Multiplex Dual-Target Reverse Transcription PCR for Subtyping Avian Influenza A(H5) Virus.

An increased risk for human infection with avian influenza A(H5N1) viruses is of concern. We developed an internally controlled, dual-target reverse transcription PCR for influenza A(H5) subtyping. This test could be used to detect influenza A(H5) in clinical samples.

Similar genetic profile in early and late stage urothelial tract cancer.

INTRODUCTION: Urothelial tract cancer (UTC) ranks as the tenth most prevalent cancer and holds the seventh position in terms of mortality worldwide. Despite its prevalence and mortality ranking, there are still gaps in the knowledge of the mutational landscape in patients with advanced disease who have limited therapeutic options after multiple lines of prior treatment. This study compares the genomic and transcriptomic landscape, and targeted treatment options between metastatic UTC (mUTC) patients treated with multiple lines of therapy compared to newly diagnosed, untreated Muscle Invasive Bladder Cancer (MIBC). METHODS: We compared genomic and clinical data from two cohorts: mUTC patients who received multiple lines of therapy and were referred to the Copenhagen Prospective Personalized Oncology (CoPPO) project at Rigshospitalet, University of Copenhagen. Data for MIBC UTC patients were acquired from the Cancer Genome Atlas Bladder Cancer (TCGA BLCA) cohort. Biopsies in CoPPO were performed at the time of enrollment. 523 highly important cancer-related genes (TrueSight Oncology-500 targeted sequencing panel) were used from both cohorts for comparative analysis. Analyses included RNA count data to compare predicted molecular subtypes in each cohort separately. RESULTS: Patients from the CoPPO cohort had a lower median age at first-line treatment than the TCGA BLCA cohort, with no significant gender disparity. The predominant histology was urothelial cell carcinoma in both cohorts. Genomic analysis revealed no significant difference between the top mutated genes in the two cohorts, specifically looking into DNA damage repair genes. Molecular subtyping indicated a higher frequency of neuroendocrine differentiation in the CoPPO cohort. 13% of patients in the CoPPO cohort received targeted therapy based on genomic findings, and 16% received non-targeted treatment, totaling 29% receiving CoPPO treatment (9 patients). The remaining 71% received best supportive care. Kaplan-Meier analysis showed a non-significant survival benefit for the intervention group in the CoPPO cohort. CONCLUSION: When focusing on 523 highly relevant cancer genes, the mutational profile of mUTC patients who have undergone numerous treatment lines resembles that of newly diagnosed MIBC. These alterations can be targeted, indicating the potential advantage of early genomic testing for personalized treatment within clinical trials.

Molecular characterizations of Cryptosporidium spp. in brown rat (Rattus norvegicus) from an animal feedlot in Xinjiang, China.

Cryptosporidium infection is a common occurrence in rodents worldwide. In this study, 435 wild brown rats were captured from an animal feedlot in Xinjiang, China, with a fecal sample obtained directly from the rectal contents of each rat. The DNA extracted from these fecal samples was analyzed for Cryptosporidium spp. using PCR targeting the SSU rRNA gene. The prevalence of Cryptosporidium infection in brown rats was found to be 5.5% (24 out of 435). Interestingly, the infection rates varied among different animal enclosures, with rates of 0% in the chicken coop (0/51), cowshed (0/3), and varying rates in other areas including the sheepfold (6.1%, 6/98), the pigsty (7.6%, 10/132), the dovecote (7.0%, 5/71), and outdoor environments (3.8%, 3/80). The study identified three species and one genotype of Cryptosporidium, namely C. occultus (n = 10), C. parvum (n = 4), C. ditrichi (n = 1), and Cryptosporidium rat genotype IV (n = 9). Additionally, two of the C. parvum isolates were successfully subtyped as IIdA19G1 (n = 2) at the gp60 gene. These results offer valuable insights into the prevalence and genetic diversity of Cryptosporidium in brown rats within the region.

First detection of tick-borne encephalitis virus (TBEV) in raw milk samples in North-Western Iran.

Tick-borne encephalitis virus (TBEV) is a significant cause of flaviviral infections affecting the human central nervous system, primarily transmitted through tick bites and the consumption of unpasteurized milk. This study aimed to assess the prevalence of TBEV and identify new natural foci of TBEV in livestock milk. In this cross-sectional study, unpasteurized milk samples were collected from livestock reared on farms and analysed for the presence and subtyping of TBEV using nested reverse transcription-polymerase chain reaction , alongside the detection of anti-TBEV total IgG antibodies using ELISA. The findings revealed that the highest prevalence of TBEV was observed in goat and sheep milk combined, whereas no TBEV was detected in cow milk samples. All identified strains were of the Siberian subtype. Moreover, the highest prevalence of anti-TBEV antibodies was detected in sheep milk. These results uncover new foci of TBEV in Iran, underscoring the importance of thermal processing (pasteurization) of milk prior to consumption to mitigate the risk of TBEV infection.

