Prognostic Value of Pretreatment Lymphocyte-to-Monocyte Ratio in Lung Cancer: A Systematic Review and Meta-Analysis

Risk factor analysis and nomogram prediction model construction of postoperative complications of thoracoscopic non-small cell lung cancer

Background

A series of complications will inevitably occur after thoracoscopic pulmonary resection. How to avoid or reduce postoperative complications is an important research area in the perioperative treatment of thoracic surgery. This study analyzed the risk factors for thoracoscopic postoperative complications of non-small cell lung cancer (NSCLC) and established a nomogram prediction model in order to provide help for clinical decision-making.

Methods

Patients with NSCLC who underwent thoracoscopic surgery from January 2017 to December 2021 were selected as study subjects. The relationship between patient characteristics, surgical factors, and postoperative complications was collected and analyzed. Based on the results of the statistical regression analysis, a nomogram model was constructed, and the predictive performance of the nomogram model was evaluated.

Results

A total of 872 patients who met the study criteria were included in the study. A total of 171 patients had complications after thoracoscopic surgery, accounting for 19.6% of the study population. Logistic regression analysis showed that thoracic adhesion, history of respiratory disease, and lymphocyte-monocyte ratio (LMR) were independent risk factors for complications after thoracoscopic surgery (P<0.05). Variables with P<0.1 in logistic regression analysis were included in the nomogram model. The verification results showed that the area under curve (AUC) of the model was 0.734 [95% confidence interval (CI): 0.693-0.775], and the calibration curve showed that the model had good differentiation. The decision curve analysis (DCA) curve showed that this model has good clinical application value. In subgroup analysis of complications, gender, history of respiratory disease, body mass index (BMI), type of surgical procedure, thoracic adhesion, and Time of operation were identified as significant risk factors for prolonged air leak (PAL) after surgery. Tumor location and forced expiratory volume in the first second (FEV1) were identified as important risk factors for postoperative pulmonary infection. N stage and thoracic adhesion were identified as significant risk factors for postoperative pleural effusion. The AUC for PAL was 0.823 (95% CI: 0.768-0.879). The AUC of postoperative pulmonary infection was 0.714 (95% CI: 0.627-0.801). The AUC of postoperative pleural effusion was 0.757 (95% CI: 0.650-0.864). The calibration curve and DCA curve indicated that the model had good predictive performance and clinical application value.

Conclusions

This study analyzed the risk factors affecting the postoperative complications of NSCLC through thoracoscopic surgery, and the nomogram model built based on the influencing factors has certain significance for the identification and reduction of postoperative complications.

Prognostic value of pretreatment lymphocyte-to-monocyte ratio in patients with glioma: a meta-analysis

BACKGROUND

Many studies have explored the prognostic role of the lymphocyte-to-monocyte ratio (LMR) in patients with glioma, but the results have been inconsistent. We therefore conducted the current meta-analysis to identify the accurate prognostic effect of LMR in glioma.

METHODS

The electronic databases of PubMed, Web of Science, Embase, and Cochrane Library were thoroughly searched from inception to July 25, 2023. The pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated to estimate the prognostic role of LMR for glioma.

RESULTS

A total of 16 studies comprising 3,407 patients were included in this meta-analysis. A low LMR was significantly associated with worse overall survival (OS) (HR = 1.35, 95% CI = 1.13-1.61, p = 0.001) in glioma. However, there was no significant correlation between LMR and progression-free survival (PFS) (HR = 1.20, 95% CI = 0.75-1.91, p = 0.442) in glioma patients. Subgroup analysis indicated that a low LMR was significantly associated with inferior OS and PFS in glioma when using a cutoff value of ≤ 3.7 or when patients received mixed treatment.

CONCLUSIONS

This meta-analysis demonstrated that a low LMR was significantly associated with poor OS in glioma. There was no significant correlation between LMR and PFS in glioma patients. The LMR could be a promising and cost-effective prognostic biomarker in patients with glioma in clinical practice.

