Based Sentinel Lymph Node Metastasis Detection in Cervical Cancer.

Background/objectives: Pathological ultrastaging, an essential part of sentinel lymph node (SLN) mapping, involves serial sectioning and immunohistochemical (IHC) staining in order to reliably detect clinically relevant metastases. However, ultrastaging is labor-intensive, time-consuming, and costly. Deep learning algorithms offer a potential solution by assisting pathologists in efficiently assessing serial sections for metastases, reducing workload and costs while enhancing accuracy. This proof-of-principle study evaluated the effectiveness of a deep learning algorithm for SLN metastasis detection in early-stage cervical cancer. Methods: We retrospectively analyzed whole slide images (WSIs) of hematoxylin and eosin (H&E)-stained SLNs from early-stage cervical cancer patients diagnosed with an SLN metastasis with either H&E or IHC. A CE-IVD certified commercially available deep learning algorithm, initially developed for detection of breast and colon cancer lymph node metastases, was employed off-label to assess its sensitivity in cervical cancer. Results: This study included 21 patients with early-stage cervical cancer, comprising 15 with squamous cell carcinoma, five with adenocarcinoma, and one with clear cell carcinoma. Among these patients, 10 had macrometastases and 11 had micrometastases in at least one SLN. The algorithm was applied to evaluate H&E WSIs of 47 SLN specimens, including 22 that were negative for metastasis, 13 with macrometastases, and 12 with micrometastases in the H&E slides. The algorithm detected all H&E macro- and micrometastases with 100% sensitivity. Conclusions: This proof-of-principle study demonstrated high sensitivity of a deep learning algorithm for detection of clinically relevant SLN metastasis in early-stage cervical cancer, despite being originally developed for adenocarcinomas of the breast and colon. Our findings highlight the potential of leveraging an existing algorithm for use in cervical cancer, warranting further prospective validation in a larger population.

Plasma-based proteomic and metabolomic characterization of lung and lymph node metastases in cervical cancer patients

Metastasis is the leading cause of mortality in cervical cancer (CC), with a particular prevalence of lymph node and lung metastases. Patients with CC who have developed distant metastases typically face a poor prognosis, and there is a scarcity of non-invasive strategies for predicting CC metastasis. In this study, we utilized label-free proteomics and untargeted metabolomics to analyze plasma samples from 25 non-metastatic, 14 with lung metastasis, and 15 with lymph node metastasis CC patients. Pathway enrichment analysis revealed a shared inflammatory process between the two metastatic groups, while the central carbon metabolism in cancer showed distinct features in the lung metastasis cohort. Additionally, cholesterol metabolism, hypoxia-inducible factor 1, and ferroptosis signaling pathways were specifically altered in the lymph node metastasis group. Utilizing the receiver operating characteristic curve analysis and Random Forest algorithm, we identified two distinct biomarker panels for the prediction of lung metastasis and lymph node metastasis, respectively. The lung metastasis panel includes properdin, neural cell adhesion molecule 1, and keratin 6 A, whereas the lymph node metastasis panel consists of quiescin sulfhydryl oxidase 1, paraoxonase 1, and keratin 6 A. Each panel exhibited significant diagnostic potential, with high area under the curve (AUC) values for lung metastasis (training set: 0.989, testing set: 0.789) and lymph node metastasis (training set: 0.973, testing set: 0.900). This study conducted an integrated proteomic and metabolomic analysis to clarify the factors contributing to lung and lymph node metastases in CC and has successfully established two biomarker panels for their prediction.

