Lactate dehydrogenase and serum tumor markers for predicting metastatic status in geriatric patients with lung adenocarcinoma

A Nomogram Based on Clinicopathological Characteristics for Estimating the Risk of Brain Metastasis from Advanced Gastric Cancer: A Multi-Center Retrospective Clinical Study

Purpose

Although brain metastasis (BM) from gastric cancer (GC) is relatively uncommon, its incidence has been increasing owing to advancements in treatment modalities. Unfortunately, patients diagnosed with BM from gastric cancer have poor life expectancy. Our study aims to establish a predictive model for brain metastasis in advanced gastric cancer patients, thus enabling the timely diagnosis of brain metastasis.

Patients and Methods

The clinicopathological features of a cohort which included 40 GC patients with brain metastasis, 32 of whom from the First Affiliated Hospital of Nanchang University, 2 from Gaoxin Branch of the First Affiliated Hospital of Nanchang University, remaining 6 from Anyang District Hospital, and 80 non-metastatic advanced GC patients from the First Affiliated Hospital of Nanchang University between 2018 and 2022. Data were retrospectively analyzed.

Results

Age, tumor size, differentiation, lymph node grade, tumor location, Lauren classification, liver metastasis, carbohydrate antigen 199 (CA199), lactate dehydrogenase (LDH), and human epidermal growth factor receptor 2 (Her-2) were associated with BM. A nomogram integrated with nine risk factors (tumor size, differentiation, lymph node grade, tumor location, Lauren classification, liver metastasis, CA-199, LDH, and Her-2) showed good performance (Area Under Curve 0.95, 95% CI: 0.91-0.98).

Conclusion

We developed and validated a nomogram that achieved individualized prediction of the possibility of BM from GC. This model enables personalized imaging review schedules for timely brain metastasis detection in advanced gastric cancer patients.

Update value and clinical application of MUC16 (cancer antigen 125)

INTRODUCTION

The largest transmembrane mucin, mucin 16 (MUC16), contains abundant glycosylation sites on the molecular surface, allowing it to participate in various molecular pathways. When cells lose polarity and become cancerous, MUC16 is overexpressed, and more of the extracellular region (cancer antigen [CA]125) is released into serum and possibly, promote the development of diseases. Thus, MUC16 plays an indispensable role in clinical research and application.

AREAS COVERED

This review summarizes the update proposed role of MUC16 in carcinogenesis and metastasis. Most importantly, we prospect its potential value in targeted therapy after screening 1226 articles published within the last 10 years from PubMed. Two reviewers screened each record and each report retrieved independently. We have summarized the progress of MUC16/CA125 in basic research and clinical application, and predicted its possible future development directions.

EXPERT OPINION

As an important noninvasive co-factor in the diagnosis of gynecological diseases, MUC16 has been used for a long time, especially in the diagnosis and treatment of ovarian cancer. The overexpression of MUC16 plays a very obvious role in regulating inflammatory response, supporting immune suppression, and promoting the proliferation, division, and metastasis of cancer cells. In the next 20 years, there will be a luxuriant clinical application of MUC16 as a target for immune monitoring and immunotherapy.

Advances in the Molecular Landscape of Lung Cancer Brain Metastasis

Lung cancer is one of the most frequent tumors that metastasize to the brain. Brain metastasis (BM) is common in advanced cases, being the major cause of patient morbidity and mortality. BMs are thought to arise via the seeding of circulating tumor cells into the brain microvasculature. In brain tissue, the interaction with immune cells promotes a microenvironment favorable to the growth of cancer cells. Despite multimodal treatments and advances in systemic therapies, lung cancer patients still have poor prognoses. Therefore, there is an urgent need to identify the molecular drivers of BM and clinically applicable biomarkers in order to improve disease outcomes and patient survival. The goal of this review is to summarize the current state of knowledge on the mechanisms of the metastatic spread of lung cancer to the brain and how the metastatic spread is influenced by the brain microenvironment, and to elucidate the molecular determinants of brain metastasis regarding the role of genomic and transcriptomic changes, including coding and non-coding RNAs. We also present an overview of the current therapeutics and novel treatment strategies for patients diagnosed with BM from NSCLC.

Preoperative diagnosis of solitary pulmonary nodules with a novel hematological index model based on circulating tumor cells

Objective

Preoperative noninvasive diagnosis of the benign or malignant solitary pulmonary nodule (SPN) is still important and difficult for clinical decisions and treatment. This study aimed to assist in the preoperative diagnosis of benign or malignant SPN using blood biomarkers.