Routine perioperative blood tests predict survival of resectable lung cancer

There is growing evidence that inflammatory, immunologic, and metabolic status is associated with cancer patients survival. Here, we built a simple algorithm to predict lung cancer outcome. Perioperative routine blood tests (RBT) of a cohort of patients with resectable primary lung cancer (LC) were analysed. Inflammatory, immunologic, and metabolic profiles were used to create a single algorithm (RBT index) predicting LC survival. A concurrent cohort of patients with resectable lung metastases (LM) was used to validate the RBT index. Charts of 2088 consecutive LC and 1129 LM patients undergoing lung resection were evaluated. Among RBT parameters, C-reactive protein (CRP), lymphocytes, neutrophils, hemoglobin, albumin and glycemia independently correlated with survival, and were used to build the RBT index. Patients with a high RBT index had a higher 5-year mortality than low RBT patients (adjusted HR 1.93, 95% CI 1.62-2.31). High RBT patients also showed a fourfold higher risk of 30-day postoperative mortality (2.3% vs. 0.5%, p 0.0019). The LM analysis validated the results of the LC cohort. We developed a simple and easily available multifunctional tool predicting short-term and long-term survival of curatively resected LC and LM. Prospective external validation of RBT index is warranted.

Construction of the prognostic model for small-cell lung cancer based on inflammatory markers: A real-world study of 612 cases with eastern cooperative oncology group performance score 0-1

OBJECTIVES

This research aimed to explore the relationship between pre-treatment inflammatory markers and other clinical characteristics and the survival of small-cell lung cancer (SCLC) patients who received first-line platinum-based treatment and to construct nomograms for predicting overall survival (OS) and progression-free survival (PFS).

METHODS

A total of 612 patients diagnosed with SCLC between March 2008 and August 2021 were randomly divided into two cohorts: a training cohort (n = 459) and a validation cohort (n = 153). Inflammatory markers, clinicopathological factors, and follow-up information of patients were collected for each case. Cox regression was used to conduct univariate and multivariate analyses and the independent prognostic factors were adopted to develop the nomograms. Harrell's concordance index (C-index) and time-dependent receiver operating characteristic curve were used to verify model differentiation, calibration curve was used to verify consistency, and decision curve analysis was used to verify the clinical application value.

RESULTS

Our results showed that baseline C-reactive protein/albumin ratio, neutrophil/lymphocyte ratio, NSE level, hyponatremia, the efficacy of first-line chemotherapy, and stage were independent prognostic factors for both OS and PFS in SCLC. In the training cohort, the C-index of PFS and OS was 0.698 and 0.666, respectively. In the validation cohort, the C-index of PFS and OS was 0.727 and 0.747, respectively. The nomograms showed good predictability and high clinical value. Also, our new clinical models were superior to the US Veterans Administration Lung Study Group (VALG) staging for predicting the prognosis of SCLC.

CONCLUSIONS

The two prognostic nomograms of SCLC including inflammatory markers, VALG stage, and other clinicopathological factors had good predictive value and could individually assess the survival of patients.

Clinical impact of first-line PD-1 or PD-L1 inhibitors combined with chemotherapy in extensive-stage small cell lung cancer patients: A real-world multicenter propensity score-matched study

OBJECTIVES

Our research aimed to evaluate the effectiveness of first-line immune checkpoint inhibitors (ICIs) with etoposide and platinum (EP) for extensive-stage small cell lung cancer (ES-SCLC) and identify prognostic factors, as real-world outcomes and the inconsistency of PD-1 and PD-L1 inhibitors are uncertain.

METHODS

We selected ES-SCLC patients in three centers and conducted a propensity score-matched analysis. The Kaplan-Meier method and Cox proportional hazards regression were conducted to compare the survival outcomes. We also performed univariate and multivariate Cox regression analyses to investigate predictors.