Diagnostic value of [18F]FDG PET/MRI in the detection of lymph node metastasis of cervical cancer: a meta-analysis

Diagnostic value of [18F]FDG PET/MRI in the detection of lymph node metastasis of cervical cancer: a meta-analysis

Preoperative scoring system for the prediction of risk of lymph node metastasis in cervical cancer

The study aimed to develop and validate a preoperative scoring system to predict the risk of lymph node metastasis (LNM) in cervical cancer (CC). A total of 426 stage IB1–IIA1 CC patients were randomly divided into two sets. A logistic regression model was used to determine independent factors that contribute to LNM. A preoperative scoring system was developed based on beta (β) coefficients. An area under the receiver operating curve (AUC) was used to test for model discrimination. Five-year overall survival (OS) rate was 91.7%. Multivariable logistic regression analysis showed that FIGO stage, tumor size, depth of invasion on MRI, and squamous cell carcinoma antigen levels were independent risk factors in the development set (all P < 0.05). The AUCs of the scoring system for the development and validation sets were 0.833 (95% CI = 0.757–0.909) and 0.767 (95% CI = 0.634–0.891), respectively. Patients who scored 0–2, 3–5, and 6–8 were classified into low-risk, medium-risk, and high-risk groups. Predicted rates were in accord with observed rates in both sets. The 5-year OS rates of the new groups were also significantly different for the entire group, development set, and validation set (all P < 0.05). LNM affects the prognosis of CC patients. The scoring system can be used to assist in evaluating the risk of LNM in CC patients preoperatively. It is easy to obtain and can provide reference for clinical treatment decision-making.

S100A7 orchestrates neutrophil chemotaxis and drives neutrophil extracellular traps (NETs) formation to facilitate lymph node metastasis in cervical cancer patients

Neutrophil extracellular traps (NETs) have been shown to promote the metastatic potential of many kinds of tumors. Our study aimed to investigate the role and mechanisms of NETs in lymph node metastasis (LNM) of cervical cancer (CCa), and evaluated the therapeutic value of targeting NETs in CCa. Immunohistochemistry demonstrated that neutrophil infiltration and NETs formation were increased in CCa patients with LNM, as well as confirming a positive correlation between S100A7 expression and neutrophil infiltration in CCa. NETs enhanced the migratory capability of CCa by activating the P38-MAPK/ERK/NFκB pathway through interaction with TLR2. Digesting NETs with deoxyribonuclease 1 (DNase 1) or inhibiting TLR2 with chloroquine eliminated the NETs-induced metastatic potential of CCa. Additionally, NETs promoted lymphangiogenesis and increased the permeability of lymphatic vessels, thus facilitating translymphatic movement of CCa. CCa-derived S100A7 exhibited a chemotactic effect on neutrophils and promoted NETs generation by elevating ROS levels rather than activating autophagy in neutrophils. The mouse model with footpad implantation illustrated that DNase 1 effectively reduced LNM in LPS-induced mice and in mice seeded with S100A7-overexpressing CCa cells. In conclusion, our study reveals a new tumor-promoting mechanism of S100A7, clarifies the crucial role and mechanism of NETs in LNM of CCa, and indicates that the NETs-targeted therapy emerges as a promising anti-metastasis therapy in CCa.

HNRNPC mediates lymphatic metastasis of cervical cancer through m6A-dependent alternative splicing of FOXM1

Cervical cancer (CCa) patients with lymph node (LN) metastasis face poor prognoses and have limited treatment options. Aberrant N6-methyladenosine (m6A) modification of RNAs are known to promote tumor metastasis, but their role in CCa remains unclear. Our study reveals that HNRNPC, an alternative splicing (AS) factor and m6A reader, increases tumor-related variants through m6A-dependent manner, thereby promoting lymphatic metastasis in CCa. We found that HNRNPC overexpression correlates with lymphatic metastasis and poorer prognoses in CCa patients. Functionally, knocking down HNRNPC markedly inhibited the migration and invasion of several CCa cell lines, while supplementing HNRNPC restored the malignant phenotypes of these cells. Mechanistically, HNRNPC regulates exon skipping of FOXM1 by binding to its m6A-modified motif. Mutating the m6A site on FOXM1 weakened the interaction between HNRNPC and FOXM1 pre-RNA, leading to a reduction in the metastasis-related FOXM1-S variant. In conclusion, our findings demonstrate that m6A-dependent alternative splicing mediated by HNRNPC is essential for lymphatic metastasis in CCa, potentially providing novel clinical markers and therapeutic strategies for patients with advanced CCa.

Cardiac metastasis in uterine cervical cancer : Asystematic review and case study.

