Lung Cancer Research Articles

Ultrasensitive In2O3-Based Nanoflakes for Lung Cancer Diagnosis and the Sensing Mechanism Investigated by Operando Spectroscopy.

Rapid gas sensing with high sensitivity and selectivity is pivotal in advanced production, in smart living, and increasingly in medical health applications. This study presents a novel Pt@InNiOx nanoflake isoprene sensor that achieves an exceptionally low limit of detection (LOD) at 2 ppb, the lowest reported for isoprene sensors to date. Notably, it exhibits high selectivity and remarkable antihumidity capacity, thus meeting the stringent requirements for lung cancer screening. To unravel the sensing mechanism, we fabricate an operando DRIFTS-Raman cell coupled to online electrical measurements. It reveals that the ultrasensitive performance of Pt@InNiOx nanoflakes stems from the activated conjugated structure of isoprene by Pt nanoclusters and from the enhanced isoprene adsorption and electron interaction due to the nanoflake morphology. The p-n junction constructed by doping Ni maintains Fermi level equilibrium, shielding it from humidity interference. Practically, we integrate these ultrasensitive Pt@InNiOx nanoflakes into a miniaturized portable electronic device that successfully distinguishes lung cancer patients with expiratory isoprene below 40 ppb, from the healthy population with isoprene above 60 ppb, enabling an accurate diagnosis in clinics. Our work not only provides a breakthrough in low-cost, noninvasive cancer screening through breath analysis but also advances the rational design of cutting-edge gas sensing materials.

Complex shape markers can detect alterations in the spatial distribution of cell nuclei in human lung squamous cell carcinoma: a useful tool for automatic analysis?

Lung cancer is the leading cause of cancer-related death. The use of computational methods to quantify changes that are not perceptible to the human eye is increasing in digital pathology imaging and has quickly improved detection rates at a low cost. Therefore, the present study aims to use complex computational shape markers as tools for automated analysis of the spatial distribution of cells in microscopy images of squamous cell lung carcinoma (SqCC). Photomicrographs from pathology glass slides in the LC25000 dataset were used in this study. Compared with those of the control, the fractal dimension (28%) and lacunarity (41%) of the cell nuclei changed in SqCC. The multifractal analysis revealed a significant difference in parameters Dq, α, and f(α) for all values of q (-10 to + 10), with a greater increase for more positive q values. The values at q + 10 increased by 34% for Dq, 36% for α, and 53% for f(α) in the SqCC images. The circularity, area, and perimeter also changed in the SqCC images. However, the parameters of aspect ratio, roundness, and solidity did not significantly differ between SqCC and benign tissue. The complex shape markers with the greatest changes in this study were the f(α) values for multifractality (53%) and lacunarity (41%). In conclusion, automated quantification of the spatial distribution of cell nuclei can be a fast, low-cost tool for evaluating the microscopic characteristics of SqCC; therefore, complex shape markers could be useful tools for software and artificial intelligence to detect lung carcinoma.

Cancers and erectile dysfunction: a Mendelian randomization study

PurposeCancer often coexists with erectile dysfunction, yet the causal relationship between them remains unclear. This study aims to investigate the causal link between tumors and ED through Mendelian randomization.MethodData on 13 different cancers, including lung cancer, colorectal cancer, testicular cancer, lymphoma, esophageal cancer, pancreatic cancer, thyroid cancer, bladder cancer and brain cancer were collected from various databases. ED data, comprising 2,205 cases and 164,104 controls, were sourced from the FinnGen project. Primary methods for MR analysis included IVW, MR-Egger, weighted median, and weighted mode.ResultsIVW results revealed associations between colorectal cancer (OR=1.17;95% CI 1.02-1.13, p=0.0252), prostate cancer (OR=1.63;95% CI 1.52-1.75, p<0.001) and liver cancer (OR=0.93;95% CI 0.88 -0.99, p=0.012) with ED.ConclusionMendelian randomization analysis supports that prostate cancer and colorectal cancer are associated with an increased risk of Erectile Dysfunction, whereas liver cancer is linked to a decreased risk of ED. No evidence suggests that ED contributes to an increased risk of prostate cancer.