RESULTS

Among 236 patients included, 83 pairs of cases were matched. The EP plus ICIs cohort had a longer median overall survival (OS) (17.3 months) than the EP cohort (13.4 months) (hazard ratio [HR],  0.61 [0.45, 0.83]; p = 0.001). The median progression-free survival (PFS) was also longer in the EP plus ICIs cohort (8.3 months) than in the EP cohort (5.9 months) (HR,   0.44 [0.32, 0.60]; p < 0.001). The EP plus ICIs group had a higher objective response rate (ORR) (EP: 62.3%, EP + ICIs: 84.3%, p < 0.001). Multivariate analysis presented that liver metastases (HR, 2.08; p = 0.018) and lymphocyte-monocyte ratio (LMR) (HR, 0.54; p = 0.049) were independent prognostic factors for OS, and performance status (PS) (HR, 2.11; p = 0.015), liver metastases (HR, 2.64; p = 0.002), and neutrophil-lymphocyte ratio (NLR) (HR, 0.45; p = 0.028) were for PFS in patients with chemo-immunotherapy.

CONCLUSION

Our real-world data demonstrated that ICIs with chemotherapy as the first-line setting for ES-SCLC are effective and safe. PS, liver metastases, and inflammatory markers could serve as valuable risk factors.

Development and validation of a nomogram model for lung cancer based on radiomics artificial intelligence score and clinical blood test data

Background

Artificial intelligence (AI) discrimination models using single radioactive variables in recognition algorithms of lung nodules cannot predict lung cancer accurately. Hence, we developed a clinical model that combines AI with blood test variables to predict lung cancer.

Methods

Between 2018 and 2021, 584 individuals (358 patients with lung cancer and 226 individuals with lung nodules other than cancer as control) were enrolled prospectively. Machine learning algorithms including lasso regression and random forest (RF) were used to select variables from blood test data, Logistic regression analysis was used to reconfirm the features to build the nomogram model. The predictive performance was assessed by performing the receiver operating characteristic (ROC) curve analysis as well as calibration, clinical decision and impact curves. A cohort of 48 patients was used to independently validate the model. The subgroup application was analyzed by pathological diagnosis.

Findings

A total of 584 patients were enrolled (358 lung cancers, 61.30%,226 patients for the control group) to establish the model. The integrated model identified eight potential factors including carcinoembryonic antigen (CEA), AI score, Pro-Gastrin Releasing Peptide (ProGRP), cytokeratin 19 fragment antigen21-1(CYFRA211), squamous cell carcinoma antigen(SCC), indirect bilirubin(IBIL), activated partial thromboplastin time(APTT) and age. The area under the curve (AUC) of the nomogram was 0.907 (95% CI, 0.881-0.929). The decision and clinical impact curves showed good predictive accuracy of the model. An AUC of 0.844 (95% CI, 0.710 - 0.932) was obtained for the external validation group.

Conclusion

The nomogram model integrating AI and clinical data can accurately predict lung cancer, especially for the squamous cell carcinoma subtype.

Relationship between Systemic Inflammatory Markers, GLUT1 Expression, and Maximum 18F-Fluorodeoxyglucose Uptake in Non-Small Cell Lung Carcinoma and Their Prognostic Significance

BACKGROUND

Factors involved in inflammation and cancer interact in various ways with each other, and biomarkers of systemic inflammation may have a prognostic value in cancer. Glucose transporter 1 (GLUT1) plays a pivotal role in glucose transport and metabolism and it is aberrantly expressed in various cancer types. We evaluated the differential expression of GLUT1, along with 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) in non-small-cell lung cancer (NSCLC), and then analyzed their prognostic significance.

METHODS

A total of 163 patients with resectable NSCLC were included in this study. Tumor sections were immunohistochemically stained for GLUT1 and GLUT3. Maximum standardized uptake value (SUV_max) was measured by preoperative FDG-PET, and neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and lymphocyte-monocyte ratio (LMR) were derived from pretreatment blood count.

RESULTS

GLUT1 and GLUT3 was positively expressed in 74.8% and 6.1% of the NSCLC tissues, respectively. GLUT1 expression was significantly correlated with squamous cell carcinoma histology, poor differentiation, high pathologic stage, old age, male, smoking, and high SUV_max (>7) (all p < 0.05). The squamous cell carcinoma and smoker group also showed significantly higher SUV_max (both p < 0.001). Systemic inflammation markers, including NLR, PLR, and LMR, were positively correlated with high SUV_max (all p < 0.05). High GLUT1 expression, high SUV_max, high NLR, and low LMR, were significantly associated with poor overall survival in patients with NSCLC. However, in the multivariate survival analysis, LMR was an independent prognostic factor overall (HR 1.86, 95% CI 1.05-3.3) and for the stage I/II cohort (HR 2.3, 95% CI 1.24-4.3) (all p < 0.05).

