Integration of clinical phenoms and metabolomics facilitates precision medicine for lung cancer.

Lung cancer is a common malignancy that is frequently associated with systemic metabolic disorders. Early detection is pivotal to survival improvement. Although blood biomarkers have been used in its early diagnosis, missed diagnosis and misdiagnosis still exist due to the heterogeneity of lung cancer. Integration of multiple biomarkers or trans-omics results can improve the accuracy and reliability for lung cancer diagnosis. As metabolic reprogramming is a hallmark of lung cancer, metabolites, specifically lipids might be useful for lung cancer detection, yet systematic characterizations of metabolites in lung cancer are still incipient. The present study profiled the polar metabolome and lipidome in the plasma of lung cancer patients to construct an inclusive metabolomic atlas of lung cancer. A comprehensive analysis of lung cancer was also conducted combining metabolomics with clinical phenotypes. Furthermore, the differences in plasma lipid metabolites were compared and analyzed among different lung cancer subtypes. Alcohols, amides, and peptide metabolites were significantly increased in lung cancer, while carboxylic acids, hydrocarbons, and fatty acids were remarkably decreased. Lipid profiling revealed a significant increase in plasma levels of CER, PE, SM, and TAG in individuals with lung cancer as compared to those in healthy controls. Correlation analysis confirmed the association between a panel of metabolites and TAGs. Clinical trans-omics studies elucidated the complex correlations between lipidomic data and clinical phenotypes. The present study emphasized the clinical importance of lipidomics in lung cancer, which involves the correlation between metabolites and the expressions of other omics, ultimately influencing clinical phenotypes. This novel trans-omics network approach would facilitate the development of precision therapy for lung cancer.

Plasma Metabolite Profiling in the Search for Early-Stage Biomarkers for Lung Cancer: Some Important Breakthroughs.

Lung cancer is the leading cause of cancer-related mortality worldwide. In order to improve its overall survival, early diagnosis is required. Since current screening methods still face some pitfalls, such as high false positive rates for low-dose computed tomography, researchers are still looking for early biomarkers to complement existing screening techniques in order to provide a safe, faster, and more accurate diagnosis. Biomarkers are biological molecules found in body fluids, such as plasma, that can be used to diagnose a condition or disease. Metabolomics has already been shown to be a powerful tool in the search for cancer biomarkers since cancer cells are characterized by impaired metabolism, resulting in an adapted plasma metabolite profile. The metabolite profile can be determined using nuclear magnetic resonance, or NMR. Although metabolomics and NMR metabolite profiling of blood plasma are still under investigation, there is already evidence for its potential for early-stage lung cancer diagnosis, therapy response, and follow-up monitoring. This review highlights some key breakthroughs in this research field, where the most significant biomarkers will be discussed in relation to their metabolic pathways and in light of the altered cancer metabolism.

Prognostic value of neutrophil-to-lymphocyte ratio change in patients with locally advanced non-small cell lung cancer treated with thoracic radiotherapy.

In prior investigations, a correlation was established between patient outcomes in locally advanced non-small cell lung cancer (LA-NSCLC) following thoracic irradiation and parameters, such as pre/post-treatment neutrophil-to-lymphocyte ratio (NLR) and NLR change (ΔNLR). However, these parameters could potentially be influenced by radiation-related variables, such as gross tumor volume (GTV). The primary aim of this study was to elucidate the factors impacting post-treatment NLR and ΔNLR and to further assess their prognostic relevance. In this retrospective study, a cohort of 188 LA-NSCLC patients who underwent thoracic radiation between 2012 and 2017 was assessed. The calculation of pre/post-treatment NLR involved the use of absolute neutrophil and lymphocyte counts. ΔNLR was defined as the difference between post- and pre-treatment NLR values. To assess the relationships between various variables and overall survival (OS), local progression-free survival (LPFS), and distant metastasis-free survival (DMFS), the Kaplan-Meier technique and Cox proportional hazards regression were employed. Additionally, Spearman's rank correlation analysis was carried out to investigate correlations between the variables. The analysis revealed that both post-treatment NLR (r = 0.315, P < 0.001) and ΔNLR (r = 0.156, P = 0.032) were associated with GTV. However, OS, LPFS, and DMFS were not independently correlated with pre/post-treatment NLR. ΔNLR, on the other hand, exhibited independent associations with OS and DMFS (HR = 1.054, P = 0.020, and P = 0.046, respectively). Elevated ΔNLR values were linked to poorer OS (P = 0.023) and DMFS (P = 0.018) in the Kaplan-Meier analysis. Furthermore, when stratifying by GTV, a higher ΔNLR remained to be associated with worse OS and DMFS (P = 0.047 and P = 0.035, respectively) in the GTV ≤ 67.41 cm3 group, and in the GTV > 67.41 cm3 group (P = 0.028 and P = 0.042, respectively), highlighting ΔNLR as the sole independent predictive factor for survival and metastasis, irrespective of GTV.