Cardiac metastasis from cervical cancer is rare and only scarcely documented. We aim to present anew case and systematically summarize the available literature. PubMed, Scopus, Web of Science, Central, and ClinicalTrials.gov were systematically searched following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria. Results were screened via title, abstract, and full text. Additionally, the reference lists of all papers chosen for the review were screened. Eighty-one papers were identified, describing 86cases in total. Cardiac metastasis occurred at all stages of cervical cancer and in all age groups. Median time from initial diagnosis to diagnosis of cardiac metastasis was 12months. Patients mainly complained of dyspnea and chest pain, 60.8% had pathologic ECG (electrocardiographic) findings. The cardiac mass was most frequently detected by transthoracic echography. The most common tumor histology was squamous cell carcinoma. Chemotherapy and surgical interventions were the main treatment modalities. Median survival after diagnosis of cardiac metastasis was 3months. This largest review on cardiac metastases from cervical cancer confirmed the heart as avery infrequent site of metastasis. There are < 100 cases described in the literature, with very poor prognosis and undefined clinical management.

RNF113A as a poor prognostic factor promotes metastasis and invasion of cervical cancer through miR197/PRP19/P38MAPK signaling pathway

It has been discovered that aberrant expression of RNF113A plays a significant role in various diseases, including esophageal cancer, hepatocellular carcinoma, and X-linked trichothiodystrophy syndrome. Nevertheless, its functional implications in cervical cancer (CC) remain unclear. The objective of this study was to investigate the role of RNF113A in both the development and prognosis of CC. To achieve this objective, a total of sixty cases were included in the follow-up investigation. The findings revealed a significant up-regulation of RNF113A protein in CC tissues compared to paired paracancerous tissues, and a high expression level of RNF113A was strongly associated with malignant phenotypes such as lymph node metastasis, differentiation degree, depth of invasion, and FIGO stage. Meanwhile, RNF113A was found to be an independent prognostic risk factor, with its high expression significantly correlating with a reduced overall survival period in patients. To elucidate the underlying cause and mechanism of the unfavorable prognosis associated with RNF113A, comprehensive functional investigations were conducted both in vitro and in vivo.Interestingly, it was revealed that RNF113A promoted migration and invasion while inhibiting apoptosis of CC cells, thereby contributing to a poor prognosis. Mechanistically, RNF113A regulated the progression and prognosis of CC through the miR197/Prp19/p38Mark signaling pathway. Overall, our findings underscore the potential clinical significance of RNF113A as an unfavorable prognostic factor in CC.

A Novel Ultrasound-Based Radiomics Model for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer

ObjectiveThe purpose of this retrospective study was to establish a combined model based on ultrasound (US)-radiomics and clinical factors to predict preoperative lymph node metastasis (LNM) in cervical cancer (CC) patients non-invasively. MethodsA total of 131 CC patients who had cervical lesions found by transvaginal sonography (TVS) from the First Affiliated Hospital of Anhui Medical University (Hefei, China) were retrospectively analyzed. The clinical independent predictors were selected using univariate and multivariate logistic regression analysis. US-radiomics features were extracted from US images; after selecting the most significant features by univariate analysis, Spearman's correlation analysis, and the least absolute shrinkage and selection operator (LASSO) algorithm; four machine-learning classification algorithms were used to build the US-radiomics model. Fivefold cross-validation was then used to test the performance of the model and compare the ability of the clinical, US-radiomics and combined models to predict LNM in CC patients. ResultsRed blood cell, platelet and squamous cell carcinoma-associated antigen were independent clinical predictors of LNM (+) in CC patients. eXtreme Gradient Boosting performed the best among the four machine-learning classification algorithms. Fivefold cross-validation confirmed that eXtreme Gradient Boosting indeed performs the best, with average area under the curve values in the training and validation sets of 0.897 and 0.898. In the three prediction models, both the US-radiomics model and the combined model showed good predictive efficacy, with average area under the curve values in the training and validation sets of 0.897, 0.898 and 0.912, 0.905, respectively. ConclusionUS-radiomics features combined with clinical factors can preoperatively predict LNM in CC patients non-invasively.