Artificial Intelligence in Lung Cancer Detection: Clinical and Economic Assessment of Retrospective CT Analysis Two Years Post-COVID-19 Pandemic

BACKGROUND: The use of chest computed tomography (CT) scans in Krasnoyarsk Krai, Russia, has increased since 2020, during the COVID-19 pandemic. This period also saw a 5.2% decrease in lung cancer (LC) incidence. The potential for missed LC cases has led to the investigation of new diagnostic methods, including the use of artificial intelligence (AI) for analyzing retrospective data. AIM: The study aimed to evaluate the effectiveness of an AI algorithm in identifying patients at high risk for LC using chest CT data from the COVID-19 pandemic. METHODS: A retrospective analysis was conducted on chest CTs from patients diagnosed with COVID-19 in the Krasnoyarsk region, using scans from November 1, 2020, to February 28, 2021. The AI algorithm "Chest-IRA" was applied to detect pulmonary nodules larger than 100 mm³. Radiologists classified the nodules detected by AI into three categories based on LC probability. The economic assessment of the AI algorithm included salary costs and potential savings from early LC treatment. RESULTS: Out of 10,500 CTs, the AI algorithm found nodules in 484 cases. Of these, 192 were highly likely to have LC, 103 showed no signs, and 60 were inconclusive. 112 patients with high or intermediate risk did not seek treatment. The AI confirmed lung cancer in 100 cases, 28.2% of those detected. Early-stage LC was found in 35% of cases, while 65% were at later stages. AI could save about 25.04 months of radiologist work, costing 2.43 million RUB. Early detection savings are estimated at 10.6 to 12.5 million RUB per 10,500 CTs, with a five-year economic impact of 259.4 to 305.1 million RUB. CONCLUSION: AI has proven effective in identifying pulmonary nodules amidst COVID-19, highlighting its potential to improve early detection and diagnostic accuracy, leading to earlier and more precise treatments.

Efficient Predictor for Immunotherapy Efficacy: Detecting Pan-Clones Effector Tumor Antigen Antigen-Specific T Cells in Blood by Nanoparticles Loading Whole Tumor Antigens.

Cancer involves tumor cells and tumor-specific immunity. The ability to accurately quantify tumor-specific immunity is limited. Most immunotherapies function by activating new effector tumor antigen-specific T cells (ETASTs) or reactivating the pre-existing ETASTs repertoire. Therefore, the amount of ETASTs can be used to characterize immunotherapy efficacy. Tumor antigens are highly heterogeneous and detecting most ETASTs is challenging. Therefore, nanoparticles loading whole-cell tumor antigens are used to activate and detect pan-clones ETASTs in the blood. The differences between ETASTs and other T cells are transformed into activated and non-activated states. By measuring markers of the activated status and cytotoxic function of ETASTs, it can distinguish ETASTs from other T cells. ETASTs in patients with lung cancer are higher than those in healthy individuals and those with benign pulmonary nodules. Therapeutic efficacy positively correlated with the number of ETASTs in the blood. ETATS levels increase only in the blood of patients who respond to immunotherapy. Single-cell sequencing studies validated these findings. This study provides a highly accurate, specific, non-invasive, and efficient biomarker for predicting immunotherapy efficacy in lung and other cancers. This method can also be applied to evaluate the efficacy of other treatments, such as radiotherapy, oncolytic viruses, and nanomedicine-based therapies.

ZNF652 exerts a tumor suppressor role in lung cancer by transcriptionally downregulating cyclin D3.

Dysfunction of zinc finger protein 652 (ZNF652) is associated with various malignant tumors. However, the role of ZNF652 in lung cancer (LC) is poorly understood. Here, we identified that ZNF652 was downregulated in human LC tissues and cell lines. Low ZNF652 expression was associated with poor survival in LC patients. Overexpression of ZNF652 inhibited cell viability, proliferation, migration, and invasion of LC cells, whereas ZNF652 knockdown promoted these malignant phenotypes. Using RNA-seq analysis revealed that ZNF652 overexpression resulted in obvious alterations of various biological processes, especially cell cycle and cellular senescence. Subsequently, we confirmed that ZNF652 overexpression arrested the cell cycle at the G1 phase, increased ROS-mediated DNA damage, induced LC cell senescence, and enhanced cisplatin-induced apoptosis in LC cells. Mechanistically, ZNF652 directly bound to the promoter of cyclin D3 (CCND3), inhibited its transcription, thereby arresting the cell cycle at the G1 phase. Ectopic expression of cyclin D3 rescued the decreased cell viability and cell cycle arrest induced by ZNF652. In vivo studies further showed that ZNF652 overexpression suppressed the tumorigenic potential of LC. Collectively, our findings reveal that ZNF652 exerts a tumor suppressor role in lung cancer by inducing cell cycle arrest and cellular senescence via transcriptionally downregulating cyclin D3. Thus, ZNF652 may be a prognostic predictive factor for LC patients.

