Feline lung-digit syndrome: A differential diagnosis for shifting, waxing and waning lameness in a cat.

The clinical presentation, cytologic findings, radiographic findings, and postmortem assessment of a cat with primary pulmonary adenocarcinoma with multiple digital metastasis are described. An unusual shifting, waxing and waning pattern of lameness, suspected to be an early manifestation of digital metastasis before any gross lesions were visible, was documented. Initial cytologic finding of a lung nodule was equivocal for diagnosis of neoplasia despite being strongly suspicious. Palliative management was short-lived, with rapid progression culminating in widespread metastasis to multiple digits, muscles, and other organs. The diagnosis of pulmonary adenocarcinoma was confirmed via necropsy and histopathology. Key clinical message: This case report highlights that feline lung-digit syndrome is an important differential diagnosis for an acute, waxing and waning, shifting leg lameness in an older cat. This pattern of lameness should raise the index of suspicion for an underlying primary lung neoplasm, and thoracic imaging (radiographs) should be considered.

Syndrome pulmonaire-digital félin : un diagnostic différentiel des boiteries changeantes, croissantes et décroissantes chez un chatLa présentation clinique, les résultats cytologiques, les résultats radiographiques et l'évaluation post mortem d'un chat atteint d'adénocarcinome pulmonaire primaire avec métastases numériques multiples sont décrits. Un schéma inhabituel de boiterie, variable, croissante et décroissante, suspecté d'être une manifestation précoce de métastases digitales avant que des lésions macroscopiques ne soient visibles, a été documenté. La découverte cytologique initiale d'un nodule pulmonaire était équivoque pour le diagnostic de néoplasie bien qu'elle soit fortement suspecte. La prise en charge palliative a été de courte durée, avec une progression rapide aboutissant à des métastases généralisées à plusieurs doigts, muscles et autres organes. Le diagnostic d'adénocarcinome pulmonaire a été confirmé par autopsie et histopathologie.Message clinique clé :Ce rapport de cas souligne que le syndrome pulmonaire-digital félin est un diagnostic différentiel important pour une boiterie aiguë, croissante et décroissante et mobile des pattes chez un chat ágé. Ce type de boiterie devrait faire suspecter une tumeur primaire du poumon sous-jacente, et une imagerie thoracique (radiographies) devrait être envisagée.(Traduit par Dr Serge Messier).

Guillain-Barré Syndrome Following Lung Adenocarcinoma Surgery: A Case Report and Literature Review.

BACKGROUND Guillain-Barre syndrome (GBS) is a rare immune-mediated peripheral nerve disorder. Among non-infectious factors, surgery has been identified as a potential trigger of the disease. This report presents the case of a 74-year-old man who developed GBS 15 days after a right lower lobectomy for lung adenocarcinoma. CASE REPORT We present a case of a patient who was a former smoker who underwent uniportal video-assisted (U-VATS) right lower lobectomy for localized lung adenocarcinoma. Fifteen days after surgery, he exhibited bilateral lower-limb weakness, widespread paresthesia, and postural instability. Comprehensive diagnostic workup, including clinical assessment, serological tests, cerebrospinal fluid (CSF) analysis, and nerve conduction studies (NCS), confirmed the diagnosis. Notably, CSF analysis revealed albumin-cytological dissociation, with albumin 453.2 mg/L, protein 757 mg/L, glucose 67 mg/dl, 3 white blood cells (WBC)/uL, and polymorphonucleates (PMN) 33%. NCS demonstrated motor and sensory abnormalities. Prompt administration of intravenous immunoglobulins (IVIG) 2 g/kg daily for 5 days resulted in complete recovery within 3 months. CONCLUSIONS This case emphasizes the importance of prompt recognition and management of GBS as a postoperative complication. Neurological examination, neuroimaging, and electrophysiological studies are essential for accurate diagnosis. IVIG therapy remains a cornerstone in GBS management, with favorable outcomes observed in this case. Enhanced awareness among clinicians about the potential association between surgery and GBS is vital to prevent more serious complications and ensure optimal patient management. Further research is crucial to determine the precise pathogenesis and mechanisms of GBS following lung surgery.

Joint DNA-RNA-based NGS for diagnosis and treatment of a rare CD47-MET fusion lung adenocarcinoma which was immunoresistant and savoltinib-sensitive: a case report.