Cervical cancer-produced neuromedin-B reprograms Schwann cells to initiate perineural invasion

Perineural invasion (PNI) is a new approach of cervical cancer invasion and metastasis, involving the cross-talk between tumor and nerve. However, the initiating signals and cellular interaction mechanisms of PNI remain largely elusive. The nerve-sparing radical hysterectomy (NSRH) proposed to improve postoperative quality of life is only applicable to cervical cancer patients without PNI. Therefore, it is important to elucidate the underlying mechanisms initiating PNI, and suggest the effective biomarkers to predict PNI before NSRH surgery. Here, we found that PNI is the characteristic of advanced cervical cancer, and Schwann cells were the antecedent cells that initiating PNI. Further, neuropeptide neuromedin B (NMB) produced by cervical cancer cells was determined to induce PNI by reprogramming Schwann cells, including driving their morphological and transcriptional changes, promoting their proliferation and migration, and initiating PNI by secreting CCL2 and directing axon regeneration. Mechanistically, cervical cancer cells-produced NMB activated its receptor NMBR in Schwann cells, and opened the T-type calcium channels to stimulate Ca2+ influx through PKA signaling, which could be blocked by the inhibitor. Clinically, combined examination of serum NMB and CCL2 levels was suggested to effectively predict PNI in cervical cancer patients. Our data demonstrate that cervical cancer-produced NMB initiates the reprograming of Schwann cells, which then direct axon regeneration, thus causing PNI onset. The elevated serum NMB and CCL2 levels may be useful for the decision-making to nerve sparing during hysterectomy surgery of cervical cancer patients.

First birth after uterine transposition in low-volume lymph node metastasis of cervical cancer: A long journey for success.

Locally advanced cervical cancer poses a significant challenge to fertility-sparing treatments. Pelvic radiotherapy impairs reproductive potential owing to ovarian, uterine, and endometrial side effects. This study presents a literature review of the main fertility-sparing therapeutic alternatives for locally advanced cervical cancer and a case report of the first childbirth following uterine transposition for gynecological malignancies.

Based on 3D-PDU and clinical characteristics nomogram for prediction of lymph node metastasis and lymph-vascular space invasion of early cervical cancer preoperatively

PurposeTo develop and validate a nomogram based on 3D-PDU parameters and clinical characteristics to predict LNM and LVSI in early-stage cervical cancer preoperatively.Materials and methodsA total of first diagnosis 138 patients with cervical cancer who had undergone 3D-PDU examination before radical hysterectomy plus lymph dissection between 2014 and 2019 were enrolled for this study. Multivariate logistic regression analyses were performed to analyze the 3D-PDU parameters and selected clinicopathologic features and develop a nomogram to predict the probability of LNM and LVSI in the early stage. ROC curve was used to evaluate model differentiation, calibration curve and Hosmer-Lemeshow test were used to evaluate calibration, and DCA was used to evaluate clinical practicability.ResultsMenopause status, FIGO stage and VI were independent predictors of LNM. BMI and maximum tumor diameter were independent predictors of LVSI. The predicted AUC of the LNM and LSVI models were 0.845 (95%CI,0.765–0.926) and 0.714 (95%CI,0.615–0.813). Calibration curve and H-L test (LNM groups P = 0.478; LVSI P = 0.783) all showed that the predicted value of the model had a good fit with the actual observed value, and DCA indicated that the model had a good clinical net benefit.ConclusionThe proposed nomogram based on 3D-PDU parameters and clinical characteristics has been proposed to predict LNM and LVSI with high accuracy, demonstrating for the first time the potential of non-invasive prediction. The probability derived from this nomogram may have the potential to provide valuable guidance for physicians to develop clinical individualized treatment plans of FIGO patients with early cervical cancer.

LIMK1 promotes the development of cervical cancer by up-regulating the ROS/Src-FAK/cofilin signaling pathway.

