Prognostic value of lactate dehydrogenase in non-small cell lung cancer patients with brain metastases: a retrospective cohort study

An abnormal metabolism-related gene, ALG3, is a potential diagnostic and prognostic biomarker for lung adenocarcinoma

BACKGROUND

To explore the abnormal metabolism-related genes that affect the prognosis of patients with lung adenocarcinoma (LUAD), and analyze the relationship with immune infiltration and competing endogenous RNA (ceRNA) network.

METHODS

Transcriptome data of LUAD were downloaded from the Cancer Genome Atlas database. Abnormal metabolism-related differentially expressed genes in LUAD were screened by the R language. Cox analysis was used to construct LUAD prognostic risk model. Kaplan-Meier test, ROC curve and nomograms were used to evaluate the predictive ability of metabolic related gene prognostic model. CIBERSORT algorithm was used to analyze the relationship between risk score and immune infiltration. The starBase database constructed a regulatory network consistent with the ceRNA hypothesis. IHC experiments were performed to verify the differential expression of ALG3 in LUAD and paracancerous samples.

RESULTS

In this study, 42 abnormal metabolism-related differential genes were screened. After survival analysis, the final 5 metabolism-related genes were used as the construction of prognosis model, including ALG3, COL7A1, KL, MST1, and SLC52A1. In the model, the survival rate of LUAD patients in the high-risk subgroup was lower than that in the low-risk group. In addition, the risk score of the constructed LUAD prognostic model can be used as an independent prognostic factor for patients. According to the analysis of CIBERSORT algorithm, the risk score is related to the infiltration of multiple immune cells. The potential ceRNA network of model genes in LUAD was constructed through the starBase database. IHC experiments revealed that ALG3 expression was upregulated in LUAD.

CONCLUSION

The prognostic model of LUAD reveals the relationship between metabolism and prognosis of LUAD, and provides a novel perspective for diagnosis and research of LUAD.

Enhanced CT-based radiomics model to predict natural killer cell infiltration and clinical prognosis in non-small cell lung cancer

Background

Natural killer (NK) cells are crucial for tumor prognosis; however, their role in non-small-cell lung cancer (NSCLC) remains unclear. The current detection methods for NSCLC are inefficient and costly. Therefore, radiomics represent a promising alternative.

Methods

We analyzed the radiogenomics datasets to extract clinical, radiological, and transcriptome data. The effect of NK cells on the prognosis of NSCLC was assessed. Tumors were delineated using a 3D Slicer, and features were extracted using pyradiomics. A radiomics model was developed and validated using five-fold cross-validation. A nomogram model was constructed using the selected clinical variables and a radiomic score (RS). The CIBERSORTx database and gene set enrichment analysis were used to explore the correlations of NK cell infiltration and molecular mechanisms.

Results

Higher infiltration of NK cells was correlated with better overall survival (OS) (P = 0.002). The radiomic model showed an area under the curve of 0.731, with 0.726 post-validation. The RS differed significantly between high and low infiltration of NK cells (P < 0.01). The nomogram, using RS and clinical variables, effectively predicted 3-year OS. NK cell infiltration was correlated with the ICOS and BTLA genes (P < 0.001) and macrophage M0/M2 levels. The key pathways included TNF-α signaling via NF-κB and Wnt/β-catenin signaling.

Conclusions

Our radiomic model accurately predicted NK cell infiltration in NSCLC. Combined with clinical characteristics, it can predict the prognosis of patients with NSCLC. Bioinformatic analysis revealed the gene expression and pathways underlying NK cell infiltration in NSCLC.

A novel lactate dehydrogenase-based risk score model to predict the prognosis of primary central nervous system germ cell tumor treated with chemoradiotherapy

BACKGROUND

The prognostic role of lactate dehydrogenase (LDH) has been confirmed in many malignant tumors, but the role of serum LDH in primary central nervous system germ cell tumor (GCT) remains unknown. This study aimed to assess the prognostic value of LDH in GCT patients and develop a nomogram to predict prognosis in patients undergoing chemoradiotherapy.