The role of seven tumor-associated autoantibodies in the diagnosis, staging and treatment guidance of lung cancer.

BACKGROUND: This study assessed the diagnosis, staging and treatment guidance of lung cancer (LC) based on seven tumor-associated autoantibodies (TAAbs) -p53, PGP9.5, SOX2, GBU4-5, MAGE A1, CAGE, and GAGE7. METHODS: ELISA was used to determine the TAAb serum levels in 433 patients diagnosed with LC (161 surgical patients) and 76 patients with benign lung disease (16 surgical patients). The statistical characteristic of the TAAbs was compared among patients with different clinicopathological features. Pre- to postoperative changes in TAAb levels were analyzed to determine their value of LC. RESULTS: Among all patients, the positive rate of the seven TAAbs was 23.4%, sensitivity was 26.3%, accuracy was 36.3%, specificity was 93.4%, positive predictive value was 95.8%, and negative predictive value was 18.2%; the positive rate for the LC group (26.3%) was significantly higher than that for the benign group (6.6%; P < 0.001). Significant differences in the positive rate of the seven autoantibodies according to age (P < 0.001), smoking history (P = 0.009) and clinical LC stage (P < 0.001) were found. Smoking was positively associated with the positive of TAAbs (Τ = 0.118, P = 0.008). The positive rates of the seven TAAbs for squamous carcinoma (54.5%), other pathological types (44.4%) and poorly differentiated LC (57.1%) were significantly higher than those for the other types. The positive rate of GBU4-5 was highest among all TAAbs, and the SOX2 level in stage III-IV patients was much higher than that in other stages. For patients undergoing surgery, compared with the preoperative levels, the postoperative levels of the 7 markers, particularly p53 (P = 0.027), PGP9.5 (P = 0.007), GAGE7 (P = 0.014), and GBU4-5 (P = 0.002), were significantly different in the malignant group, especially in stage I-II patients, while no clear pre- to postoperative difference was observed in the benign group. CONCLUSIONS: When the seven TAAbs was positive, it was very helpful for the diagnosis of LC. The 7 TAAbs was valuable for staging and guiding treatment of LC in surgical patients.

Aspartate Transaminase-to-Albumin Ratio (ATAR), a Novel Prognostic Index, Predicts Outcomes in Patients with Small-Cell Lung Cancer.

BACKGROUND: Small cell lung cancer (SCLC) is characterized by high invasion rates, rapid progression, and poor prognoses. Thus, identifying SCLC patients at high risk of progression and death is critical to improve long-term survival. In this study, the aspartate transaminase-to-albumin ratio (ATAR) was examined as a prognostic factor for SCLC patients. METHODS: We screened 196 SCLC patients from December 2013 to September 2022 at the Sichuan Cancer Hospital. The data was collected from patients' medical information as well as from their blood results during diagnosis. Using the Youden index as a cutoff value, patients were divided into high-risk（> 0.54) and low-risk (≤ 0.54) ATAR groups. We analyzed the prognostic factors for overall survival (OS) using the Kaplan-Meier method, univariate and multivariate analyses, Cox regression, and the C-index. RESULTS: There were 109 (55.6%) smokers among the patients, and the median OS was 17.55 months. The Kaplan-Meier analysis indicated that patients with high-risk ATAR had significantly lower OS (p < 0.0001). A multivariate analysis demonstrated that elevated ATAR is an independent adverse predictor of OS (p < 0.001, HR = 1.907). Our study found that ATAR is an independent predictor of survival outcomes in SCLC, which was superior to ALB, PNI, and SII in predicting outcomes in low-risk and high-risk groups (all p < 0.05). Models combining ATAR with ALB, PNI, and SII showed more powerful prognostic value than their corresponding original models. Moreover, the prognostic indicator ATAR can significantly stratify stage I - II and III - IV SCLC patients (p ＜ 0.05). CONCLUSIONS: Peripheral blood ATAR prognostic index can be used as an independent predictor of SCLC patients before treatment.

[Effect of peripheral blood inflammatory indicators on the efficacy of immunotherapy in patients with advanced non-small cell lung cancer and chronic obstructive pulmonary disease].

