KIAA1429 regulates lung adenocarcinoma proliferation and metastasis through the PI3K/AKT pathway by modulating ARHGAP30 expression.

BACKGROUND: Alterations in epigenetic factors are recognized as key contributors to the emergence of human cancer. The active and reversible alteration of N6-methyladenosine (m6A) RNA is crucial for controlling gene activity and determining cellular destiny. Even with these insights, the triggering of KIAA1429 (also called VIRMA) and its role in lung adenocarcinoma (LUAD) is mostly unclear. As a result, the objective of this study was to elucidate how KIAA1429 contributes to cancer development in LUAD. METHODS: This study utilized multiple methods for investigation, encompassing the in vitro functional examination of KIAA1429 in lung adenocarcinoma cells, transcriptome sequencing, methylation RNA immunoprecipitation sequencing (MeRIP-seq), as well as RNA stability tests to ascertain the half-life and stability of the target genes. RESULTS: The results indicated that modifying the expression of KIAA1429 regulated the proliferation and metastasis of LUAD. By employing transcriptome sequencing alongside MeRIP-seq analysis, the research pinpointed genes affected by m6A alterations triggered by KIAA1429. In a more detailed manner, it was discovered that KIAA1429 plays a regulatory role in the expression of ARHGAP30. Suppressing KIAA1429 results in reduced m6A levels in the mRNA of the target gene ARHGAP30, boosting its stability and expression, thus inhibiting tumor proliferation and metastasis. CONCLUSION: This study revealed the activation mechanism and pivotal function of KIAA1429 in LUAD tumor development, paving the way for molecular-based interventions for LUAD.

Prognostic model based on B cell marker genes for NSCLC patients under neoadjuvant immunotherapy by integrated analysis of single-cell and bulk RNA-sequencing data.

BACKGROUND: Neoadjuvant immunotherapy has evolved as an effective option to treat non-small cell lung cancer (NSCLC). B cells play essential roles in the immune system as well as cancer progression. However, the repertoire of B cells and its association with clinical outcomes remains unclear in NSCLC patients receiving neoadjuvant immunotherapy. METHODS: Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data for LUAD samples were accessed from the TCGA and GEO databases. LUAD-related B cell marker genes were confirmed based on comprehensive analysis of scRNA-seq data. We then constructed the B cell marker gene signature (BCMGS) and validated it. In addition, we evaluated the association of BCGMS with tumor immune microenvironment (TIME) characteristics. Furthermore, we validated the efficacy of BCGMS in a cohort of NSCLC patients receiving neoadjuvant immunotherapy. RESULTS: A BCMGS was constructed based on the TCGA cohort and further validated in three independent GSE cohorts. In addition, the BCMGS was proven to be significantly associated with TIME characteristics. Moreover, a relatively higher risk score indicated poor clinical outcomes and a worse immune response among NSCLC patients receiving neoadjuvant immunotherapy. CONCLUSIONS: We constructed an 18-gene prognostic signature derived from B cell marker genes based on scRNA-seq data, which had the potential to predict the prognosis and immune response of NSCLC patients receiving neoadjuvant immunotherapy.

Machine learning-based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis.

BACKGROUND: Radiomics is increasingly utilized to distinguish pulmonary nodules between lung adenocarcinoma (LUAD) and tuberculosis (TB). However, it remains unclear whether different segmentation criteria, such as the inclusion or exclusion of the cavity region within nodules, affect the results. METHODS: A total of 525 patients from two medical centers were retrospectively enrolled. The radiomics features were extracted according to two regions of interest (ROI) segmentation criteria. Multiple logistic regression models were trained to predict the pathology: (1) The clinical model relied on clinical-radiological semantic features; (2) The radiomics models (radiomics+ and radiomics-) utilized radiomics features from different ROIs (including or excluding cavities); (3) the composite models (composite+ and composite-) incorporated both above. RESULTS: In the testing set, the radiomics+/- models and the composite+/- models still possessed efficient prediction performance (AUC ≥ 0.94), while the AUC of the clinical model was 0.881. In the validation set, the AUC of the clinical model was only 0.717, while that of the radiomics+/- models and the composite+/- models ranged from 0.801 to 0.825. The prediction performance of all the radiomics+/- and composite+/- models were significantly superior to that of the clinical model (p < 0.05). Whether the ROI segmentation included or excluded the cavity had no significant effect on these models (radiomics+ vs. radiomics-, composite+ model vs. composite-) (p > 0.05). CONCLUSIONS: The present study established a machine learning-based radiomics strategy for differentiating LUAD from TB lesions. The ROI segmentation including or excluding the cavity region may exert no significant effect on the predictive ability.

