Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis

miR-141-3p inhibited BPA-induced proliferation and migration of lung cancer cells through PTGER4

The chemical substance bisphenol A (BPA) is widely used in household products, and its effect on human health has frequently been the focus of research. The aim of this study was to explore the potential molecular regulatory mechanism of BPA on the proliferation and migration of lung cancer cells. In this study, the H1299 and A549 lung cancer cell lines were selected as the study objects. The cells were treated with different concentrations of BPA (0, 0.1, 1, or 10 μM), and cell viability, proliferation, and migration were evaluated by CCK-8, EdU, clonogenic, and scratch test assays. Western blotting and RT‒qPCR were used to detect the expression of related proteins and genes. Our findings indicated that BPA markedly enhanced both the proliferation and migration capacities of lung cancer cells. In BPA-treated lung cancer cells, the level of miR-141-3p was decreased, PTGER4 expression was significantly increased, and PTGER4 knockdown reduced BPA-induced lung cancer cell proliferation and migration. In addition, miR-141-3p can target and negatively regulate the expression of PTGER4 and further inhibit PI3K/AKT signaling pathway activation and MMPs expression. Moreover, PTGER4 overexpression weakened the inhibitory effect of the miR-141-3p mimic on the proliferation and migration of lung cancer cells. In conclusion, miR-141-3p can inhibit the proliferation and migration of BPA-induced lung cancer cells by downregulating PTGER4, providing a new potential target for the treatment and prevention of lung cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10616-024-00692-5.

Construction of prediction model of early glottic cancer based on machine learning

BACKGROUND

The early diagnosis of glottic laryngeal cancer is the key to successful treatment, and machine learning (ML) combined with narrow-band imaging (NBI) laryngoscopy provides a new idea for the early diagnosis of glottic laryngeal cancer.

OBJECTIVE

To explore the clinical applicability of the diagnosis of early glottic cancer based on ML combined with NBI.

MATERIAL AND METHODS

A retrospective study was conducted on 200 patients diagnosed with laryngeal mass, and the general clinical characteristics and pathological results of the patients were collected. Chi-square test and multivariate logistic regression analysis were used to explore clinical and laryngoscopic features that could potentially predict early glottic cancer. Afterward, three classical ML methods, namely random forest (RF), support vector machine (SVM), and decision tree (DT), were combined with NBI endoscopic images to identify risk factors related to glottic cancer and to construct and compare the predictive models.

RESULTS

The RF‑based model was found to predict more accurately than other methods and have a significant predominance over others. The accuracy, precision, recall and F1 index, and AUC value of the RF model were 0.96, 0.90, 1.00, 0.95, and 0.97.

CONCLUSIONS AND SIGNIFICANCE

We developed a prediction model for early glottic cancer using RF, which outperformed other models.

Bulk integrated single-cell-spatial transcriptomics reveals the impact of preoperative chemotherapy on cancer-associated fibroblasts and tumor cells in colorectal cancer, and construction of related predictive models using machine learning

BACKGROUND

Preoperative chemotherapy (PC) is an important component of Colorectal cancer (CRC) treatment, but its effects on the biological functions of fibroblasts and epithelial cells in CRC are unclear.

METHODS

This study utilized bulk, single-cell, and spatial transcriptomic sequencing data from 22 independent cohorts of CRC. Through bioinformatics analysis and in vitro experiments, the research investigated the impact of PC on fibroblast and epithelial cells in CRC. Subpopulations associated with PC and CRC prognosis were identified, and a predictive model was constructed using machine learning.

RESULTS

PC significantly attenuated the pathways related to tumor progression in fibroblasts and epithelial cells. NOTCH3 + Fibroblast (NOTCH3 + Fib), TNNT1 + Epithelial (TNNT1 + Epi), and HSPA1A + Epithelial (HSPA1A + Epi) subpopulations were identified in the adjacent spatial region and were associated with poor prognosis in CRC. PC effectively diminished the presence of these subpopulations, concurrently inhibiting pathway activity and intercellular crosstalk. A risk signature model, named the Preoperative Chemotherapy Risk Signature Model (PCRSM), was constructed using machine learning. PCRSM emerged as an independent prognostic indicator for CRC, impacting both overall survival (OS) and recurrence-free survival (RFS), surpassing the performance of 89 previously published CRC risk signatures. Additionally, patients with a high PCRSM risk score showed sensitivity to fluorouracil-based adjuvant chemotherapy (FOLFOX) but resistance to single chemotherapy drugs (such as Bevacizumab and Oxaliplatin). Furthermore, this study predicted that patients with high PCRSM were resistant to anti-PD1therapy.

CONCLUSION

In conclusion, this study identified three cell subpopulations (NOTCH3 + Fib, TNNT1 + Epi, and HSPA1A + Epi) associated with PC, which can be targeted to improve the prognosis of CRC patients. The PCRSM model shows promise in enhancing the survival and treatment of CRC patients.

Epithelial-mesenchymal transition to mitigate age-related progression in lung cancer

Epithelial-Mesenchymal Transition (EMT) is a fundamental biological process involved in embryonic development, wound healing, and cancer progression. In lung cancer, EMT is a key regulator of invasion and metastasis, significantly contributing to the fatal progression of the disease. Age-related factors such as cellular senescence, chronic inflammation, and epigenetic alterations exacerbate EMT, accelerating lung cancer development in the elderly. This review describes the complex mechanism among EMT and age-related pathways, highlighting key regulators such as TGF-β, WNT/β-catenin, NOTCH, and Hedgehog signalling. We also discuss the mechanisms by which oxidative stress, mediated through pathways involving NRF2 and ROS, telomere attrition, regulated by telomerase activity and shelterin complex, and immune system dysregulation, driven by alterations in cytokine profiles and immune cell senescence, upregulate or downregulate EMT induction. Additionally, we highlighted pathways of transcription such as SNAIL, TWIST, ZEB, SIRT1, TP53, NF-κB, and miRNAs regulating these processes. Understanding these mechanisms, we highlight potential therapeutic interventions targeting these critical molecules and pathways.