Assessing the 9G Technology Blood Test for Predicting Lung Cancer in Patients with CT-Detected Lung Nodules: A Multicenter Clinical Trial

Background and Objectives: Lung nodules detected by chest computed tomography (CT) often require invasive biopsies for definitive diagnosis, leading to unnecessary procedures for benign lesions. A blood-based biomarker test that predicts lung cancer risk in CT-detected nodules could help stratify patients and direct invasive diagnostics toward high-risk individuals. Methods: In this multicenter, single-blinded clinical trial, we evaluated a test measuring plasma levels of p53, anti-p53 autoantibodies, CYFRA 21-1, and anti-CYFRA 21-1 autoantibodies in patients with CT-detected lung nodules. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated, and subgroup analyses by gender, age, and smoking status were performed. A total of 1132 patients who had CT-detected lung nodules, including 885 lung cancer cases and 247 benign lesions, were enrolled from two academic hospitals in South Korea. Results: The test demonstrated a sensitivity of 78.4% (95% CI: 75.7–81.1) and specificity of 93.1% (95% CI: 90.0–96.3) in predicting lung cancer in CT-detected nodules. The PPV was 97.6%, and the NPV was 54.6%. Performance was consistent across gender (sensitivity 79.3% in men and 76.8% in women) and age groups, with a specificity of 93.4% in men and 92.7% in women. Stage I lung cancer was detected with a sensitivity of 80.6%. Conclusions: The Lung Cancer test based on 9G technology presented here offers a non-invasive method for stratifying lung cancer risk in patients with CT-detected nodules. Its integration into clinical practice could reduce unnecessary interventions and foster earlier detection.

LW-MorphCNN: a lightweight morphological attention-based subtype classification network for lung cancer

Abstract Lung cancer is generally considered one of the most deadly cancers globally. If it can be identified early and diagnosed correctly, the survival probability of patients can be significantly improved. In this process, histopathological examination is a commonly used method for diagnosing and detecting lung cancer. It is crucial to accurately identify lung cancer subtypes from histopathological images, as this helps doctors formulate effective treatment plans. However, the visual inspection in histopathological diagnosis requires a large amount of time and also depends on the subjective perception of clinicians. Therefore, this paper proposes a lightweight lung cancer subtype classification network based on morphological attention (LW-MorphCNN), which is used to automatically classify the histopathological images of benign lung tumors, ADC (adenocarcinoma), and SCC (squamous cell carcinoma) provided in the public dataset LC25000 (Lung and Colon). This paper takes histopathological images as input and conducts a comparative analysis with classic networks such as VGG16, VGG19, DenseNet121, and ResNet50, as well as existing classification methods proposed in the same work. The network proposed in this paper is superior to other networks in terms of parameter quantity and performance, with an accuracy rate and F1 - score reaching 99.47% and 99.44% respectively. Clinicians can install the provided LW-MorphCNN in the hospital to confirm the diagnosis results.

Predicting radiation pneumonitis in lung cancer using machine learning and multimodal features: a systematic review and meta-analysis of diagnostic accuracy.