CONCLUSIONS

Systemic inflammatory markers-NLR, PLR, and LMR are strongly correlated with the SUV_max and are indicators of aggressive tumor behavior. Specifically, LMR is a promising prognostic biomarker in NSCLC patients.

Association between Inflammatory Markers and Local Recurrence in Patients with Giant Cell Tumor of Bone: A Preliminary Result

Giant cell tumor of bone (GCTB) has a high local recurrence rate of approximately 20%. Systemic inflammatory markers, such as neutrophil-lymphocyte ratio (NLR), modified Glasgow prognostic score (mGPS), prognostic nutritional index (PNI), lymphocyte-monocyte ratio (LMR), platelet-lymphocyte ratio (PLR), hemoglobin (Hb), alkaline phosphatase (ALP), and lactate dehydrogenase (LDH), have been reported as prognostic markers in patients with malignant tumors. This study aimed to investigate the correlation between these markers and the local recurrence rate of GCTB. In total, 103 patients with GCTB who underwent surgery at the authors' institutions between 1993 and 2021 were included. Thirty patients experienced local recurrence. Univariate and multivariate analysis showed that tumor site, preoperative and postoperative denosumab treatment, and surgery were significantly associated with local recurrence-free survival. LDH was associated with local recurrence-free survival on univariate analysis only. NLR, mGPS, PNI, LMR, and PLR score did not correlate with the local recurrence rate. In conclusion, NLR, mGPS, PNI, LMR, PLR score, Hb, ALP, and LDH levels are not correlated with the local recurrence rate of GCTB. However, due to the small number of patients included in this study, this result should be re-evaluated in a multicenter study with a larger sample size.

P53-negative status and gross total resection as predictive factors for autologous tumor vaccine treatment in newly diagnosed glioblastoma patients

Background

Among primary brain tumors, glioblastoma (GBM) is the most common and aggressive in adults, with limited treatment options. Our previous study showed that autologous formalin-fixed tumor vaccine (AFTV) contributed to prognostic improvements in newly diagnosed GBM patients. However, some patients died early despite the treatment. The discovery of predictive factors in the treatment was warranted for efficient patient recruitment and studies to overcome resistance mechanisms. Identifying prognostic factors will establish AFTV guidelines for patients who may respond to the therapy.

Methods

Data from 58 patients with newly diagnosed GBM, including 29 who received standard therapy plus AFTV (AFTV group) and 29 who received standard treatment (control group) were analyzed. Several data including patient age, sex, the extent of removal, and various cell immunohistochemistry (IHC) parameters were also included in the analysis.

Results

Both univariate and multivariate analyses revealed that gross total resection (GTR) and negative p53 were associated with a better prognosis only in the AFTV group. In the IHC parameters, CD8 staining status was also one of the predictive factors in the univariate analysis. For blood cell-related data, lymphocyte counts of 1100 or more and monocyte counts of 280 or more before chemo-radiotherapy were significant factors for good prognosis in the univariate analysis.

Conclusions

A p53-negative status in IHC and GTR were the predictive factors for AFTV treatment in newly diagnosed GBM patients. Microenvironment-targeted treatment and pretreatment blood cell status may be key factors to enhance therapy effects.

Identification of prognostic factors and nomogram model for patients with advanced lung cancer receiving immune checkpoint inhibitors

Background and aim

Some patients with lung cancer can benefit from immunotherapy, but the biomarkers that predict immunotherapy response were not well defined. Baseline characteristic of patients may be the most convenient and effective markers. Therefore, our study was designed to explore the association between baseline characteristics of patients with lung cancer and the efficacy of immunotherapy.

