Serum Lactate Dehydrogenase(LDH) and Gamma-Glutamyl Transferase (GGT) Correlate Optimally with Nottingham Prognostic Index for Breast Cancer

Serum Lactate Dehydrogenase(LDH) and Gamma-Glutamyl Transferase (GGT) Correlate Optimally with Nottingham Prognostic Index for Breast Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Serum Lactate Dehydrogenase(LDH) and Gamma-Glutamyl Transferase (GGT) Correlate Optimally with Nottingham Prognostic Index for Breast Cancer Sylvery Mwesige, Victor Meza Kyaruzi, Mungeni Misidai, Mabula Mchembe, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4409898/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background LDH and GGT have been identified as breast cancer serum prognostic markers. Serum level of LDH has been found to increase due to uplifted anaerobic glycolysis in malignant neoplastic conditions. GGT has also been noted to rise in circulation indicating the extent of oxidative stress within the body. Their levels are high in advanced BC cases. Meanwhile, in our setting, there are no serum markers done on a routine basis in breast cancer surveillance among women and predicting the prognosis. Thus, these markers can augment the available tool(s) in predicting breast cancer prognosis since they are widely available, accessible, and economically affordable. Objective This study aimed to assess the correlation between LDH/GGT with NPI among Breast Cancer Women. Study Methods A prospective cross-sectional study was conducted for 12 months. The data were collected by interviewing patients, patients’ files and from the hospital’s electronic database (Jeeva) and then were filled in the structured checklist. IBM SPSS version 27 was used to analyse the data. Mean, median and standard deviation were used to present numerical variables. Categorical variables were presented as frequencies or proportions The correlation was assessed and analyzed by Spearman correlation coefficient and Linear Regression models while ROC was used to determine the accuracy. Results Out of the 104 patients who underwent radical mastectomy for Breast Cancer had a mean age of 48.51 ± 12.80. Among them, 27 (26%) were diagnosed below 40 years of age. Lactate Dehydrogenase Gamma-Glutamyl Transferase Nottingham Prognostic index Correlation Breast Cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Breast cancer is one of the most common malignancies of solid organs among females next to cervical cancer globally( 1 ). It affects women in both high-income and low-income countries or settings ( 2 ). In sub-Saharan Africa, it accounts for 19.5% of all cancer types( 2 – 4 ). In addition to the diagnostic and prognosis determination tool available; there are serum biomarkers that have evolved in predicting breast cancer prognosis in terms of response to treatment, disease-free survival and overall survival. Amongst them; serum levels of LDH and GGT have been tested and found to be useful in prognostic stratification. LDH is almost available in all body cells but predominantly found in the liver, kidney, breast, brain, cardiac muscles, skeletal muscles and red blood cells, particularly in the mitochondria with a normal value in circulation of about 125-220U/L. Its main function is to reversibly catalyse the conversion of lactate to pyruvate in anaerobic respiration due to high metabolic demand. Medical conditions such as breast malignancy, bacterial meningitis, liver disease, acute kidney injury, acute myocardial infarction, bone fracture, and skeletal muscle trauma can lead to an increase of LDH in circulation. In malignant conditions; even in the presence of oxygen, cancerous cells release larger quantities of LDH to facilitate glycolysis for the production of energy, and lactate to meet the metabolic requirement in highly proliferating cells. This phenomenon is referred to as the Warburg effect( 6 ). LDH makes malignant cells resist the body immune response by inhibiting CD8 + T cells and activation of Natural killer(NK) cells by increasing the expression of vascular endothelial growth factor. Furthermore, LDH promotes angiogenesis within the tumour hence facilitating migration and metastasis of tumour cells( 4 ). The pre-treatment LDH level of about 244U/L and above has been noted to indicate poor prognosis among breast cancer patients( 7 ). GGT is a membrane-bound enzyme that catalyzes the catabolism of reduced glutathione to cysteine and glycine in Meister’s -glutamyl cycle. As a result, it delivers cysteine for intracellular synthesis of glutathione, which is one of the powerful antioxidants and plays a role in protecting cells from injury resulting from oxygen free radicals. The level of GGT in circulation marks the extent of oxidative stress/imbalance in the body. Its normal level in circulation is about 0-29U/L. It is potentially found in the liver, pancreas, heart and kidney. Conditions that have been documented to cause elevation of GGT include Cholestasis, malignancy, alcoholic liver disease, pancreatitis, heart failure and diabetes mellitus( 8 ). The amount of circulating GGT is found to be directly proportional to the turnover rate of malignant cells. Thus GGT gives a clue on the apoptotic balance and extent of detoxification in breast cancer conditions hence we can predict disease progression rate, response to chemotherapy and disease overall survival( 4 , 9 ).In breast cancer patients, those with pre-treatment serum levels of GGT ≥ 29U/L have been noted to have unfavourable outcomes( 10 ). Amongst models that have been validated; the Nottinghman Prognostic Index (NPI) which was devised by Galea in 1982 still holds the ability to predict the outcome in independent populations by comparing the predicted and observed outcomes ( 13 , 20 ).In this study, it is going to be used as a gold standard in categorizing patients for assessing the accuracy of serum markers in predicting the prognosis of non-metastatic BC disease( 13 ). Meanwhile, in our setting, we do not have any serological or serum markers that are done on a routine basis during breast cancer surveillance and prognosis stratification. TNM staging has been used for ages in decision-making and even determining the prognosis of patients before intervention however breast cancer patients with similar TNM stages may have different prognosis. There is a need to augment the available with serum markers particularly LDH and GGT that have been found to correlate with pathological parameters like tumour grade, number of positive nodes for the tumour, molecular subtypes and response to either adjuvant or palliative chemotherapy( 41 ). Tests for LDH and GGT are widely available, easy to perform and relatively cheap. Their correlation with pathological parameters that are we only obtain after surgery would augment clinical TNM staging and immunohistochemical markers in the prognostic stratification of patients before initiating any treatment.Post-operative levels at an interval can be used to predict the disease recurrence. Thus higher levels in these patients may be an alarming/ warning sign of local recurrence or distant metastasis. Materials and Research Methods A prospective cross-sectional study was conducted at Muhimbili National Hospital in the Department of Surgery. We aimed to determine the correlation of LDH/GGT with NPI among women with Breast Cancer. We also aimed to determine the predictive accuracy of LDH and GGT on prognosis based on NPI and Clinical TNM stage in patients who underwent modified radical mastectomy for breast cancer. Study Area Muhimbili National Hospital is a National Referral Hospital and a University teaching hospital and Research Center located in Dar es Salaam, Tanzania. It has about 1,500-bed capacity attending 2,200 outpatients a day and 1,200–1500 inpatients per week. The Department of Surgery has 20 qualified Surgeons who perform mastectomies regularly and there are 5 attending Oncologists from the Department of Clinical Oncology. Patients with stage I to III breast cancer are attended as outpatients and admitted a day or two before surgery for preparations. On average 49 women with breast neoplastic conditions at various clinical stages are admitted monthly and among them, 16 (32.2%) undergo a mastectomy on an elective basis. Sample Estimation and Selection The sample size was estimated by the Openepi formula as described in Kelsey( 42 ). A total sample of 104 patients was selected and their results were used to determine the correlation of the markers and NPI at a power of 80%, margin of error of less than 5% and confidence interval of 95%. From April 2023 to March 2024, a non-probability convenient sampling technique was applied. During the study,104 women with histological diagnoses of breast cancer in the surgical ward scheduled for modified radical mastectomy were studied after consenting. The patients with Non-epithelial breast tumours, clinical stage IV disease, features of the hepatobiliary disease(jaundice, dilated biliary duct or identified focal biliary pathology from radiological metastatic workups, particularly Thoraco-abdominal CT scan were excluded. Patients with co-existing lymphoma, musculoskeletal trauma within 7 days, Heart failure, Myocardial Infarction, Renal failure and Diabetes mellitus identified during routine pre-operative physician review were excluded as well. Data Collection Methods And Measurement Social-demographic data including Age, Menopausal status, age at diagnosis, and history of Neoadjuvant therapy was obtained by interviewing study participants. The clinical TNM stage as per The American Joint Committee on Cancer(AJCC),8th edition was established from a physical examination. A thorough assessment of the affected breast was done on all participants. Features of skin involvement such