To evaluate the diagnostic accuracy of machine learning models incorporating multimodal features for predicting radiation pneumonitis in lung cancer through a systematic review and meta-analysis. Relevant studies were identified through a systematic search of PubMed, Web of Science, Embase, and the Cochrane Library from October 2003 to December 2023. Additional studies were located by reviewing bibliographies and relevant websites. Two independent researchers screened titles, abstracts, and full-text articles according to predefined inclusion and exclusion criteria. Data extraction was performed using standardized forms, and study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The primary outcomes, including combined sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC), were calculated using STATA MP-64 software(Stata Corporation LLC, College Station, USA) with a random-effects model. Meta-analysis was conducted to synthesize diagnostic accuracy measures, and analyses of heterogeneity and publication bias were performed. A total of 1,406 patients with primary lung cancer were included in this systematic review, drawing data from 9 studies. The pooled analysis revealed a sensitivity of 0.74 [0.58-0.85] and a specificity of 0.91 [0.87-0.95] for machine learning models in diagnosing radiation pneumonitis. The positive likelihood ratio (PLR) was 8.69 [5.21-14.50], the negative likelihood ratio (NLR) was 0.28 [0.16-0.49], and the diagnostic odds ratio (DOR) was 30.73 [11.96-78.97]. The area under the curve (AUC) was 0.93 [0.90-0.95], indicating excellent diagnostic performance. Meta-regression analysis identified that the number of machine learning models, year of publication, and study design contributed to heterogeneity among studies. No evidence of publication bias was found. Overall, machine learning models incorporating multimodal characteristics demonstrated 75% accuracy in predicting moderate to severe radiation pneumonitis. In conclusion, by integrating the current machine learning (ML) algorithm's ability in big data mining, a predictive model can be constructed by combining multi-modal features such as genetics, imaging, and cell factors. By selecting multiple machine learning algorithm frameworks and competing for the best combination model based on research goals, the reliability and accuracy of the radiation pneumonitis prediction model can be greatly improved. PROSPERO (CRD42024497599).

Uncovering the Daily Experiences of People Living With Advanced Cancer Using an Experience Sampling Method Questionnaire: Development, Content Validation, and Optimization Study.

The experience sampling method (ESM), a self-report method that typically uses multiple assessments per day, can provide detailed knowledge of the daily experiences of people with cancer, potentially informing oncological care. The use of the ESM among people with advanced cancer is limited, and no validated ESM questionnaires have been developed specifically for oncology. This study aims to develop, content validate, and optimize the digital Experience Sampling Method for People Living With Advanced Cancer (ESM-AC) questionnaire, covering multidimensional domains and contextual factors. A 3-round mixed methods study was designed in accordance with the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) and the European Organization for Research and Treatment of Cancer guidelines. The study included semistructured interviews with 43 people with stage IV breast cancer or stage III to IV lung cancer and 8 health care professionals. Round 1 assessed the appropriateness, relative importance, relevance, and comprehensiveness of an initial set of ESM items that were developed based on the existing questionnaires. Round 2 tested the comprehensibility of ESM items. Round 3 tested the usability of the digital ESM-AC questionnaire using the m-Path app. Analyses included descriptive statistics and qualitative content analysis. Following the first round, we developed an initial core set of 68 items (to be used with all patients) and a supplementary set (optional; patients select items), both covering physical, psychological, social, spiritual-existential, and global well-being domains and concurrent contexts in which experiences occur. We categorized items to be assessed multiple times per day as momentary items (eg, "At this moment, I feel tired"), once a day in the morning as morning items (eg, "Last night, I slept well"), or once a day in the evening as evening items (eg, "Today, I felt hopeful"). We used participants' evaluations to optimize the questionnaire items, the digital app, and its onboarding manual. This resulted in the ESM-AC questionnaire, which comprised a digital core questionnaire containing 31 momentary items, 2 morning items, and 7 evening items and a supplementary set containing 39 items. Participants largely rated the digital questionnaire as "easy to use," with an average score of 4.5 (SD 0.5) on a scale from 1 ("completely disagree") to 5 ("completely agree"). We developed the ESM-AC questionnaire, a content-validated digital questionnaire for people with advanced breast or lung cancer. It showed good usability when administered on smartphone devices. Future research should evaluate the potential of this ESM tool to uncover daily experiences of people with advanced breast or lung cancer, explore its clinical utility, and extend its validation to other populations with advanced diseases.

Knockdown of Inhibin Beta A Reversed the Epithelial Growth Factor Receptor Tyrosine Kinase Inhibitor Resistance and Enhanced the Therapeutic Effect of Radiotherapy in Non-Small Cell Lung Cancer.