Targeted therapy and immunotherapy are both important in the treatment of non-small-cell lung cancer (NSCLC). Accurate diagnose and precise treatment are key in achieving long survival of patients. MET fusion is a rare oncogenic factor, whose optimal detection and treatment are not well established. Here, we report on a 32-year-old female lung adenocarcinoma patient with positive PD-L1 and negative driver gene detected by DNA-based next-generation sequencing (NGS). A radical resection of the primary lesion after chemotherapy combined with PD-1 checkpoint inhibitor administration indicated primary immuno-resistance according to her pathological response and rapid relapse. A rare CD47-MET was detected by RNA-based NGS, which was confirmed by fluorescence in situ hybridization. Multiplex immunofluorescence revealed a PD-L1 related heterogeneous immunosuppressive microenvironment with little distribution of CD4+ T cells and CD8+ T cells. Savolitinib therapy resulted in a progression-free survival (PFS) of >12 months, until a new secondary resistance mutation in MET p.D1228H was detected by re-biopsy and joint DNA-RNA-based NGS after disease progression. In this case, CD47-MET fusion NSCLC was primarily resistant to immunotherapy, sensitive to savolitinib, and developed secondary MET p.D1228H mutation after targeted treatment. DNA-RNA-based NGS is useful in the detection of such molecular events and tracking of secondary mutations in drug resistance. To this end, DNA-RNA-based NGS may be of better value in guiding precise diagnosis and individualized treatment in this patient population.

Ultrasensitive PD-L1-Expressing Exosome Immunosensors Based on a Chemiluminescent Nickel-Cobalt Hydroxide Nanoflower for Diagnosis and Classification of Lung Adenocarcinoma.

Programmed death ligand-1 (PD-L1)-expressing exosomes are considered a potential marker for diagnosis and classification of lung adenocarcinoma (LUAD). There is an urgent need to develop highly sensitive and accurate chemiluminescence (CL) immunosensors for the detection of PD-L1-expressing exosomes. Herein, N-(4-aminobutyl)-N-ethylisopropanol-functionalized nickel-cobalt hydroxide (NiCo-DH-AA) with a hollow nanoflower structure as a highly efficient CL nanoprobe was synthesized using gold nanoparticles as a "bridge". The resulting NiCo-DH-AA exhibited a strong and stable CL emission, which was ascribed to the exceptional catalytic capability and large specific surface area of NiCo-DH, along with the capacity of AuNPs to facilitate free radical generation. On this basis, an ultrasensitive sandwich CL immunosensor for the detection of PD-L1-expressing exosomes was constructed by using PD-L1 antibody-modified NiCo-DH-AA as an effective signal probe and rabbit anti-CD63 protein polyclonal antibody-modified carboxylated magnetic bead as a capture platform. The immunosensor demonstrated outstanding analytical performance with a wide detection range of 4.75 × 103-4.75 × 108 particles/mL and a low detection limit of 7.76 × 102 particles/mL, which was over 2 orders of magnitude lower than the reported CL method for detecting PD-L1-expressing exosomes. Importantly, it was able to differentiate well not only between healthy persons and LUAD patients (100% specificity and 87.5% sensitivity) but also between patients with minimally invasive adenocarcinoma and invasive adenocarcinoma (92.3% specificity and 52.6% sensitivity). Therefore, this study not only presents an ultrasensitive and accurate diagnostic method for LUAD but also offers a novel, simple, and noninvasive approach for the classification of LUAD.

Bioinformatics and experimental approach identify lipocalin 2 as a diagnostic and prognostic indicator for lung adenocarcinoma.

