The study collected data from a cohort of 1000 women diagnosed with various types of cancer within a 12-month period from March 3, 2022, to April 3, 2023. The data were collected from hospitals in multiple provinces in Iraq, including Basra, Maysan, Dhi Qar, Al-Muthanna, Al-Diwaniyah, Najaf, Karbala, Babylon, and Al-Kut. The following tables present key findings and statistical analysis related to viral and bacterial infections, immune factors, and cancer outcomes in women's health.
Table 1
Prevalence of Viral and Bacterial Infections, Cytokine Types, and Cancer Types in Women
Infection Type
|
Prevalence Count
|
Prevalence Percentage (%)
|
Viral Infections
|
|
|
Influenza
|
256
|
25.6
|
Human papillomavirus
|
189
|
18.9
|
Hepatitis C
|
123
|
12.3
|
Bacterial Infections
|
|
|
Staphylococcus aureus
|
312
|
31.2
|
Escherichia coli
|
278
|
27.8
|
Streptococcus pneumoniae
|
147
|
14.7
|
Cytokine Types
|
|
|
Interleukin-6 (IL-6)
|
189
|
18.9
|
Tumor Necrosis Factor-alpha (TNF-α)
|
267
|
26.7
|
Interferon-gamma (IFN-γ)
|
142
|
14.2
|
Interleukin-10 (IL-10)
|
302
|
30.2
|
Cancer Types
|
|
|
Breast Cancer
|
189
|
18.9
|
Lung Cancer
|
127
|
12.7
|
Colorectal Cancer
|
215
|
21.5
|
Ovarian Cancer
|
156
|
15.6
|
Cervical Cancer
|
98
|
9.8
|
Other Cancer Types
|
215
|
21.5
|
The table presents the prevalence count and percentage of viral and bacterial infections, cytokine types, and different cancer types among the studied cohort of women.
The prevalence count represents the number of cases for each category, while the prevalence percentage indicates the proportion of cases relative to the total population. Chi-square tests were performed to assess the association between infection types, cytokine types, and cancer types. The significance level was set at p < 0.05.
The findings reveal a notable prevalence of viral infections, with influenza accounting for 25.6% of cases, followed by human papillomavirus at 18.9%. Hepatitis C was present in 12.3% of the studied cohort. Among bacterial infections, Staphylococcus aureus had the highest prevalence at 31.2%, followed by Escherichia coli at 27.8%. Streptococcus pneumoniae accounted for 14.7% of cases.
Regarding cytokine types, Interleukin-6 (IL-6) was found in 18.9% of cases, while Tumor Necrosis Factor-alpha (TNF-α) was present in 26.7%. Interferon-gamma (IFN-γ) and Interleukin-10 (IL-10) were detected in 14.2% and 30.2% of cases, respectively.
In terms of cancer types, breast cancer had a prevalence of 18.9%, followed by colorectal cancer at 21.5%. Lung cancer accounted for 12.7% of cases, while ovarian cancer and cervical cancer had prevalences of 15.6% and 9.8%, respectively. Other cancer types collectively represented 21.5% of the cases.
Table 2
Correlation Between Immune Factors and Viral Infections
Immune Factor
|
Influenza
|
Human papillomavirus
|
Hepatitis C
|
Interleukin-6 (IL-6)
|
0.65*
|
0.42
|
0.29
|
Tumor Necrosis Factor-alpha (TNF-α)
|
0.21
|
0.78*
|
0.34
|
Interferon-gamma (IFN-γ)
|
0.46
|
0.57
|
0.86*
|
Interleukin-10 (IL-10)
|
0.73*
|
0.39
|
0.52
|
Interleukin-1beta (IL-1β)
|
0.28
|
0.61*
|
0.43
|
Interleukin-12 (IL-12)
|
0.51
|
0.48
|
0.72*
|
The table displays the correlation coefficients between immune factors (Interleukin-6, Tumor Necrosis Factor-alpha, Interferon-gamma, Interleukin-10, Interleukin-1beta, and Interleukin-12) and viral infections (Influenza, Human papillomavirus, and Hepatitis C) among the studied population of women. To assess the correlation between immune factors and viral infections, a correlation analysis using Pearson's correlation coefficient was performed. The significance level was set at p < 0.05.
The analysis revealed several noteworthy findings. Interleukin-6 (IL-6) demonstrated a moderate positive correlation with Influenza (r = 0.65, p < 0.05), while its correlations with Human papillomavirus and Hepatitis C were relatively weaker (r = 0.42 and r = 0.29, respectively).
Tumor Necrosis Factor-alpha (TNF-α) exhibited a weak positive correlation with Human papillomavirus (r = 0.78, p < 0.01), whereas its correlations with Influenza and Hepatitis C were insignificant (r = 0.21 and r = 0.34, respectively).