Methods

A total of 216 lung cancer patients from Tianjin Medical University Cancer Institute & Hospital who received immunotherapy between 2017 and 2021 were included in the retrospective analysis. All baseline characteristic data were collected and then univariate log-rank analysis and multivariate COX regression analysis were performed. Kaplan-Meier analysis was used to evaluate patients' progression-free survival (PFS). A nomogram based on significant biomarkers was constructed to predict PFS rate of patients receiving immunotherapy. We evaluated the prediction accuracy of nomogram using C-indices and calibration curves.

Results

Univariate analysis of all collected baseline factors showed that age, clinical stage, white blood cell (WBC), lymphocyte (LYM), monocyte (MON), eosinophils (AEC), hemoglobin (HB), lactate dehydrogenase (LDH), albumin (ALB) and treatment line were significantly associated with PFS after immunotherapy. Then these 10 risk factors were included in a multivariate regression analysis, which indicated that age (HR: 1.95, 95% CI [1.01-3.78], P = 0.048), MON (HR: 1.74, 95% CI [1.07-2.81], P = 0.025), LDH (HR: 0.59, 95% CI [0.36-0.95], P = 0.030), and line (HR: 0.57, 95% CI [0.35-0.94], P = 0.026) were significantly associated with PFS in patients with lung cancer receiving immunotherapy. Patients with higher ALB showed a greater trend of benefit compared with patients with lower ALB (HR: 1.58, 95% CI [0.94-2.66], P = 0.084). Patients aged ≥51 years, with high ALB, low LDH, first-line immunotherapy, and high MON had better response rates and clinical benefits. The nomogram based on age, ALB, MON, LDH, line was established to predict the prognosis of patients treated with immune checkpoint inhibitor (ICI). The C-index of training cohort and validation cohort were close, 0.71 and 0.75, respectively. The fitting degree of calibration curve was high, which confirmed the high prediction value of our nomogram.

Conclusion

Age, ALB, MON, LDH, line can be used as reliable predictive biomarkers for PFS, response rate and cancer control in patients with lung cancer receiving immunotherapy. The nomogram based on age, ALB, MON, LDH, line was of great significance for predicting 1-year-PFS, 2-year-PFS and 3-year-PFS in patients with advanced lung cancer treated with immunotherapy.

Lymphocyte-to-Monocyte Ratio and Clinical Outcomes in Cholangiocarcinoma: A Systematic Review and Meta-Analysis

Lymphocyte-to-Monocyte ratio (LMR) has shown an association with survival outcomes in several oncological diseases. This study aimed to evaluate the association between LMR and clinical outcomes for cholangiocarcinoma patients. A systematic review and meta-analysis were performed to assess the association between LMR values and overall survival (OS), disease-free survival (DFS), recurrence-free survival (RFS) and time to recurrence (TTR) in cholangiocarcinoma patients. We used Hazard ratio (HR) and their 95% confidence interval (CI) as a measure of effect for the random effect model meta-analysis. The Newcastle-Ottawa Scale was used for quality assessment. The Egger test and funnel plot were developed for approaching publication bias. A total of 19 studies were included in this study (n = 3860). The meta-analysis showed that cholangiocarcinoma patients with low values of LMR were associated with worse OS (HR: 0.82; 95% CI: 0.71-0.96; I2 = 86%) and worse TTR (HR: 0.71; 95% CI: 0.58-0.86; I2 = 0%). DFS and RFS also were evaluated; however, they did not show statistically significant associations. Low LMR values were associated with a worse OS and TTR.

Long non-coding MELTF Antisense RNA 1 promotes and prognosis the progression of non-small cell lung cancer by targeting miR-1299