BACKGROUND: lipocalin 2 (LCN2) is a secreted glycoprotein that plays key roles in tumorigenesis and progression. Interestingly, LCN2 appears to have a contradictory function in developing lung adenocarcinoma (LUAD). Thus, we intend to explore the role of LCN2 in LUAD through bioinformatics and experimental validation. METHODS: LCN2 expression of LUAD was investigated in the TCGA, TIMER and HPA databases. The relationship between LCN2 and prognosis was investigated by KM plotter, TCGA and GEO databases. GO, KEGG and protein-protein interactions network analysis were conducted to investigate the potential mechanism of LCN2. The relevance of LCN2 to cancer-immune infiltrates was investigated in the TCGA and TIMER databases. Quantitative reverse transcription PCR, western blot and enzyme-linked immunosorbent assay were performed to identify the expression level of LCN2 in cells and serum samples. The CCK-8, wound healing and transwell assay were used to confirm the effect of LCN2 on cell proliferation, migration and invasion in LUAD. The receiver operating characteristic curve was utilized to assess the diagnostic efficiency of LCN2 further. RESULTS: LCN2 expression was significantly upregulated in LUAD (P < 0.05), and was correlated with the clinical stage, tumor size, lymph node metastasis and distant metastasis (P < 0.05). There was a high correlation between high LCN2 and worse prognosis in LUAD. Functional network analysis suggested that LCN2 was associated with multiple signal pathways in cancers, such as JAK-STAT, TNF, NF-κB, HIF-1 and PI3K-Akt signal pathways. In addition, the knockdown of LCN2 significantly inhibited the ability of cell proliferation, migration and invasion. Immune infiltration analysis indicated that LCN2 is associated with multiple immune cell infiltration. Notably, LCN2 demonstrated high diagnostic efficiency for LUAD (AUC = 0.818, P < 0.05), especially for stage III-IV patients could reach 0.895. CONCLUSIONS: LCN2 as an oncogenic glycoprotein promotes the cancer progression related to immune infiltrates, which might be a potential diagnostic and prognostic marker in LUAD.

The expression characteristic and prognostic role of Siglec-15 in lung adenocarcinoma.

Sialic acid-binding immunoglobulin-like lectin-15 (Siglec-15) has been identified as an immune suppressor and a promising candidate for immunotherapy of cancer management. However, the association between Siglec-15 expression and clinicopathological features of lung adenocarcinoma (LUAD), especially the prognostic role, is not fully elucidated. In this present study, a serial of bioinformatics analyses in both tissue and cell levels were conducted to provide an overview of Siglec-15 expression. Real-time quantitative PCR (qPCR) test, western blotting assay, and immunohistochemistry (IHC) analyses were conducted to evaluate the expression of Siglec-15 in LUAD. Survival analysis and Kaplan-Meier curve were employed to describe the prognostic parameters of LUAD. The results of bioinformatics analyses demonstrated the up-regulation of Siglec-15 expression in LUAD. The data of qPCR, western blotting, and IHC analyses further proved that the expression of Siglec-15 in LUAD tissues was significantly increased than that in noncancerous tissues. Moreover, the expression level of Siglec-15 protein in LUAD was substantially associated with TNM stage. LUAD cases with up-regulated Siglec-15 expression, positive N status, and advance TNM stage suffered a critical unfavorable prognosis. In conclusion, Siglec-15 could be identified as a novel prognostic biomarker in LUAD and targeting Siglec-15 may provide a promising strategy for LUAD immunotherapy.

Pleural effusion supernatant: a reliable resource for cell-free DNA in molecular testing of lung cancer.

INTRODUCTION: DNA extracted from malignant pleural effusion (PE) sediments is the traditional source of tumor DNA for predictive biomarker molecular testing (MT). Few recent studies have proposed the utility of cell-free DNA (cfDNA) extracted from effusion cytology centrifuged supernatants (CCS) in MT. The aim of this study was to assess the feasibility and utility of molecular testing on cfDNA extracted from PE CCS in lung cancer patients. MATERIALS AND METHODS: The study was of prospective design. All PE CCS were collected and stored. Subsequently, in patients confirmed as primary lung adenocarcinoma (LUAD) and where patient matched effusion sediment/tissue biopsy/plasma was being tested for EGFR mutations, cfDNA extraction and EGFR MT by real-time polymerase chain reaction (qPCR) were performed. Custom panel targeted next-generation sequencing (NGS) (Ion Torrent; Thermo Fisher, Carlsbad, CA) was also performed wherever feasible. RESULTS: Out of 299 PE CCS collected, 20 CCS samples were included in the study. Concordant EGFR mutations were detected in pleural effusion CCS of 10 of 11 (91%) EGFR mutant cases as per qPCR performed on the matched sediment DNA (n = 8), lung biopsy (n = 2), and plasma (n = 1) samples. In 1 positive sample, CCS detected additional EGFR T790M mutation. Among 10 CCS samples also tested by NGS, additional EGFR mutations missed by qPCR were picked up in 2 (2 of 10). Success of mutation detection in CCS cfDNA did not correlate with cfDNA quantity or tumor fraction in sediment. CONCLUSIONS: cfDNA from effusion CCS is a reliable and independent source of tumor DNA highly amenable for MT and complement results from other tumor DNA sources for comprehensive mutation profiling in LUAD patients.