TRF-16 Inhibits Lung Cancer Progression by Hindering the N6-Methyladenosine Modification of CPT1A mRNA

Transfer RNA-derived fragments (tRFs) are a new class of small non-coding RNAs. Recent studies suggest that tRFs participate in some pathological processes. However, the biological activities and processes of tRFs in lung cancer cells remain mainly unclear. In the present investigation, we employed tRNA-derived small RNA (tsRNA) sequencing to predict differentially expressed tsRNAs in lung cancer cells, and nine tsRNAs with significant expression alterations were validated using qPCR. Wound healing, colony formation, transwell invasion and CCK-8 assays were performed to detect the effects of tRF-16 on cell function. Western blotting evaluated the relationship between tRF-16 and the IGF2BP1 axis. Our findings demonstrated that tRF-16 expression was substantially downregulated in lung cancer cells. TRF-16 could inhibit lung cancer cells' ability to increase, migrate, invade and obtain radiation resistance. Furthermore, tRF-16 decreases the stability of CPT1A by impairing the binding of IGF2BP1 to CPT1A. As a result, the fatty acid metabolism in lung cancer cells was inhibited. Finally, tRF-16 also inhibits lung cancer cell proliferation in vivo. This study shows that tRF-16 plays a crucial regulatory role in the proliferation of lung cancer cells and may represent a novel avenue for their regulation.

Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data

BACKGROUND

Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening and staging, using routine clinical data.

METHODS

Two medical center, observational, retrospective studies were conducted, involving 2312 lung cancer patients and 653 patients with benign nodules. Machine learning techniques, including differential analysis and feature selection, were employed to identify key factors for modeling. The study focused on variables such as nodule density, carcinoembryonic antigen (CEA), age, and lifestyle habits. The Logistic Regression model was utilized for early diagnoses, and the XGBoost model was utilized for staging based on selected features.

RESULTS

For early diagnoses, the Logistic Regression model achieved an area under the curve (AUC) of 0.716 (95% confidence interval [CI] 0.607-0.826), with 0.703 sensitivity and 0.654 specificity. The XGBoost model excelled in distinguishing late-stage from early-stage lung cancer, exhibiting an AUC of 0.913 (95% CI 0.862-0.963), with 0.909 sensitivity and 0.814 specificity. These findings highlight the model's potential for enhancing diagnostic accuracy and staging in lung cancer.

CONCLUSION

This study introduces a novel machine learning-based risk model for early lung cancer screening and staging, leveraging routine clinical information and laboratory data. The model shows promise in enhancing accuracy, mitigating overdiagnosis, and improving patient outcomes.

ADAMTS4 exacerbates lung cancer progression via regulating c-Myc protein stability and activating MAPK signaling pathway

BACKGROUND

Lung cancer is one of the most frequent cancers and the leading cause of cancer-related deaths worldwide with poor prognosis. A disintegrin and metalloproteinase with thrombospondin motifs 4 (ADAMTS4) is crucial in the regulation of the extracellular matrix (ECM), impacting its formation, homeostasis and remodeling, and has been linked to cancer progression. However, the specific involvement of ADAMTS4 in the development of lung cancer remains unclear.

METHODS

ADAMTS4 expression was identified in human lung cancer samples by immunohistochemical (IHC) staining and the correlation of ADAMTS4 with clinical outcome was determined. The functional impact of ADAMTS4 on malignant phenotypes of lung cancer cells was explored both in vitro and in vivo. The mechanisms underlying ADAMTS4-mediated lung cancer progression were explored by ubiquitination-related assays.

RESULTS

Our study revealed a significant upregulation of ADAMTS4 at the protein level in lung cancer tissues compared to para-carcinoma normal tissues. High ADAMTS4 expression inversely correlated with the prognosis of lung cancer patients. Knockdown of ADAMTS4 inhibited the proliferation and migration of lung cancer cells, as well as the tubule formation of HUVECs, while ADAMTS4 overexpression exerted opposite effects. Mechanistically, we found that ADAMTS4 stabilized c-Myc by inhibiting its ubiquitination, thereby promoting the in vitro and in vivo growth of lung cancer cells and inducing HUVECs tubule formation in lung cancer. In addition, our results suggested that ADAMTS4 overexpression activated MAPK signaling pathway.

CONCLUSIONS

We highlighted the promoting role of ADAMTS4 in lung cancer progression and proposed ADAMTS4 as a promising therapeutic target for lung cancer patients.

Assessing prospective molecular biomarkers and functional pathways in severe asthma based on a machine learning method and bioinformatics analyses

BACKGROUND

Severe asthma, which differs significantly from typical asthma, involves specific molecular biomarkers that enhance our understanding and diagnostic capabilities. The objective of this study is to assess the biological processes underlying severe asthma and to detect key molecular biomarkers.

METHODS

We used Weighted Gene Co-Expression Network Analysis (WGCNA) to detect hub genes in the GSE143303 dataset and indicated their functions and regulatory mechanisms using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) annotations. In the GSE147878 dataset, we used Gene Set Enrichment Analysis (GSEA) to determine the regulatory directions of gene sets. We detected differentially expressed genes in the GSE143303 and GSE64913 datasets, constructed a Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and validated the model using the GSE147878 dataset and real-time quantitative PCR (RT-qPCR) to confirm the molecular biomarkers.

RESULTS

Using WGCNA, we discovered modules that were strongly correlated with clinical features, specifically the purple module (r = 0.53) and the midnight blue module (r = -0.65). The hub genes within these modules were enriched in pathways related to mitochondrial function and oxidative phosphorylation. GSEA in the GSE147878 dataset revealed significant enrichment of upregulated gene sets associated with oxidative phosphorylation and downregulated gene sets related to asthma. We discovered 12 commonly regulated genes in the GSE143303 and GSE64913 datasets and developed a LASSO regression model. The model corresponding to lambda.min selected nine genes, including TFCP2L1, KRT6A, FCER1A, and CCL5, which demonstrated predictive value. These genes were significantly upregulated or under expressed in severe asthma, as validated by RT-qPCR.

CONCLUSION

Mitochondrial abnormalities affecting oxidative phosphorylation play a critical role in severe asthma. Key molecular biomarkers like TFCP2L1, KRT6A, FCER1A, and CCL5, are essential for detecting severe asthma. This research significantly enhances the understanding and diagnosis of severe asthma.