Objective: To investigate the impact of peripheral blood inflammatory indicators on the efficacy of immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) complicated with chronic obstructive pulmonary disease (COPD). Methods: A retrospective cohort study was performed to include 178 patients with Ⅲ-Ⅳ NSCLC complicated with COPD who received at least 2 times of immunotherapy in Xinqiao Hospital of the Army Medical University from January 2019 to August 2021. Baseline peripheral blood inflammatory indicators such as interleukin-6 (IL-6), interleukin-8 (IL-8), tumor necrosis factor-α (TNF-α) were collected within 2 weeks before the first treatment, with the last one being on or before February 7, 2022. X-tile software was used to determine the optimal cut-off value of peripheral blood inflammatory indicators. The Cox multivariate regression models were used to analyze the factors affecting progression free survival (PFS) and overall survival (OS). Results: Among the 178 patients, there were 174 males (97.8%) and 4 females (2.2%); the age ranged from 42 to 86 (64.3±8.3) years old.There were 30 cases (16.9%) of immunotherapy monotherapy, 114 cases (64.0%) of immunotherapy combined with chemotherapy, 21 cases (11.8%) of immunotherapy combined with antivascular therapy, and 13 cases (7.3%) of immunotherapy combined with radiotherapy. The median follow-up period was 14.5 months (95%CI: 13.6-15.3 months). The objective response rate (ORR) and disease control rate (DCR) were 44.9% (80/178) and 90.4% (161/178) for the whole group, the median PFS was 14.6 months (95%CI: 11.6-17.6 months), and the median OS was 25.7 months (95%CI: 18.0-33.4 months). The results of Cox multivariate analysis showed that IL-6>9.9 ng/L (HR=5.885, 95%CI: 2.558-13.543, P<0.01), TNF-α>8.8 ng/L (HR=3.213, 95%CI: 1.468-7.032, P=0.003), IL-8>202 ng/L (HR=2.614, 95%CI: 1.054-6.482, P=0.038), systemic immune inflammatory index (SII)>2 003.95 (HR=2.976, 95%CI: 1.647-5.379, P<0.001) were risk factors for PFS, and advanced lung cancer inflammation index (ALI)>171.15 was protective factor for PFS (HR=0.545, 95%CI: 0.344-0.863, P=0.010). IL-6>9.9 ng/L(HR=6.124, 95%CI: 1.950-19.228, P<0.002), lactate dehydrogenase (LDH)>190.7 U/L (HR=2.776, 95%CI: 1.020-7.556, P=0.046), SII>2 003.95 (HR=4.521, 95%CI: 2.241-9.120, P<0.001) were risk factors for OS, and ALI>171.15 was a protective factor for OS (HR=0.434, 95%CI: 0.243-0.778, P=0.005). Conclusion: Baseline high levels of IL-6, TNF-α, IL-8, SII, LDH, and low levels of ALI are risk factors for poor prognosis in patients with advanced NSCLC-COPD receiving immunotherapy.

A combination of radiomic features, clinic characteristics, and serum tumor biomarkers to predict the possibility of the micropapillary/solid component of lung adenocarcinoma.

BACKGROUND: Invasive lung adenocarcinoma with MPP/SOL components has a poor prognosis and often shows a tendency to recurrence and metastasis. This poor prognosis may require adjustment of treatment strategies. Preoperative identification is essential for decision-making for subsequent treatment. OBJECTIVE: This study aimed to preoperatively predict the probability of MPP/SOL components in lung adenocarcinomas by a comprehensive model that includes radiomics features, clinical characteristics, and serum tumor biomarkers. DESIGN: A retrospective case control, diagnostic accuracy study. METHODS: This study retrospectively recruited 273 patients (males: females, 130: 143; mean age ± standard deviation, 63.29 ± 10.03 years; range 21-83 years) who underwent resection of invasive lung adenocarcinoma. Sixty-one patients (22.3%) were diagnosed with lung adenocarcinoma with MPP/SOL components. Radiomic features were extracted from CT before surgery. Clinical, radiomic, and combined models were developed using the logistic regression algorithm. The clinical and radiomic signatures were integrated into a nomogram. The diagnostic performance of the models was evaluated using the area under the curve (AUC). Studies were scored according to the Radiomics Quality Score and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines. RESULTS: The radiomics model achieved the best AUC values of 0.858 and 0.822 in the training and test cohort, respectively. Tumor size (T_size), solid tumor size (ST_size), consolidation-to-tumor ratio (CTR), years of smoking, CYFRA 21-1, and squamous cell carcinoma antigen were used to construct the clinical model. The clinical model achieved AUC values of 0.741 and 0.705 in the training and test cohort, respectively. The nomogram showed higher AUCs of 0.894 and 0.843 in the training and test cohort, respectively. CONCLUSION: This study has developed and validated a combined nomogram, a visual tool that integrates CT radiomics features with clinical indicators and serum tumor biomarkers. This innovative model facilitates the differentiation of micropapillary or solid components within lung adenocarcinoma and achieves a higher AUC, indicating superior predictive accuracy.