Quantitative proteomics revealed protein biomarkers to distinguish malignant pleural effusion from benign pleural effusion.

To identify protein biomarkers capable of early prediction regarding the distinguishing malignant pleural effusion (MPE) from benign pleural effusion (BPE) in patients with lung disease. A four-dimensional data independent acquisition (4D-DIA) proteomic was performed to determine the differentially expressed proteins in samples from 20 lung adenocarcinoma MPE and 30 BPE. The significantly differential expressed proteins were selected for Gene Ontology (GO) enrichment and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis. Protein biomarkers with high capability to discriminate MPE from BPE patients were identified by Random Forest (RF) algorithm prediction model, whose diagnostic and prognostic efficacy in primary tumors were further explored in public datasets, and were validated by ELISA experiment. 50 important proteins (30 up-regulated and 20 down-regulated) were selected out as potential markers to distinguish the MPE from BPE group. GO analysis revealed that those proteins involving the most important cell component is extracellular space. KEGG analysis identified the involvement of cellular adhesion molecules pathway. Furthermore, the Area Under Curve (AUC) of these proteins were ranged from 0.717 to 1.000，with excellent diagnostic properties to distinguish the MPE. Finally, significant survival and gene and protein expression analysis demonstrated BPIFB1, DPP4, HPRT1 and ABI3BP had high discriminating values. SIGNIFICANCE: We performed a 4D-DIA proteomics to determine the differentially expressed proteins in pleural effusion samples from MPE and BPE. Some potential protein biomarkers were identified to distinguish the MPE from BPE patients., which may provide helpful diagnostic and therapeutic insights for lung cancer. This is significant because the median survival time of patients with MPE is usually 4-12 months, thus, it is particularly important to diagnose MPE early to start treatments promptly. The most common causes of MPE are lung cancers, while pneumonia and tuberculosis are the main causes of BPE. If more diagnostic markers could be identified periodically, there would be an important significance to clinical diagnose and treatment with drugs in lung cancer patients.

CRTAC1 identified as a promising diagnosis and prognostic biomarker in lung adenocarcinoma.

CRTAC1, one of the pyroptosis-related genes, has been identified as a protective factor in certain kinds of cancer, such as gastric adenocarcinoma and bladder cancer. The study aimed to investigate the role of CRTAC1 in lung adenocarcinoma (LUAD). LUAD datasets were obtained from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA), pyroptosis-related genes from GeneCard. Limma package used to find differentially expressed genes (DEGs), least absolute shrinkage and selection operator (LASSO) regression and weighted genes co-expression network analysis (WGCNA) to identify CRTAC1 as hub gene. CRTAC1 expression was confirmed in a real-world cohort using quantitative polymerase chain reaction (qPCR) and Western Blot (WB) analyses. Cellular experiments were conducted to investigate CRTAC1's potential oncogenic mechanisms. CRTAC1 mRNA expression was significantly lower in LUAD tissues (p < 0.05) and showed high accuracy in diagnosing LUAD. Reduced CRTAC1 expression was associated with a poor prognosis. Higher CRTAC1 expression correlated with increased immune cell infiltration. Individuals with high CRTAC1 expression showed increased drug sensitivity. Additionally, qPCR and WB analyses showed that CRTAC1 expression was lower in tumor tissue compared to adjacent normal tissue at both the RNA and protein levels. Upregulation of CRTAC1 significantly inhibited LUAD cell proliferation, invasion, and migration in cellular experiments. CRTAC1 has the potential to serve as a diagnostic and prognostic biomarker in LUAD.

Detection of fucosylated extracellular vesicles miR-4732-5p related to diagnosis of early lung adenocarcinoma by the electrochemical biosensor.