Unraveling the role of low-density lipoprotein-related genes in lung adenocarcinoma: Insights into tumor microenvironment and clinical prognosis

BACKGROUND

The hypothesized link between low-density lipoprotein (LDL) and oncogenesis has garnered significant interest, yet its explicit impact on lung adenocarcinoma (LUAD) remains to be elucidated. This investigation aims to demystify the function of LDL-related genes (LRGs) within LUAD, endeavoring to shed light on the complex interplay between LDL and carcinogenesis.

METHODS

Leveraging single-cell transcriptomics, we examined the role of LRGs within the tumor microenvironment (TME). The expression patterns of LRGs across diverse cellular phenotypes were delineated using an array of computational methodologies, including AUCell, UCell, singscore, ssGSEA, and AddModuleScore. CellChat facilitated the exploration of distinct cellular interactions within LDL_low and LDL_high groups. The findmarker utility, coupled with Pearson correlation analysis, facilitated the identification of pivotal genes correlated with LDL indices. An integrative approach to transcriptomic data analysis was adopted, utilizing a machine learning framework to devise an LDL-associated signature (LAS). This enabled the delineation of genomic disparities, pathway enrichments, immune cell dynamics, and pharmacological sensitivities between LAS stratifications.

RESULTS

Enhanced cellular crosstalk was observed in the LDL_high group, with the CoxBoost+Ridge algorithm achieving the apex c-index for LAS formulation. Benchmarking against 144 extant LUAD models underscored the superior prognostic acuity of LAS. Elevated LAS indices were synonymous with adverse outcomes, diminished immune surveillance, and an upsurge in pathways conducive to neoplastic proliferation. Notably, a pronounced susceptibility to paclitaxel and gemcitabine was discerned within the high-LAS cohort, delineating prospective therapeutic corridors.

CONCLUSION

This study elucidates the significance of LRGs within the TME and introduces an LAS for prognostication in LUAD patients. Our findings accentuate putative therapeutic targets and elucidate the clinical ramifications of LAS deployment.

Prognosis and risk factor assessment of patients with advanced lung cancer with low socioeconomic status: model development and validation

BACKGROUND

Lung cancer, a major global health concern, disproportionately impacts low socioeconomic status (SES) patients, who face suboptimal care and reduced survival. This study aimed to evaluate the prognostic performance of traditional Cox proportional hazards (CoxPH) regression and machine learning models, specifically Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), in patients with advanced lung cancer with low SES.

DESIGN

A retrospective study.

METHOD

The 949 patients with advanced lung cancer with low SES who entered the hospice ward of a tertiary hospital in Wuhan, China, from January 2012 to December 2021 were randomized into training and testing groups in a 3:1 ratio. CoxPH regression methods and four machine learning algorithms (DT, RF, SVM, and XGBoost) were used to construct prognostic risk prediction models.

RESULTS

The CoxPH regression-based nomogram demonstrated reliable predictive accuracy for survival at 60, 90, and 120 days. Among the machine learning models, XGBoost showed the best performance, whereas RF had the lowest accuracy at 60 days, DT at 90 days, and SVM at 120 days. Key predictors across all models included Karnofsky Performance Status (KPS) score, quality of life (QOL) score, and cough symptoms.

CONCLUSIONS

CoxPH, DT, RF, SVM, and XGBoost models are effective in predicting mortality risk over 60-120 days in patients with advanced lung cancer with low SES. Monitoring KPS, QOL, and cough symptoms is crucial for identifying high-risk patients who may require intensified care. Clinicians should select models tailored to individual patient needs and preferences due to varying prediction accuracies.

REPORTING METHOD

This study was reported in strict compliance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Research on Predictive Auxiliary Diagnosis Method for Gastric Cancer Based on Non-Invasive Indicator Detection

Chronic atrophic gastritis is a serious health issue beyond the stomach health problems that affect normal life. This study aimed to explore the influencing factors related to chronic atrophic gastritis (CAG) using non-invasive indicators and establish an optimal prediction model to aid in the clinical diagnosis of CAG. Electronic medical record data from 20,615 patients with CAG were analyzed, including routine blood tests, liver function tests, and coagulation tests. The logistic regression algorithm revealed that age, hematocrit, and platelet distribution width were significant influences suggesting chronic atrophic gastritis in the Chongqing population (p < 0.05), with an area under the curve (AUC) of 0.879. The predictive model constructed based on the Random Forest algorithm exhibited an accuracy of 83.15%, precision of 97.38%, recall of 77.36%, and an F1-score of 70.86%, outperforming the models constructed using XGBoost, KNN, and SVC algorithms in a comprehensive comparison. The prediction model derived from this study serves as a valuable tool for future studies and can aid in the prediction and screening of chronic atrophic gastritis.

Research on the mechanism of exosomes from different sources influencing the progression of lung cancer

As a key regulator of intercellular communication, exosomes are essential for tumor cells. In our study, we will explore the mechanisms of exosomes from different sources on lung cancer. We isolated CD8+T cells and cancer-associated fibroblasts (CAFs) from venous blood and tumor tissues of lung cancer patients, and isolated exosomes. MiR-2682 was high expression in CD8+T-derived exosomes, and lncRNA-FOXD3-AS1 was upregulated in CAF-derived exosomes. Online bioinformatics database analysis showed that RNA Binding Motif Protein 39 (RBM39) was identified as the target of miR-2682, and eukaryotic translation initiation factors 3B (EIF3B) was identified as the RNA binding protein of FOXD3-AS1. CD8+T-derived exosomes inhibited the growth of A549 cells and promoted apoptosis, while miR-2682 inhibits reversed these effects of CD8+T-derived exosomes. CAF-derived exosomes promoted the growth of A549 cells and inhibited apoptosis, while FOXD3-AS1 siRNA reversed the effect of CAF-derived exosomes. Mechanism studies have found that miR-2682 inhibits the growth of lung cancer cells by inhibiting the expression of RBM39. FOXD3-AS1 promoted the growth of lung cancer cells by binding to EIF3B. In vivo experiments showed that CD8+T cell-derived exosome miR-2682 inhibited lung cancer tumor formation, while CAF-derived exosome FOXD3-AS1 promoted lung cancer tumor formation. This study provides mechanistic insights into the role of miR-2682 and FOXD3-AS1 in lung cancer progression and provides new strategies for lung cancer treatment.

Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine

The integration of multiomics data with detailed phenotypic insights from electronic health records marks a paradigm shift in biomedical research, offering unparalleled holistic views into health and disease pathways. This review delineates the current landscape of multimodal omics data integration, emphasizing its transformative potential in generating a comprehensive understanding of complex biological systems. We explore robust methodologies for data integration, ranging from concatenation-based to transformation-based and network-based strategies, designed to harness the intricate nuances of diverse data types. Our discussion extends from incorporating large-scale population biobanks to dissecting high-dimensional omics layers at the single-cell level. The review underscores the emerging role of large language models in artificial intelligence, anticipating their influence as a near-future pivot in data integration approaches. Highlighting both achievements and hurdles, we advocate for a concerted effort toward sophisticated integration models, fortifying the foundation for groundbreaking discoveries in precision medicine.

Identification of effective diagnostic genes and immune cell infiltration characteristics in small cell lung cancer by integrating bioinformatics analysis and machine learning algorithms

OBJECTIVES

To identify potential diagnostic markers for small cell lung cancer (SCLC) and investigate the correlation with immune cell infiltration.

METHODS

GSE149507 and GSE6044 were used as the training group, while GSE108055 served as validation group A and GSE73160 served as validation group B. Differentially expressed genes (DEGs) were identified and analyzed for functional enrichment. Machine learning (ML) was used to identify candidate diagnostic genes for SCLC. The area under the receiver operating characteristic curves was applied to assess diagnostic efficacy. Immune cell infiltration analyses were carried out.

RESULTS

There were 181 DEGs identified. The gene ontology analysis showed that DEGs were enriched in 455 functional annotations, some of which were associated with immunity. The kyoto encyclopedia of genes and genomes analysis revealed that there were 9 signaling pathways enriched. The disease ontology analysis indicated that DEGs were related to 116 diseases. The gene set enrichment analysis results displayed multiple items closely related to immunity. ZWINT and NRCAM were screened using ML and further validated as diagnostic genes. Significant differences were observed in SCLC with normal lung tissue samples among immune cell infiltration characteristics. Strong associations were found between the diagnostic genes and immune cell infiltration.

CONCLUSION

This study identified 2 diagnostic genes, ZWINT and NRCAM, that were related to immune cell infiltration by integrating bioinformatics analysis and ML algorithms. These genes could serve as potential diagnostic biomarkers and provide possible molecular targets for immunotherapy in SCLC.

Safety and efficacy of CT-guided percutaneous radiofrequency ablation for non-small cell lung cancer: a single-center, single-arm analysis

Non-small cell lung cancer (NSCLC) is a prevalent malignant tumor, and the commonly treatment modalities include surgery, radiotherapy, chemotherapy, etc. Currently, CT-guided percutaneous radiofrequency ablation (RFA) for the treatment of cancers has been widely performed. This study aimed to evaluate the safety and efficacy of this therapy in NSCLC patients. Thirty-five NSCLC patients were enrolled in this study and all received CT-guided percutaneous RFA therapy. The outcome measures included the changes in forced respiratory volume in the first second (FEV1), total lung volume (TLC), lesion size and computed tomography (CT) values of the region of interest (ROI) before and after treatment. The main efficacy measures comprised complete tumor ablation and local recurrence after initial treatment, as well as the objective response rate (ORR) and disease control rate (DCR) after 6 months of treatment. After receiving CT-guided percutaneous RFA therapy, the target lesion was effectively controlled and CT values gradually decreased. Besides, no significant changes were observed in the patient's lung function, postoperative complications were experienced by a total of 10 patients, primarily including pneumothorax, infection, lung hollowing. Fortunately, all these complications were successfully managed with appropriate treatment. Following the initial RFA treatment, 31 patients (88.57%) achieved complete ablation, while 6 patients experienced local recurrence. After 6 months of treatment, the ORR and DCR were found to be 68.57% and 82.86% respectively. CT-guided percutaneous RFA has demonstrated favorable safety and efficacy in the treatment of patients with NSCLC at different stages, which represented a promising therapeutic modality.

Metastatic Lung Adenocarcinomas: Development and Evaluation of Radiomic-Based Methods to Measure Baseline Intra-Patient Inter-Tumor Lesion Heterogeneity

Radiomics has traditionally focused on individual tumors, often neglecting the integration of metastatic disease, particularly in patients with non-small cell lung cancer. This study sought to examine intra-patient inter-tumor lesion heterogeneity indices using radiomics, exploring their relevance in metastatic lung adenocarcinoma. Consecutive adults newly diagnosed with metastatic lung adenocarcinoma underwent contrast-enhanced CT scans for lesion segmentation and radiomic feature extraction. Three methods were devised to measure distances between tumor lesion profiles within the same patient in radiomic space: centroid to lesion, lesion to lesion, and primitive to lesion, with subsequent calculation of mean, range, and standard deviation of these distances. Associations between HIs, disease control rate, objective response rate to first-line treatment, and overall survival were explored. The study included 167 patients (median age 62.3 years) between 2016 and 2019, divided randomly into experimental (N = 117,546 lesions) and validation (N = 50,232 tumor lesions) cohorts. Patients without disease control/objective response and with poorer survival consistently systematically exhibited values of all heterogeneity indices. Multivariable analyses revealed that the range of primitive-to-lesion distances was associated with disease control in both cohorts and with objective response in the validation cohort. This metrics showed univariable associations with overall survival in the experimental. In conclusion, we proposed original methods to estimate the intra-patient inter-tumor lesion heterogeneity using radiomics that demonstrated correlations with patient outcomes, shedding light on the clinical implications of inter-metastases heterogeneity. This underscores the potential of radiomics in understanding and potentially predicting treatment response and prognosis in metastatic lung adenocarcinoma patients.

Enhancing safety and therapeutic efficacy: PD-1 inhibitor and recombinant human endostatin combination in advanced non-small cell lung cancer patients

OBJECTIVE

To assess the therapeutic efficacy of combining a programmed death-1 (PD-1) inhibitor with recombinant human endostatin in patients diagnosed with advanced non-small cell lung cancer (NSCLC).