as ulceration, peaud’orange, satellite nodules and inflammation were noted during the inspection. For participants who had undergone breast imaging; the size of the tumour was recorded from a mammogram or breast ultrasound report. For those with palpable lump(s) and had not undergone any imaging; the size was estimated on palpating the affected breast. For all cases; the greatest dimension was considered, and the largest lump was considered in the case of multiple lumps. Through palpation and radio imaging reports if any; the status of regional lymph nodes was determined and recorded. Since the study involved patients undergoing modified radical mastectomy, patients with distant metastasis were excluded right away based on clinical features and metastatic radio imaging particularly thoracoabdominal CT scan which is currently recommended( 43 , 44 ). However, a chest X-ray and abdominal ultrasound were used for patients in the early stages of the disease. Blood Sample Collection Aseptic precautions were observed, and approximately 3 ml of blood was collected from the antecubital vein of each subject into a red top bottle. To avoid hemolysis that may result in raised LDH; the specimen was sent to the lab immediately after collection for analysis. After allowing the blood sample to clot, the serum was extracted by centrifuging it at 3000 rpm for roughly 10 minutes. Before analysis, serum samples were kept at -20°C. By catalyzing the reaction between pyruvate and NADH to form NAD and lactate and measuring absorbance at 340 nm, the amount of LDH in serum was measured by employing a Stat Fax 3300 semi-automated analyzer (USA). Similarly, the amount of 5-amino-2-nitrobenzonate generated during the catalytic reaction of L-Glutamyl-3carboxy-4-nitroanilide with glycylglycine at 405 nm was used for evaluating the level of GGT with the semi-automated analyzer (Stat Fax 3300, USA) and analytical reagent kits from Analyticon Biotechnologies AG Muhlenberg, Germany. The results were saved in U/L and uploaded to the hospital's electronic database (Jeeva). The cutoff values were 280 U/L for LDH and 30 U/L for GGT ( 7 , 10 ). Histopathology data Histological type, tumour grade and Immunohistochemical results(PR, ER, HER2, Ki-67) were obtained either pre-operatively or post-operatively. The status of the margins, pathological tumour size and the number of positive regional lymph nodes were obtained postoperatively on the entire tissue submitted. All these data were found on the histopathology report by searching in the hospital database(Jeeva) using the patient’s file number. A senior Mmed Pathology Resident was contacted to review and report on the missing parameters from the histopathology report for a few patients and the NPI was calculated from the formula; NPI=(0.2 x S) + N + G whereby S-Pathological size of the tumour in cm N-Positive lymph node for the tumor whereby 0 nodes = 1, 1–3 nodes = 2, > 3 nodes = 3 G-Tumor grade where Grade I = 1, Grade II = 2, Grade III = 3. Data Handling and Analysis The data for each participant was correctly filled in an adapted European Society of Medical Oncology(ESMO) checklist before analysis. Data were analysed using version 27 of the International Bussiness Machine Statistical Product and Service Solution (IBM SPSS. The correlation was assessed using a simple linear regression by determining the Spearman correlation coefficient. The ability of LDH and GGT in discriminating breast cancer women into favourable and unfavourable prognoses based on NPI and clinical TNM stage was assessed using a binary Logistic Regression model, the accuracy was determined on the Area under Curve (AUC) on a Receiver Operating Characteristic Curve (ROC). Results Socio-demographic characteristics The mean age of the patients selected to participate in this research was 48.51 (± 12.80). Of them, 60 (57.7%) belonged to the age range between 41–60 years, and 21 (20.2%) fell into the age category of 21–40 years. Amongst them, a total of 53 individuals (51%) had gone through formal education whereas 10 individuals (9.5%) had not obtained any formal education. 54(51.9%) had public patient bills. The remaining patients were either insured or private at the same time. Out of the total participants, 77 (74%) had a breast cancer diagnosis at the age of 40 or older. Table 1 . Table 1 Socio-demographic characteristics of women with breast cancer at MNH, N = 104 Variable Mean \(\pm\) SD Frequency (%) Age (years) 48.51 ( \(\pm\) 12.80) Age at diagnosis < 40 years 27 (26.00 ) ≥ 40 years 77 (74.0 0 ) Level of education None 10 ( 9.60) Primary 53 ( 51.00) Secondary 21 ( 20.20) College 20 (19.20) Billing Category Public 54 (51.90 ) Private/ Insured 50 (48.10 ) Pre-operative clinical characteristics 53 patients, or 51% of the total, were postmenopausal. Regarding the clinical stage, 57 individuals (54.80%) were in stage III, 46 individuals (44.20%) were in stage II, and 1 individual (1.0%) was in stage I of the disease. 44 patients (42.30%) had high LDH (> 280 U/L), with a median of 240.5 (195.50, 357.75). With a median score of 31.99 (24.30,47.57), elevated GGT (> 30U/L) was identified in 62 (59.60%) cases. Post Operative characteristics In regards to histological types, invasive ductal carcinoma accounted for 89 (85.60%) of the cases, followed by invasive lobular carcinoma 12 (11.50%)), and indeterminate 3 (2.90%). Luminal A made up 32 (30.80%) of the patients, followed by triple negative 11 (10.60%), Luminal B 8 (7.70%), and Her 2 Enriched 3 (2.90%). NPI of women with breast cancer Following histopathological evaluation, the data (pathological tumour size, histological grade, and the number of lymph nodes containing a tumour) were used to calculate the NPI. The mean NPI was 4.89 ± 2.11. Of those evaluated, 62 (59.62%) had a favourable prognosis and 42 (40.38%) had an unfavourable prognosis Table 2 . Table 2 Clinicopathological characteristics of women with breast cancer at MNH, N = 104 Variable Mean \(\pm\) SD Median (IQR) Frequency (%) Menopausal status Premenopausal 51 ( 49.00 ) Post-menopausal 53 ( 51.00 ) Clinical TNM stage Early stage ( stage 1 & 2) 47 ( 45.20) Advanced stage ( stage 3) 57 ( 54.80) NAT Yes 36 (34.6) No 68 (65.4) LDH 240.5 (195.50, 357.75) LDH Level Normal or Low (≤ 280 U/L) 60 ( 57.70) High (> 280 U/L) 44 (42.30) GGT 31.99 (24.30,47.57) GGT Level Normal or Low (≤ 30U/L) 42 (40.40) High (> 30 U/L ) 62 (59.60) Histological type Ductal 89 (85.60) Lobular 12 (11.50) Other 3 (2.90) NPI 4.89 ± 2.11 NPI Category Favourable (≤ 5.4) 62 ( 59.60) Unfavorable (> 5.4) 42 (40.40) Correlation of LDH and NPI To determine whether there is a relationship between the LDH level and the NPI among breast cancer patients, a correlation test was undertaken. There is a significant positive correlation, as demonstrated by the Spearman correlation coefficient, r = 0.660, p < 0.01 To assess whether there is any degree of dependence for the NPI on changes in LDH value, a linear regression analysis was also carried out. After deriving the statistical model y = 3.49 + 4.21 * x, it was determined that the two variables had a positive relationship with a regression coefficient of b = 4.21, p < 0.001. This model shows that the change in NPI is increased by 4.21 for each increment of one value of LDH Fig. 1 . The coefficient of determination, R 2 was found to be 0.283, p < 0.001. This implies that LDH alone could predict the category of NPI by 28.3%. In addition to that, ANOVA was executed and a significant difference in variance for LDH levels was observed between patients who had favourable NPI (variance = 913.5) compared to unfavourable NPI group(variance = 1348.6). The mean LDH varied significantly between favourable and unfavourable NPI groups. The mean LDH of patients with NPI > 5.4 was 491.5 (95% CI: 419.4–563.6), while the mean LDH of patients with NPI ≤ 5.4 was 229.4 (95% CI: 170.0 − 288.7, p < 0,001 and F = 31). Figure 2 . Correlation of GGT and NPI The Spearman correlation coefficient was computed to see whether there was a correlation at all between GGT and NPI. There is a relatively weak positive correlation,r = 0.455 between the two, despite a substantial association, p < 0.01 . Furthermore, an analysis of variance was conducted, and it was found that there was an insignificant difference in variance for GGT levels between the two groups. The mean GGT did not differ significantly between the favourable and unfavourable NPI groups. Patients with NPI > 5.4 had a mean GGT of 175.72 (95% CI: 53.72–297.67), while those with NPI ≤ 5.4 had an average GGT of 32.87(95% CI: -67.50 ─ 133.25), p = 0.148 and F = 2.17. Figure 3 The accuracy of LDH and GGT in predicting breast cancer prognosis based on NPI. The ability of LDH and GGT to discriminate breast cancer women into favourable and unfavourable prognosis groups based on NPI was determined using the ROC. The Area Under the Curve was 0.845 (0.766–0.923, 95% CI, p < 0.001) and 0.725(0.623–0.827,95% CI, p < 0.001) respectively. Figure 4 The accuracy of LDH and GGT in predicting breast cancer prognosis based on clinical TNM stage. Based on clinical TNM stage LDH and GGT can correctly discriminate breast cancer women into favourable and unfavourable groups. The Area Under the Curve was computed and found to be 0.870 (0.801–0.939, 95% CI, p < 0.001) and 0.759(0.665–0.852,95% CI, p < 0.001) respectively. Figure 5 Using the Youden index, the optimal effectiveness of the markers in patient classification was also assessed. Only LDH showed statistical significance, with an index of 58% for NPI and 65% for the clinical TNM stage. For GGT, where an index of 44% for NPI and 45% for TNM was noted, it was comparatively insignificant. Table 3 Table 3 Prognostic accuracy and precision values for LDH and GGT Biomarker