Our preliminary investigation has identified the potential of serum fucosylated extracellular vesicles (EVs) miR-4732-5p in the early diagnosis of lung adenocarcinoma (LUAD) by a fucose-captured strategy utilizing lentil lectin (LCA)-magnetic beads and subsequent screening of high throughput sequencing and validation of real-time quantitative polymerase chain reaction (RT-qPCR). Considering the relatively complicated procedure, expensive equipment, and stringent laboratory condition, we have constructed an electrochemical biosensor assay for the detection of miR-4732-5p. miR-4732-5p is extremely low in serum, down to the fM level, so it needs to be detected by highly sensitive electrochemical methods based on the Mg2+-dependent DNAzyme splitting nucleic acid lock (NAL) cycle and hybridization chain reaction (HCR) signal amplification. In this study, signal amplification is achieved through the dual amplification reactions using NAL cycle in combination with HCR. In addition, hybridized DNA strands bind to a large number of methylene blue (MB) molecules to enhance signaling. Based on the above strategy, we further enhance our signal amplification strategies to improve detection sensitivity and accuracy. The implementation of this assay proceeded as follows: initially, miR-4732-5p was combined with NAL, and then Mg2+-dependent DNAzyme splitted NAL to release auxiliary DNA (S1) strands, which were subsequently captured by the immobilized capture probe DNA (C1) strands on the electrode surface. Following this, abundant quantities of DNA1 (H1) and DNA2 (H2) tandems were generated by HCR, and S1 strands then hybridized with the H1 and H2 tandems through base complementary pairing. Finally, MB was bonded to the H1 and H2 tandems through π-π stacking interaction, leading to the generation of a signal current upon the detection of a potential capable of inducing a redox change of MB by the electrode. Furthermore, we evaluated the performance of our developed electrochemical biosensor assay. The results demonstrated that our assay is a reliable approach, characterized by its high sensitivity (with a detection limit of 2.6 × 10-17 M), excellent specificity, good accuracy, reproducibility, and stability. Additionally, it is cost-effective, requires simple operation, and is portable, making it suitable for the detection of serum fucosylated extracellular vesicles miR-4732-5p. Ultimately, this development has the potential to enhance the diagnostic efficiency for patients with early-stage LUAD.

Machine-learning developed an iron, copper, and sulfur-metabolism associated signature predicts lung adenocarcinoma prognosis and therapy response.

BACKGROUND: Previous studies have largely neglected the role of sulfur metabolism in LUAD, and no study has combine iron, copper, and sulfur-metabolism associated genes together to create prognostic signatures. METHODS: This study encompasses 1564 LUAD patients, 1249 NSCLC patients, and over 10,000 patients with various cancer types from diverse cohorts. We employed the R package ConsensusClusterPlus to separate patients into different ICSM (Iron, Copper, and Sulfur-Metabolism) subtypes. Various machine-learning methods were utilized to develop the ICSMI. Enrichment analyses were conducted using ClusterProfiler and GSVA, while IOBR quantified immune cell infiltration. GISTIC2.0 and maftools were utilized for CNV and SNV data analysis. The Oncopredict package predicted drug information based on GDSC1. TIDE algorithm and cohorts GSE91061 and IMvigor210 evaluated patient response to immunotherapy. Single-cell data was processed using the Seurat package, AUCell package calculated cells geneset activity scores, and the Scissor algorithm identified ICSMI-associated cells. In vitro experiments was conducted to explore the role of ICSMRGs in LUAD. RESULTS: Unsupervised clustering identified two distinct ICSM subtypes of LUAD, each with unique clinical characteristics. The ICSMI, comprising 10 genes, was constructed using integrated machine-learning methods. Its prognostic power was validated in 10 independent datasets, revealing that LUAD patients with higher ICSMI levels had poorer prognoses. Furthermore, ICSMI demonstrated superior predictive abilities compared to 102 previously published signatures. A nomogram incorporating ICSMI and clinical features exhibited high predictive performance. ICSMI positively correlated with patients gene mutations, and integrated analysis of bulk and single-cell transcriptome data revealed its association with TME modulators. Cells representing the high-ICSMI phenotype exhibited more malignant features. LUAD patients with high ICSMI levels exhibited sensitivity to chemotherapy and targeted therapy but displayed resistance to immunotherapy. In a comprehensive analysis across various cancers, ICSMI retained significant prognostic value and emerged as a risk factor for the majority of cancer patients. CONCLUSIONS: ICSMI provides critical prognostic insights for LUAD patients, offering valuable insights into the tumor microenvironment and predicting treatment responsiveness.

Invasion and Grading of Pulmonary Non-Mucinous Adenocarcinoma.

Lung adenocarcinoma staging and grading were recently updated to reflect the link between histologic growth patterns and outcomes. The lepidic growth pattern is regarded as "in-situ," whereas all other patterns are regarded as invasive, though with stratification. Solid, micropapillary, and complex glandular patterns are associated with worse prognosis than papillary and acinar patterns. These recent changes have improved prognostic stratification. However, multiple pitfalls exist in measuring invasive size and in classifying lung adenocarcinoma growth patterns. Awareness of these limitations and recommended practices will help the pathology community achieve consistent prognostic performance and potentially contribute to improved patient management.