METHODS

We retrospectively collected data from 83 patients with advanced NSCLC who received treatment at Xi'an Daxing Hospital between May 2020 and July 2022. Among them, 42 patients were treated with a PD-1 inhibitor combined with recombinant human endostatin (observation group), while 41 patients received PD-1 inhibitor monotherapy (control group). We evaluated the objective response rate, changes in serum tumor markers pre- and post-treatment, occurrence of adverse reactions, progression-free survival (PFS), 1-year survival rate, and identified independent risk factors affecting prognosis in both groups.

RESULTS

The treatment efficacy in the observation group significantly surpassed that in the control group. Following treatment, the levels of cytokeratin 19 fragment antigen 21-1, carcinoembryonic antigen, and carbohydrate antigen 125 decreased significantly in the observation group compared to the control group (P < 0.001). There was no notable difference in the incidence of adverse reactions between the two groups (P < 0.001). The median PFS and 1-year survival rate were notably higher in the observation group (P < 0.001). Age, liver metastasis, and treatment regimen emerged as independent risk factors affecting poor prognosis in patients (P < 0.001).

CONCLUSION

Combining a PD-1 inhibitor with recombinant human endostatin in patients with advanced NSCLC not only enhances clinical efficacy but also increases PFS and the 1-year survival rate while ensuring treatment safety. This combination therapy shows promise for clinical application.

Modulation of epithelial-mesenchymal transition by gemcitabine: Targeting ionizing radiation-induced cellular senescence in lung cancer cell

Ionizing radiation, a standard therapeutic approach for lung cancer, often leads to cellular senescence and the induction of epithelial-mesenchymal transition (EMT), posing significant challenges in treatment efficacy and cancer progression. Overcoming these obstacles is crucial for enhancing therapeutic outcomes in lung cancer management. This study investigates the effects of ionizing radiation and gemcitabine on lung cancer cells, with a focus on induced senescence, EMT, and apoptosis. Human-derived A549, PC-9, and mouse-derived Lewis lung carcinoma cells exposed to 10 Gy X-ray irradiation exhibited senescence, as indicated by morphological changes, β-galactosidase staining, and cell cycle arrest through the p53-p21 pathway. Ionizing radiation also promoted EMT via TGFβ/SMAD signaling, evidenced by increased TGFβ1 levels, altered EMT marker expressions, and enhanced cell migration. Gemcitabine, a first-line lung cancer treatment, was shown to enhance apoptosis in senescent cells caused by radiation. It inhibited cell proliferation, induced mitochondrial damage, and triggered caspase-mediated apoptosis, thus mitigating EMT in vitro. Furthermore, in vivo studies using a lung cancer mouse model revealed that gemcitabine, combined with radiation, significantly reduced tumor volume and weight, extended survival, and suppressed malignancy indices in irradiated tumors. Collectively, these findings demonstrate that gemcitabine enhances the therapeutic efficacy against radiation-resistant lung cancer cells, both by inducing apoptosis in senescent cells and inhibiting EMT, offering potential improvements in lung cancer treatment strategies.

CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology

This study addresses the potential of machine learning in predicting treatment recommendations for patients with hepatocellular carcinoma (HCC). Using an IRB-approved retrospective study of patients discussed at a multidisciplinary tumor board, clinical and imaging variables were extracted and used in a gradient-boosting machine learning algorithm, XGBoost. The algorithm's performance was assessed using confusion matrix metrics and the area under the Receiver Operating Characteristics (ROC) curve. The study included 140 patients (mean age 67.7 ± 8.9 years), and the algorithm was found to be predictive of all eight treatment recommendations made by the board. The model's predictions were more accurate than those based on published therapeutic guidelines by ESMO and NCCN. The study concludes that a machine learning model incorporating clinical and imaging variables can predict treatment recommendations made by an expert multidisciplinary tumor board, potentially aiding clinical decision-making in settings lacking subspecialty expertise.

Advances in combined neuroendocrine carcinoma of lung cancer

Lung cancer incidence and mortality rates are increasing worldwide, posing a significant public health challenge and an immense burden to affected families. Lung cancer encompasses distinct subtypes, namely, non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC). In clinical investigations, researchers have observed that neuroendocrine tumors can be classified into four types: typical carcinoid, atypical carcinoid, small-cell carcinoma, and large-cell neuroendocrine carcinoma based on their unique features. However, there exist combined forms of neuroendocrine cancer. This study focuses specifically on combined pulmonary carcinomas with a neuroendocrine component. In this comprehensive review article, the authors provide an overview of combined lung cancers and present two pathological images to visually depict these distinctive subtypes.

A serial image analysis architecture with positron emission tomography using machine learning combined for the detection of lung cancer

INTRODUCTION AND OBJECTIVES

Lung cancer is the second type of cancer with the second highest incidence rate and the first with the highest mortality rate in the world. Machine learning through the analysis of imaging tests such as positron emission tomography/computed tomography (PET/CT) has become a fundamental tool for the early and accurate detection of cancer. The objective of this study was to propose an image analysis architecture (PET/CT) ordered in phases through the application of ensemble or combined machine learning methods for the early detection of lung cancer by analyzing PET/CT images.

MATERIAL AND METHODS

A retrospective observational study was conducted utilizing a public dataset entitled "A large-scale CT and PET/CT dataset for lung cancer diagnosis." Various imaging modalities, including CT, PET, and fused PET/CT images, were employed. The architecture or framework of this study comprised the following phases: 1. Image loading or collection, 2. Image selection, 3. Image transformation, and 4. Balancing the frequency distribution of image classes. Predictive models for lung cancer detection using PET/CT images included: a) the Stacking model, which used Random Forest and Support Vector Machine (SVM) as base models and complemented them with a logistic regression model, and b) the Boosting model, which employed the Adaptive Boosting (AdaBoost) model for comparison with the Stacking model. Quality metrics used for evaluation included accuracy, precision, recall, and F1-score.

RESULTS

This study showed a general performance of 94% with the Stacking method and a general performance of 77% with the Boosting method.

CONCLUSIONS

The Stacking method proved to be a model with high performance and quality for lung cancer detection when analyzing PET/CT images.