Cutpoint Sensitivity (%) Specificity (%) PPV (%) NPV (%) Youden’s Index AUC 95%CI Metric score P-value NPI status LDH 270.00 80.95 77.42 70.83 85.71 0.58 0.845 0.766 -0.923 1.58 0.000 GGT 39.00 61.90 82.26 70.27 76.12 0.44 0.725 0.623–0.827 1.44 0.000 Clinical stage status LDH 231.00 82.46 82.98 85.45 79.59 0.65 0.870 0.801–0.939 1.65 0.000 GGT 38.60 57.89 87.23 84.62 63.08 0.451 0.759 0.665–0.852 1.45 0.000 Discussion The purpose of this study was to ascertain whether there was a correlation between LDH/GGT and the Nottingham Prognostic Index concerning breast cancer patients. The correlation found between LDH and NPI suggests that high levels of lactate release in highly malignant breast cancer cells are indicative of a predictive value for lactate concentration in breast cancer. While lactate serves as an energy source for advancing epithelial cancer cells, excessive lactate concentrations seriously impair macrophage function by reducing the generation of natural killer cells and causing T cells to undergo apoptosis. The study found a statistically significant difference (p < 0.001) in the means of LDH between individuals with a favourable NPI and those with an unfavourable NPI. This observation aligns with the physiological reaction of lactate to malignant neoplasms as described earlier. A high positive correlation was observed between LDH and NPI, with a Spearman correlation value of r = 0.66 and p = 0.01; the mean NPI was 4.89 ± 2.11. According to the regression coefficient of (b = 4.21.5), there is a one-score increase in the probability of an unfavourable prognosis for every four-fold rise in LDH. Individuals with LDH levels ≥ 280 U/L are more likely to have greater NPI levels, which is associated with a worse prognosis. According to the linear regression model, the coefficient of determination was 0.283, which indicates that LDH alone can distinguish between breast cancer patients with favourable and unfavourable prognosis groups with an accuracy of roughly 28.3%. Increased lactate concentration was correlated with poorer NPI but is also a marker sensitive to tumour grades, according to a study by Cheung SM et al. The two showed a significant correlation with p = 0.0495( 45 – 47 ). Additionally, it was investigated if lactate could accurately classify breast cancer patients with favourable and unfavourable prognoses based on NPI. With a sensitivity of 80.95% and specificity of 77.42%.Its discriminatory capacity is useful with the AUC found to be 0.85. According to our research, women with breast cancer who have a lactate level of more than 270 U/L are at likelihood for an unfavourable prognosis. Our results are in line with those of a different study by He J et al that evaluated the predictive power of the lactate-lactate-to-albumin ratio for non-metastatic breast cancer patients' disease-free survival. It was discovered that the lactate-to-albumin ratio, with an area under the curve (AUC) of 0.709, a sensitivity of 94.1%, and a specificity of 38.5%, is an independent predictor of prognosis in patients with breast cancer. The fact that their study only included Caucasian women and included the patients' patterns of dietary status might account for the discrepancy in specificity. ( 48 ). Furthermore, it has been observed that lactate can distinguish between locally advanced illness and early (stages I–II) disease. 58% of the individuals in our research had a locally advanced disease. This might be explained by the low level of education, ignorance, and the state of the economy in our area. Even though this study only included one patient with stage I disease, the accuracy of LDH in properly classifying breast cancer patients with favourable and unfavourable prognoses based on clinical stage was also assessed and noted to be significantly helpful, the AUC = 0.870. Serum lactate levels were compared in the previous research at different stages of the disease. The results of the interstage comparison showed that there was a significant increase between stages II, III and IV, but not between stages I and II( 4 , 19 , 41 ). On the other hand, a high/rapid turnover of malignant cells releases the enzymes into the bloodstream, which in turn reflects on the tumour burden, leading to an elevated serum GGT. The measured serum GGT and NPI in the current investigation showed an unremarkable correlation with a Spearman correlation coefficient, of r = 0.455. This is in contrast to findings from earlier research by Mohammed Saheb SK and Abdalla M Jarari et al., where the GGT levels in the blood were considerably greater in breast cancer patients than in the controls Along the progressing stage, particularly across stages III and IV, the amount of this specific enzyme was noticeably greater. Since our study only included patients in the non-metastasizing group and other studies included patients in the stage IV group, where the correlation is highly significant, it is possible that this accounts for the discrepancy between the findings of the two studies( 17 , 29 ). Based on the clinical TNM stage, the efficacy of GGT in classifying patients with breast cancer into early and locally progressed diseases was also evaluated. With a 57.9% sensitivity and an 87.2% specificity, the AUC was 0.759. These results are in line with those of another study conducted by Mohammed SSK et al. in which the activity of serum GGT was found to significantly increase between stage II to stage IV patients but an interstage comparison revealed a non-significant increase between stages I and II( 4 ). Based on NPI and cTNM, we evaluated the accuracy of GGT in classifying breast cancer women into favourable and unfavourable groups in our study based on NPI and clinical TNM stage. Its specificity and sensitivity were determined to be (62%, 82%), and (58%, 87%), respectively. Its discriminatory capacity/utility was somewhat limited with an AUC of 0.73 and 0.76. These results imply that the enzyme may be used but with limitations as a discriminant marker in breast cancer patients concerning their tumour load, notwithstanding the weak correlation that currently exists. The distinction between our study's cutoff values and those of the other research it cited was also clearly visible. The values for LDH were found to be 270 U/L and 231 U/L for the NPI and clinical TNM stage respectively, whereas the value for GGT was found to be 39 U/L for both. Strengths and Limitations The testing of easy, affordable, and accessible markers (enzymes) in breast cancer, which is practically a public health challenge, is one of this study's many remarkable strengths. Nevertheless, it has its drawbacks as well. The fact that our study is a single site, has a limited sample size, and was conveniently sampled all contribute to selection bias, which limits the study's capacity to be broadly applied. Furthermore, Immunohistochemical (IHC) markers as one of the prognostic parameters took longer to produce results because some patients did not have them checked out before surgery and were thus ordered following a histological evaluation of the entire breast tissue. Since IHC markers are not covered by National Health Insurance, most of the participants did not pay for them timely hence missing their results at the time of analysis. Conclusion Even though these enzyme markers only have unspecific diagnostic magnitude, the measurement of their levels can be carried out before treatment and as part of a follow-up plan. The current study concludes that blood levels of glutamyltranspeptidase and lactate dehydrogenase may serve as indicators for breast cancer treatment modalities and disease prognostication in conjunction with other potential factors. To validate this research's findings, nonetheless, further multicentric and bigger sample size studies must be undertaken. The markers can also be used during follow-up to assess response to treatment, and disease recurrence/disease progression. Declarations Acknowledgement I extend my profound appreciation to the patients who enthusiastically became part of the research we conducted. Authors’ Contributions The primary researcher, Sylvery Mwesige, wrote the full-text paper and produced the manuscript. He has access to all of the study's data. Victor Meza Kyaruzi contributed to the study's conception and design as well as its statistical analysis and interpretation. Mabula D.P.M Mchembe and Mungeni Misidai played administrative roles and offered material and technical support. Funding No funds were received for this paper. Availability of data and materials All research materials pertinent to this study are accessible and will be handed down by the relevant author upon justifiable request Ethical Approval The Muhimbili University of Health and Allied Sciences Institutional Review Board approved the study protocol(IRB-MUHAS REF.No.DA.282/298/01.C/1845. The Muhimbili National Hospital Research and Consultancy Department was approached for permission before any data was captured(REF.No.MNH/CRTCU/Perm/2023/488). Before taking part, each participant in this academic study signed a written consent. All ethical principles were observed throughout the investigation. The research was completed while upholding the confidentiality of participants' identities and other pertinent personal information Consent for publication Not applicable Competing Interests The author declares neither financial nor non-financial interests that may be relevant to this study. References Rajeswari G, Srinivas PS, Rama KS, Sai K, Suresh E. Study of serum LDH and GGT levels in carcinoma breast. 2016;7(1):31–4. Bray F, Mccarron P, Parkin DM. The changing global patterns of female breast cancer incidence and mortality. 