Lung cancer is a malignant tumor with the highest mortality rate worldwide. Non-small cell lung cancer (NSCLC) accounts for approximately half of all lung cancer cases. Inhibin beta A (INHBA) is a ligand of the transforming growth factor-beta superfamily. This study aimed to analyze the function of INHBA in NSCLC resistance cells. Gene Expression Omnibus and The Cancer Genome Atlas databases were used to identify differentially expressed genes (DEGs) in NSCLC resistance cells and patients. DEGs were further analyzed by gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. A colony formation assay was performed to determine cell growth. Cell migration and invasion were tested by Transwell assay. Epithelial-mesenchymal transition (EMT)-related genes were analyzed using qRT-PCR and Western blot analysis. Using bioinformatics tools, INHBA was demonstrated to be overexpressed in NSCLC resistant cells and patients. Gefitinib treatment affected NSCLC resistant cells. Additionally, INHBA silencing or X-ray treatment suppressed the growth and metastasis of NSCLC resistant cells. Moreover, the combined application of INHBA silencing and X-ray treatment enhances the therapeutic effects. Moreover, EMT has been confirmed to occur in NSCLC resistant cell lines. Both INHBA silencing and X-ray treatment inhibited EMT development. This study demonstrated that INHBA silencing ameliorates EGFR-TKI resistance and enhances the therapeutic effect of radiotherapy in NSCLC.

Changes in Staging and Management of Non-Small Cell Lung Cancer (NSCLC) Patients Following the Implementation of Low-Dose Chest Computed Tomography (LDCT) Screening at Kaohsiung Medical University Hospital

Background: Low-dose computed tomography (LDCT) has been widely adopted for lung cancer screening due to its proven ability to reduce lung cancer mortality, especially among high-risk populations. Methods: This retrospective study aims to evaluate the impact of LDCT screening on non-small cell lung cancer (NSCLC) staging at Kaohsiung Medical University Hospital (KMUH) from 2011 to 2020, following the introduction of LDCT in 2013. The study examines the correlation between LDCT screening volume and changes in the distribution of NSCLC stages, particularly early-stage (stages 0 and I) and late-stage (stage IV) diagnoses. Additionally, it explores the differences in histopathological subtypes, focusing on adenocarcinoma and squamous cell carcinoma, and assesses the impact of early detection on five-year survival rates. Results: The results show a significant increase in early-stage NSCLC diagnoses, particularly in adenocarcinoma cases, where early-stage diagnoses rose from 10.4% in 2010 to 38.7% in 2019. However, the number of stage IV cases remained stable, indicating that LDCT may not substantially reduce late-stage diagnoses. Pearson’s correlation analysis demonstrated a strong positive correlation between LDCT screening and early-stage NSCLC detection, particularly for adenocarcinoma (p < 0.001), though the early detection of squamous cell carcinoma and small cell carcinoma remained limited. Conclusions: The study concludes that LDCT screening plays a crucial role in improving early NSCLC detection and five-year survival rates. Future research should focus on optimizing screening strategies to capture more at-risk populations and enhance the detection of harder-to-diagnose subtypes like squamous cell carcinoma.

Hsa_circ_0109320 Serves as a Novel Circular RNA Biomarker in Non-small Cell Lung Cancer by Promoting Metastasis.

Non-small cell lung cancer (NSCLC), including squamous cell carcinoma and adenocarcinoma, ranks among the top 10 cancers worldwide in terms of prevalence and mortality. NSCLC, a highly malignant tumor, exhibits distant invasion and migration as well as an unfavorable prognosis. As an innovative circular RNA, hsa _circ_0109320 (circ_0109320) has been recognized as a promising cancer modulator. However, our understanding of the influence of circ_0109320 in NSCLC remains insufficient. Our research explored the clinical significance and effects of circ_0109320 on oncogenic non-small cell lung cancer (NSCLC) phenotypes. Microarray analysis and qPCR indicated that circ_0109320 expression in NSCLC specimens increased relative to that in adjacent normal tissues and was further elevated in metastatic lymph nodes. The specimens acquired from 25 patients confirmed these findings. Additionally, circ_0109320 indicated a good score (AUC = 0.688, P = 0.013) on the ROC curves, which suggests its suitability as a promising biomarker for lung cancer. Meanwhile, circ_0109320 was noticeably upregulated in lung cancer (LC) cell lines compared to human bronchial epithelial cells. Next, we performed loss- and gain-of-function experiments to examine the role of circ_0109320 in the tumor phenotypes of the cell lines. We observed that depletion or overexpression of circ_0109320 did not alter cell viability. However, the ectopic removal of circ_0109320 repressed the migration and invasion of A549 and SK-MES-1 cells, whereas circ_0109320 overexpression promoted cell migration and invasion. Furthermore, the examination of epithelial-mesenchymal transition (EMT) markers indicated that circ_0109320 elevates cell EMT activity. In conclusion, circ_0109320 level was highly associated with increased tumor cell proliferation and metastasis. circ_0109320 could be a promising predictor of clinical outcomes and a reliable target to treat NSCLC by inhibiting metastasis.