Precise Molecular Subtyping Reveals Heterogeneity of Lung Adenocarcinoma Based on DNA Methylation.

BACKGROUND: Due to the high heterogeneity of lung adenocarcinoma (LUAD), which restricts the effectiveness of therapy, precise molecular subgrouping of LUAD is of great significance. Clinical research has demonstrated the significant potential of DNA methylation as a classification indicator for human malignancies. METHODS: WGML framework (which was developed based on weighted gene correlation network analysis (WGCNA), Gene Ontology (GO), and machine learning) was developed to precisely subgroup molecular subtypes of LUAD. This framework included two parts: the WG algorithm and the machine learning part. The WG algorithm part was an original algorithm used to obtain a crucial module, which was characterized by weighted correlation network analysis, functional annotation, and mathematical algorithms. The machine learning part utilized the Boruta algorithm, random forest algorithm, and Gradient Boosting Regression Tree algorithm to select feature genes. Then, based on the results of the WGML framework, subtypes were computed by the hierarchical clustering algorithm. A series of analyses, including dimensionality reduction methods, survival analysis, clinical stage analysis, immune infiltration analysis, tumor environment analysis, immune checkpoints analysis, TIDE analysis, CYT analysis, somatic mutation analysis, and drug sensitivity analysis, were utilized to demonstrate the effectiveness of subgrouping. GEO datasets were used to externally validate the results. Meanwhile, another subgrouping method of LUAD from another study was employed to compare with the WGML framework. RESULT: By importing DNA methylation data into the WGML framework, nine genes were obtained to further subgroup LUAD. Three subtypes, the Carcinogenesis subtype, Immune-infiltration subtype, and Chemoresistance subtype, were identified. The dimensionality reduction method exhibited great distinctness between subtypes. A series of analyses were employed to exhibit the difference among the three subtypes and to demonstrate the accuracy of the definition of subtypes. Besides, the WGML framework was compared with a LUAD subgrouping method from another research, which demonstrated that WGML had better efficiency for subgrouping LUAD. CONCLUSION: This study provides a novel LUAD subgrouping framework named WGML for the accurate subgrouping of lung adenocarcinoma.

Methods in DNA methylation array dataset analysis: A review.

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

Enhancing RNA detection and breast cancer subtyping with a universal 3D-hybridization chain reaction system.

Breast cancer, a globally prevalent malignancy, is characterized by pronounced heterogeneity. Accurate subtyping requires the simultaneous detection of different biomarkers, which is crucial for personalized treatment strategies. However, existing methodologies are hindered by limited versatility and sensing performance. To overcome these hurdles, this study presents a universal 3D-Hybridization Chain Reaction (3D-HCR) system for RNA detection and subtype-specific diagnosis of breast cancer. The system integrated a universal trigger for HCR, thereby circumventing the need for complex sequence design and enabling the analysis of various RNA targets. Leveraging the spatial-confinement effect offered by DNA nanocarriers, this system exhibited superior amplification efficiency, achieving detection limits of 3.83 pM and 4.96 pM for PD-L1 mRNA and miR-21, respectively. Importantly, the system could differentiate between triple-negative breast cancer and estrogen receptor-positive breast cancer in both living cells and clinical tissues. These findings underscore the potential of the universal 3D-HCR system as a promising tool in clinical diagnostics. With its proven proficiency in breast cancer diagnostics and versatility in RNA analysis, this system holds the promise of broadening the horizons of precision medicine.

Comprehensive molecular epidemiology of Cryptosporidium species in Japan.

Cryptosporidium species, causing diarrheal illnesses in humans and animals worldwide, are under investigation for their molecular epidemiology in Japan. The study focuses on detecting Cryptosporidium species in humans, animals, water, and the environment, revealing three species in people: C. parvum, C. meleagridis, and C. hominis. Subtype IIa of the C. parvum gp60 gene is prevalent, indicating potential zoonotic transmission. Animal studies identified sixteen species, mainly cattle and pets, with C. parvum (subtype IIa) common in cattle and C. canis and C. felis prevalent in pets. Additionally, C. bovis and C. ryanae were found in cattle and sika deer. Knowledge gaps exist, particularly in water and environmental source typing, with limited research revealing five species and five genotypes, suggesting a significant role of water in transmission. Further research is needed to understand the molecular diversity and transmission dynamics across humans, animals, water, and the environment in Japan.