2004;229–39. Chao CA, Huang L, Visvanathan K, Mwakatobe K, Masalu N, Rositch AF. Understanding women ’ 's perspectives on breast cancer is essential for cancer control : knowledge , risk awareness , and care- seeking in Mwanza , Tanzania. 2020;1–11. Sk MS, Kasibabu A. 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Br J Cancer [Internet]. 2014;110(7):1688–97. Available from: http://dx.doi.org/10.1038/bjc.2014.120 Al O, Zaimi A, Brahmi SA, Afqir S. Nottingham Prognostic Index is an Applicable Prognostic Tool in Non-Metastatic Triple-Negative Breast Cancer. 2019;20:59–63. Hillyar C, Rizki H, Abbassi O, Miles-Dua S, Clayton G, Gandamihardja T, et al. Correlation between Oncotype DX, PREDICT and the Nottingham Prognostic Index: Implications for the management of early breast cancer. Cureus. 2020;12(4). Hearne BJ, Teare MD, Butt M, Donaldson L. Comparison of Nottingham Prognostic Index and Adjuvant Online prognostic tools in young women with breast cancer : review of a single-institution experience. 2015;1–7. Albergaria A, Ricardo S, Milanezi F, Carneiro V, Amendoeira I, Vieira D, et al. Nottingham Prognostic Index in Triple-Negative Breast Cancer : a reliable prognostic tool ? 2011; Green AR, Soria D, Stephen J, Powe DG, Nolan CC, Kunkler I, et al. Nottingham Prognostic Index Plus : Validation of a clinical decision making tool in breast cancer in an independent series. 2016;(January):32–40. Rejali M, Tazhibi M, Mokarian F, Gharanjik N, Mokarian R. The Performance of the Nottingham Prognosis Index and the Adjuvant Online Decision Making Tool for Prognosis in Early ‑ stage Breast Cancer Patients. 2015; Cheung SM, Husain E, Masannat Y, Miller ID, Wahle K, Heys SD, et al. Lactate concentration in breast cancer using advanced magnetic resonance spectroscopy. Br J Cancer [Internet]. 2020;(April). Available from: http://dx.doi.org/10.1038/s41416-020-0886-7 Teichgraeber DC, Guirguis MS, Whitman GJ. Breast Cancer Staging : Updates in the AJCC Cancer Staging Manual , 8th Edition , and Current Challenges for Radiologists , From the AJR Special Series on Cancer Staging. 2021;(August):278–90. Liu D, Zhang L. Prognostic signi fi cance of serum lactate dehydrogenase in patients with breast cancer : a meta-analysis. 2019;3611–9. Chen HL, Zhou MQ, Tian W, Meng KX, He HF. Effect of age on breast cancer patient prognoses: A population-based study using the SEER 18 database. PLoS One. 2016;11(10):1–11. Brandt J, Garne PP, Tengrup I, Manjer J. Age at diagnosis in relation to survival following breast cancer: A cohort study. World J Surg Oncol. 2015;13(1):1–11. Bell RJ. Menopausal status at diagnosis of breast cancer and risk of metastatic recurrence. Vol. 28, Menopause (New York, N.Y.). United States; 2021. p. 1079–80. Lao C, Elwood M, Kuper-Hommel M, Campbell I, Lawrenson R. Impact of menopausal status on risk of metastatic recurrence of breast cancer. Menopause. 2021 Jul;28(10):1085–92. Oncology B, Pathology H. Breast Cancer in Lean Postmenopausal Women. 2019;487:483–7. Bundred J, Michael S, Bowers S, Barnes N, Jauhari Y, Plant D, et al. Do surgical margins matter after mastectomy? A systematic review. Eur J Surg Oncol [Internet]. 2020;46(12):2185–94. Available from: https://doi.org/10.1016/j.ejso.2020.08.015 Wang H, Mao X. Evaluation of the efficacy of neoadjuvant chemotherapy for breast cancer. Drug Des Devel Ther. 2020;2423–33. Kumar S, Badhe BA, Krishnan KM, Sagili H. Study of tumour cellularity in locally advanced breast carcinoma on neo-adjuvant chemotherapy. J Clin diagnostic Res JCDR. 2014;8(4):FC09. Basnyat A, Jha A, Pathak R, Shrestha B. Study of Serum Lactate Dehydrogenase and Gamma-Glutamyl Transpeptidase in Breast Cancer Patients receiving Chemotherapy. J Trop Life Sci. 2017;7(2):128–32. Kelsey JL, Whittemore AS, Evans AS, Thompson WD. Methods in observational epidemiology. Vol. 10. Monographs in Epidemiology and; 1996. James J, Teo M, Ramachandran V, Law M, Stoney D, Cheng M. Performance of CT scan of abdomen and pelvis in detecting asymptomatic synchronous metastasis in breast cancer. Int J Surg. 2017;46:164–9. James J, Teo M, Ramachandran V, Law M, Stoney D, Cheng M. A critical review of the chest CT scans performed to detect asymptomatic synchronous metastasis in new and recurrent breast cancers. World J Surg Oncol. 2019;17:1–7. Cheung SM, Husain E, Masannat Y, Miller ID, Wahle K, Heys SD, et al. Lactate concentration in breast cancer using advanced magnetic resonance spectroscopy. Br J Cancer. 2020;123(2):261–7. Rizwan A, Serganova I, Khanin R, Karabeber H, Ni X, Thakur S, et al. Relationships between LDH-A, lactate, and metastases in 4T1 breast tumors. Clin Cancer Res. 2013;19(18):5158–69. Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science (80- ). 2009;324(5930):1029–33. He J, Tong L, Wu P, Wu Y, Shi W, Chen L. Prognostic Significance of Preoperative Lactate Dehydrogenase to Albumin Ratio in Breast Cancer : A Retrospective Study. 2023;(February):507–14. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4409898","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":304313181,"identity":"49175a42-238b-47f4-9f9e-d0670c7f6c40","order_by":0,"name":"Sylvery Mwesige","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACZgY2BgYDCyCL/+MDIMnDR1gLM0iLBIhpbADSwkaENSA1YC1mYJKgFoPj/Mce/iiQSOyXPpBW+TXHToaNgfnhoxv4tBxmZjfmMZBInNmXcOy27LZkoMPYjI1z8GgxO8zMJg30i7HBGca225LbmIFaeNikCWmR/AHUYn+Gma1Ycls9cVokgA6TM+BhY2P8uO0wYS32h5nNpEFaJM7wMEszbjvOw8ZMwC+S/QefSf74Y8PD38PD+PHntmp7fvbmh4/xaUEBzDxgkljlIMD4gxTVo2AUjIJRMGIAAARqODNuN3mkAAAAAElFTkSuQmCC","orcid":"","institution":"Muhimbili University of Health and Allied Sciences","correspondingAuthor":true,"prefix":"","firstName":"Sylvery","middleName":"","lastName":"Mwesige","suffix":""},{"id":304313182,"identity":"a3aa03ec-2c85-4d4b-bcb8-c6d18212dbd0","order_by":1,"name":"Victor Meza Kyaruzi","email":"","orcid":"","institution":"Evidence-Based Scientific Consortium (EBASC)","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"Meza","lastName":"Kyaruzi","suffix":""},{"id":304313183,"identity":"3527a563-f676-4e40-910e-ef9533056855","order_by":2,"name":"Mungeni Misidai","email":"","orcid":"","institution":"Muhimbili University of Health and Allied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mungeni","middleName":"","lastName":"Misidai","suffix":""},{"id":304313184,"identity":"a92c4677-2031-4a7a-9f3f-dd74f8c1e4cc","order_by":3,"name":"Mabula Mchembe","email":"","orcid":"","institution":"Muhimbili University of Health and Allied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mabula","middleName":"","lastName":"Mchembe","suffix":""},{"id":304313187,"identity":"b4d0a9b1-21fb-41b2-b7fa-e25ccf3819c7","order_by":4,"name":"Ally Mwanga","email":"","orcid":"","institution":"Muhimbili University of Health and Allied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ally","middleName":"","lastName":"Mwanga","suffix":""}],"badges":[],"createdAt":"2024-05-12 23:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4409898/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4409898/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57452011,"identity":"2f884989-9729-4a6d-8bc8-25a43fdb5851","added_by":"auto","created_at":"2024-05-30 20:56:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":204972,"visible":true,"origin":"","legend":"\u003cp\u003eRegression scatter plot for LDH and NPI\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4409898/v1/d66e7c4db52a62f4018c8b82.png"},{"id":57452009,"identity":"3e190664-ac78-4e12-9344-153e05ad7a64","added_by":"auto","created_at":"2024-05-30 20:56:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17921,"visible":true,"origin":"","legend":"\u003cp\u003eThe LDH mean difference between the NPI Categories\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4409898/v1/2bd9314f813660d48c7e3f71.png"},{"id":57452010,"identity":"a64f035c-2853-42b0-886b-7ddf66ec1543","added_by":"auto","created_at":"2024-05-30 20:56:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17810,"visible":true,"origin":"","legend":"\u003cp\u003eThe GGT mean difference among the NPI Categories\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4409898/v1/db50153a68d07910283c0508.png"},{"id":57452504,"identity":"0b7c4404-db1f-434c-8fd0-03dd2f7352af","added_by":"auto","created_at":"2024-05-30 21:04:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":179360,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Receiver Operating Curve displaying the Area Under Curve for NPI status discrimination by LDH and GGT\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4409898/v1/268e00a5f49b0bbc0832bdb3.png"},{"id":57452012,"identity":"6277052d-8082-4176-bdc0-0e6fc9875c6a","added_by":"auto","created_at":"2024-05-30 20:56:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":146916,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Receiver Operating Curve displaying the Area Under Curve for breast cancer prognostic stage discrimination by LDH and GGT.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4409898/v1/2265df31c4e3f7245378b086.png"},{"id":60475599,"identity":"ce065e09-d64b-4431-99f8-a868e9252c02","added_by":"auto","created_at":"2024-07-17 07:39:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1372985,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4409898/v1/fb1f1b79-5e98-4444-83b1-007c432cf1bf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Serum Lactate Dehydrogenase(LDH) and Gamma-Glutamyl Transferase (GGT) Correlate Optimally with Nottingham Prognostic Index for Breast Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is one of the most common malignancies of solid organs among females next to cervical cancer globally(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It affects women in both high-income and low-income countries or settings (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In sub-Saharan Africa, it accounts for 19.5% of all cancer types(\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to the diagnostic and prognosis determination tool available; there are serum biomarkers that have evolved in predicting breast cancer prognosis in terms of response to treatment, disease-free survival and overall survival. Amongst them; serum levels of LDH and GGT have been tested and found to be useful in prognostic stratification.