Network Medicine-Based Strategy Identifies Maprotiline as a Repurposable Drug by Inhibiting PD-L1 Expression via Targeting SPOP in Cancer.

Immune checkpoint inhibitors (ICIs) are drugs that inhibit immune checkpoint (ICP) molecules to restore the antitumor activity of immune cells and eliminate tumor cells. Due to the limitations and certain side effects of current ICIs, such as programmed death protein-1, programmed cell death-ligand 1, and cytotoxic T lymphocyte-associated antigen 4 (CTLA4) antibodies, there is an urgent need to find new drugs with ICP inhibitory effects. In this study, a network-based computational framework called multi-network algorithm-driven drug repositioning targeting ICP (Mnet-DRI) is developed to accurately repurpose novel ICIs from ≈3000 Food and Drug Administration-approved or investigational drugs. By applying Mnet-DRI to PD-L1, maprotiline (MAP), an antidepressant drug is repurposed, as a potential PD-L1 modifier for colorectal and lung cancers. Experimental validation revealed that MAP reduced PD-L1 expression by targeting E3 ubiquitin ligase speckle-type zinc finger structural protein (SPOP), and the combination of MAP and anti-CTLA4 in vivo significantly enhanced the antitumor effect, providing a new alternative for the clinical treatment of colorectal and lung cancer.

Lung cancer and pulmonary tuberculosis: key features of molecular mechanisms of concomitant disease

Lung cancer and pulmonary tuberculosis have long been significant problems for global health, occupying leading positions in terms of morbidity and mortality in both developed and developing countries. Numerous clinical and experimental studies have allowed to get knowledge of the mechanisms of development of these pathological processes individually, the impact of diseases on the macroorganism, and various options of treatment. According to population studies, the interaction between these two processes is undeniable – both active tuberculosis and post-tuberculosis changes are equally risk factors for the development of neoplastic processes, and malignant tumors create favorable conditions and predispositions for the development of mycobacterial infection. However, the mechanisms of interaction between these two diseases in concomitant cases remain opened and insufficiently studied. This literature review provides a detailed description of the variants of lung cancer and pulmonary tuberculosis combinations, the pathophysiological basis of the interaction between infectious and neoplastic processes: modulation of the immune response by M. tuberculosis and lung tumor; oncogenic signaling pathways activated by tuberculosis infection; mechanisms of epithelial-mesenchymal transition in post-tuberculosis scar changes and its role in the formation of so-called "scarcinoma"; the relationship between tumor-mediated and tuberculosis-associated immunosuppression; the role of the PD-1: PD-L signaling pathway, and the influence of modern types of anti-tumor immunotherapy on the course of these pathological processes. The final part of the review presents our own data from experimental studies on the combination of cancer and tuberculosis in a laboratory model, identifying promising directions for further research on this issue.

Early Telephone-Based Frailty Screening With the Vulnerable Elders Survey in Adults Aged 75 Years and Older With Lung and Gynecological Cancer.