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

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

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

GTSE1: A potential prognostic and diagnostic biomarker in various tumors including lung adenocarcinoma.

OBJECTIVE: This research was aimed to comprehensively investigate the expression levels, diagnostic and prognostic implications, and the relationship with immune infiltration of G2 and S phase-expressed-1 (GTSE1) across 33 tumor types, including lung adenocarcinoma (LUAD), through gene expression profiling. METHODS: GTSE1 mRNA expression data together with clinical information were acquired from Xena database of The Cancer Genome Atlas (TCGA), ArrayExpress, and Gene Expression Omnibus (GEO) database for this study. The Wilcoxon rank-sum test was used to detect differences in GTSE1 expression between groups. The ability of GTSE1 to accurately predict cancer status was evaluated by calculating the area under the curve (AUC) value for the receiver operating characteristic curve. Additionally, we investigated the predictive value of GTSE1 in individuals diagnosed with neoplasms using univariate Cox regression analysis as well as Kaplan-Meier curves. Furthermore, the correlation between GTSE1 expression and levels of immune infiltration was assessed by utilizing the Tumor Immune Estimate Resource (TIMER) database to calculate the Spearman rank correlation coefficient. Finally, the pan-cancer analysis findings were validated by examining the association between GTSE1 expression and prognosis among patients with LUAD. RESULTS: GTSE1 exhibited significantly increased expression levels in a wide range of tumor tissues in contrast with normal tissues (p < 0.05). The expression of GTSE1 in various tumors was associated with clinical features, overall survival, and disease-specific survival (p < 0.05). In immune infiltration analyses, a strong correlation of the level of immune infiltration with the expression of GTSE1 was observed. Furthermore, GTSE1 demonstrated good discriminative and diagnostic value for most tumors. Additional experiments confirmed the relationship between elevated GTSE1 expression and unfavorable prognosis in individuals diagnosed with LUAD. These findings indicated the crucial role of GTSE1 expression level in influencing the development and immune infiltration of different types of tumors. CONCLUSIONS: GTSE1 might be a potential biomarker for the prognosis of pan-cancer. Meanwhile, it represented a promising target for immunotherapy.

Diagnostic Value of Immunostaining for Thyroid Transcription Factor 1 (TTF1) and Paired Box 8 (PAX8) in Distinguishing Pulmonary Metastases of Mesonephric and Mesonephric-like Adenocarcinomas from Primary Lung Adenocarcinomas.

BACKGROUND/AIM: Both mesonephric adenocarcinoma (MA) and mesonephric-like adenocarcinoma (MLA) express thyroid transcription factor 1 (TTF1). TTF1 is also considered a highly sensitive and specific diagnostic marker for primary lung adenocarcinoma (PLA). However, distinguishing PLA from pulmonary metastatic MA/MLA (PMM) based on the expression of TTF1 alone can be difficult. This study aimed to investigate the expression of TTF1 and paired box 8 (PAX8) and assess their value in distinguishing PMM from PLA. PATIENTS AND METHODS: We reviewed the electronic medical records and pathology slides of eight PMM cases. We conducted immunostaining for TTF1 and PAX8 in 6, 8, and 21 cases of primary MA/MLA, PMM, and PLA, respectively. RESULTS: Two patients with stage IB uterine MLA developed lung metastases at 5 and 57 months after hysterectomy. Solitary pulmonary nodules were suspected to be primary lung cancer in two patients. Compared to primary tumors, all matched PMMs exhibited reduced TTF1 immunoreactivity. In contrast, the majority of PLAs showed uniform and intense TTF1 expression. All except one PMM exhibited diffuse and strong PAX8 expression, while only one PLA showed focal and weak PAX8 expression. CONCLUSION: Immunostaining for TTF1 and PAX8 can help in distinguishing PMM from PLA in the diagnosis of pulmonary lesions detected in patients with a history of MA/MLA.

Lung Cancer Diagnosis on Virtual Histologically Stained Tissue Using Weakly Supervised Learning.