Single-cell RNA sequencing analysis reveals transcriptional heterogeneity of multiple primary lung cancer.

INTRODUCTION: With the advancements in early diagnosis, more and more patients with multiple primary lung cancer (MPLC) have been identified. However, the progression of MPLC involves complex changes in cell composition and metabolic function, which remains largely controversial. OBJECTIVE: Our study aims to comprehensively reveal the cellular characteristics and inter-cellular connections of MPLC. METHODS: We performed scRNA-seq from 23 samples of six MPLC patients, combined with bulk whole-exome sequencing. We performed trajectory analysis to investigate the transition of different cell types during the development of MPLC. RESULTS: A total of 1 67 397 cells were sequenced derived from tumour and adjacent tissues of MPLC patients, and tumour, normal, immune and stromal cells were identified. Two states of epithelial cells were identified, which were associated with immune response and cell death, respectively. Furthermore, both CD8+ naïve and memory T cells participated in the differentiation of CD8+ T cells. The terminal states of CD8+ T cells were exhausted T cells and cytotoxic T cells, which positively regulated cell death and were implicated in the regulation of cytokine production, respectively. Two main subpopulations of B cells with distinct functions were identified, which participate in the regulation of the immune response and antigen presentation, respectively. In addition, we found a specific type of endothelial cells that were abundant in tumour samples, with an increasing trend from normal to tumour samples. CONCLUSIONS: Our study showed the comprehensive landscape of different cells of MPLC, which might reveal the key cellular mechanisms and, therefore, may provide new insights into the early diagnosis and treatment of MPLC.

Multimodal analysis of cell-free DNA whole-methylome sequencing for cancer detection and localization.

Multimodal epigenetic characterization of cell-free DNA (cfDNA) could improve the performance of blood-based early cancer detection. However, integrative profiling of cfDNA methylome and fragmentome has been technologically challenging. Here, we adapt an enzyme-mediated methylation sequencing method for comprehensive analysis of genome-wide cfDNA methylation, fragmentation, and copy number alteration (CNA) characteristics for enhanced cancer detection. We apply this method to plasma samples of 497 healthy controls and 780 patients of seven cancer types and develop an ensemble classifier by incorporating methylation, fragmentation, and CNA features. In the test cohort, our approach achieves an area under the curve value of 0.966 for overall cancer detection. Detection sensitivity for early-stage patients achieves 73% at 99% specificity. Finally, we demonstrate the feasibility to accurately localize the origin of cancer signals with combined methylation and fragmentation profiling of tissue-specific accessible chromatin regions. Overall, this proof-of-concept study provides a technical platform to utilize multimodal cfDNA features for improved cancer detection.

Peripheral blood inflammatory biomarkers dynamics reflect treatment response and predict prognosis in non-small cell lung cancer patients with neoadjuvant immunotherapy.

Neoadjuvant immunotherapy has significantly changed the therapeutic approach for treating patients with surgically resectable non-small cell lung cancer (NSCLC). Here, peripheral blood inflammation-based biomarkers as well as previously less focused eosinophil fraction, modified Glasgow prognostic score (mGPS), and prognostic nutritional index (PNI) were systematically included to comprehensively analyze their potential in predicting neoadjuvant immunotherapy efficacy and prognosis. We enrolled 189 patients (94 in training and 95 in validation cohorts) with stage I-III B surgically resectable NSCLC treated with neoadjuvant immunotherapy from the National Cancer Center of China. Baseline and post-treatment eosinophils fraction, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), monocyte-to-lymphocyte ratio (MLR), PNI, mGPS, and their changes were calculated and analyzed for correlation with neoadjuvant immunotherapy efficacy and prognosis. In patients in the major pathological response (MPR) group, the post-treatment eosinophil fraction was significantly high, and NLR, PLR, SII, and MLR were significantly lower compared to the non-MPR group in both the training and validation cohorts. The receiver operating characteristic curve showed that post-treatment, eosinophil fraction and SII and their changing were two of the most important factors. Univariate and multivariate logistic regression analyses showed that post-treatment eosinophil fraction, SII, mGPS, and ΔSII could independently predict MPR in patients treated with neoadjuvant immunotherapy. Survival analysis showed a significant correlation between high post-treatment NLR, PLR, SII, mGPS, and their changes in ΔNLR and ΔSII elevation with poor overall survival and event-free survival of patients. Our results suggest that inflammatory biomarkers could predict the patient's response to neoadjuvant immunotherapy and prognosis.