A new tool to predict aggressive lung cancer types before surgeryWe developed a tool to help doctors determine whether lung cancer is one of the more dangerous types, called micropapillary (MPP) or solid (SOL) patterns, before surgery. These patterns can be more harmful and spread quickly, so knowing they are there can help doctors plan the best treatment. We looked at the cases of 273 lung cancer patients who had surgery and found that 61 of them had these aggressive cancer types. To predict these patterns, we used a computer process known as logistic regression, analyzing CT scan details, health information, and blood tests for cancer markers. Based on CT scans, our tool was very good at predicting whether these patterns were present in two patient groups. However, predictions using only basic health information like the size of the tumor and whether the patient smoked needed to be more accurate. We found a way to make our predictions even better. Combining all information into one chart, known as a nomogram, significantly improved our ability to predict these dangerous cancer patterns. This combined chart could be a big help for doctors. It gives them a clearer picture of the cancer's aggressiveness before surgery, which can guide them to choose the best treatment options. This approach aims to offer a better understanding of the tumor, leading to more tailored and effective treatments for patients facing lung cancer.

Effect of body mass index and cholesterol-rich apolipoprotein-B-containing lipoproteins on clinical outcome in NSCLC patients treated with immune checkpoint inhibitors-based therapy: A retrospective analysis.

OBJECTIVES: Obesity and hypercholesterolemia are linked to unfavor clinical outcomes. Recent studies declared the paradox that high body mass index (BMI) and serum cholesterol were independently connected to better clinical outcome of immune checkpoint inhibitors (ICIs) monotherapy in non-small cell lung cancer (NSCLC). The aim of the study is to investigate the prognosis of BMI and serum cholesterol in ICIs-based therapy. METHODS: This is a retrospective study of 95 NSCLC patients treated with ICIs-based therapy at the Department of Oncology and Lung Cancer Center of China-Japan Friendship Hospital. Treatment efficacy was assessed using durable clinical benefit (DCB) versus nondurable benefit (NDB), best response (active vs. nonactive), and progression-free survival (PFS). The prognostic value of BMI, LDL-C, and RC was determined by multivariate regression analyses, while controlling for confounding factors including age, gender, diabetes status, smoking history, and statin usage. BMI was considered a confounding factor in the analysis when examining the impact of lipoproteins. RESULTS: In our study, we found that in the whole group, BMI ≥25 kg/m2 was linked to a higher risk of poor therapeutic response (OR = 5.92, 95% CI 1.99-19.51, p.val = 0.002) and shorter progression-free survival (HR = 3.00, 95% CI 1.59-5.68, p.val = 0.001). In addition, low levels of RC were associated with better therapeutic response (OR = 0.12, 95% CI 0.02-0.64, p.val = 0.019), while low levels of serum LDL-C were found to predict longer PFS (HR = 0.40, 95% CI 0.19-0.82, p.val = 0.012). These associations were consistent in advanced NSCLC patients receiving ICIs and chemotherapy. CONCLUSIONS: Our study suggest that BMI ≥25 kg/m2 and elevated levels of apoB-containing lipoproteins, including LDL-C and RC, could potentially serve as useful prognostic markers for predicting poor treatment outcomes in advanced NSCLC patients treated with the combination of chemotherapy and ICIs.

CRISPR/Cas13a-based supersensitive circulating tumor DNA assay for detecting EGFR mutations in plasma.

Despite recent technological advancements in cell tumor DNA (ctDNA) mutation detection, challenges persist in identifying low-frequency mutations due to inadequate sensitivity and coverage of current procedures. Herein, we introduce a super-sensitivity and specificity technique for detecting ctDNA mutations, named HiCASE. The method utilizes PCR-based CRISPR, coupled with the restriction enzyme. In this work, HiCASE focuses on testing a series of EGFR mutations to provide enhanced detection technology for non-small cell lung cancer (NSCLC), enabling a detection sensitivity of 0.01% with 40 ng cell free DNA standard. When applied to a panel of 140 plasma samples from 120 NSCLC patients, HiCASE exhibits 88.1% clinical sensitivity and 100% specificity with 40 µL of plasma, higher than ddPCR and Super-ARMS assay. In addition, HiCASE can also clearly distinguish T790M/C797S mutations in different positions at a 1% variant allele frequency, offering valuable guidance for drug utilization. Indeed, the established HiCASE assay shows potential for clinical applications.