\u003c/p\u003e \u003cp\u003eLDH is almost available in all body cells but predominantly found in the liver, kidney, breast, brain, cardiac muscles, skeletal muscles and red blood cells, particularly in the mitochondria with a normal value in circulation of about 125-220U/L. Its main function is to reversibly catalyse the conversion of lactate to pyruvate in anaerobic respiration due to high metabolic demand. Medical conditions such as breast malignancy, bacterial meningitis, liver disease, acute kidney injury, acute myocardial infarction, bone fracture, and skeletal muscle trauma can lead to an increase of LDH in circulation.\u003c/p\u003e \u003cp\u003eIn malignant conditions; even in the presence of oxygen, cancerous cells release larger quantities of LDH to facilitate glycolysis for the production of energy, and lactate to meet the metabolic requirement in highly proliferating cells. This phenomenon is referred to as the Warburg effect(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLDH makes malignant cells resist the body immune response by inhibiting CD8\u0026thinsp;+\u0026thinsp;T cells and activation of Natural killer(NK) cells by increasing the expression of vascular endothelial growth factor. Furthermore, LDH promotes angiogenesis within the tumour hence facilitating migration and metastasis of tumour cells(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The pre-treatment LDH level of about 244U/L and above has been noted to indicate poor prognosis among breast cancer patients(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGGT is a membrane-bound enzyme that catalyzes the catabolism of reduced glutathione to cysteine and glycine in Meister\u0026rsquo;s -glutamyl cycle. As a result, it delivers cysteine for intracellular synthesis of glutathione, which is one of the powerful antioxidants and plays a role in protecting cells from injury resulting from oxygen free radicals. The level of GGT in circulation marks the extent of oxidative stress/imbalance in the body. Its normal level in circulation is about 0-29U/L. It is potentially found in the liver, pancreas, heart and kidney. Conditions that have been documented to cause elevation of GGT include Cholestasis, malignancy, alcoholic liver disease, pancreatitis, heart failure and diabetes mellitus(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe amount of circulating GGT is found to be directly proportional to the turnover rate of malignant cells. Thus GGT gives a clue on the apoptotic balance and extent of detoxification in breast cancer conditions hence we can predict disease progression rate, response to chemotherapy and disease overall survival(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).In breast cancer patients, those with pre-treatment serum levels of GGT\u0026thinsp;\u0026ge;\u0026thinsp;29U/L have been noted to have unfavourable outcomes(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmongst models that have been validated; the Nottinghman Prognostic Index (NPI) which was devised by Galea in 1982 still holds the ability to predict the outcome in independent populations by comparing the predicted and observed outcomes (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).In this study, it is going to be used as a gold standard in categorizing patients for assessing the accuracy of serum markers in predicting the prognosis of non-metastatic BC disease(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMeanwhile, in our setting, we do not have any serological or serum markers that are done on a routine basis during breast cancer surveillance and prognosis stratification.\u003c/p\u003e \u003cp\u003eTNM staging has been used for ages in decision-making and even determining the prognosis of patients before intervention however breast cancer patients with similar TNM stages may have different prognosis.\u003c/p\u003e \u003cp\u003eThere is a need to augment the available with serum markers particularly LDH and GGT that have been found to correlate with pathological parameters like tumour grade, number of positive nodes for the tumour, molecular subtypes and response to either adjuvant or palliative chemotherapy(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTests for LDH and GGT are widely available, easy to perform and relatively cheap.\u003c/p\u003e \u003cp\u003eTheir correlation with pathological parameters that are we only obtain after surgery would augment clinical TNM staging and immunohistochemical markers in the prognostic stratification of patients before initiating any treatment.Post-operative levels at an interval can be used to predict the disease recurrence. Thus higher levels in these patients may be an alarming/ warning sign of local recurrence or distant metastasis.\u003c/p\u003e"},{"header":"Materials and Research Methods","content":"\u003cp\u003eA prospective cross-sectional study was conducted at Muhimbili National Hospital in the Department of Surgery.\u003c/p\u003e \u003cp\u003eWe aimed to determine the correlation of LDH/GGT with NPI among women with Breast Cancer. We also aimed to determine the predictive accuracy of LDH and GGT on prognosis based on NPI and Clinical TNM stage in patients who underwent modified radical mastectomy for breast cancer.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eMuhimbili National Hospital is a National Referral Hospital and a University teaching hospital and Research Center located in Dar es Salaam, Tanzania.\u003c/p\u003e \u003cp\u003eIt has about 1,500-bed capacity attending 2,200 outpatients a day and 1,200\u0026ndash;1500 inpatients per week.\u003c/p\u003e \u003cp\u003eThe Department of Surgery has 20 qualified Surgeons who perform mastectomies regularly and there are 5 attending Oncologists from the Department of Clinical Oncology. Patients with stage I to III breast cancer are attended as outpatients and admitted a day or two before surgery for preparations. On average 49 women with breast neoplastic conditions at various clinical stages are admitted monthly and among them, 16 (32.2%) undergo a mastectomy on an elective basis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSample Estimation and Selection\u003c/h2\u003e \u003cp\u003eThe sample size was estimated by the Openepi formula as described in Kelsey(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). A total sample of 104 patients was selected and their results were used to determine the correlation of the markers and NPI at a power of 80%, margin of error of less than 5% and confidence interval of 95%.\u003c/p\u003e \u003cp\u003eFrom April 2023 to March 2024, a non-probability convenient sampling technique was applied. During the study,104 women with histological diagnoses of breast cancer in the surgical ward scheduled for modified radical mastectomy were studied after consenting.\u003c/p\u003e \u003cp\u003eThe patients with Non-epithelial breast tumours, clinical stage IV disease, features of the hepatobiliary disease(jaundice, dilated biliary duct or identified focal biliary pathology from radiological metastatic workups, particularly Thoraco-abdominal CT scan were excluded. Patients with co-existing lymphoma, musculoskeletal trauma within 7 days, Heart failure, Myocardial Infarction, Renal failure and Diabetes mellitus identified during routine pre-operative physician review were excluded as well.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Collection Methods And Measurement\u003c/h2\u003e \u003cp\u003eSocial-demographic data including Age, Menopausal status, age at diagnosis, and history of Neoadjuvant therapy was obtained by interviewing study participants.\u003c/p\u003e \u003cp\u003eThe clinical TNM stage as per The American Joint Committee on Cancer(AJCC),8th edition was established from a physical examination. A thorough assessment of the affected breast was done on all participants. Features of skin involvement such as ulceration, peaud\u0026rsquo;orange, satellite nodules and inflammation were noted during the inspection. For participants who had undergone breast imaging; the size of the tumour was recorded from a mammogram or breast ultrasound report. For those with palpable lump(s) and had not undergone any imaging; the size was estimated on palpating the affected breast. For all cases; the greatest dimension was considered, and the largest lump was considered in the case of multiple lumps. Through palpation and radio imaging reports if any; the status of regional lymph nodes was determined and recorded.\u003c/p\u003e \u003cp\u003eSince the study involved patients undergoing modified radical mastectomy, patients with distant metastasis were excluded right away based on clinical features and metastatic radio imaging particularly thoracoabdominal CT scan which is currently recommended(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). However, a chest X-ray and abdominal ultrasound were used for patients in the early stages of the disease.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eBlood Sample Collection\u003c/h2\u003e \u003cp\u003eAseptic precautions were observed, and approximately 3 ml of blood was collected from the antecubital vein of each subject into a red top bottle. To avoid hemolysis that may result in raised LDH; the specimen was sent to the lab immediately after collection for analysis.