Plasma extracellular vesicle long RNAs predict response to neoadjuvant immunotherapy and survival in patients with non-small cell lung cancer.

Neoadjuvant immunotherapy has brought new hope for patients with non-small cell lung cancer (NSCLC). However, limited by the lack of clinically feasible markers, it is still difficult to select NSCLC patients who respond well and to predict patients' clinical outcomes before the treatment. Before the treatment, we isolated plasma extracellular vesicles (EVs) from three cohorts (discovery, training and validation) of 78 NSCLC patients treated with neoadjuvant immunotherapy. To identify differentially-expressed EV long RNAs (exLRs), we employed RNA-seq in the discovery cohort. And we subsequently used qRT-PCR to establish and validate the predictive signature in the other two cohorts. We have identified 8 candidate exLRs from 27 top-ranked exLRs differentially expressed between responders and non-responders, and tested their expression with qRT-PCR in the training cohort. We finally identified H3C2 (P = 0.029), MALAT1 (P = 0.043) and RPS3 (P = 0.0086) significantly expressed in responders for establishing the predictive signature. Integrated with PD-L1 expression, our signature performed well in predicting immunotherapeutic responses in the training (AUC=0.892) and validation cohorts (AUC=0.747). Furthermore, our signature was proven to be a predictor for favorable prognosis of patients treated with neoadjuvant immunotherapy, which demonstrates the feasibility of our signature in clinical practices (P = 0.048). Our results demonstrate that the exLR-based signature could accurately predict responses to neoadjuvant immunotherapy and prognosis in NSCLC patients.

Utility of 18F-FDG uptake in predicting major pathological response to neoadjuvant immunotherapy in patients with resectable non­small cell lung cancer.

PURPOSE: The aim of present study was to investigate the efficiency of 18F-FDG uptake in predicting major pathological response (MPR) in resectable non-small cell lung cancer (NSCLC) patients with neoadjuvant immunotherapy. METHODS: A total of 104 patients with stage I-IIIB NSCLC were retrospectively derived from National Cancer Center of China, of which 36 cases received immune checkpoint inhibitors (ICIs) monotherapy (I-M) and 68 cases with ICI combination therapy (I-C). 18F-FDG PET-CT scans were performed at baseline and after neoadjuvant therapy (NAT). Receiver-operating characteristic (ROC) curve analyses were conducted and area under ROC curve (AUC) was calculated for biomarkers including maximum standardized uptake value (SUVmax), inflammatory biomarkers, tumor mutation burden (TMB), PD-L1 tumor proportion score (TPS) and iRECIST. RESULTS: Fifty-four resected NSCLC tumors achieved MPR (51.9%, 54/104). In both neoadjuvant I-M and I-C cohorts, post-NAT SUVmax and the percentage changes of SUVmax (ΔSUVmax%) were significantly lower in the patients with MPR versus non-MPR (p < 0.01), and were also negatively correlated with the degree of pathological regression (p < 0.01). The AUC of ΔSUVmax% for predicting MPR was respectively 1.00 (95% CI: 1.00-1.00) in neoadjuvant I-M cohort and 0.94 (95% CI: 0.86-1.00) in I-C cohort. Baseline SUVmax had a statistical prediction value for MPR only in I-M cohort, with an AUC up to 0.76 at the threshold of 17.0. ΔSUVmax% showed an obvious advantage in MPR prediction over inflammatory biomarkers, TMB, PD-L1 TPS and iRECIST. CONCLUSION: 18F-FDG uptake can predict MPR in NSCLC patients with neoadjuvant immunotherapy.