METHODS

A total of 161 patients with GCT were included in this study. Using a restricted cubic spline (RCS) model, the optimal cutoff point for LDH was determined to be 217 U/L. The survival of GCT patients was evaluated using the Kaplan-Meier method and log-rank test to analyze the effects of LDH levels. Univariate Cox regression, multivariate Cox regression, and LASSO Cox regression were conducted to identify prognostic factors, which were incorporated into a nomogram for predicting overall survival (OS). The predictive accuracy of the nomogram was assessed using the C-index, calibration curve, area under the time-dependent receiver operating characteristic curve (time-dependent AUC), and risk group stratification. The net benefits of the nomogram at different threshold probabilities were quantified using decision curve analysis (DCA).

RESULTS

The high-LDH group had significantly shorter OS compared to the low-LDH group (P = 0.016). Based on the SYSUCC cohort, three variables were shown to be significant factors for OS and were incorporated in the nomogram: LDH, histopathology, and dissemination. It showed good discrimination ability, with C-index of 0.789 (95% CI, 0.671-0.907). Additionally, the clinical usefulness of the nomogram was confirmed by calibration curves and time-dependent AUC. DCA further highlighted the potential of the nomogram to guide clinical treatment strategies for patients. Moreover, there was a significant difference in OS among patients categorized into different risk groups (P < 0.001).

CONCLUSION

LDH levels may serve as a reliable predictor for assessing the therapeutic effect of chemoradiotherapy in GCT. The developed nomogram exhibits high accuracy in predicting survival outcomes, aiding in the classification of prognostic groups, and supporting informed clinical decision-making.

Exploration of a novel prognostic model based on nomogram in non-small cell lung cancer patients with distant organ metastasis: implications for immunotherapy

Background

Evidence for the effects of immunotherapy in non-small cell lung cancer (NSCLC) patients with distant organ metastasis is insufficient, and the predictive efficacy of established markers in tissue and blood is elusive. Our study aimed to determine the prognostic factors and develop a survival prognosis model for these patients.

Methods

A total of 100 advanced NSCLC patients with distant organ metastases, who received single or combination immune checkpoint inhibitors (ICIs) in Xijing Hospital between June 2018 and June 2021, were enrolled for retrospective analysis. The major clinicopathological parameters were collected, and associated survival outcomes were followed up by telephone or inpatient follow-up for nearly 3 years to assess prognoses. The survival prognosis model was established based on univariate and multivariate Cox regression analyses to determine the candidate prognostic factors.

Results

From the start of immunotherapy to the last follow-up, 77 patients progressed and 42 patients died, with a median follow-up of 18 months [95% confidence interval (CI): 15-19.9]. The median progression-free survival (PFS) and overall survival (OS) were 8 months (95% CI: 5.6-10.4) and 21 months (95% CI: 8.9-33.1), respectively. Multivariate Cox proportional hazards analysis showed Eastern Cooperative Oncology Group performance status (ECOG PS), body mass index (BMI), age-adjusted Charlson comorbidity index (ACCI), lactate dehydrogenase (LDH), and absolute neutrophil count (ANC) were correlated significantly with OS. Based on these five predictive factors, a nomogram and corresponding dynamic web page were constructed with a concordance index (C-index) of 0.81 and a 95% CI of 0.778-0.842. Additionally, the calibration plot and time-receiver operating characteristic (ROC) curve validated the precision of the model at 6-, 12-, and 18-month area under the curves (AUCs) reached 0.934, 0.829, and 0.846, respectively. According to the critical point of the model, patients were further divided into a high-risk total point score (TPS) >258, middle-risk (204< TPS ≤258), and low-risk group (TPS ≤204), and significant OS differences were observed among the three subgroups (median OS: 4.8 vs. 13.0 vs. 32.9 months).

Conclusions

A feasible and practical model based on clinical characteristics has been developed to predict the prognosis of NSCLC patients with distant organ metastasis undergoing immunotherapy.

Assessing the role of colonic and other anatomical sites uptake by [18 F]FDG-PET/CT and immune-inflammatory peripheral blood indexes in patients with advanced non-small cell lung cancer treated with first-line immune checkpoint inhibitors

BACKGROUND

Inflammation in non-small cell lung cancer (NSCLC) may impair the response to immune checkpoint inhibitors (ICIs) and can be indicated by peripheral blood inflammatory indexes. 2-deoxy-2-[18 F]fluoro-D-glucose positron emission tomography/computed tomography ([18 F] FDG-PET/CT) may be used as a marker of inflammation by measuring glucose metabolism in different colonic sites.