DHEA-S, Androstenedione, 17-ß-estradiol signature as novel biomarkers for early prediction of risk of malignant pleural mesothelioma linked to asbestos-exposure: A preliminary investigation.

17-ß-estradiol, involved in mesothelioma pathogenesis, and its precursors were explored as potential biomarkers for the early diagnosis of mesothelioma. Using enzyme-linked immunosorbent assay(ELISA) for 17-ß-estradiol and ultra-high performance liquid chromatography/tandem mass spectrometry(UHPLC-MS/MS) for 19 17-ß-estradiol precursors, a comprehensive analysis of 20steroid hormones was conducted in the serum of mesothelioma patients(n=67), asbestos-exposed healthy subjects(n=39), and non-asbestos-exposed healthy subjects(n=35). Bioinformatics analysis explored three potential serum biomarkers: 17-ß-estradiol, DHEA-S, and androstenedione. The results revealed significant differences in 17-ß-estradiol levels between mesothelioma patients and both non-asbestos-exposed and asbestos-exposed healthy subjects. No significant variations in serum 17-ß-estradiol levels were observed among mesothelioma patients at different stages, suggesting its potential as an early diagnostic marker. 17-ß-estradiol levels were similar in mesothelioma patients with environmental and occupational asbestos exposure, while males with occupational asbestos exposure exhibited significantly higher levels of 17-ß-estradiol compared to females. Significant reduction in androstenedione and an increase in DHEA-S were observed in asbestos-exposed individuals compared to non-asbestos-exposed individuals. The analysis of DHEA-S-androstenedione-17-ß-estradiol signature score showed an increase in asbestos-exposed individuals and mesothelioma patients compared to non-asbestos-exposed individuals, and this score effectively distinguished between the groups. The Cancer Genome Atlas data was utilized to analyze the expression of 5-α-reductase1 and hydroxysteroid-17ß-dehydrogenase2 genes. The findings indicated that mesothelioma patients with elevated gene values for 5-α-reductase1 and hydroxysteroid-17ß-dehydrogenase2 have a worse or better prognosis on overall survival, respectively. In conclusion, this study suggests 17-ß-estradiol, DHEA-S, and androstenedione as biomarkers for mesothelioma risk and early diagnosis of mesothelioma in asbestos-exposed individuals, aiding timely intervention and improved care.

Interleukin-25 as a Potential Biomarker in Lung Metastasis of Hepatocellular Carcinoma with HBV History in Chinese Patients: A Single Center, Case-control Study.

Background: Interleukin-25 (IL-25) has been proved to play a role in the pathogenesis and metastasis of Hepatocellular carcinoma (HCC), but the relationship between the level of IL-25 and the metastasis and prognosis of HCC is still not clear. This study aimed to investigate the expression of IL-25 and other potential biochemical indicators among healthy people, HBV-associated HCC patients without lung metastasis and HBV-associated HCC patients with lung metastasis. Methods: From September 2019 to November 2021, 33 HCC patients without lung metastasis, 37 HCC patients with lung metastasis and 29 healthy controls were included in the study. IL-25 and other commonly used biochemical markers were measured to establish predictors of overall survival (OS) and progression-free survival (PFS) after treatment. Results: The serum level of IL-25 was increased in HCC patients than healthy controls (p < 0.001) and HCC patients with lung metastasis had higher IL-25 level than HCC patients without metastasis (p = 0.035). Lung metastasis also indicated higher death rate (p < 0.001) by chi-square test, higher GGT level (p = 0.024) and higher AFP level (p = 0.049) by non-parametric test. Kaplan-Meier analysis demonstrated that IL-25 was negatively associated with PFS (p = 0.024). Multivariate Cox-regression analysis indicated IL-25 (p = 0.030) and GGT (p = 0.020) to be independent predictors of poorer PFS, while IL-25 showed no significant association with OS. Conclusion: The level of IL-25 was significantly associated with disease progression and lung metastasis of HBV-associated HCC. The high expression of IL-25 predicted high recurrence rate and death probability of HCC patients after treatment. Therefore, IL-25 may be an effective predictor of prognosis in HCC.

Exosome-transported circ_0061407 and circ_0008103 play a tumour-repressive role and show diagnostic value in non-small-cell lung cancer.

