Neutrophil-to-Lymphocyte Ratio in Ovarian Cancer Patients with Low CA125 Concentration

Prognostic Value of The Neutrophil-to-Lymphocyte Ratio, Platelet-to-Lymphocyte Ratio, and Platelet Count for Platinum-Sensitive Recurrent Epithelial Ovarian Cancer

OBJECTIVE

To study the prognostic value of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and platelet count in patients with platinum-sensitive recurrent epithelial ovarian cancer (PS-ROC).

METHODS

This was a retrospective study on a database of platinum-sensitive recurrent epithelial ovarian cancer patients who received treatment at HRH Princess Maha Chakri Sirindhorn Medical Center (MSMC) between January 2010 and December 2020. The patients' demographic data, surgical factors, pathological factors, laboratory findings, and response to treatment were reviewed from the patients' medical records. Survival analysis was conducted using the Kaplan-Meier survival estimate and Cox regression model.

RESULTS

In total, 56 patients were recruited in this study. The median overall survival (OS) and progression-free survival (PFS) were 33 (95%CI 23-43) and 11 (95%CI 8-16) months, respectively. Survival analysis showed a high PLR was associated with decreased OS compared with low value but no significant difference in PFS. High NLR was associated with poor OS and PFS. There was no association between the platelet count and survival outcome (OS and PFS). In the multivariable Cox regression analysis, the NLR, PLR, and platelet count were not significant prognostic factors for survival outcome. However, low hemoglobin and a decreased disease-free interval were significantly associated with poor PFS. A white blood cell count (WBC) ≥ 8,000 cells/mm3 was a poor prognostic factor for overall survival (Adjusted HR 7.64; 95%CI: 2.21-26.42; p-value = 0.001).

CONCLUSIONS

The NLR, PLR, and platelet count were not associated with both the OS or PFS in patients with PS-ROC. However, the WBC level is an easy, readily available, and economical way to predict survival outcomes in PS-ROC patients and may help physicians to tailor therapeutic interventions in the future.

Association between Rheumatoid Arthritis Disease Activity and Risk of Ovarian Malignancy in Middle-Aged and Elderly Women

Objective

To investigate the risk of ovarian malignancy in middle-aged and elderly women with rheumatoid arthritis (RA) and its correlation with disease activity.

Methods

219 middle-aged and elderly (age ≥ 40) female RA patients who were treated at the Department of Rheumatology and Immunology of the Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine from August 2019 to September 2020 were selected. Their general information such as age and medical history was collected. RA disease activity-related indicators include rheumatoid factor (RF), anticyclic citrullinated peptide antibody (ACPA), ESR, CRP, and ovarian malignancy risk-related indicators including alpha fetoprotein (AFP), carcinoembryonic antigen (CEA), CA125, CA199, and human epididymis protein 4 (HE4) were detected. According to Risk of Ovarian Malignancy Algorithm (ROMA), they were divided into a low-risk group (ROMA-low, premenopausal: ROMA ≤ 11.4%, postmenopausal: ROMA ≤ 29.9%) and a high-risk group (ROMA-high, premenopausal: ROMA > 11.4%, postmenopausal: ROMA > 29.9%) for ovarian malignancy. Meanwhile, according to the DAS28-ESR, they were divided into the general disease activity group (DAS28-ESR ≤ 5.1) and the high disease activity group (DAS28-ESR > 5.1). SPSS 25.0 software was used to compare the differences among groups and to analyze the correlation between ovarian malignancy risk and RA disease activity.

Results

Compared with the ROMA-low group, the levels of RF, ACCP, CDAI, SDAI, DAS28-ESR, and DAS28-CRP in the ROMA-high group were significantly increased (P < 0.05). HE4 and ROMA in the high disease activity group were significantly higher than general disease activity group (P < 0.05). Spearman correlation analysis showed that age (r = 0.472), RF (r = 0.221), ACPA (r = 0.156), CDAI (r = 0.226), SDAI (r = 0.221), DAS28-ESR (r = 0.254), DAS28-CRP (r = 0.208), medications (r = 0.189), and CA199 (r = 0.250) were correlated with ROMA (P < 0.05). Multivariate regression analysis showed that ESR (OR = 1.11), SDAI (OR = 1.02), DAS28-ESR (OR = 1.33), DAS28-CRP (OR = 1.26), and CA199 (OR = 1.03) were independent risk factors for high risk of ovarian malignancy (P < 0.05). Subgroup analysis showed that CA199 is an effect modification factor for DAS28-ESR (P < 0.05).

Conclusion

The risk of ovarian malignancy is significantly increased in middle-aged and elderly women with high disease activity with rheumatoid arthritis. In clinical, full attention should be paid to the risk of ovarian malignancy in this population. Screening in time, especially in patients with increased DAS28-ESR and CA199 at the same time, is needed.