Single-cell RNA-sequencing data reveals the genetic source of extracellular vesicles in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is invasive cancer and the complex mechanisms underlying carcinogenesis remain unclear. Extracellular vesicles (EVs), secreted by most cell types, serve as a critical factor in tumorigenesis via intercellular communications. Our study aims to investigate the cellular origin of EVs in ESCC, and unveil the unknown molecular and cellular mechanisms underlying cell-cell communications. Six ESCC patients were enrolled and single-cell RNA sequencing (scRNA-seq) analyses were conducted to screen different cell subpopulations. The genetic origin of EVs was tracked using the supernatant from different cellular extracts. Nanoparticle tracking analysis (NTA), western blot analysis, and transmission electron microscopy (TEM) were performed for validation. Using scRNA-seq analysis, eleven cell subpopulations were identified in ESCC. Differences in gene expression in EVs between malignant and non-malignant esophageal tissues were found. Our findings demonstrated that epithelial cells releasing EVs were the most prevalent in malignant tissues, while endothelial cells and fibroblasts releasing EVs were predominant in non-malignant tissues. Furthermore, the high levels of gene expression in EVs released from these cells were correlated significantly with a worse prognosis. Our findings revealed the genetic origin of EVs in malignant and non-malignant esophageal tissues and provided a comprehensive overview of the associated cell-cell interactions in ESCC.

Association between radiotherapy and risk of second primary malignancies in patients with resectable lung cancer: a population-based study.

BACKGROUND: The most common form of treatment for non-metastatic lung cancer is surgery-based combination therapy, which may also include adjuvant radiotherapy or chemotherapy. Second primary malignancies (SPMs) are uncommon but significant radiation side effects in patients with resectable lung cancer, and SPMs have not been adequately investigated. Our study aims to assess the correlations of radiotherapy with the development of SPMs in patients with resectable lung cancer. METHODS: We screened for any primary malignancy that occurred more than five years after the diagnosis of resectable lung cancer. Based on the large cohort of the Surveillance, Epidemiology and End Results database, radiotherapy-correlated risks were estimated using the Poisson regression analysis and the cumulative incidence of SPMs was calculated using Fine-Gray competing risk regression analysis. RESULTS: Among the 62,435 patients with non-metastatic lung cancer undergoing surgery, a total of 11,341 (18.16%) patients have received radiotherapy. Our findings indicated that radiotherapy was substantially related to a high risk of main second solid malignancies (RR = 1.21; 95%CI, 1.08 to 1.35) and a negligible risk of main second hematologic malignancies (RR = 1.08; 95%CI, 0.84 to 1.37). With the greatest number of patients, the risk of acquiring a second primary gastrointestinal cancer was the highest overall (RR = 1.77; 95 percent CI, 1.44 to 2.15). The cumulative incidence and standardized incidence ratios of SPMs revealed similar findings. Furthermore, the young and the elderly may be more vulnerable, and the highest risk of acquiring most SPMs was seen more than ten years after lung cancer diagnosis. Additionally, more attention should be paid to the second primary gastrointestinal cancer in young individuals with resectable lung cancer. CONCLUSION: After receiving radiotherapy, an increased risk of developing second primary solid and gastrointestinal cancers was observed for patients with resectable lung cancer. The prevention of SPMs associated with radiotherapy requires further attention.

Global burden and temporal trends in incidence and mortality of oesophageal cancer.

INTRODUCTION: Oesophageal cancer is a prevalent and deadly cancer around the world. OBJECTIVES: We aimed to present a comprehensive analysis of the global geographic patterns and temporal trends in the mortality and incidence of oesophageal cancer. METHODS: The mortality and incidence data of oesophageal cancer in 2020 were obtained from the GLOBOCAN database. Based on World Health Organization (WHO) mortality database and the Cancer Incidence in Five Continents (CI5), we also retrieved the mortality and incidence age-standardized rates (ASRs) of oesophageal cancer. The average annual percentage changes (AAPCs) of mortality and incidence were calculated using the joinpoint regression analysis. RESULTS: Globally, 0.54 million deaths and 0.6 million new cases were identified in 2020. In the majority of countries of South America and Asia, the mortality and incidence trends have substantially decreased, but trends in European countries have varied. The prevalence in European nations varied, but the incidence in most other continents decreased dramatically. In terms of mortality, the global average rate was 5.6 per 100000, ranging from 16.7 (Malawi) to 0.28 (Belize). European countries varied in mortality, such as Norway (AAPC, male: 0.68; female: 0.89) and Ireland (AAPC, male: -0.96; female: -1.52). Most non-European countries saw large decreases in mortality, such as Singapore (AAPC, male: -4.78; female: -6.89). The elderly had more noticeable trends in mortality and incidence in most countries. CONCLUSIONS: We have identified different trends in mortality and incidence among European countries, whereas declining trends were identified in most non-European countries. However, increasing trends were identified in specific subgroups of some countries, such as men in Thailand. For populations with rising mortality and incidence trends, more preventative efforts are required.