\u003c/p\u003e \u003cp\u003eAfter allowing the blood sample to clot, the serum was extracted by centrifuging it at 3000 rpm for roughly 10 minutes. Before analysis, serum samples were kept at -20\u0026deg;C. By catalyzing the reaction between pyruvate and NADH to form NAD and lactate and measuring absorbance at 340 nm, the amount of LDH in serum was measured by employing a Stat Fax 3300 semi-automated analyzer (USA).\u003c/p\u003e \u003cp\u003eSimilarly, the amount of 5-amino-2-nitrobenzonate generated during the catalytic reaction of L-Glutamyl-3carboxy-4-nitroanilide with glycylglycine at 405 nm was used for evaluating the level of GGT with the semi-automated analyzer (Stat Fax 3300, USA) and analytical reagent kits from Analyticon Biotechnologies AG Muhlenberg, Germany. The results were saved in U/L and uploaded to the hospital's electronic database (Jeeva). The cutoff values were 280 U/L for LDH and 30 U/L for GGT (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHistopathology data\u003c/h2\u003e \u003cp\u003eHistological type, tumour grade and Immunohistochemical results(PR, ER, HER2, Ki-67) were obtained either pre-operatively or post-operatively. The status of the margins, pathological tumour size and the number of positive regional lymph nodes were obtained postoperatively on the entire tissue submitted. All these data were found on the histopathology report by searching in the hospital database(Jeeva) using the patient\u0026rsquo;s file number. A senior Mmed Pathology Resident was contacted to review and report on the missing parameters from the histopathology report for a few patients and the NPI was calculated from the formula; NPI=(0.2 x S)\u0026thinsp;+\u0026thinsp;N\u0026thinsp;+\u0026thinsp;G whereby\u003c/p\u003e \u003cp\u003eS-Pathological size of the tumour in cm\u003c/p\u003e \u003cp\u003eN-Positive lymph node for the tumor whereby 0 nodes\u0026thinsp;=\u0026thinsp;1, 1\u0026ndash;3 nodes\u0026thinsp;=\u0026thinsp;2, \u0026gt;\u0026thinsp;3 nodes\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003cp\u003eG-Tumor grade where Grade I\u0026thinsp;=\u0026thinsp;1, Grade II\u0026thinsp;=\u0026thinsp;2, Grade III\u0026thinsp;=\u0026thinsp;3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Handling and Analysis\u003c/h2\u003e \u003cp\u003eThe data for each participant was correctly filled in an adapted European Society of Medical Oncology(ESMO) checklist before analysis. Data were analysed using version 27 of the International Bussiness Machine Statistical Product and Service Solution (IBM SPSS. The correlation was assessed using a simple linear regression by determining the Spearman correlation coefficient. The ability of LDH and GGT in discriminating breast cancer women into favourable and unfavourable prognoses based on NPI and clinical TNM stage was assessed using a binary Logistic Regression model, the accuracy was determined on the Area under Curve (AUC) on a Receiver Operating Characteristic Curve (ROC).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSocio-demographic characteristics\u003c/h2\u003e \u003cp\u003eThe mean age of the patients selected to participate in this research was 48.51 (\u0026plusmn;\u0026thinsp;12.80). Of them, 60 (57.7%) belonged to the age range between 41\u0026ndash;60 years, and 21 (20.2%) fell into the age category of 21\u0026ndash;40 years. Amongst them, a total of 53 individuals (51%) had gone through formal education whereas 10 individuals (9.5%) had not obtained any formal education. 54(51.9%) had public patient bills. The remaining patients were either insured or private at the same time. Out of the total participants, 77 (74%) had a breast cancer diagnosis at the age of 40 or older.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics of women with breast cancer at MNH, N\u0026thinsp;=\u0026thinsp;104\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.51 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e12.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at diagnosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (26.00 )\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; 40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (74.0 0 )\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevel of education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 ( 9.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 ( 51.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 ( 20.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (19.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBilling Category\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (51.90 )\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate/ Insured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (48.10 )\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePre-operative clinical characteristics\u003c/h2\u003e \u003cp\u003e53 patients, or 51% of the total, were postmenopausal. Regarding the clinical stage, 57 individuals (54.80%) were in stage III, 46 individuals (44.20%) were in stage II, and 1 individual (1.0%) was in stage I of the disease. 44 patients (42.30%) had high LDH (\u0026gt;\u0026thinsp;280 U/L), with a median of 240.5 (195.50, 357.75). With a median score of 31.99 (24.30,47.57), elevated GGT (\u0026gt;\u0026thinsp;30U/L) was identified in 62 (59.60%) cases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePost Operative characteristics\u003c/h2\u003e \u003cp\u003eIn regards to histological types, invasive ductal carcinoma accounted for 89 (85.60%) of the cases, followed by invasive lobular carcinoma 12 (11.50%)), and indeterminate 3 (2.90%). Luminal A made up 32 (30.80%) of the patients, followed by triple negative 11 (10.60%), Luminal B 8 (7.70%), and Her 2 Enriched 3 (2.90%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eNPI of women with breast cancer\u003c/h2\u003e \u003cp\u003eFollowing histopathological evaluation, the data (pathological tumour size, histological grade, and the number of lymph nodes containing a tumour) were used to calculate the NPI. The mean NPI was 4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11. Of those evaluated, 62 (59.62%) had a favourable prognosis and 42 (40.38%) had an unfavourable prognosis Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological characteristics of women with breast cancer at MNH, N\u0026thinsp;=\u0026thinsp;104\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMenopausal status\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePremenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 ( 49.00 )\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-menopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 ( 51.00 )\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical TNM stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly stage ( stage 1 \u0026amp; 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 ( 45.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced stage ( stage 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 ( 54.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNAT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (34.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (65.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240.5 (195.50, 357.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDH Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal or Low (\u0026le;\u0026thinsp;280 U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 ( 57.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;280 U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (42.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGGT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.99 (24.30,47.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGGT Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal or Low (\u0026le;\u0026thinsp;30U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (40.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;30 U/L )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (59.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistological type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuctal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (85.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLobular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (11.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (2.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNPI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNPI Category\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFavourable (\u0026le;\u0026thinsp;5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 ( 59.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnfavorable (\u0026gt;\u0026thinsp;5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (40.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of LDH and NPI\u003c/h2\u003e \u003cp\u003eTo determine whether there is a relationship between the LDH level and the NPI among breast cancer patients, a correlation test was undertaken. There is a significant positive correlation, as demonstrated by the Spearman correlation coefficient, r\u0026thinsp;=\u0026thinsp;0.660, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003cp\u003eTo assess whether there is any degree of dependence for the NPI on changes in LDH value, a linear regression analysis was also carried out. After deriving the statistical model y\u0026thinsp;=\u0026thinsp;3.49\u0026thinsp;+\u0026thinsp;4.21 * x, it was determined that the two variables had a positive relationship with a regression coefficient of b\u0026thinsp;=\u0026thinsp;4.