Role of Systemic Inflammatory Reaction in Female Genital Organ Malignancies - State of the Art

Systemic inflammatory reaction (SIR) is an unfavorable prognostic factor in many malignancies and has a role in all stages of the neoplastic process: initiation, promotion, and disease progression. Analysis of SIR can be performed by assessing indicators (eg, lymphocyte-to-neutrophil, platelet-to-lymphocyte, and monocyte-to-neutrophil ratios) and products of neutrophils and lymphocytes (ie, the systemic immune-inflammation index), or by examining the relationship between levels of C-reactive protein and albumin (based on the Glasgow Prognostic Score, modified Glasgow Prognostic Score, and C-reactive protein-to-albumin ratio). Risk stratification is essential in the clinical management of cancer; hence, the evaluation of these factors has potential applications in the clinical management of patients with cancer and in the development of new therapeutic targets. This review summarizes the current knowledge on SIR indicators and presents their clinical utility in malignancies of the female genital organs.

Research Progress in Prognostic Factors and Biomarkers of Ovarian Cancer

Ovarian cancer is a serious threat to women's health; its early diagnosis rate is low and prone to metastasis and recurrence. The current conventional treatment for ovarian cancer is a combination of platinum and paclitaxel chemotherapy based on surgery. The recurrence and progression of ovarian cancer with poor prognosis is a major challenge in treatment. With rapid advances in technology, understanding of the molecular pathways involved in ovarian cancer recurrence and progression has increased, biomarker-guided treatment options can greatly improve the prognosis of patients. This review systematically discusses and summarizes existing and new information on prognostic factors and biomarkers of ovarian cancer, which is expected to improve the clinical management of patients and lead to effective personalized treatment.

Spatial cytotoxic and memory T cells in tumor predict superior survival outcomes in patients with high-grade serous ovarian cancer

Although the association between tumor‐infiltrating CD3⁺ T and CD8⁺ T cells and superior survival in high‐grade serous ovarian cancer (HGSOC) has been observed, the different spatial localization of tumor‐infiltrating lymphocytes (TILs) possesses heterogeneous effects. We performed localized measurements in 260 HGSOC from 2 independent cohorts represented in tissue microarray format to determine the localized expression pattern and clinical significance of CD3⁺ T, CD8⁺ T, and CD45RO⁺ cells in HGSOC. Different density of spatial localization of CD3⁺ T, CD8⁺ T, and CD45RO⁺ cells exhibited heterogeneous association with OS. The combination of the center of the tumor and invasive margin localized CD8⁺T cells (CD8^CT&IM) with the same margin localized CD45RO (CD45RO^CT&IM) was the most robust prognostic predictor. Immune score (IS) was constructed by integrating FIGO stage with CD8^CT&IM and CD45RO^IM&CT and had the best prognostic value in HGSOC. The low‐, intermediate‐, and high‐IS groups were observed in 44.7%, 41.6%, and 13.7% of patients, respectively. Low‐IS identified patients were at higher risk of death compared to high‐IS identified patients (HR = 12.426; 95% CI 5.317–29.039, p < 0.001); meanwhile, we evaluate the RMSTs over 10 years of follow‐up and obtained RMST values of 104.09 months (95% CI 96.31–111.87 months) in the high‐IS group, 75.26 months (95% CI 59.92–90.60 months) in the intermediate‐IS group, and 48.68 months (95%CI 38.82–58.54 months) in the low‐IS group. In general, spatial localization can modulate the clinical effects of TILs in HGSOC. Thus, the spatial expression of CD8 and CD45RO could aid clinicians to determine the follow‐up plan of patients with HGSOC.

Evaluation of Combined Cancer Markers With Lactate Dehydrogenase and Application of Machine Learning Algorithms for Differentiating Benign Disease From Malignant Ovarian Cancer

BACKGROUND

The differential diagnosis of ovarian cancer is important, and there has been ongoing research to identify biomarkers with higher performance. This study aimed to evaluate the diagnostic utility of combinations of cancer markers classified by machine learning algorithms in patients with early stage ovarian cancer, which has rarely been reported.

METHODS

In total, 730 serum samples were assayed for lactate dehydrogenase (LD), neutrophil-to-lymphocyte ratio (NLR), human epididymis protein 4 (HE4), cancer antigen 125 (CA125), and risk of ovarian malignancy algorithm (ROMA). Among them, 53 were diagnosed with early stage ovarian cancer, and the remaining 677 were diagnosed with benign disease.

RESULTS

The areas under the receiver operating characteristic curves (ROC-AUCs) of the ROMA, HE4, CA125, LD, and NLR for discriminating ovarian cancer from non-cancerous disease were .707, .680, .643, .657, and .624, respectively. ROC-AUC of the combination of ROMA and LD (.709) was similar to that of single ROMA in the total population. In the postmenopausal group, ROC-AUCs of HE4 and CA125 combined with LD presented the highest value (.718). When machine learning algorithms were applied to ROMA combined with LD, the ROC-AUC of random forest was higher than that of other applied algorithms in the total population (.757), showing acceptable performance.

CONCLUSION

Our data suggest that the combinations of ovarian cancer-specific markers with LD classified by random forest may be a useful tool for predicting ovarian cancer, particularly in clinical settings, due to easy accessibility and cost-effectiveness. Application of an optimal combination of cancer markers and algorithms would facilitate appropriate management of ovarian cancer patients.