Methods

A total of 286 patients were recruited for this study. The serum FR⁺CTC, TK1, TP, TPS, ALB, Pre-ALB, ProGRP, CYFRA21-1, NSE, CA50, CA199, and CA242 were detected and analyzed.

Results

In the univariate analysis, age, FR⁺CTC, TK1, CA50, CA19.9, CA242, ProGRP, NSE, CYFRA21-1, and TPS showed the statistical significance of a correlation with malignant SPNs (P <0.05). The highest performing biomarker is FR⁺CTC (odd ratio [OR], 4.47; 95% CI: 2.57-7.89; P <0.001). The multivariate analysis identified that age (OR, 2.69; 95% CI: 1.34-5.59, P = 0.006), FR⁺CTC (OR, 6.26; 95% CI: 3.09-13.37, P <0.001), TK1 (OR, 4.82; 95% CI: 2.4-10.27, P <0.001), and NSE (OR, 2.06; 95% CI: 1.07-4.06, P = 0.033) are independent predictors. A prediction model based on age, FR⁺CTC, TK1, CA50, CA242, ProGRP, NSE, and TPS was developed and presented as a nomogram, with a sensitivity of 71.1% and a specificity of 81.3%, and the AUC was 0.826 (95% CI: 0.768-0.884).

Conclusions

The novel prediction model based on FR⁺CTC showed much stronger performance than any single biomarker, and it can assist in predicting benign or malignant SPNs.

Exosomes in malignant pleural effusion from lung cancer patients impaired the cytotoxicity of double-negative T cells

CD3⁺CD4^-CD8^- double-negative T (DNT) cells are new weapons in cancer immunotherapy. Here, we explored DNT cells in malignant pleural effusions (MPEs) from lung cancer patients. DNT cells, especially TCRαβ⁺CD56^- DNT cells, were increased in MPE from lung cancer patients. DNT cells highly expressed PD-1, TRAIL, NKG2D and DNAM-1. In contrast, FasL was barely detected in DNT cells. Compared with non-MPE cells, MPE-derived DNT cells expressed much higher levels of PD-1 and TRAIL. DNT cells from healthy peripheral blood donors potentially killed lung cancers, which was decreased by MPE supernatant. Exosomes from MPE supernatant expressed PD-1 and CEACAM1 and impaired the cytotoxicity of DNT cells. Blocking PD-1 and TIM3 rescued the cytotoxicity of DNT cells treated with MPE-derived exosomes. Overall, we demonstrated that the frequency of DNT cells in MPE from lung cancer patients was increased and that MPE-derived exosomes impaired the cytotoxicity of DNT cells via the PD-1/PD-L1 and CEACAM1/TIM3 pathways.

Elevated lactate dehydrogenase predicts poor prognosis of acute ischemic stroke

Background

Lactate dehydrogenase (LDH) is associated with the prognosis of many diseases, but the relationship between LDH and the poor prognosis (recurrence and death) of acute ischemic stroke (AIS) has not been fully clarified. This study aimed to investigate the association between admission LDH level and poor prognosis in patients with AIS.

Methods

This retrospective study enrolled AIS patients treated in Taizhou Hospital of Zhejiang Province from July 2019 to December 2019. Poor prognosis included AIS recurrence and all-cause death at 3, 6, and 18 months. The correction between LDH and poor prognosis or all-cause death was assessed. Lasso Cox expression and multivariate Cox expression analyses were used to evaluate the association of LDH with the risk of poor prognosis and all-cause death, respectively. A nomogram was constructed to evaluate the predictive Values of LDH for the poor prognosis and all-cause death of AIS.

Results

732 patients were included in the study. Multivariate analysis shows that admission LDH levels were significantly correlated with poor prognosis [odds ratio (OR),1.003; 95% confidence interval (95% CI), 1.001–1.005; P = 0.001] and all-cause death (OR, 1.005; 95% CI, 1.000–1.009; P = 0.031). The correlation analysis showed that admission LDH level was positively correlated with National Institutes of Health Stroke Scale (NIHSS) score and modified Rankin Scale (mRS) score. Time-dependent receiver operating characteristic (td-ROC) curves analysis showed that the AUC values of admission LDH level for predicting prognosis of AIS patients in 3-month, 6-month, 12-month and 18-month were 0.706 (95% CI, 0.604–0.810), 0.653 (95% CI, 0.583–0.723), 0.616 (95% CI, 0.556–60676) and 0.610 (95% CI, 0.552–0.680), respectively. And td-ROC also showed that the AUC values of admission LDH level for predicting all-cause death of AIS patients in 3-month, 6-month,12-month and 18-month were 0.861 (95% CI, 0.764–0.958), 0.824 (95% CI, 0.753–0.890), 0.726 (95% CI, 0.633–0.819) and 0.715 (95% CI, 0.622–0.807), respectively. The nomograms were constructed to create the predictive models of the poor prognosis and all-cause death of AIS.