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. This model shows that the change in NPI is increased by 4.21 for each increment of one value of LDH Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe coefficient of determination, R\u003csup\u003e2\u003c/sup\u003e was found to be 0.283, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. This implies that LDH alone could predict the category of NPI by 28.3%.\u003c/p\u003e \u003cp\u003eIn addition to that, ANOVA was executed and a significant difference in variance for LDH levels was observed between patients who had favourable NPI (variance\u0026thinsp;=\u0026thinsp;913.5) compared to unfavourable NPI group(variance\u0026thinsp;=\u0026thinsp;1348.6).\u003c/p\u003e \u003cp\u003eThe mean LDH varied significantly between favourable and unfavourable NPI groups. The mean LDH of patients with NPI\u0026thinsp;\u0026gt;\u0026thinsp;5.4 was 491.5 (95% CI: 419.4\u0026ndash;563.6), while the mean LDH of patients with NPI\u0026thinsp;\u0026le;\u0026thinsp;5.4 was 229.4 (95% CI: 170.0\u0026thinsp;\u0026minus;\u0026thinsp;288.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0,001 and F\u0026thinsp;=\u0026thinsp;31). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of GGT and NPI\u003c/h2\u003e \u003cp\u003eThe Spearman correlation coefficient was computed to see whether there was a correlation at all between GGT and NPI. There is a relatively weak positive correlation,r\u0026thinsp;=\u0026thinsp;0.455 between the two, despite a substantial association,\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, an analysis of variance was conducted, and it was found that there was an insignificant difference in variance for GGT levels between the two groups. The mean GGT did not differ significantly between the favourable and unfavourable NPI groups. Patients with NPI\u0026thinsp;\u0026gt;\u0026thinsp;5.4 had a mean GGT of 175.72 (95% CI: 53.72\u0026ndash;297.67), while those with NPI\u0026thinsp;\u0026le;\u0026thinsp;5.4 had an average GGT of 32.87(95% CI: -67.50 ─ 133.25), \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.148\u003c/em\u003e and F\u0026thinsp;=\u0026thinsp;2.17. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe accuracy of LDH and GGT in predicting breast cancer prognosis based on NPI.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe ability of LDH and GGT to discriminate breast cancer women into favourable and unfavourable prognosis groups based on NPI was determined using the ROC. The Area Under the Curve was 0.845 (0.766\u0026ndash;0.923, 95% CI, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) and 0.725(0.623\u0026ndash;0.827,95% CI, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) respectively. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe accuracy of LDH and GGT in predicting breast cancer prognosis based on clinical TNM stage.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on clinical TNM stage LDH and GGT can correctly discriminate breast cancer women into favourable and unfavourable groups. The Area Under the Curve was computed and found to be 0.870 (0.801\u0026ndash;0.939, 95% CI, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) and 0.759(0.665\u0026ndash;0.852,95% CI, \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) respectively. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing the Youden index, the optimal effectiveness of the markers in patient classification was also assessed. Only LDH showed statistical significance, with an index of 58% for NPI and 65% for the clinical TNM stage. For GGT, where an index of 44% for NPI and 45% for TNM was noted, it was comparatively insignificant. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrognostic accuracy and precision values for LDH and GGT\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCutpoint\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYouden\u0026rsquo;s Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMetric score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eNPI status\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.766 -0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.623\u0026ndash;0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical stage status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e231.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.801\u0026ndash;0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.665\u0026ndash;0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe purpose of this study was to ascertain whether there was a correlation between LDH/GGT and the Nottingham Prognostic Index concerning breast cancer patients. The correlation found between LDH and NPI suggests that high levels of lactate release in highly malignant breast cancer cells are indicative of a predictive value for lactate concentration in breast cancer.\u003c/p\u003e \u003cp\u003eWhile lactate serves as an energy source for advancing epithelial cancer cells, excessive lactate concentrations seriously impair macrophage function by reducing the generation of natural killer cells and causing T cells to undergo apoptosis. The study found a statistically significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the means of LDH between individuals with a favourable NPI and those with an unfavourable NPI. This observation aligns with the physiological reaction of lactate to malignant neoplasms as described earlier. A high positive correlation was observed between LDH and NPI, with a Spearman correlation value of r\u0026thinsp;=\u0026thinsp;0.66 and p\u0026thinsp;=\u0026thinsp;0.01; the mean NPI was 4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11.\u003c/p\u003e \u003cp\u003eAccording to the regression coefficient of (b\u0026thinsp;=\u0026thinsp;4.21.5), there is a one-score increase in the probability of an unfavourable prognosis for every four-fold rise in LDH. Individuals with LDH levels\u0026thinsp;\u0026ge;\u0026thinsp;280 U/L are more likely to have greater NPI levels, which is associated with a worse prognosis. According to the linear regression model, the coefficient of determination was 0.283, which indicates that LDH alone can distinguish between breast cancer patients with favourable and unfavourable prognosis groups with an accuracy of roughly 28.3%. Increased lactate concentration was correlated with poorer NPI but is also a marker sensitive to tumour grades, according to a study by Cheung SM et al. The two showed a significant correlation with p\u0026thinsp;=\u0026thinsp;0.0495(\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, it was investigated if lactate could accurately classify breast cancer patients with favourable and unfavourable prognoses based on NPI. With a sensitivity of 80.95% and specificity of 77.42%.Its discriminatory capacity is useful with the AUC found to be 0.85. According to our research, women with breast cancer who have a lactate level of more than 270 U/L are at likelihood for an unfavourable prognosis.\u003c/p\u003e \u003cp\u003eOur results are in line with those of a different study by He J et al that evaluated the predictive power of the lactate-lactate-to-albumin ratio for non-metastatic breast cancer patients' disease-free survival. It was discovered that the lactate-to-albumin ratio, with an area under the curve (AUC) of 0.709, a sensitivity of 94.1%, and a specificity of 38.5%, is an independent predictor of prognosis in patients with breast cancer. The fact that their study only included Caucasian women and included the patients' patterns of dietary status might account for the discrepancy in specificity.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Furthermore, it has been observed that lactate can distinguish between locally advanced illness and early (stages I\u0026ndash;II) disease. 58% of the individuals in our research had a locally advanced disease. This might be explained by the low level of education, ignorance, and the state of the economy in our area. Even though this study only included one patient with stage I disease, the accuracy of LDH in properly classifying breast cancer patients with favourable and unfavourable prognoses based on clinical stage was also assessed and noted to be significantly helpful, the AUC\u0026thinsp;=\u0026thinsp;0.870. Serum lactate levels were compared in the previous research at different stages of the disease. The results of the interstage comparison showed that there was a significant increase between stages II, III and IV, but not between stages I and II(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, a high/rapid turnover of malignant cells releases the enzymes into the bloodstream, which in turn reflects on the tumour burden, leading to an elevated serum GGT. The measured serum GGT and NPI in the current investigation showed an unremarkable correlation with a Spearman correlation coefficient, of r\u0026thinsp;=\u0026thinsp;0.455. This is in contrast to findings from earlier research by Mohammed Saheb SK and Abdalla M Jarari et al., where the GGT levels in the blood were considerably greater in breast cancer patients than in the controls\u003c/p\u003e \u003cp\u003eAlong the progressing stage, particularly across stages III and IV, the amount of this specific enzyme was noticeably greater. Since our study only included patients in the non-metastasizing group and other studies included patients in the stage IV group, where the correlation is highly significant, it is possible that this accounts for the discrepancy between the findings of the two studies(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the clinical TNM stage, the efficacy of GGT in classifying patients with breast cancer into early and locally progressed diseases was also evaluated. With a 57.9% sensitivity and an 87.2% specificity, the AUC was 0.759. These results are in line with those of another study conducted by Mohammed SSK et al. in which the activity of serum GGT was found to significantly increase between stage II to stage IV patients but an interstage comparison revealed a non-significant increase between stages I and II(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on NPI and cTNM, we evaluated the accuracy of GGT in classifying breast cancer women into favourable and unfavourable groups in our study based on NPI and clinical TNM stage. Its specificity and sensitivity were determined to be (62%, 82%), and (58%, 87%), respectively. Its discriminatory capacity/utility was somewhat limited with an AUC of 0.73 and 0.76. These results imply that the enzyme may be used but with limitations as a discriminant marker in breast cancer patients concerning their tumour load, notwithstanding the weak correlation that currently exists.