Evolving treatment landscape in thymic epithelial tumors: From mechanism to therapy.

Thymic epithelial tumors (TETs) are rare neoplasms of the anterior mediastinum that arise from thymic epithelial cells. Although surgery is the preferred treatment for resectable TETs, the options for unresectable or recurrent advanced-stage TETs are limited beyond platinum-based chemotherapy. The evolving landscape of TET treatments is marked by significant advancements in targeted therapies and immunotherapies, particularly with anti-angiogenic agents and immune checkpoint inhibitors (ICIs). While monotherapies demonstrated certain efficacy, the development of combination strategies is vital for improving patient outcomes. This review consolidates progress in anti-angiogenic therapies and ICIs, emphasizing the evolution of combination therapies of TETs. Furtherly, we particularly discuss new first-line strategies based on these advancements and emphasizes exploring novel treatments like antibody-drug conjugates, immunomodulatory drugs and cytokine-based agents for TETs. Mechanistically, the molecular features of TETs integrated with clinical diagnosis and targeted therapy, and immunophenotyping of TETs along with its impact on the efficacy and safety of immunotherapy are discussed. Thus, this review systemizes the development in the treatment landscape of TETs, integrating the corresponding molecular and immune mechanisms, aiming to provide new references for the treatment of TETs.

Themis: advancing precision oncology through comprehensive molecular subtyping and optimization.

Recent advances in tumor molecular subtyping have revolutionized precision oncology, offering novel avenues for patient-specific treatment strategies. However, a comprehensive and independent comparison of these subtyping methodologies remains unexplored. This study introduces 'Themis' (Tumor HEterogeneity analysis on Molecular subtypIng System), an evaluation platform that encapsulates a few representative tumor molecular subtyping methods, including Stemness, Anoikis, Metabolism, and pathway-based classifications, utilizing 38 test datasets curated from The Cancer Genome Atlas (TCGA) and significant studies. Our self-designed quantitative analysis uncovers the relative strengths, limitations, and applicability of each method in different clinical contexts. Crucially, Themis serves as a vital tool in identifying the most appropriate subtyping methods for specific clinical scenarios. It also guides fine-tuning existing subtyping methods to achieve more accurate phenotype-associated results. To demonstrate the practical utility, we apply Themis to a breast cancer dataset, showcasing its efficacy in selecting the most suitable subtyping methods for personalized medicine in various clinical scenarios. This study bridges a crucial gap in cancer research and lays a foundation for future advancements in individualized cancer therapy and patient management.

Analysis of convolutional neural networks for fronto-temporal dementia biomarker discovery.

PURPOSE: Frontotemporal lobe dementia (FTD) results from the degeneration of the frontal and temporal lobes. It can manifest in several different ways, leading to the definition of variants characterised by their distinctive symptomatologies. As these variants are detected based on their symptoms, it can be unclear if they represent different types of FTD or different symptomatological axes. The goal of this paper is to investigate this question with a constrained cohort of FTD patients in order to see if the heterogeneity within this cohort can be inferred from medical images rather than symptom severity measurements. METHODS: An ensemble of convolutional neural networks (CNNs) is used to classify diffusion tensor images collected from two databases consisting of 72 patients with behavioural variant FTD and 120 healthy controls. FTD biomarkers were found using voxel-based analysis on the sensitivities of these CNNs. Sparse principal components analysis (sPCA) is then applied on the sensitivities arising from the patient cohort in order to identify the axes along which the patients express these biomarkers. Finally, this is correlated with their symptom severity measurements in order to interpret the clinical presentation of each axis. RESULTS: The CNNs result in sensitivities and specificities between 83 and 92%. As expected, our analysis determines that all the robust biomarkers arise from the frontal and temporal lobes. sPCA identified four axes in terms of biomarker expression which are correlated with symptom severity measurements. CONCLUSION: Our analysis confirms that behavioural variant FTD is not a singular type or spectrum of FTD, but rather that it has multiple symptomatological axes that relate to distinct regions of the frontal and temporal lobes. This analysis suggests that medical images can be used to understand the heterogeneity of FTD patients and the underlying anatomical changes that lead to their different clinical presentations.