Conclusion

Higher LDH levels are independently associated with poor prognosis and all-cause death of AIS.

Diagnostic value of combination of exfoliative cytology with CA125, CEA, NSE, CYFRA21-1 and CA15-3 for lung cancer

Background: To explore the diagnostic value of combination of exfoliative cytology with detection of tumor markers carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), neuron specific enolase (NSE), cytokeratin 19 fragment antigen 21-1 (CYFRA21-1) and CA15-3 for lung cancer.

Methods: A total of 256 patients were enrolled, including 164 males and 92 females aged (64.51±22.68) years old. Among them, 189 patients (100 males and 89 females) were randomly selected as Tumor group, and the remaining 67 patients were used for validation. Another 514 healthy people receiving physical examination in our hospital during the same period were selected, from which 397 cases (266 males and 131 females) were randomly selected as No Tumor group, and the remaining 117 cases were used for validation. The biochemical criteria were detected in all subjects. The diagnostic value of each index for lung cancer was analyzed using receiver operating characteristic (ROC) curves.

Results: The results of ROC curve analysis revealed that in Tumor group, the area under curve (AUC) of exfoliative cytology, CA125, CYFRA21-1, CA15-3, CEA and NSE was ≥0.7, while that of CA72-4, CA19-9, TSGF, AFP, CA242, SCCAg and CA50 was <0.7. The indices in each factor were comprehensively assessed, and then exfoliative cytology, CA125, CA15-3, CYFRA21-1, CEA and NSE were screened to establish the lung cancer prediction model. The diagnostic value was comparable between the prediction model and the combined detection of 9 indices (Z=1.682, P=0.079).

Conclusions: The lung cancer prediction model balances sensitivity and specificity without reducing the diagnostic efficiency.

Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis

Artificial Intelligence (AI) is a branch of computer science that includes research in robotics, language recognition, image recognition, natural language processing, and expert systems. AI is poised to change medical practice, and oncology is not an exception to this trend. As the matter of fact, lung cancer has the highest morbidity and mortality worldwide. The leading cause is the complexity of associating early pulmonary nodules with neoplastic changes and numerous factors leading to strenuous treatment choice and poor prognosis. AI can effectively enhance the diagnostic efficiency of lung cancer while providing optimal treatment and evaluating prognosis, thereby reducing mortality. This review seeks to provide an overview of AI relevant to all the fields of lung cancer. We define the core concepts of AI and cover the basics of the functioning of natural language processing, image recognition, human-computer interaction and machine learning. We also discuss the most recent breakthroughs in AI technologies and their clinical application regarding diagnosis, treatment, and prognosis in lung cancer. Finally, we highlight the future challenges of AI in lung cancer and its impact on medical practice.

Lymphocyte percentage as a valuable predictor of prognosis in lung cancer

Lymphocytes and neutrophils are involved in the immune response against cancer. This study aimed to investigate the relationship between lymphocyte percentage/neutrophil percentage and the clinical characteristics of lung cancer patients, and to explore whether they could act as valuable predictors to ameliorate lung cancer prognosis. A total of 1312 patients were eligible to be recruited. Lymphocyte percentage and neutrophil percentage were classified based on their reference ranges. Survival curves were determined using Kaplan–Meier method, and univariate and multivariate cox regression analyses were performed to identify the significant predictors. Decision curve analysis was used to evaluate the clinical benefit. The results of both training and validation cohorts indicated that lymphocyte percentage exhibited high correlation with clinical characteristics and metastasis of lung cancer patients. Both lymphocyte percentage and neutrophil percentage were closely associated with survival status (all p < 0.0001). Low lymphocyte percentage could act as an indicator of poor prognosis; it offered a higher clinical benefit when combined with the clinical characteristic model. Our findings suggested that pretreatment lymphocyte percentage served as a reliable predictor of lung cancer prognosis, and it was also an accurate response indicator in lung adenocarcinoma and advanced lung cancer. Measurement of lymphocyte percentage improved the clinical utility of patient characteristics in predicting mortality of lung cancer patients.