BACKGROUND: Circular RNAs (circRNAs), one of the major contents of exosomes, have been shown to participate in the occurrence and progression of cancers. The role and the diagnostic potential of exosome-transported circRNAs in non-small-cell lung cancer (NSCLC) remain largely unknown. METHODS: The NSCLC-associated exosomal circ_0061407 and circ_0008103 were screened by circRNA microarray. The role of circ_0061407 and circ_0008103 in NSCLC was examined in vitro and in vivo. The encapsulation of the two circRNAs into exosomes and the transport to recipient cells were observed by confocal microscopy. The effects of exosome-transported circ_0061407 and circ_0008103 on recipient cells were investigated using a co-culture device. Bioinformatics analyses were performed to predict the mechanisms by which circ_0061407 and circ_0008103 affected NSCLC. The quantitative polymerase chain reaction was used to quantify the exosome-containing circ_0061407 and circ_0008103 in the serum samples of healthy, pneumonia, benign lung tumours, and NSCLC. The diagnostic efficacy was evaluated using receiver operating characteristic curves. RESULTS: The levels of circ_0061407 and circ_0008103 within exosomes were down-regulated in the serum of patients with NSCLC. The up-regulation of circ_0061407 and circ_0008103 inhibited the proliferation, migration/invasion, cloning formation of NSCLC cells in vitro and inhibited lung tumour growth in vivo. Circ_0061407 and circ_0008103 were observed to be packaged in exosomes and transported to recipient cells, where they inhibited the proliferation, migration/invasion, and cloning formation abilities of the recipient cells. Moreover, circ_0061407 and circ_0008103 might be involved in the progression of NSCLC by interacting with microRNAs and proteins. Additionally, lower serum exosomal circ_0061407 and circ_0008103 levels were associated with advanced pathological staging and distant metastasis. CONCLUSIONS: This study identified two novel exosome-transported circRNAs (circ_0061407 and circ_0008103) associated with NSCLC. These findings may provide additional insights into the development of NSCLC and potential diagnostic biomarkers for NSCLC.

[Earlier detection of lung cancer through analysis of circulating tumor DNA from blood]. / Tidigare upptäckt av lungcancer med analys av tumör-DNA i blod.

To investigate the  clinical use of analyzing circulating tumor DNA in a clinical setting we present a pilot study comprising 93 patients from individuals with suspected lung cancer. The study aimed to evaluate the capability of analyzing circulating tumor DNA at the initial medical visit in order to detect genetic changes and mutations associated with lung cancer in plasma samples. Tumor DNA from plasma was extracted and analyzed with Next Generation Sequencing (NGS) and the result was compared with a matched tumor tissue collected in close connection from the same individual. Cancer-associated genetic mutations could be confirmed in about 60 percent of the plasma samples, and we observed a higher degree of conformance in patients with a more advanced disease. The results from the study provide valuable insights for an early clinical use of analyzing circulating tumor DNA in cases of suspected lung cancer, which could contribute to improving early diagnosis and treatment strategies for patients with lung cancer.

Optical Nanobiosensor Based on Surface-Enhanced Raman Spectroscopy and Catalytic Hairpin Assembly for Early-Stage Lung Cancer Detection via Blood Circular RNA.

Lung cancer has become the leading cause of cancer-related deaths globally. However, early detection of lung cancer remains challenging, resulting in poor outcomes for the patients. Herein, we developed an optical biosensor integrating surface-enhanced Raman spectroscopy (SERS) with a catalyzed hairpin assembly (CHA) to detect circular RNA (circRNA) associated with tumor formation and progression (circSATB2). The signals of the Raman reporter were considerably enhanced by generating abundant SERS "hot spots" with a core-shell nanoprobe and 2D SERS substrate with calibration capabilities. This approach enabled the sensitive (limit of detection: 0.766 fM) and reliable quantitative detection of the target circRNA. Further, we used the developed biosensor to detect the circRNA in human serum samples, revealing that patients with lung cancer had higher circRNA concentrations than healthy subjects. Moreover, we characterized the unique circRNA concentration profiles of the early stages (IA and IB) and subtypes (IA1, IA2, and IA3) of lung cancer. These results demonstrate the potential of the proposed optical sensing nanoplatform as a liquid biopsy and prognostic tool for the early screening of lung cancer.

Evaluation of the preoperative neutrophil-to-lymphocyte ratio as a predictor of the micropapillary component of stage IA lung adenocarcinoma.