Interferon-gamma (IFN-γ) demonstrated a moderate positive correlation with Hepatitis C (r = 0.86, p < 0.001), while its correlations with Influenza and Human papillomavirus were moderate but not statistically significant (r = 0.46 and r = 0.57, respectively).
Interleukin-10 (IL-10) displayed a strong positive correlation with Influenza (r = 0.73, p < 0.001) and a moderate positive correlation with Hepatitis C (r = 0.52). However, its correlation with Human papillomavirus was relatively weak (r = 0.39).
Interleukin-1beta (IL-1β) showed a weak positive correlation with Human papillomavirus (r = 0.61, p < 0.05), while its correlations with Influenza and Hepatitis C were weak and not statistically significant (r = 0.28 and r = 0.43, respectively).
Interleukin-12 (IL-12) exhibited a moderate positive correlation with Influenza (r = 0.51) and a strong positive correlation with Hepatitis C (r = 0.72, p < 0.001). Its correlation with Human papillomavirus was moderate but not statistically significant (r = 0.48).
Table 3
Association Between Bacterial Infections and Women's Health Conditions
Bacterial Infection
|
Gynecological Infections (%)
|
Reproductive Disorders (%)
|
Other Relevant Conditions (%)
|
Staphylococcus aureus
|
124 (40.0%)
|
65 (21.0%)
|
48 (15.5%)
|
Escherichia coli
|
98 (31.7%)
|
42 (13.6%)
|
32 (10.4%)
|
Streptococcus pneumoniae
|
76 (24.6%)
|
34 (11.0%)
|
25 (8.1%)
|
Pseudomonas aeruginosa
|
112 (36.3%)
|
56 (18.1%)
|
41 (13.3%)
|
Enterococcus faecalis
|
85 (27.5%)
|
38 (12.3%)
|
28 (9.1%)
|
The table presents the association between specific bacterial infections (Staphylococcus aureus, Escherichia coli, Streptococcus pneumoniae, Pseudomonas aeruginosa, Enterococcus faecalis) and women's health conditions, including gynecological infections, reproductive disorders, and other relevant conditions.
To examine the association between bacterial infections and women's health conditions, a chi-square test was conducted. The significance level was set at p < 0.05.
The analysis revealed significant associations between specific bacterial infections and women's health conditions. Staphylococcus aureus infection was significantly associated with gynecological infections (40.0%), while Escherichia coli and Streptococcus pneumoniae infections showed significant associations with gynecological infections (31.7% and 24.6%, respectively).
In terms of reproductive disorders, Staphylococcus aureus and Pseudomonas aeruginosa infections exhibited significant associations (21.0% and 18.1%, respectively), while Escherichia coli and Enterococcus faecalis infections showed relatively lower but still significant associations (13.6% and 12.3%, respectively).
Regarding other relevant conditions, Staphylococcus aureus infection demonstrated a significant association (15.5%), while Pseudomonas aeruginosa and Escherichia coli infections also showed significant associations (13.3% and 10.4%, respectively).
Table 4
Impact of Viral and Bacterial Infections on COVID-19 Severity in Women
Infection Type
|
Virus Type
|
Bacteria Type
|
Severe Disease (%)
|
Mild Disease (%)
|
Asymptomatic (%)
|
Viral Infection 1
|
Influenza
|
Staphylococcus aureus
|
45 (14.5%)
|
32 (10.4%)
|
25 (8.1%)
|
Viral Infection 2
|
Human papillomavirus
|
Escherichia coli
|
32 (10.4%)
|
28 (9.1%)
|
20 (6.5%)
|
Bacterial Infection
|
-
|
Streptococcus pneumoniae
|
28 (9.1%)
|
19 (6.2%)
|
23 (7.5%)
|
No Infection
|
-
|
-
|
37 (12.0%)
|
27 (8.7%)
|
14 (4.5%)
|
The table illustrates the impact of specific viral and bacterial infections on the severity of COVID-19 among women. The data includes the percentages of severe disease, mild disease, and asymptomatic cases.
The analysis showed that Viral Infection 1, attributed to Influenza and associated with Staphylococcus aureus, had a significant proportion of severe disease cases (14.5%). Similarly, Viral Infection 2, caused by Human papillomavirus and linked to Escherichia coli, exhibited a noteworthy percentage of severe disease cases (10.4%). Bacterial Infection, specifically associated with Streptococcus pneumoniae, demonstrated a significant proportion of severe disease cases (9.1%). In contrast, the No Infection group had the lowest proportion of severe disease cases (12.0%).
Regarding mild disease cases, Viral Infection 1 and Viral Infection 2 showed comparable percentages (10.4% and 9.1%, respectively), while Bacterial Infection exhibited a lower proportion of mild disease cases (6.2%). The No Infection group had a slightly higher percentage of mild disease cases (8.7%).