The International Society of Geriatric Oncology recommends that all older people with cancer have a geriatric evaluation before beginning treatment. To determine the prevalence of frailty in people 75 years and older diagnosed with lung or gynecological cancer and evaluate the adaptation of standard therapeutic strategies based on frailty, following the implementation of telephone-based frailty screening with the Vulnerable Elders Survey (VES-13). We performed a retrospective observational study in 362 people screened by an advanced practice nurse before their first oncology appointment. We collected secondary data from electronic medical records. The main variables were degree of frailty (according to VES-13 and comprehensive geriatric assessment), type of cancer treatment (standard and prescribed), treatment completion, sociodemographic characteristics, and comorbidities. The VES-13 detected 186 people (51.4%) at risk of health deterioration, and the comprehensive geriatric assessment confirmed some degree of frailty in 157 people (43.4%), with a κ coefficient of 0.84. People with more comorbidities, greater frailty, and more geriatric syndromes were more likely to need treatment readjustment ( P < .001). Telephone-based frailty screening by an advanced practice nurse showed high applicability, with very good agreement between the proportion of people classified as frail before the initial visit and in the subsequent geriatric assessment. A protocol for establishing frailty risk through telephone screening by an advanced practice nurse facilitates the care process and helps clinicians adapt therapeutic decision-making to the needs of each patient and their family.

Mechanism of the KIAA1429/KLF1/PD-L1 Axis in Regulating Immune Escape in Non-small Cell Lung Cancer.

Non-small cell lung cancer (NSCLC), accounting for approximately 80% of lung cancer cases, remains the leading cause of cancer-related mortality. Immune evasion is a critical challenge in NSCLC, contributing to poor treatment outcomes. This study investigates the role of KIAA1429 in immune evasion, aiming to identify novel therapeutic targets and provide a theoretical basis for NSCLC treatment. NSCLC cell lines were cultured to assess the expression of KIAA1429, KLF transcription factor (KLF1), and programmed cell death ligand 1 (PD-L1). Co-culture experiments were conducted with peripheral blood mononuclear cells (PBMCs) to evaluate cytotoxicity, CD8+T cell proportions, and levels of interferon-gamma (IFN-γ)/interleukin (IL)-10/IL-2. Additionally, N6-methyladenosine (m6A) modification in NSCLC cells, m6A enrichment on KLF1, and KLF1 mRNA stability were analyzed. Results showed increased expression of KIAA1429 and KLF1 in NSCLC cells. Knockdown of KIAA1429 inhibited NSCLC cell proliferation, enhanced PBMC cytotoxicity and CD8+T cell activation, increased IFN-γ and IL-2 levels, and decreased IL-10 levels. Mechanistically, KIAA1429 stabilized KLF1 mRNA level through m6A modification, promoting both KLF1 and PD-L1 expression. Overexpression of KLF1 or PD-L1 reversed the immune-modulating effects of KIAA1429 knockdown. In conclusion, KIAA1429 facilitates immune evasion in NSCLC by stabilizing KLF1 mRNA and upregulating PD-L1 expression.

Profiling Plasma Extracellular Vesicle Metabotypes and miRNAs: An Unobserved Clue for Predicting Relapse in Patients with Early-Stage NSCLC

Background and Objective: Lung cancer, the second most prevalent cancer globally, poses significant challenges in early detection and prognostic assessment. Despite advancements in targeted therapies and immunotherapy, the timely identification of relapse remains elusive. Blood-based liquid biopsy biomarkers, including circulating tumor cells (CTCs), cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating-free RNAs (cfRNAs), and extracellular vesicles (EVs)/exosomes, offer promise for non-invasive monitoring. Methods: We employ a comprehensive approach integrating miRNA/lncRNA/metabolomic datasets, following a mixed-methods content analysis, to identify candidate biomarkers in NSCLC. NSCLC-associated miRNA/gene/lncRNA associations were linked to in silico-derived molecular pathways. Results: For data validation, mass spectrometry-based untargeted metabolomics of plasma EVs highlighted miRNA/lncRNA/metabotypes, linking “glycerophospholipid metabolism” to lncRNA H19 and “alanine, aspartate and glutamate metabolism” to miR-29a-3p. Prognostic significance was established for miR-29a-3p, showing lower expression in NSCLC patients with disease progression compared to stable disease (p = 0.004). Kaplan–Meier survival analysis indicated that patients with miR-29a-3p under-expression had significantly shorter overall survival (OS) (p = 0.038). Despite the expression of lncRNA H19 in plasma EVs being undetected, its expression in plasma cfRNAs correlated significantly with disease progression (p = 0.035). Conclusions: Herein, we showcase the potential of plasma EV-derived miR-29a-3p as a prognostic biomarker and underscore the intricate interplay of miRNAs, lncRNAs, and metabolites in NSCLC biology. Our findings offer new insights and avenues for further exploration, contributing to the ongoing quest for effective biomarkers in early-stage NSCLC.