Combining Lactate Dehydrogenase and Fibrinogen: Potential Factors to Predict Therapeutic Efficacy and Prognosis of Patients with Small-Cell Lung Cancer

Background

A growing interest exists in identifying reliable and low-cost biomarkers or factors that could predict the therapeutic response, prognosis, recurrence, and survival in small-cell lung cancer (SCLC). This study aimed to investigate the better predictors of chemotherapy efficacy and prognosis in patients with SCLC receiving first-line chemotherapy and radiotherapy.

Materials and Methods

This study retrospectively retrieved the medical records of patients with SCLC treated with first-line platinum-based chemotherapy and radiotherapy from January 2016 to June 2019 in the First Affiliated Hospital of Nanjing Medical University. Plasma biochemical parameters, clinical features, and overall survival (OS) time were collected. The independent effects of plasma parameters on patient survival were assessed by conducting univariate and multivariate Cox regression analyses. The optimal cut-off values of independent risk factors in the ROC curve and Kaplan–Meier survival analysis were determined using MedCalc software.

Results

Statistically significant differences in lactate dehydrogenase (LDH) and fibrinogen (Fbg) were found between the complete remission + partial remission group and the non-responders, which consisted of stable-disease and progressive-disease groups, after first-line chemotherapy. Multivariate Cox regression analysis showed that LDH and Fbg were independent risk factors in predicting PFS (LDH HR: 1.013, 95% CI: 1.002–1.030, P = 0.037; Fbg HR: 1.622, 95% CI: 1.094–2.526, P = 0.017) and OS (LDH HR: 1.021, 95% CI: 1.008–1.034, P = 0.001; Fbg HR: 2.168, 95% CI: 1.324–3.550, P = 0.002). The AUC of LDH and Fbg was 0.77 and 0.745, respectively. The cut-off value of LDH and Fbg in predicting OS was 263 U/L and 4.03 g/L. When these two data were combined, the AUC reached 0.832, better than that of LDH and Fbg alone. The objective response rate (ORR) and OS were significantly different among these three different groups according to the addition of the assigned value (P < 0.05).

Conclusion

Combined retreatment serum LDH and Fbg levels may be a better potential biomarker for predicting the clinical efficacy of chemotherapy and the prognosis of individuals with SCLC. Combining these two parameters could improve prediction efficacy.

A nomogram to predict the overall survival of patients with symptomatic extensive-stage small cell lung cancer treated with thoracic radiotherapy

Background

Small cell lung cancer (SCLC) makes up 13% of lung malignancies. Only one-third of SCLC patients received their diagnosis at the limited stage. Treatment for symptomatic extensive-stage (ES) SCLC with persistent intrathoracic disease is still controversial. The present research aimed to analyze the impact of palliative thoracic radiotherapy (TRT) as a treatment for this patient group and build a prognostic nomogram.

Methods

In this retrospective, multi-center study, we analyzed 120 patients with ES-SCLC and a World Health Organization performance status of 1–2 who were diagnosed between March 2014 and September 2019. A nomogram was formulated to predict the patients’ 1- and 2-year overall survival (OS).

Results

The study cohort had a median age of 62 years, and males accounted for 85% of enrollees. A significant extension was observed in the median OS in the TRT group compared to the no TRT group (P<0.001). When the patients were stratified by TRT dose, no significant differences in OS were noted (P=0.530). However, higher levels of inflammatory markers prior to TRT were associated with a shorter OS (neutrophil-to-lymphocyte ratio, P=0.002; platelet/lymphocyte ratio, P=0.023). The nomogram’s Harrell’s concordance (C)-statistic reached 0.70, and the calibration curve analysis revealed goodness of fit.

Conclusions

The neutrophil-to-lymphocyte ratio is an independent factor predicting survival in ES-SCLC patients treated with palliative TRT. Our nomogram, which incorporates immunological markers, has higher accuracy than existing models for the prediction of individuals’ chances of survival, and it could be a significant tool for clinicians in the development of tailored therapeutic strategies.