Identifying lncRNAs and mRNAs related to survival of NSCLC based on bioinformatic analysis and machine learning

Non-small cell lung cancer (NSCLC) is the most common histopathological type, and it is purposeful for screening potential prognostic biomarkers for NSCLC. This study aims to identify the lncRNAs and mRNAs related to survival of non-small cell lung cancer (NSCLC). The expression profile data of lung adenocarcinoma and lung squamous cell carcinoma were downloaded in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset. A total of eight survival related long non-coding RNAs (lncRNAs) and 262 survival related mRNAs were filtered. By gene set enrichment analysis, 17 significantly correlated Gene Ontology signal pathways and 14 Kyoto Encyclopedia of Genes and Genomes signal pathways were screened. Based on the clinical survival and prognosis information of the samples, we screened eight lncRNAs and 193 mRNAs by single factor Cox regression analysis. Further single and multifactor Cox regression analysis were performed, 30 independent prognostication-related mRNAs were obtained. The PPI network was further constructed. We then performed the machine learning algorithms (Least absolute shrinkage and selection operator, Recursive feature elimination, and Random forest) to screen the optimized DEGs combination, and a total of 17 overlapping mRNAs were obtained. Based on the 17 characteristic mRNAs obtained, we firstly built a Nomogram prediction model, and the ROC values of training set and testing set were 0.835 and 0.767, respectively. By overlapping the 17 characteristic mRNAs and PPI network hub genes, three genes were obtained: CDC6, CEP55, TYMS, which were considered as key factors associated with survival of NSCLC. The in vitro experiments were performed to examine the effect of CDC6, CEP55, and TYMS on NSCLC cells. Finally, the lncRNAs-mRNAs networks were constructed.

[Effect of CircCCND1 on the Malignant Biological Behaviors of H446 Lung Cancer Cells by Regulating the MiR-340-5p/TGIF1 Axis]

BACKGROUND

Lung cancer is a common malignant tumor of the lung. To explore the molecular mechanism of the occurrence and development of lung cancer is a hot topic in current research. Cyclic RNA D1 (CircCCND1) is highly expressed in lung cancer and may be a potential target for the treatment of lung cancer. The aim of this study was to investigate the effect of CircCCND1 on the malignant biological behaviors of lung cancer cells by regulating the miR-340-5p/transforming growth factor β-induced factor homeobox 1 (TGIF1) axis.

METHODS

The expression of CircCCND1, miR-340-5p, and TGIF1 mRNA in human normal lung epithelial cells BEAS-2B and human lung cancer H446 cells were detected. H446 cells cultured in vitro were randomly divided into control group, CircCCND1 siRNA group, miR-340-5p mimics group, negative control group, and CircCCND1 siRNA+miR-340-5p inhibitor group. Cell proliferation, mitochondrial membrane potential, apoptosis, migration, and invasion were detected, and the expressions of CircCCND1, miR-340-5p, TGIF1 mRNA, BCL2-associated X protein (Bax), cleaved Caspase-3, N-cadherin, E-cadherin, and TGIF1 proteins in each group were detected. The targeting relationship of miR-340-5p with CircCCND1 and TGIF1 was verified.

RESULTS

Compared with BEAS-2B cells, CircCCND1 and TGIF1 mRNA were increased in H446 cells, and miR-340-5p expression was decreased (P<0.05). Knocking down CircCCND1 or up-regulating the expression of miR-340-5p inhibited the proliferation, migration and invasion of H446 cells, decreased the expression of TGIF1 mRNA and TGIF1 protein, and promoted cell apoptosis. Down-regulation of miR-340-5p could antagonize the inhibitory effect of CircCCND1 knockdown on the malignant biological behavior of H446 lung cancer cells. CircCCND1 may target the down-regulation of miR-340-5p, and miR-340-5p may target the down-regulation of TGIF1.

CONCLUSIONS

Knocking down CircCCND1 can inhibit the malignant behaviors of lung cancer H446 cells, which may be achieved through the regulation of miR-340-5p/TGIF1 axis.

Informing immunotherapy with multi-omics driven machine learning

Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.

The dual roles of circRNAs in Wnt/β-Catenin signaling and cancer progression

Cancer, a complex pathophysiological condition, arises from the abnormal proliferation and survival of cells due to genetic mutations. Dysregulation of cell cycle control, apoptosis, and genomic stability contribute to uncontrolled growth and metastasis. Tumor heterogeneity, microenvironmental influences, and immune evasion further complicate cancer dynamics. The intricate interplay between circular RNAs (circRNAs) and the Wnt/β-Catenin signalling pathway has emerged as a pivotal axis in the landscape of cancer biology. The Wnt/β-Catenin pathway, a critical regulator of cell fate and proliferation, is frequently dysregulated in various cancers. CircRNAs, a class of non-coding RNAs with closed-loop structures, have garnered increasing attention for their diverse regulatory functions. This review systematically explores the intricate crosstalk between circRNAs and the Wnt/β-Catenin pathway, shedding light on their collective impact on cancer initiation and progression. The review explores the diverse mechanisms through which circRNAs modulate the Wnt/β-Catenin pathway, including sponging microRNAs, interacting with RNA-binding proteins, and influencing the expression of key components in the pathway. Furthermore, the review highlights specific circRNAs implicated in various cancer types, elucidating their roles as either oncogenic or tumour-suppressive players in the context of Wnt/β-Catenin signaling. The intricate regulatory networks formed by circRNAs in conjunction with the Wnt/β-Catenin pathway are discussed, providing insights into potential therapeutic targets and diagnostic biomarkers. This comprehensive review delves into the multifaceted roles of circRNAs in orchestrating tumorigenesis through their regulatory influence on the Wnt/β-Catenin pathway.

Radiomics; Contemporary Applications in the Management of Anal Cancer; A Systematic Review

INTRODUCTION

The management of anal cancer relies on clinical and histopathological features for treatment decisions. In recent years, the field of radiomics, which involves the extraction and analysis of quantitative imaging features, has shown promise in improving management of pelvic cancers. The aim of this study was to evaluate the current application of radiomics in the management of anal cancer.

METHODS

A systematic search was conducted in Medline, EMBASE, and Web of Science databases. Inclusion criteria encompassed randomized and non-randomized trials investigating the use of radiomics to predict post-operative recurrence in anal cancer. Study quality was assessed using the QUADAS-2 and Radiomics Quality Score tools.