Comprehensive analysis of the immunological implication and prognostic value of CXCR4 in non-small cell lung cancer.

CXCR4 (C-X-C chemokine receptor type 4) is the most commonly expressed of all chemokine receptors in malignant tumors. However, studies on CXCR4 in non-small cell lung cancer (NSCLC) tumor immune microenvironment, including those determining its immune efficacy and prognostic potential, are still scarce. Therefore, in this study, we determined the ability of CXCR4 to predict immunotherapy response and prognosis in NSCLC using immunohistochemical staining and RT-PCR, respectively, in two independent cohorts from the National Cancer Center of China. We analyzed transcriptome sequencing data and clinical information from multiple public databases to assess immune cell infiltration in NSCLC and constructed immune risk prognostic signatures based on CXCR4-related immunomodulators. We found that immune cell infiltration is significant differences in NSCLC tissues and is moderately correlated with CXCR4 expression. High CXCR4 expression was significantly associated with poor prognosis in NSCLC patients and a higher response rate to immunotherapy. The ROC curve showed that CXCR4 expression exhibited excellent performance in predicting the efficacy of immunotherapy in NSCLC. We identified 30 CXCR4-related immunomodulators in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) and constructed immune prognostic signatures based on CXCR4-related immunomodulators and CXCR4-related mutant genes. The signature-based prognostic risk score showed good performance in predicting patient prognosis in both LUAD and LUSC; high risk scores were significantly associated with poor prognosis (P < 0.0001) and was established as an independent prognostic factor by multivariate Cox regression. We postulate that CXCR4 is a potential predictive marker of immunotherapy efficacy in NSCLC and should be used in clinical settings. Moreover, the constructed signatures may be valuable in predicting patient prognosis in NSCLC.

Integrating microarray-based spatial transcriptomics and single-cell RNA-sequencing reveals tissue architecture in esophageal squamous cell carcinoma.

BACKGROUND: The tumor microenvironment (TME) serves as an important factor in tumorigenesis and metastasis. Although distinct cell subsets can be identified via single-cell RNA sequencing (scRNA-seq), the spatial composition of cells within the TME is difficult to characterise. METHODS: Tissue samples were collected from three patients with esophageal squamous cell carcinoma (ESCC), and scRNA-seq was performed to identify distinct cell subsets. In addition, a microarray-based spatial transcriptomics (ST) method was used to characterise the spatial landscape of expression data via an array of spots. Using multimodal intersection analysis (MIA) to integrate scRNA-seq and ST, the exact cellular components of the tumor and stromal regions were annotated. FINDINGS: The subpopulations of seven stromal cells were identified within the TME of ESCC, and the architecture of scRNA-seq-determined subsets was mapped in cancer and stromal regions. The distribution of various stromal cells and their subpopulations was heterogeneous. Compared with immune cells, non-immune stromal cells were significantly enriched in the TME. Most subsets of epithelial cells were enriched in the cancer regions, whereas inflammatory cancer-associated fibroblasts were correlated with the stromal regions. Furthermore, TME features were different between metastatic and non-metastatic samples and between the primary and metastatic sites of the metastatic sample. INTERPRETATION: This study revealed the spatial landscape of various cell subsets within the TME and the potential cross-talk among diverse cells, which provides novel insights into cancer intervention. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Plasma extracellular vesicle long RNA profiling identifies a diagnostic signature for stage I lung adenocarcinoma.