Lung adenocarcinoma (LUAD) is the most common primary lung cancer and accounts for 40% of all lung cancer cases. The current gold standard for lung cancer analysis is based on the pathologists' interpretation of hematoxylin and eosin (H&E)-stained tissue slices viewed under a brightfield microscope or a digital slide scanner. Computational pathology using deep learning has been proposed to detect lung cancer on histology images. However, the histological staining workflow to acquire the H&E-stained images and the subsequent cancer diagnosis procedures are labor-intensive and time-consuming with tedious sample preparation steps and repetitive manual interpretation, respectively. In this work, we propose a weakly supervised learning method for LUAD classification on label-free tissue slices with virtual histological staining. The autofluorescence images of label-free tissue with histopathological information can be converted into virtual H&E-stained images by a weakly supervised deep generative model. For the downstream LUAD classification task, we trained the attention-based multiple-instance learning model with different settings on the open-source LUAD H&E-stained whole-slide images (WSIs) dataset from the Cancer Genome Atlas (TCGA). The model was validated on the 150 H&E-stained WSIs collected from patients in Queen Mary Hospital and Prince of Wales Hospital with an average area under the curve (AUC) of 0.961. The model also achieved an average AUC of 0.973 on 58 virtual H&E-stained WSIs, comparable to the results on 58 standard H&E-stained WSIs with an average AUC of 0.977. The attention heatmaps of virtual H&E-stained WSIs and ground-truth H&E-stained WSIs can indicate tumor regions of LUAD tissue slices. In conclusion, the proposed diagnostic workflow on virtual H&E-stained WSIs of label-free tissue is a rapid, cost effective, and interpretable approach to assist clinicians in postoperative pathological examinations. The method could serve as a blueprint for other label-free imaging modalities and disease contexts.

TIMM17A overexpression in lung adenocarcinoma and its association with prognosis.

Lung adenocarcinoma (LUAD), a leading cause of cancer-related mortality worldwide, demands a deeper understanding of its molecular mechanisms and the identification of reliable biomarkers for better diagnosis and targeted therapy. Leveraging data from the Cancer Genome Atlas (TCGA), the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and the Human Protein Atlas (HPA), we investigated the mRNA and protein expression profiles of TIMM17A and assessed its prognostic significance through Kaplan-Meier survival curves and Cox regression analysis. Through Gene Set Enrichment Analysis, we explored the regulatory mechanisms of TIMM17A in LUAD progression and demonstrated its role in modulating the proliferative capacity of A549 cells, a type of LUAD cell, via in vitro experiments. Our results indicate that TIMM17A is significantly upregulated in LUAD tissues, correlating with clinical staging, lymph node metastasis, overall survival, and progression-free survival, thereby establishing it as a critical independent prognostic factor. The construction of a nomogram model further enhances our ability to predict patient outcomes. Knockdown of TIMM17A inhibited the growth of LUAD cells. The potential of TIMM17A as a biomarker and therapeutic target for LUAD presents a promising pathway for improving patient diagnosis and treatment strategies.

Proteomic profiling of tumor microenvironment and prognosis risk prediction in stage I lung adenocarcinoma.

OBJECTIVES: With the increasing popularity of CT screening, more cases of early-stage lung cancer are being diagnosed. However, 24.5% of stage I non-small-cell lung cancer (NSCLC) patients still experience treatment failure post-surgery. Biomarkers to predict lung cancer patients at high risk of recurrence are needed. MATERIALS AND METHODS: We collected protein mass spectrometry data from the Taiwan Lung Cancer Moonshot Project and performed bioinformatics analysis on proteins with differential expressions between tumor and adjacent normal tissues in 74 stage I lung adenocarcinoma (LUAD) cases, aiming to explore the tumor microenvironment related prognostic biomarkers. Findings were further validated in 6 external cohorts. RESULTS: The analysis of differentially expressed proteins revealed that the most enriched categories of diseases and biological functions were cellular movement, immune cell trafficking, and cancer. Utilizing proteomic profiling of the tumor microenvironment, we identified five prognostic biomarkers (ADAM10, MIF, TEK, THBS2, MAOA). We then developed a risk score model, which independently predicted recurrence-free survival and overall survival in stage I LUAD. Patients with high risk scores experienced worse recurrence-free survival (adjusted hazard ratio = 8.28, p < 0.001) and overall survival (adjusted hazard ratio = 6.88, p = 0.013). Findings had been also validated in the external cohorts. CONCLUSION: The risk score model derived from proteomic profiling of tumor microenvironment can be used to predict recurrence risk and prognosis of stage I LUAD.