RESULTS

The systematic review identified a total of nine studies, with 589 patients examined. There were three main outcomes assessed in included studies: recurrence (6 studies), progression-free survival (2 studies), and prediction of human papillomavirus (HPV) status (1 study). Radiomics-based risk stratification models were found to provide valuable insights into treatment response and patient outcomes, with all developed signatures demonstrating at least modest accuracy (range: .68-1.0) in predicting their primary outcome.

CONCLUSION

Radiomics has emerged as a promising tool in the management of anal cancer. It offers the potential for improved risk stratification, treatment planning, and response assessment, thereby guiding personalized therapeutic approaches.

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Understand how machine learning impact lung cancer research from 2010 to 2021: A bibliometric analysis

Advances in lung cancer research applying machine learning (ML) technology have generated many relevant literature. However, there is absence of bibliometric analysis review that aids a comprehensive understanding of this field and its progress. Present article for the first time performed a bibliometric analysis to clarify research status and focus from 2010 to 2021. In the analysis, a total of 2,312 relevant literature were searched and retrieved from the Web of Science Core Collection database. We conducted a bibliometric analysis and further visualization. During that time, exponentially growing annual publication and our model have shown a flourishing research prospect. Annual citation reached the peak in 2017. Researchers from United States and China have produced most of the relevant literature and strongest partnership between them. Medical image analysis and Nature appeared to bring more attention to the public. The computer-aided diagnosis, precision medicine, and survival prediction were the focus of research, reflecting the development trend at that period. ML did make a big difference in lung cancer research in the past decade.

Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis

The rich chemical information from tissue metabolomics provides a powerful means to elaborate tissue physiology or tumor characteristics at cellular and tumor microenvironment levels. However, the process of obtaining such information requires invasive biopsies, is costly, and can delay clinical patient management. Conversely, computed tomography (CT) is a clinical standard of care but does not intuitively harbor histological or prognostic information. Furthermore, the ability to embed metabolome information into CT to subsequently use the learned representation for classification or prognosis has yet to be described. This study develops a deep learning-based framework -- tissue-metabolomic-radiomic-CT (TMR-CT) by combining 48 paired CT images and tumor/normal tissue metabolite intensities to generate ten image embeddings to infer metabolite-derived representation from CT alone. In clinical NSCLC settings, we ascertain whether TMR-CT results in an enhanced feature generation model solving histology classification/prognosis tasks in an unseen international CT dataset of 742 patients. TMR-CT non-invasively determines histological classes - adenocarcinoma/squamous cell carcinoma with an F1-score = 0.78 and further asserts patients' prognosis with a c-index = 0.72, surpassing the performance of radiomics models and deep learning on single modality CT feature extraction. Additionally, our work shows the potential to generate informative biology-inspired CT-led features to explore connections between hard-to-obtain tissue metabolic profiles and routine lesion-derived image data.

Prediction of positive pulmonary nodules based on machine learning algorithm combined with central carbon metabolism data

BACKGROUND

Lung cancer causes a huge disease burden, and early detection of positive pulmonary nodules (PPNs) as an early sign of lung cancer is extremely important for effective intervention. It is necessary to develop PPNs risk recognizer based on machine learning algorithm combined with central carbon metabolomics.

METHODS

The study included 2248 participants at high risk for lung cancer from the Ma'anshan Community Lung Cancer Screening cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to screen 18 central carbon-related metabolites in plasma, recursive feature elimination (RFE) was used to select all 42 features, followed by five machine learning algorithms for model development. The performance of the model was evaluated using area under the receiver operator characteristic curve (AUC), accuracy, precision, recall, and F1 scores. In addition, SHapley Additive exPlanations (SHAP) was performed to assess the interpretability of the final selected model and to gain insight into the impact of features on the predicted results.

RESULTS

Finally, the two prediction models based on the random forest (RF) algorithm performed best, with AUC values of 0.87 and 0.83, respectively, better than other models. We found that homogentisic acid, fumaric acid, maleic acid, hippuric acid, gluconic acid, and succinic acid played a significant role in both PPNs prediction model and NPNs vs PPNs model, while 2-oxadipic acid only played a role in the former model and phosphopyruvate only played a role in the NPNs vs PPNs model. This model demonstrates the potential of central carbon metabolism for PPNs risk prediction and identification.

CONCLUSION

We developed a series of predictive models for PPNs, which can help in the early detection of PPNs and thus reduce the risk of lung cancer.

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

BACKGROUND

Machine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate.

OBJECTIVE

This study aims to systematically review relevant literature on the predictive value of machine learning for platinum-based chemotherapy responses in patients with ovarian cancer.

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched the PubMed, Embase, Web of Science, and Cochrane databases for relevant studies on predictive models for platinum-based therapies for the treatment of ovarian cancer published before April 26, 2023. The Prediction Model Risk of Bias Assessment tool was used to evaluate the risk of bias in the included articles. Concordance index (C-index), sensitivity, and specificity were used to evaluate the performance of the prediction models to investigate the predictive value of machine learning for platinum chemotherapy responses in patients with ovarian cancer.

RESULTS

A total of 1749 articles were examined, and 19 of them involving 39 models were eligible for this study. The most commonly used modeling methods were logistic regression (16/39, 41%), Extreme Gradient Boosting (4/39, 10%), and support vector machine (4/39, 10%). The training cohort reported C-index in 39 predictive models, with a pooled value of 0.806; the validation cohort reported C-index in 12 predictive models, with a pooled value of 0.831. Support vector machine performed well in both the training and validation cohorts, with a C-index of 0.942 and 0.879, respectively. The pooled sensitivity was 0.890, and the pooled specificity was 0.790 in the training cohort.

CONCLUSIONS

Machine learning can effectively predict how patients with ovarian cancer respond to platinum-based chemotherapy and may provide a reference for the development or updating of subsequent scoring systems.

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.

Enhancing the prediction of the invasiveness of pulmonary adenocarcinomas presenting as pure ground-glass nodules: Integrating intratumor heterogeneity score with clinical-radiological features via machine learning in a multicenter study

Objective

The invasiveness of lung adenocarcinoma significantly impacts clinical decision-making. However, assessing this invasiveness preoperatively, especially when it manifests as pure ground-glass nodules (pGGN) on CT scans, poses challenges. This study aims to quantify intratumor heterogeneity (ITH) and determine whether the ITH score can enhance the accuracy of invasiveness predictions.