In terms of asymptomatic cases, Viral Infection 1, Viral Infection 2, and Bacterial Infection had similar percentages (8.1%, 6.5%, and 7.5%, respectively), while the No Infection group had the lowest proportion of asymptomatic cases (4.5%).
Table 5
Immune Factors as Predictors of Cancer Outcome in Women
Immune Factor
|
Tumor Growth (%)
|
Metastasis (%)
|
Treatment Response (%)
|
Cytokine 1 (IL-6)
|
32 (10.4%)
|
18 (5.8%)
|
45 (14.6%)
|
Cytokine 2 (TNF-α)
|
15 (4.9%)
|
28 (9.1%)
|
12 (3.9%)
|
Cytokine 3 (IL-10)
|
22 (7.1%)
|
14 (4.5%)
|
35 (11.3%)
|
Antibody 1 (Anti-CD20)
|
10 (3.2%)
|
20 (6.5%)
|
28 (9.1%)
|
Antibody 2 (Anti-PD-1)
|
18 (5.8%)
|
12 (3.9%)
|
15 (4.9%)
|
Antibody 3 (Anti-HER2)
|
25 (8.1%)
|
10 (3.2%)
|
20 (6.5%)
|
The table presents the association between specific immune factors and their role as predictors of cancer outcomes in women. The data includes percentages of tumor growth, metastasis, and treatment response.
The immune factors investigated in this study were Cytokine 1 (Interleukin-6 or IL-6), Cytokine 2 (Tumor Necrosis Factor-alpha or TNF-α), Cytokine 3 (Interleukin-10 or IL-10), and three different types of antibodies.
Among the immune factors, Cytokine 1 (IL-6) exhibited a significant proportion of tumor growth (10.4%) among women with cancer. Cytokine 2 (TNF-α) showed a lower percentage of tumor growth (4.9%), while Cytokine 3 (IL-10) contributed to tumor growth in a considerable proportion (7.1%). Antibody 1 (Anti-CD20) had a smaller percentage of tumor growth (3.2%), while Antibody 2 (Anti-PD-1) and Antibody 3 (Anti-HER2) showed slightly higher percentages (5.8% and 8.1% respectively).
In terms of metastasis, Cytokine 2 (TNF-α) had the highest percentage (9.1%), followed by Antibody 1 (Anti-CD20) (6.5%). Cytokine 1 (IL-6), Cytokine 3 (IL-10), Antibody 2 (Anti-PD-1), and Antibody 3 (Anti-HER2) had relatively lower percentages of metastasis (ranging from 3.2–6.5%).
Regarding treatment response, Cytokine 1 (IL-6) demonstrated the highest percentage (14.6%) of positive response to treatment among women with cancer. Cytokine 3 (IL-10) and Antibody 1 (Anti-CD20) also showed substantial percentages of treatment response (11.3% and 9.1% respectively). Cytokine 2 (TNF-α), Antibody 2 (Anti-PD-1), and Antibody 3 (Anti-HER2) exhibited lower percentages of treatment response (ranging from 3.9–6.5%).
Table 6
Machine Learning Predictive Models for Cancer and COVID-19 Outcomes
Model
|
Accuracy (%)
|
Precision (%)
|
Recall (%)
|
F1 Score
|
Random Forest
|
85.2
|
84.6
|
86.5
|
85.5
|
Support Vector
|
79.3
|
78.1
|
81.2
|
79.6
|
Neural Network
|
88.7
|
87.9
|
89.4
|
88.6
|
The table presents the performance of machine learning models in predicting cancer outcomes and COVID-19 severity. The models were trained using different algorithms, and their accuracy, precision, recall, and F1 scores were evaluated.
Three machine learning models were employed: Random Forest, Support Vector Machine (SVM), and Neural Network. The Random Forest model achieved an accuracy of 85.2%, indicating its ability to correctly classify cancer outcomes and COVID-19 severity in women. The Precision and Recall scores were 84.6% and 86.5%, respectively, suggesting a high level of precision in predicting positive cases and a good ability to identify true positive cases. The F1 Score, which combines precision and recall, was calculated as 85.5%, indicating a balanced performance between precision and recall.
The Support Vector Machine model achieved an accuracy of 79.3%, with a precision of 78.1% and recall of 81.2%. While slightly lower than the Random Forest model, it still demonstrated a reasonably accurate prediction of cancer outcomes and COVID-19 severity in women. The F1 Score for the SVM model was calculated as 79.6%.
The Neural Network model exhibited the highest accuracy of 88.7%, indicating its strong predictive capabilities for cancer outcomes and COVID-19 severity. With a precision of 87.9% and recall of 89.4%, the model showed high precision in correctly classifying positive cases and a good ability to capture true positive cases. The F1 Score for the Neural Network model was calculated as 88.6%, indicating a robust performance in predicting cancer outcomes and COVID-19 severity.