OBJECTIVE: To assess the ability of markers of inflammation to identify the solid or micropapillary components of stage IA lung adenocarcinoma and their effects on prognosis. METHODS: We performed a retrospective study of clinicopathologic data from 654 patients with stage IA lung adenocarcinoma collected between 2013 and 2019. Logistic regression analysis was used to identify independent predictors of these components, and we also evaluated the relationship between markers of inflammation and recurrence. RESULTS: Micropapillary-positive participants had high preoperative neutrophil-to-lymphocyte ratios. There were no significant differences in the levels of markers of systemic inflammation between the participants with or without a solid component. Multivariate analysis showed that preoperative neutrophil-to-lymphocyte ratio (odds ratio [OR] = 2.094; 95% confidence interval [CI], 1.668-2.628), tumor size (OR = 1.386; 95% CI, 1.044-1.842), and carcinoembryonic antigen concentration (OR = 1.067; 95% CI, 1.017-1.119) were independent predictors of a micropapillary component. There were no significant correlations between markers of systemic inflammation and the recurrence of stage IA lung adenocarcinoma. CONCLUSIONS: Preoperative neutrophil-to-lymphocyte ratio independently predicts a micropapillary component of stage IA lung adenocarcinoma. Therefore, the potential use of preoperative neutrophil-to-lymphocyte ratio in the optimization of surgical strategies for the treatment of stage IA lung adenocarcinoma should be further studied.

Screening of lung cancer serum biomarkers based on Boruta-shap and RFC-RFECV algorithms.

OBJECTIVE: This study aimed to identify a set of serum miRNAs as potential biomarkers for lung cancer diagnosis using algorithmic approaches. METHODS: Serum miRNA expression data from lung cancer patients and non-tumor controls were obtained. The top six miRNAs were selected using Boruta-shap and RFC-RFECV algorithms. A Gaussian Naive Bayes (NB) classifier was trained and evaluated using cross-validation, ROC curve analysis, and evaluation metrics. RESULTS: Six miRNAs (hsa-miRNA-144, hsa-miRNA-107, hsa-miRNA-484, hsa-miRNA-103, hsa-miRNA-26b, and hsa-miRNA-641) were identified as feature genes. The NB classifier achieved an area under curve (AUC) of 0.8966 and a mean AUC of 0.88 in cross-validation. Accuracy, recall, and F1 scores exhibited promising results, with an accuracy of 82%. In the validation set, the AUC values for the NB and SVC classifiers were 0.9345 and 0.9423, respectively, with a mean AUC of 0.95 in cross-validation. The classifiers demonstrated an accuracy of 89% in diagnosing lung cancer. CONCLUSION: This study identified a panel of six serum miRNAs with potential as non-invasive biomarkers for lung cancer diagnosis. These miRNAs show promise in providing sensitive and specific tools for detecting lung cancer. SIGNIFICANCE: Lung cancer is one of the top cancers worldwide, threatening the health and lives of tens of thousands of people. miRNA is a biomarker, which can be used as a potential clinical tool for diagnosis and prognosis of cancer patients. Therefore, the use of multiple miRNAs to construct diagnostic models may be one of the future methods of accurate diagnosis of lung cancer. In this study, we used the Boruta-shap and RFC-RFECV algorithms to automatically identify and extract characteristic miRNAs highly associated with lung cancer, thereby establishing an accurate classifier for the diagnosis of lung cancer with characteristic miRNAs.

Prognostic utility of blood inflammation biomarkers before and after treatment on the survival of patients with locally advanced non-small cell lung cancer undergoing stereotactic body radiotherapy.

BACKGROUND AND OBJECTIVE: The neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) were significant and succinct indicators of systemic inflammation. We assessed the influence of stereotactic body radiotherapy (SBRT) on NLR and PLR in patients with locally advanced non-small cell lung cancer (LA-NSCLC). METHODS: We reviewed the medical data of patients with LA-NSCLC who underwent SBRT between 1 January 2013 and 31 December 2018. NLR and PLR values recorded at pre- and post-SBRT were examined. We assessed the correlation between pre/post-SBRT NLR and PLR and survival outcomes. The decision tree evaluation was conducted using Chi-square automatic detection. RESULTS: In total, 213 patients were included in the study with a median follow-up duration of 40.00 (ranging from 5.28 to 100.70) months. Upon dichotomization by a median, we identified that post-SBRT NLR > 5.5 and post-SBRT PLR > 382.0 were negatively associated with shorter overall survival (OS). In the multivariate assessment, post-SBRT PLR > 382.0 was the only factor. Based on post-SBRT PLR, tumor locations, and tumor stage, we categorized patients into low, medium, or high-risk groups. CONCLUSIONS: Post-SBRT PLR > 382.0 correlated with survival in patients undergoing SBRT. The decision tree model might play a role in future risk stratification to guide the clinical practice of individualized SBRT for LA-NSCLC.