\u003c/p\u003e \u003cp\u003eThe distinction between our study's cutoff values and those of the other research it cited was also clearly visible. The values for LDH were found to be 270 U/L and 231 U/L for the NPI and clinical TNM stage respectively, whereas the value for GGT was found to be 39 U/L for both.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eThe testing of easy, affordable, and accessible markers (enzymes) in breast cancer, which is practically a public health challenge, is one of this study's many remarkable strengths. Nevertheless, it has its drawbacks as well. The fact that our study is a single site, has a limited sample size, and was conveniently sampled all contribute to selection bias, which limits the study's capacity to be broadly applied.\u003c/p\u003e \u003cp\u003eFurthermore, Immunohistochemical (IHC) markers as one of the prognostic parameters took longer to produce results because some patients did not have them checked out before surgery and were thus ordered following a histological evaluation of the entire breast tissue. Since IHC markers are not covered by National Health Insurance, most of the participants did not pay for them timely hence missing their results at the time of analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eEven though these enzyme markers only have unspecific diagnostic magnitude, the measurement of their levels can be carried out before treatment and as part of a follow-up plan. The current study concludes that blood levels of glutamyltranspeptidase and lactate dehydrogenase may serve as indicators for breast cancer treatment modalities and disease prognostication in conjunction with other potential factors. To validate this research's findings, nonetheless, further multicentric and bigger sample size studies must be undertaken.\u003c/p\u003e \u003cp\u003eThe markers can also be used during follow-up to assess response to treatment, and disease recurrence/disease progression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI extend my profound appreciation to the\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003epatients who enthusiastically became part of the research we conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary researcher, Sylvery Mwesige, wrote the full-text paper and produced the manuscript. He has access to all of the study\u0026apos;s data.\u0026nbsp;\u003cbr\u003e\u0026nbsp;Victor Meza Kyaruzi contributed to the study\u0026apos;s conception and design as well as its statistical analysis and interpretation.\u003cbr\u003e\u0026nbsp;Mabula D.P.M Mchembe and Mungeni Misidai played administrative roles and offered material and technical support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funds were received for this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll research materials pertinent to this study\u0026nbsp;are accessible and will be handed down by the relevant author upon justifiable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The Muhimbili University of Health and Allied Sciences Institutional Review Board approved the study protocol(IRB-MUHAS REF.No.DA.282/298/01.C/1845. The Muhimbili National Hospital Research and Consultancy Department was approached for permission before any data was captured(REF.No.MNH/CRTCU/Perm/2023/488). Before taking part, each participant in this academic study signed a written consent. All ethical principles were observed throughout the investigation. The research was completed while upholding the confidentiality of participants\u0026apos; identities and other pertinent personal information\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares neither financial nor non-financial interests that may be relevant to this study.\u003c/p\u003e"},{"header":"References ","content":"\u003col\u003e\n\u003cli\u003eRajeswari G, Srinivas PS, Rama KS, Sai K, Suresh E. 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Cureus. 2020;12(4). \u003c/li\u003e\n\u003cli\u003eHearne BJ, Teare MD, Butt M, Donaldson L. Comparison of Nottingham Prognostic Index and Adjuvant Online prognostic tools in young women with breast cancer : review of a single-institution experience. 2015;1\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eAlbergaria A, Ricardo S, Milanezi F, Carneiro V, Amendoeira I, Vieira D, et al. Nottingham Prognostic Index in Triple-Negative Breast Cancer : a reliable prognostic tool ? 2011; \u003c/li\u003e\n\u003cli\u003eGreen AR, Soria D, Stephen J, Powe DG, Nolan CC, Kunkler I, et al. Nottingham Prognostic Index Plus : Validation of a clinical decision making tool in breast cancer in an independent series. 2016;(January):32\u0026ndash;40. \u003c/li\u003e\n\u003cli\u003eRejali M, Tazhibi M, Mokarian F, Gharanjik N, Mokarian R. 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Effect of age on breast cancer patient prognoses: A population-based study using the SEER 18 database. PLoS One. 2016;11(10):1\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eBrandt J, Garne PP, Tengrup I, Manjer J. Age at diagnosis in relation to survival following breast cancer: A cohort study. World J Surg Oncol. 2015;13(1):1\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eBell RJ. Menopausal status at diagnosis of breast cancer and risk of metastatic recurrence. Vol. 28, Menopause (New York, N.Y.). United States; 2021. p. 1079\u0026ndash;80. \u003c/li\u003e\n\u003cli\u003eLao C, Elwood M, Kuper-Hommel M, Campbell I, Lawrenson R. Impact of menopausal status on risk of metastatic recurrence of breast cancer. Menopause. 2021 Jul;28(10):1085\u0026ndash;92. \u003c/li\u003e\n\u003cli\u003eOncology B, Pathology H. Breast Cancer in Lean Postmenopausal Women. 2019;487:483\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eBundred J, Michael S, Bowers S, Barnes N, Jauhari Y, Plant D, et al. 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Clin Cancer Res. 2013;19(18):5158\u0026ndash;69. \u003c/li\u003e\n\u003cli\u003eVander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science (80- ). 2009;324(5930):1029\u0026ndash;33. \u003c/li\u003e\n\u003cli\u003eHe J, Tong L, Wu P, Wu Y, Shi W, Chen L. Prognostic Significance of Preoperative Lactate Dehydrogenase to Albumin Ratio in Breast Cancer : A Retrospective Study. 2023;(February):507\u0026ndash;14. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lactate Dehydrogenase, Gamma-Glutamyl Transferase, Nottingham Prognostic index, Correlation, Breast Cancer","lastPublishedDoi":"10.21203/rs.3.rs-4409898/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4409898/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLDH and GGT have been identified as breast cancer serum prognostic markers. Serum level of LDH has been found to increase due to uplifted anaerobic glycolysis in malignant neoplastic conditions. GGT has also been noted to rise in circulation indicating the extent of oxidative stress within the body. Their levels are high in advanced BC cases. Meanwhile, in our setting, there are no serum markers done on a routine basis in breast cancer surveillance among women and predicting the prognosis. Thus, these markers can augment the available tool(s) in predicting breast cancer prognosis since they are widely available, accessible, and economically affordable.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to assess the correlation between LDH/GGT with NPI among Breast Cancer Women.\u003c/p\u003e\u003ch2\u003eStudy Methods\u003c/h2\u003e \u003cp\u003eA prospective cross-sectional study was conducted for 12 months. The data were collected by interviewing patients, patients\u0026rsquo; files and from the hospital\u0026rsquo;s electronic database (Jeeva) and then were filled in the structured checklist. IBM SPSS version 27 was used to analyse the data. Mean, median and standard deviation were used to present numerical variables. Categorical variables were presented as frequencies or proportions The correlation was assessed and analyzed by Spearman correlation coefficient and Linear Regression models while ROC was used to determine the accuracy.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOut of the 104 patients who underwent radical mastectomy for Breast Cancer had a mean age of 48.51\u0026thinsp;\u0026plusmn;\u0026thinsp;12.80. Among them, 27 (26%) were diagnosed below 40 years of age.\u003c/p\u003e","manuscriptTitle":"Serum Lactate Dehydrogenase(LDH) and Gamma-Glutamyl Transferase (GGT) Correlate Optimally with Nottingham Prognostic Index for Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-30 20:56:16","doi":"10.21203/rs.3.rs-4409898/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"12eb0f70-eb23-4397-bbf0-408aecb5587a","owner":[],"postedDate":"May 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-17T07:31:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-30 20:56:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4409898","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4409898","identity":"rs-4409898","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}