In this study, we investigated the mechanism of action of LIMK1 in cervical cancer progression. The biological role of LIMK1 in regulating the growth, invasion, and metastasis of cervical cancer was studied in SiHa, CaSki cells and nude mice tumor models. The role of LIMK1 in the growth of cervical cancer was evaluated by HE staining. The role of LIMK1 in the invasion, metastasis, and proliferation of cervical cancer was evaluated by cell scratch, Transwell, and monoclonal experiments. The interaction among LIMK1, ROS, and Src was evaluated by Western blotting. The effects of regulating ROS and p-Src expression on LIMK1 in the migration/invasion and proliferation of cervical cancer cells were evaluated through cellular functional assays. Overexpression of LIMK1 promoted tumor growth in nude mice. Cell scratch, Transwell, and monoclonal experiments suggested that LIMK1 promoted the invasion, metastasis, and proliferation of cervical cancer cells. Western blotting suggested that LIMK1 can promote the expression of ROS-related proteins NOX2, NOX4, p-Src, and downstream proteins p-FAK, p-ROCK1/2, p-Cofilin-1, F-actin and inhibit the expression of p-SHP2 protein. Correction experiments showed that LIMK1 regulated the expression of p-FAK and p-Cofilin-1 proteins by regulating ROS and p-Src. Through the detection of cervical cancer cell functions, it was found that the activation of ROS and p-Src induced by LIMK1 is an early event that promotes the migration, proliferation, and invasion of cervical cancer cells. LIMK1 promotes the expression of F-actin and promotes the development of cervical cancer by regulating the oxidative stress/Src-mediated p-FAK/p-ROCK1/2/p-Cofilin-1 pathway.

Increasing the accuracy and reproducibility of positron emission tomography radiomics for predicting pelvic lymph node metastasis in patients with cervical cancer using 3D local binary pattern-based texture features

BackgroundThe reproducibility of positron emission tomography (PET) radiomics features is affected by several factors, such as scanning equipment, drug metabolism time and reconstruction algorithm. We aimed to explore the role of 3D local binary pattern (LBP)-based texture in increasing the accuracy and reproducibility of PET radiomics for predicting pelvic lymph node metastasis (PLNM) in patients with cervical cancer. MethodsWe retrospectively analysed data from 177 patients with cervical squamous cell carcinoma. They underwent 18F-fluorodeoxyglucose (18F-FDG)whole-body PET/computed tomography (PET/CT), followed by pelvic 18F-FDG PET/magnetic resonance imaging (PET/MR). We selected reproducible and informative PET radiomics features using Lin's concordance correlation coefficient, least absolute shrinkage and selection operator algorithm, and established 4 models, PET/CT, PET/CT-fusion, PET/MR and PET/MR-fusion, using the logistic regression algorithm. We performed receiver operating characteristic (ROC) curve analysis to evaluate the models in the training data set (65 patients who underwent radical hysterectomy and pelvic lymph node dissection) and test data set (112 patients who received concurrent chemoradiotherapy or no treatment). The DeLong test was used for pairwise comparison of the ROC curves among the models. ResultsThe distribution of age, squamous cell carcinoma (SCC), International Federation of Gynaecology and Obstetrics stage and PLNM between the training and test data sets were different (P < 0.05). The LBP-transformed radiomics features (50/379) had higher reproducibility than the original radiomics features (9/107). Accuracy of each model in predicting PLNM was as follows: training data set: PET/CT = PET/CT-fusion = PET/MR-fusion (0.848) and test data set: PET/CT = PET/CT-fusion (0.985) > PET/MR = PET/MR-fusion (0.954). There was no statistical difference between the ROC curve of PET/CT and PET/MR models in both data sets (P > 0.05). ConclusionsThe LBP-transformed radiomics features based on PET images could increase the accuracy and reproducibility of PET radiomics in predicting pelvic lymph node metastasis in cervical cancer to allow the model to be generalised for clinical use across multiple centres.