Background: The early diagnosis of lung adenocarcinoma (LUAD) is particularly challenging. Recent studies have reported that extracellular vesicles (EVs) include both small and long RNA. However, the profile and diagnosis-related significance of EV long RNA (exLR) profiles for early LUAD remain unclear. Methods: A case-control analysis was carried out involving 110 participants, including 64 stage I LUAD cases, 24 benign pulmonary nodule (BPN) cases, and 22 healthy controls (HCs). The analysis was performed on the plasma samples' exLR profile based on exLR sequencing. The d-signature was identified using the least absolute shrinkage and selection operator (LASSO) method and a training set (n=48), and validation was completed through use of an internal validation set (n=32) and an external validation set (n=30). Results: A diagnostic signature (d-signature) encompassing 8 exLR markers (NFKBIA, NDUFB10, SLC7A7, ARPC5, SEPTIN9, HMGN1, H4C2, and lnc-PLA2G1B-2:3) was identified for the detection of LUAD. This d-signature exhibited a high level of accuracy, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.991 in the training group, 0.921 in the internal validation group, and 0.9 in the external validation group. Moreover, the d-signature could distinguish adenocarcinomas in situ (AIS) and minimally invasive adenocarcinomas (MIA) from the noncancerous controls (NCs), with AUCs of 0.934 and 0.909, respectively, in the combined cohorts. Conclusions: This study initially characterized the plasma exLR profile of early LUAD and reported on an exLR-based diagnostic signature for the detection of LUAD. This d-signature could be a promising noninvasive biomarker for the early detection and routine screening of LUAD.

TIGIT/CD47 dual high expression predicts prognosis and is associated with immunotherapy response in lung squamous cell carcinoma.

BACKGROUND: Recent studies indicated that T cell immunoreceptor with immunoglobulin and ITIM domains (TIGIT) and cluster of differentiation 47 (CD47) have emerged as new potential immunotherapy targets. However, the roles of TIGIT and CD47 in lung squamous cell carcinoma (LUSC) have not been fully illustrated. METHODS: The specimens and clinicopathological information from 190 LUSC patients who underwent surgeries in our center were retrospectively collected. Immunohistochemical staining for TIGIT and CD47 was conducted. Transcriptional and clinical data of 479 LUSC were downloaded from The Cancer Genome Atlas (TCGA). RESULTS: In the TCGA LUSC cohort, 142 (29.6%) cases were TIGIT/CD47 dual high expression at RNA level. The expression levels of TIGIT and CD47 were significantly correlated (p < 0.001). The proportions of patients with high TIGIT expression (p = 0.001) and high TIGIT/CD47 dual high expression (p = 0.049) were both higher in female cases. Advanced TNM stage (p = 0.006) and TIGIT/CD47 dual high expression (p = 0.047) were independent prognostic factors for LUSC. In the 190 LUSC cohort of our center, 75 (39.5%) cases were TIGIT/CD47 dual high expression at protein level. Cross-table analysis showed a correlation between TIGIT and CD47 expression. Older age (p = 0.001), advanced TNM stage (p < 0.001) and TIGIT/CD47 dual high expression (p = 0.046) were independent prognostic factors in our cohort. CONCLUSION: We found that TIGIT and CD47 dual high expression was associated with poor prognosis in LUSC. We speculated that patients with dual high expression of CD47/TIGIT might be suitable for new target immunotherapy in the future.

Comprehensive analysis of the prognostic impact and immune implication of KIAA1429 in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is the most common lung cancer worldwide. N6-methyladenosine (m6A) methylation is a messenger RNA (mRNA) modification that plays a key role in tumor growth, immune microenvironment, and immunotherapy response. This study investigated the expression level, mutation status, prognostic value, and predictive ability for response to anti-PD-1 immunotherapy of the m6A methyltransferase KIAA1429 in LUAD. Methods: This study examined multiple public data cohorts and independent samples from National Cancer Center (NCC) to evaluate the clinical significance and prognostic value of KIAA1429 in LUAD using bioinformatics techniques and immunohistochemical staining. We also evaluated the predictive value of KIAA1429 expression for anti-PD-1 immunotherapy efficacy. GSEA analysis was performed using KIAA1429 RNA-seq data at the tumor tissue level and cellular level to explore the potential molecular mechanism. Results: In public databases, KIAA1429 was significantly associated with clinicopathological parameters in LUAD patients and had the potential to predict patient prognosis. The mutation characteristics of KIAA1429-related genes were analyzed and TP53, TTN, CSMD3, and other genes showed high mutation frequencies in LUAD. An independent cohort of 415 samples confirmed that high KIAA1429 expression was significantly associated with poorer prognosis in LUAD patients. Analysis of a small immunotherapy cohort showed that patients with high expression of KIAA1429 had better response after immunotherapy, and the proportion of patients with immunotherapy response was higher in this group. Conclusions: Our study confirmed that KIAA1429 was highly expressed in LUAD and was significantly associated with poor prognosis. Moreover, KIAA1429 may serve as a potential marker to predict the efficacy of immunotherapy in LUAD.