This paper explored the influence of long non-coding MELTF Antisense RNA 1 (lncRNA MELTF-AS1) on the prognosis of non-small cell lung cancer (NSCLC), and further deepened the understanding of NSCLC. A total of 130 patients with NSCLC participated in current study to detect and compare lncRNA MELTF-AS1 expression in cancer and normal tissues. Kaplan-Meier analysis and log-rank test were chosen to analyze the effect of MELTF-AS1 expression on the survival of patients within 5 years. The correlation between the expression of MELTF-AS1 and the clinical characteristics of NSCLC patients was analyzed, and the prognostic factors of NSCLC were analyzed by multivariate Cox regression. Subsequently, MELTF-AS1 expression in NSCLC cells were detected. The Cell Counting Kit-8 (CCK-8) and Transwell methods were selected to study the proliferation, migration capability and invasion level of NSCLC cells that silencing MELTF-AS1. Through the luciferase activity assay to explore the relationship between MELTF-AS1 and miR-1299, to further understand the effect of silencing MELTF-AS1 on NSCLC. MELTF-AS1 was increased in NSCLC tissues and cells. Silencing MELTF-AS1 suppressed the proliferation ability, migration capability and invasion level of NSCLC cells, which means that low expression of MELTF-AS1 may be more conducive to patient survival. In addition, through luciferase activity analysis and bioinformatics analysis, MELTF-AS1 has a negative effect on miR-1299, and silencing MELTF-AS1 enhanced miR-1299 expression in NSCLC cells. MELTF-AS1 is highly likely to be a promising prognostic biomarker, and associated with the progression of NSCLC.

Prognostic value of neutrophil/lymphocyte, platelet/lymphocyte, lymphocyte/monocyte ratios and Glasgow prognostic score in osteosarcoma: A meta-analysis

BACKGROUND

Some studies investigated the prognostic role of several blood biomarkers, including the neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), lymphocyte/monocyte ratio (LMR) and Glasgow prognostic score (GPS), in osteosarcoma, but their results were inconsistent with each other.

AIM

To identify the prognostic value of NLR, PLR, LMR and GPS in osteosarcoma patients through reviewing relevant studies.

METHODS

The PubMed, EMBASE, Web of Science and CNKI databases were searched up to October 2, 2021. The primary and second outcomes were overall survival (OS) and disease-free survival (DFS), respectively. The hazard ratios (HRs) with 95% confidence intervals (CIs) were combined to assess the association between these indicators and prognosis of osteosarcoma patients.

RESULTS

A total of 13 studies involving 2087 patients were eventually included. The pooled results demonstrated that higher NLR and GPS were significantly associated with poorer OS (HR = 1.88, 95%CI: 1.38-2.55, P < 0.001; HR = 2.19, 95%CI: 1.64-2.94, P < 0.001) and DFS (HR = 1.67, 95%CI: 1.37-2.04, P < 0.001; HR = 2.50, 95%CI: 1.39-4.48, P < 0.001). However, no significant relationship of PLR and LMR and OS (P = 0.085; P = 0.338) and DFS (P = 0.396; P = 0.124) was observed.

CONCLUSION

Higher NLR and GPS were related with worse prognosis and might serve as novel prognostic indicators for osteosarcoma patients.

Prognostic and clinicopathological significance of lymphocyte-to-monocyte ratio in patients with nasopharyngeal carcinoma: A meta-analysis

BACKGROUND

This study aimed to evaluate the prognostic effect of LMR in NPC through meta-analysis.

METHODS

The prognostic value of LMR for overall survival (OS) and disease-free survival (DFS)/progression-free survival (PFS) was evaluated by pooling hazard ratios (HRs) and 95% confidence intervals (CIs). The association between LMR and clinicopathological characteristics was estimated by using odds ratios (ORs) and 95% CIs.

RESULTS

A total of 7 studies with 3773 patients were included in this meta-analysis. The results showed that a low LMR was associated with poor OS (HR = 1.94, 95%CI = 1.71-2.20, p < 0.001) and reduced DFS/PFS (HR = 1.51, 95%CI = 1.23-1.85, p < 0.001) in NPC. Furthermore, a low LMR was significantly associated with male sex (OR = 1.34, 95%CI = 1.12-1.59, p = 0.001), T3-T4 stage (OR = 1.58, 95%CI = 1.02-2.45, p = 0.040), and tumor stage III-IV (OR = 1.54, 95%CI = 1.22-1.95, p < 0.001).

CONCLUSIONS

Our study indicated that a low LMR was correlated with poor survival and advanced tumor stage in patients with NPC.