Carbohydrate antigen 19-9 - tumor marker: Past, present, and future

Carbohydrate antigen 19-9 (CA 19-9) is a cell surface glycoprotein complex most commonly associated with pancreatic ductal adenocarcinoma (PDAC). Koprowski first described it in 1979 using a mouse monoclonal antibody in a colorectal carcinoma cell line. Historically, it is one of the most commonly used tumor markers for diagnosing, managing, and prognosticating PDAC. Additionally, elevated CA 19-9 levels are used as an indication for surgery in suspected benign pancreatic conditions. Another common application of CA 19-9 in the biliary tract includes its use as an adjunct in diagnosing cholangiocarcinoma. However, its clinical value is not limited to the hepatopancreatobiliary system. The reality is that the advancing literature has broadened the clinical value of CA 19-9. The potential value of CA 19-9 in patients' workup extends its reach to gastrointestinal cancers - such as colorectal and oesophageal cancer - and further beyond the gastrointestinal tract - including urological, gynecological, pulmonary, and thyroid pathologies. Apart from its role in investigations, CA 19-9 presents a potential therapeutic target in PDAC and acute pancreatitis. In a bid to consolidate its broad utility, we appraised and reviewed the biomarker's current utility and limitations in investigations and management, while discussing the potential applications for CA 19-9 in the works for the future.

Inflammatory Marker Predicts Outcome of Oral Squamous Cell Carcinoma Receiving Chemo-Radiotherapy

Background

Inflammation is involved in the progression of oral squamous cell carcinoma. We therefore postulate that a blood-based inflammatory marker, the neutrophil-to-lymphocyte ratio, could help to predict the prognosis in patients with oral squamous cell carcinoma receiving chemo-radiotherapy, and build a prognostic nomogram.

Methods

In 111 cases of oral squamous cell carcinoma, several baseline variables, including inflammatory markers and lactate dehydrogenase, were measured within the week of chemo-radiotherapy initiation as predictors of mortality and separate risk scores were developed.

Results

Dichotomized neutrophil-to-lymphocyte ratio was found to be associated with AJCC stage (p<0.001). Patients with oral squamous cell carcinoma with a low neutrophil-to-lymphocyte ratio survived longer than those with a high neutrophil-to-lymphocyte ratio (overall survival HR=2.39, 95% CI 1.24-4.61, p=0.009). The Harrell's concordance (C)-statistic of the nomogram was 0.74 and the calibration curve demonstrated the goodness of fit.

Conclusion

The inflammatory marker neutrophil-to-lymphocyte ratio, obtained before chemo-radiotherapy, was an independent factor of survival prediction for oral squamous cell carcinoma. The nomogram incorporating immunological markers could more accurately predict individualized survival probability than the existing models. These findings are significant for the creation of personalized treatment strategies in the clinical setting.

Cancer/testis antigen LDHC promotes proliferation and metastasis by activating the PI3K/Akt/GSK-3β-signaling pathway and the in lung adenocarcinoma

The cancer/testis antigen lactate dehydrogenase-C4 (LDHC) is a specific isoenzyme of the LDH family that regulates invasion and metastasis in some malignancies; however, little is known regarding its role in progression of lung adenocarcinoma (LUAD). Thus, we investigated LDHC expression by immunohistochemistry, and analyzed its clinical significance in 88 LUAD specimens. The role and molecular mechanisms subserving LDHC in cellular proliferation, migration, and invasion were explored both in vitro and in vivo. As a result, we found that high LDHC expression was significantly correlated with clinicopathological features of aggressive LUAD and a poor prognosis. Overexpression of LDHC induced LUAD cells to produce lactate and ATP, increased their metastatic and invasive potential-, and accelerated xenograft tumor growth. We further demonstrated that overexpression of LDHC affected the expression of cell proliferation-related proteins (cyclin D1 and c-Myc) and epithelial-mesenchymal transition (EMT)-related proteins (MMP-2, MMP-9, E-cadherin, Vimentin, Twist, Slug, and Snail) both in vitro and in vivo. Finally, excessive activation of LDHC enhanced the phosphorylation levels of AKT and GSK-3β, revealing activation of the PI3K/Akt/GSK-3β oncogenic-signaling pathways. Treatment with a PI3K inhibitor reversed the effects of LDHC overexpression by inhibiting cellular proliferation, migration, and invasion, with diminished levels of p-Akt and p-GSK3β. PI3K inhibition also reversed cell proliferation-related and EMT-related proteins in LDHC-overexpressing A549 cells. In conclusion, LDHC promotes proliferation, migration, invasion, and EMT in LUAD cells via activation of the PI3K/Akt/GSK-3β pathway.