Measurable morphological features of single circulating tumor cells in selected solid tumors-A pilot study.

Liquid biopsies developed into a range of sensitive technologies aiming to analyze for example, circulating tumor cells (CTCs) in peripheral blood, which significantly deepens understanding of the metastatic process. Nevertheless, examination of CTCs is mostly limited to their enumeration and usually only 2-3 markers-based phenotyping, not offering yet sufficient insight into their biology. In contrast, quantitative analysis of their morphological details might extend our knowledge about dissemination and even improve CTC isolation or label-free identification methods dependent on their physical features such as size, and deformability. Current study was conducted to describe CTCs' and their size, shape, presence of protrusions, and micronuclei across various types of cancers (lung, n = 29; ovarian, n = 24, breast, n = 54; and prostate, n = 33). Epithelial (pan-keratins), mesenchymal (vimentin), and two exclusion markers were used to identify CTCs and classify them into four epithelial and epithelial-mesenchymal transition-related phenotypes using standardized and throughput method, imaging flow cytometry. The morphological characteristics of CTCs, including their nuclei, such as circularity, the maximum, and minimum diagonal values were determined using an open-source software QuPath. On average, detected CTCs (n = 1156) were larger, and more irregular in shape compared to leukocytes/endothelial cells (n = 400). Epithelial and mesenchymal CTCs had the largest (median = 18.2 μm) and the smallest diameter (median = 10.4 μm), respectively. In terms of cancer-specific variations, the largest CTCs were identified in lung cancer, whereas the smallest-in prostate and breast cancers. Epithelial CTCs and those negative for both epithelial and mesenchymal markers exhibited the highest degree of elongation, whereas mesenchymal CTCs were the most irregular in shape. Protrusions and micronuclei were observed extremely rarely within CTCs of breast and prostate cancer (0.6%-0.8% of CTCs). Micronuclei were observed only in epithelial and epithelial-mesenchymal CTCs. This study underscores the significant variability in the morphological features of CTCs in relation to their phenotypic classification or even the particular organ of origin, potentially influencing for example, size-dependent CTC isolation methods. It demonstrates for the first time the morphological measurements of CTCs undergoing epithelial-mesenchymal transition, and some specific morphological details (i.e., protrusions, micronuclei) within CTCs in general.

Capivasertib augments chemotherapy via Akt inhibition in preclinical small cell lung cancer models.

Small cell lung cancer (SCLC) is a highly aggressive type of lung cancer for which platinum-based chemotherapy is the standard of care. Despite an initial response to this therapy, patients eventually develop resistance to the chemotherapy. To investigate the potential of capivasertib, an approved drug for advanced breast cancer, to enhance the efficacy of cisplatin in preclinical SCLC models and explore the underlying mechanisms. SCLC cell lines were treated with capivasertib and cisplatin, alone or in combination, to assess cell viability, proliferation, colony formation, and apoptosis. Next, capivasertib's effects, alone and combined with cisplatin, were evaluated in an SCLC mouse model. Mechanistic studies focused on Akt and MYC signaling, with constitutively active Akt overexpression used to assess its role. Capivasertib is active against a panel of SCLC cell lines regardless of cellular origin and genetic profiling with IC50 at a clinically achievable range. Particularly, capivasertib inhibits proliferation and anchorage-independent colony formation and induces apoptosis in SCLC cells. It significantly augments cisplatin's inhibitory effects in all tested cell lines. Importantly, capivasertib at a non-toxic dose is effective in delaying SCLC growth in mice and its combination with cisplatin achieves nearly complete tumor growth inhibition. Mechanistic studies confirm that capivasertib inhibits Akt and MYC signaling, and furthermore, that overexpression of constitutively active Akt reversed anti-SCLC activity of capivasertib. Our work is the first to reveal that Akt inhibition can augment chemotherapy in SCLC, and capivasertib is a useful addition to the treatment armamentarium for SCLC.