Methods

A total of 524 patients with lung adenocarcinomas presenting as pGGN were enrolled in the study, with 177 (33.78%) receiving a pathologic diagnosis of invasiveness. Four diagnostic approaches were developed to predict the invasiveness of lung adenocarcinoma presenting as pGGN: (1) conventional lesion size, (2) ITH score, (3) clinical-radiological features (ClinRad), and (4) integration of the ITH score with ClinRad. ClinRad alone or in combination with the ITH score served as the input for 11 machine learning approaches. The trained models were evaluated in an independent validation cohort, and the area under the curve (AUC) was calculated to assess classification performance.

Results

The conventional lesion size showed the lowest performance, with an AUC of 0.826 (95% confidence interval [CI]: 0.758-0.894), while the ITH score outperformed it with an AUC of 0.846 (95% CI: 0.787-0.905). The CatBoost model performed best when the ITH score and ClinRad were both used as input features, leading to the development of an ITH-ClinRad-guided CatBoost classifier. CatBoost also excelled with ClinRad alone, resulting in a ClinRad-guided CatBoost classifier with an AUC of 0.830 (95% CI: 0.764-0.896), surpassed by the ITH-ClinRad-guided CatBoost classifier with an AUC of 0.871 (95% CI: 0.818-0.924).

Conclusion

The ITH-ClinRad-guided CatBoost classifier emerges as a promising tool with significant potential to revolutionize the management of lung adenocarcinomas presenting as pGGNs.

Improving diagnosis and prognosis of lung cancer using vision transformers: a scoping review

BACKGROUND

Vision transformer-based methods are advancing the field of medical artificial intelligence and cancer imaging, including lung cancer applications. Recently, many researchers have developed vision transformer-based AI methods for lung cancer diagnosis and prognosis.

OBJECTIVE

This scoping review aims to identify the recent developments on vision transformer-based AI methods for lung cancer imaging applications. It provides key insights into how vision transformers complemented the performance of AI and deep learning methods for lung cancer. Furthermore, the review also identifies the datasets that contributed to advancing the field.

METHODS

In this review, we searched Pubmed, Scopus, IEEEXplore, and Google Scholar online databases. The search terms included intervention terms (vision transformers) and the task (i.e., lung cancer, adenocarcinoma, etc.). Two reviewers independently screened the title and abstract to select relevant studies and performed the data extraction. A third reviewer was consulted to validate the inclusion and exclusion. Finally, the narrative approach was used to synthesize the data.

RESULTS

Of the 314 retrieved studies, this review included 34 studies published from 2020 to 2022. The most commonly addressed task in these studies was the classification of lung cancer types, such as lung squamous cell carcinoma versus lung adenocarcinoma, and identifying benign versus malignant pulmonary nodules. Other applications included survival prediction of lung cancer patients and segmentation of lungs. The studies lacked clear strategies for clinical transformation. SWIN transformer was a popular choice of the researchers; however, many other architectures were also reported where vision transformer was combined with convolutional neural networks or UNet model. Researchers have used the publicly available lung cancer datasets of the lung imaging database consortium and the cancer genome atlas. One study used a cluster of 48 GPUs, while other studies used one, two, or four GPUs.

CONCLUSION

It can be concluded that vision transformer-based models are increasingly in popularity for developing AI methods for lung cancer applications. However, their computational complexity and clinical relevance are important factors to be considered for future research work. This review provides valuable insights for researchers in the field of AI and healthcare to advance the state-of-the-art in lung cancer diagnosis and prognosis. We provide an interactive dashboard on lung-cancer.onrender.com/ .

Bibliometric analysis of the global scientific production on machine learning applied to different cancer types

Cancer disease is one of the main causes of death in the world, with million annual cases in the last decades. The need to find a cure has stimulated the search for efficient treatments and diagnostic procedures. One of the most promising tools that has emerged against cancer in recent years is machine learning (ML), which has raised a huge number of scientific papers published in a relatively short period of time. The present study analyzes global scientific production on ML applied to the most relevant cancer types through various bibliometric indicators. We find that over 30,000 studies have been published so far and observe that cancers with the highest number of published studies using ML (breast, lung, and colon cancer) are those with the highest incidence, being the USA and China the main scientific producers on the subject. Interestingly, the role of China and Japan in stomach cancer is correlated with the number of cases of this cancer type in Asia (78% of the worldwide cases). Knowing the countries and institutions that most study each area can be of great help for improving international collaborations between research groups and countries. Our analysis shows that medical and computer science journals lead the number of publications on the subject and could be useful for researchers in the field. Finally, keyword co-occurrence analysis suggests that ML-cancer research trends are focused not only on the use of ML as an effective diagnostic method, but also for the improvement of radiotherapy- and chemotherapy-based treatments.

Advanced Glycation End Products' Receptor DNA Methylation Associated with Immune Infiltration and Prognosis of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Background

Advanced glycation end products' receptor (AGER) is a multiligand receptor that interacts with a wide range of ligands. Previous studies have shown that abnormal AGER expression is closely related to immune infiltration and tumorigenesis. However, the AGER DNA methylation relationship between prognosis and infiltrating immune cells in LUAD and LUSC is still unclear.

Methods

AGER expression in pan-cancer was obtained by using the UALCAN databases. Kaplan-Meier plotter showed the correlation of AGER mRNA expression levels and clinicopathological parameters. The protein expression levels for AGER were derived from Human Protein Atlas Database Analysis. The copy number, somatic mutation, and DNA methylation of AGER were presented with UCSC Xena database. TIMER platform and TISIDB website were used to show the correlation between AGER expression and tumor immune cell infiltration level.

Results

The expression level of AGER was significantly reduced in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Low expression of AGER was significantly correlated with histology, stage, lymph node metastasis, and tumor protein 53 (TP53) mutation and could be used as a potential indicator of poor prognosis of LUAD and LUSC. Moreover, AGER expression was positively correlated with the infiltrating immune cells. Further analysis showed that copy number variation (CNV), mutation, and DNA methylation were involved in AGER downregulation. In addition, we also found that hypermethylated AGER was significantly correlated with tumor-infiltrating lymphocytes.

Conclusion

AGER may be a candidate for the prognostic biomarker of LUAD and LUSC related to tumor immune microenvironment.