METHODS

This retrospective analysis aimed to investigate the correlation between [18 F] FDGPET/CT SUVratio in six gastrointestinal districts, the spleen, the pharynx and the larynx alongside the most avid tumor lesion with peripheral blood inflammatory indexes, including the neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammatory index (SII, i.e., NLR times platelets) and lactate dehydrogenase (LDH), in patients with [18 F] FDG-PET/CT staged IV NSCLC who received first-line immune checkpoint inhibitors (ICIs). The role of SUVratios and peripheral blood inflammatory indexes in predicting overall survival (OS) and progression-free survival (PFS) was then explored.

RESULTS

A total of 43 patients were treated with first-line ICI alone (58%) or in combination with chemotherapy (42%). A significant correlation was only found between the rectosigmoid SUVratio and NLR (p = 0.0465). NLR >5.5 and LDH > 333.5 were associated with a worse OS (p = 0.033 and p = 0.009, respectively). The SII was associated with a worse PFS in patients treated with ICI alone (p = 0.033). None of the SUVratios were significantly associated with OS or PFS, although a high left colon SUVratio showed a trend toward a worse PFS.

CONCLUSION

There was no significant correlation between [18 F]FDG PET/CT uptake in different anatomical sites, and in the tumor, and systemic immune-inflammatory indexes. The prognostic role of high left colon SUVratio deserves further investigation.

Factors associated with failure to start consolidation durvalumab after definitive chemoradiation for locally advanced NSCLC

Introduction

The introduction of consolidation immunotherapy after chemoradiotherapy has improved outcome for patients with locally advanced non-small cell lung cancer. However, not all patients receive this treatment. This study identifies factors associated with failure to start durvalumab as consolidation therapy with the aim of optimizing treatment, supportive care and prehabilitation to ensure that more patients complete the planned treatment.

Materials and methods

Patients from two clinical trials and a named patient use program, were included in this study. All patients received platinum-doublet chemotherapy concomitant with radiotherapy to a total dose of 60-66 gray. Patient characteristics, cancer treatment, toxicity, performance status and laboratory data before and after chemoradiotherapy were recorded and patients who never started durvalumab were compared with those who did.

Results

A total of 101 patients were included, of which 83 started treatments with durvalumab after chemoradiotherapy. The 18 patients who did not start durvalumab had significantly higher lactate dehydrogenase at baseline and a worse performance status, cumulative toxicity and higher c-reactive protein after completed chemoradiotherapy. Data also suggest that pre-treatment diabetes and reduced hemoglobin and/or diffusion capacity of the lungs for carbon monoxide contribute to the risk of treatment abruption.

Conclusion

Treatment plan disruption rate was 18%. Systemic inflammation and performance status were associated with failure to receive durvalumab after chemoradiation. Further studies are needed to confirm findings and prospective trials should investigate whether prehabilitation and supportive treatment could help more patients finishing the planned treatment.

Clinical Trial Registration

clinicaltrials.gov, identifier NCT03798535; NCT04392505.

Multi-Biomarkers Panel in Identifying Benign and Malignant Lung Diseases and Pathological Types of Lung Cancer

With the discovery of many tumor markers, there are new strategies for the early diagnosis and treatment of lung cancer and the prediction of prognosis. We examined the multi-protein markers panel (4MP, consisting of Pro-SFTPB, CA125, Cyfra21-1, and CEA) diagnosis performance in differentiating benign and malignant lung diseases and identifying pathological types of lung cancer. Meantime, the complementary performance of three conventional tumor markers (NSE, SCC, and Pro-GRP) for 4MP was assessed. A total of 294 patients with lung cancer or benign lung disease are contained in this study. The AUCs of 4MP and 7MP (NSE, SCC, Pro-GRP, and 4MP) in distinguishing benign lung disease and lung cancer were 0.808 and 0.832, respectively. In distinguishing SQCLC and SCLC, the AUCs were 0.716 and 0.985, respectively. In distinguishing LADC and SCLC, the AUCs were 0.849 and 0.998, respectively. This study demonstrated that 4MP can distinguish lung cancer from benign disease. Traditional biomarkers NSE, SCC, and Pro-GRP can significantly improve the performance of 4MP in the differentiation of LADC, SQCLC, and SCLC, which is expected to contribute to the accurate diagnosis and personalized treatment of patients.