Asiatic acid inhibits cervical cancer cell proliferation and migration via PI3K/AKT/mTOR signaling pathway

Cervical cancer (CC) is a malignant tumor of the female reproductive system that typically occurs in cervical cells and has high incidence and mortality rates, strong metastatic ability, and poor prognosis. Asiatic acid (AA) exhibits anti-inflammatory, anti-depressant, and anti-tumor effects. However, the molecular targets and mechanisms underlying AA-mediated inhibition of CC metastasis remain unclear. AA affects the proliferation, metastasis, and epithelial-mesenchymal transition (EMT) process of CC cell lines. MTT experiments verified that AA inhibited the proliferation ability of CC cells, and the effect of AA on the lateral and longitudinal migration ability of CC was evaluated through wound healing and Transwell assays. Western blotting was used to explore whether AA inhibits EMT process in HeLa and C33a cells. Currently, targeting the PI3K/AKT/mTOR pathway as a strategy for cancer treatment remains an evolving field. However, the molecular mechanism by which AA inhibits CC via the PI3K/AKT/mTOR pathway remains unclear and requires further investigation.

Hsa_circ_0002454 controls cervical cancer cell growth and metastasis by targeting SDC4 through miR-654-3p

hsa_circ_0002454 controls cervical cancer cell growth and metastasis by targeting SDC4 through miR-654-3p

Clinical Significance of Low Volume Lymph Node Metastasis in Early Stage Cervical Cancer: A Single Centre Case Series

Background and Objectives: Lymph node involvement is uncommon in early-stage cervical cancer (7-20%), with low volume nodal disease (micrometastasis and isolated tumour cells) representing a further small subset (2-23%) of this small population. Fewer data are available on the impact of low volume metastasis in cervical cancer. The current consensus is to treat this group of patients in the same way as for nodal macrometastasis although the prognostic significance of low volume lymph node metastasis still remains contested. The purpose of this study was to retrospectively analyze a cohort of patients who underwent primary surgery for early-stage cervical cancer and where the post operative histopathology report demonstrated low volume lymph node metastasis. The aim was to determine the frequency of recurrence and progression free survival in groups with isolated tumor cells (&lt;0.2 mm), micrometastasis (0.2 – 2 mm) and macrometastasis (&gt;2 mm). Methods: Retrospective review had identified 18 patients who underwent primary surgery in single Gynaecology Cancer Centre between 2007 and 2018 for cervical cancer with subsequent histologically demonstrated lymph node metastasis. Progression free survival curves were generated for each patient group based on their histological type of lymph node metastasis. Log rank, chi squared test statistic was calculated to test for statistically significant difference in survival between groups. Results: A total of 18 patients were identified as having isolated tumor cells (n=7), micrometastasis (n=5) or macrometastasis (n=6) on post operative lymph node histopathology. Follow up data demonstrated a total of 6 recurrences overall, with the highest number in the “macrometasitasis“ group (66.7%), followed by “isolated tumor cells” group (28.6%). No recurrence was observed in the “micrometastasis” group. The difference in progression-free survival between groups was not statistically significant at alpha = 0.05 (p=0.0685). Conclusion: Further research and larger studies are required to evaluate the prognostic significance and the overall outcome in patients with isolated tumor cell lymph node metastasis in early stage cervical cancer.

Abdominal Parietal Metastasis from Cervical Cancer: A Review of One of the Most Uncommon Sites of Recurrence Including a Report of a New Case.

Cervical cancer is the fourth most common cancer in women, the highest mortality being found in low- and middle-income countries. Abdominal parietal metastases in cervical cancer are a very rare entity, with an incidence of 0.1-1.3%, and represent an unfavorable prognostic factor with the survival rate falling to 17%. Here, we present a review of cases of abdominal parietal metastasis in recent decades, including a new case of a 4.5 cm abdominal parietal metastasis at the site of the scar of the former drain tube 28 months after diagnosis of stage IIB cervical cancer (adenosquamous carcinoma), treated by external radiotherapy with concurrent chemotherapy and intracavitary brachytherapy and subsequent surgery (type B radical hysterectomy). The tumor was resected within oncological limits with the histopathological result of adenosquamous carcinoma. The case study highlights the importance of early detection and appropriate treatment of metastases in patients with cervical cancer. The discussion explores the potential pathways for parietal metastasis and the impact of incomplete surgical procedures on the development of metastases. The conclusion emphasizes the poor prognosis associated with this type of metastasis in cervical cancer patients and the potential benefits of surgical resection associated with systemic therapy in improving survival rates.