Severe hypoglycemia and finger clubbing in a patient with a BRCA1 mutation in a solitary fibrous tumor: a case report.

Solitary fibrous tumors (SFTs) are rare tumors that stem from mesenchymal cells of submesothelial tissues belonging to the pleura. They can occur in many places such as the spinal canal, intracranial, neck, kidney, liver, pelvis, limbs and other places, most commonly in the chest and abdomen. Pleural SFTs are one of the most common types, and are common in middle-aged people. Pleural SFTs can have an insidious expression, such that the illness can progress for years before diagnosis. SFTs can induce paraneoplastic syndromes, such as reactive hypoglycemia [Doege-Potter syndrome (DPS)] or hypertrophic osteoarthropathy [Pierre-Marie-Bamberger syndrome (PMBS)]. In this article, we report a case study of a 51-year-old man with pleural SFTs. Preoperative imaging examinations, including chest X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), showed a huge mass in the right thoracic cavity, compressing surrounding tissues and organs and may invade other tissues. In addition, he suffers from severe hypoglycemia and finger clubbing, and has successfully undergone a complete resection, and now attends regular follow-up appointments. The paraneoplastic syndromes have resolved, and no recurrence has been found. Importantly, we used next-generation sequencing (NGS) to explore the molecular characteristics of the patient's pathological tissue at the DNA level and mRNA level, and found that breast cancer gene 1 (BRAC1) mutations may be an important pathogenic factor.

Comprehensive Analysis of PD-L1 Expression, Immune Infiltrates, and m6A RNA Methylation Regulators in Esophageal Squamous Cell Carcinoma.

Background: Esophageal squamous cell carcinoma (ESCC) is one of the most common cancer types and represents a threat to global public health. N6-Methyladenosine (m6A) methylation plays a key role in the occurrence and development of many tumors, but there are still few studies investigating ESCC. This study attempts to construct a prognostic signature of ESCC based on m6A RNA methylation regulators and to explore the potential association of these regulators with the tumor immune microenvironment (TIME). Methods: The transcriptome sequencing data and clinical information of 20 m6A RNA methylation regulators in 453 patients with ESCC (The Cancer Genome Atlas [TCGA] cohort, n = 95; Gene Expression Omnibus [GEO] cohort, n = 358) were obtained. The differing expression levels of m6A regulators between ESCC and normal tissue were evaluated. Based on the expression of these regulators, consensus clustering was performed to investigate different ESCC clusters. PD-L1 expression, immune score, immune cell infiltration and potential mechanisms among different clusters were examined. LASSO Cox regression analysis was utilized to obtain a prognostic signature based on m6A RNA methylation modulators. The relationship between the risk score based on the prognostic signature and the TIME of ESCC patients was studied in detail. Results: Six m6A regulators (METTL3, WTAP, IGF2BP3, YTHDF1, HNRNPA2B1 and HNRNPC) were observed to be significantly highly expressed in ESCC tissues. Two molecular subtypes (clusters 1/2) were determined by consensus clustering of 20 m6A modulators. The expression level of PD-L1 in ESCC tissues increased significantly and was significantly negatively correlated with the expression levels of YTHDF2, METL14 and KIAA1429. The immune score, CD8 T cells, resting mast cells, and regulatory T cells (Tregs) in cluster 2 were significantly increased. Gene set enrichment analysis (GSEA) shows that this cluster involves multiple hallmark pathways. We constructed a five-gene prognostic signature based on m6A RNA methylation, and the risk score based on the prognostic signature was determined to be an independent prognostic indicator of ESCC. More importantly, the prognostic value of the prognostic signature was verified using another independent cohort. m6A regulators are related to TIME, and their copy-number alterations will dynamically affect the number of tumor-infiltrating immune cells. Conclusion: Our study established a strong prognostic signature based on m6A RNA methylation regulators; this signature was able to accurately predict the prognosis of ESCC patients. The m6A methylation regulator may be a key mediator of PD-L1 expression and immune cell infiltration and may strongly affect the TIME of ESCC.