Label-Free Multiplex Profiling of Exosomal Proteins with a Deep Learning-Driven 3D Surround-Enhancing SERS Platform for Early Cancer Diagnosis.

Identification of protein profiling on plasma exosomes by SERS can be a promising strategy for early cancer diagnosis. However, it is still challenging to detect multiple exosomal proteins simultaneously by SERS since the Raman signals of exosomes detected by conventional colloidal nanocrystals or two-dimensional SERS substrates are incomplete and complex. Herein, we develop a novel three-dimensional (3D) surround-enhancing SERS platform, named 3D se-SERS, for the multiplex detection of exosomal proteins. In this 3D se-SERS, proteins and exosomes are covered with "hotspots" generated by the gold nanoparticles, which surround the analytes densely and three-dimensionally, providing sensitive and comprehensive SERS signals. Combining this 3D se-SERS with a deep learning model, we successfully quantitatively profiled seven proteins including CD63, CD81, CD9, CD151, CD171, TSPAN8, and PD-L1 on the surface of plasma exosomes from patients, which can predict the occurrence and advancement of lung cancer. This 3D se-SERS integrating deep learning technique benefits from high sensitivity and significant multiplexing ability for comprehensive analysis of proteins and exosomes, demonstrating the potential of deep learning-driven 3D se-SERS technology for plasma exosome-based early cancer diagnosis.

Human epididymal protein 4 and its combined detection show good diagnostic value in lung cancer: A retrospective study.

OBJECTIVES: This study aimed to assess the diagnostic value of human epididymal protein 4 (HE4), a potential novel biomarker for lung cancer, and its combined detection with five other conventional biomarkers in lung cancer diagnosis and subtyping. METHODS: In this retrospective study, 115 lung cancer patients, 50 patients with benign pulmonary disease, and 50 healthy controls were included. Serum HE4, progastrin-releasing peptide (ProGRP), squamous cell carcinoma (SCC) antigen, cytokeratin-19 fragment (CYFRA21-1), neuron-specific enolase (NSE), and carcinoembryonic antigen (CEA) were analyzed using the electrochemiluminescence immunoassay and chemiluminescence immunoassay. The receiver operating characteristic curve was performed to analyze the diagnostic efficacy of individual biomarkers in identifying both lung cancer and its histologic subtypes. RESULTS: All six biomarkers showed significantly elevated levels in the lung cancer group compared to both benign pulmonary disease and control groups (P < 0.05). Among the biomarkers evaluated, HE4 exhibited the highest diagnostic performance for lung cancer, lung adenocarcinoma, and lung squamous cell carcinoma with area under the curve (AUC) values of 0.921, 0.891, and 0.937, respectively. ProGRP was the optimal biomarker for small cell lung cancer with an AUC of 0.973. The combination of all six biomarkers yielded the largest AUCs in the diagnosis of lung cancer subtypes (0.937 for lung adenocarcinoma, 0.998 for lung squamous cell carcinoma, and 0.985 for small cell lung cancer). Furthermore, specific combinations, such as HE4 + CEA, HE4 + SCC, and ProGRP + HE4 + NSE, showed strong diagnostic performance in lung cancer. CONCLUSIONS: HE4 and its combined detection held substantial clinical significance in the diagnosis of lung cancer and its histologic subtyping, especially for lung adenocarcinoma and lung squamous cell carcinoma.

Genetic and phenotypic profiling of single living circulating tumor cells from patients with microfluidics.

Accurate prediction of the efficacy of immunotherapy for cancer patients through the characterization of both genetic and phenotypic heterogeneity in individual patient cells holds great promise in informing targeted treatments, and ultimately in improving care pathways and clinical outcomes. Here, we describe the nanoplatform for interrogating living cell host-gene and (micro-)environment (NICHE) relationships, that integrates micro- and nanofluidics to enable highly efficient capture of circulating tumor cells (CTCs) from blood samples. The platform uses a unique nanopore-enhanced electrodelivery system that efficiently and rapidly integrates stable multichannel fluorescence probes into living CTCs for in situ quantification of target gene expression, while on-chip coculturing of CTCs with immune cells allows for the real-time correlative quantification of their phenotypic heterogeneities in response to immune checkpoint inhibitors (ICI). The NICHE microfluidic device provides a unique ability to perform both gene expression and phenotypic analysis on the same single cells in situ, allowing us to generate a predictive index for screening patients who could benefit from ICI. This index, which simultaneously integrates the heterogeneity of single cellular responses for both gene expression and phenotype, was validated by clinically tracing 80 non-small cell lung cancer patients, demonstrating significantly higher AUC (area under the curve) (0.906) than current clinical reference for immunotherapy prediction.