The Clinicopathological Significance of Tumor Cell Subtyping in Appendiceal Neuroendocrine Tumors: A Series of 135 Tumors.

Appendiceal neuroendocrine tumors (NETs) are common and often are identified as incidental lesions at the time of appendectomy. The guidelines for management are based on tumor size, degree of invasion, and the Ki67 proliferation index. Most small bowel NETs are composed of serotonin-producing EC-cells, but there are multiple other neuroendocrine cell types. In the rectum, there are L-cell tumors that express peptide YY (PYY), glucagon-like peptides (GLPs), and pancreatic polypeptide (PP); they are thought to have a better prognosis than serotonin-producing tumors. We investigated whether the appendix has distinct neuroendocrine tumor types based on cell type and whether that distinction has clinical significance. We collected 135 appendiceal NETs from the pathology archives of UHN Toronto and UHCMC (Cleveland). We analyzed the expression of biomarkers including CDX2, SATB2, PSAP, serotonin, glucagon (that detects GLPs), PYY, and pancreatic polypeptide (PP) and correlated the results with clinicopathologic parameters. Immunohistochemistry identified three types of appendiceal NETs. There were 75 (56%) classified as EC-cell tumors and 37 (27%) classified as L-cell tumors; the remaining 23 (17%) expressed serotonin and one of the L-cell biomarkers and were classified as mixed. EC-cell tumors were significantly larger with more extensive invasion involving the muscularis propria, subserosa, and mesoappendix compared with L-cell tumors. Mixed tumors were intermediate in all of these parameters. Both EC-cell and mixed tumors had lymphatic and/or vascular invasion while L-cell tumors had none. Unlike EC-cell NETs, L-cell tumors were not associated with lymph node metastasis. Tumor type correlated with pT stage and the only patient with distant metastatic disease in this series had an EC-cell tumor. Our study confirms that appendiceal NETs are not a homogeneous tumor population. There are at least three types of appendiceal NET, including EC-cell, L-cell, and mixed tumors. This information is important for surveillance of patients, as monitoring urinary 5HIAA levels is only appropriate for patients with serotonin-producing tumors, whereas measurement of GLPs and/or PP is more appropriate for patients with L-cell tumors. Our data also show that tumor type is of significance with EC-cell tumors exhibiting the most aggressive behavior.

Evidence for planning and motor subtypes of stuttering based on resting state functional connectivity.

We tested the hypothesis, generated from the Gradient Order Directions Into Velocities of Articulators (GODIVA) model, that adults who stutter (AWS) may comprise subtypes based on differing connectivity within the cortico-basal ganglia planning or motor loop. Resting state functional connectivity from 91 AWS and 79 controls was measured for all GODIVA model connections. Based on a principal components analysis, two connections accounted for most of the connectivity variability in AWS: left thalamus - left posterior inferior frontal sulcus (planning loop component) and left supplementary motor area - left ventral premotor cortex (motor loop component). A k-means clustering algorithm using the two connections revealed three clusters of AWS. Cluster 1 was significantly different from controls in both connections; Cluster 2 was significantly different in only the planning loop; and Cluster 3 was significantly different in only the motor loop. These findings suggest the presence of planning and motor subtypes of stuttering.

Diagnostic potential of protein serum biomarkers for distinguishing small and non-small cell lung cancer in patients with suspicious lung lesions.

BACKGROUND: Biomarkers play a role in identifying, managing, and predicting cancer outcomes. In lung cancer, they are used at various time points. Doubts remain regarding their accuracy for differential diagnosis and histological subtyping. A diagnostic test study was conducted. It included malignant lesions and controls with benign lesions. Before lung biopsy, all patients had the following biomarkers measured in serum (Pro-GRP,NSE,CYFRA21-1,SCC-Ag,CEA). METHODS: The predictive capacity of serum biomarkers was evaluated to discriminate between lung cancer and benign pathology. The accuracy was also assessed for distinguishing between SCLC and NSCLC and explored their ability to perform histological subtyping. RESULTS: 93 patients were included, 60 with lung cancer, 33 with benign pathology. Pro-GRP and NSE were elevated in SCLC compared with NSCLC or nonmalignant disease. The most accurate for differentiating between malignant and benign pathology were CEA and CYFRA21-1. Pro-GRP had a poor predictive capacity for distinguishing NSCLC from SCLC. However, combined with CEA and CYFRA21-1, performance improved. For SCLC, the diagnostic capacity of Pro-GRP increased by combining with biomarkers, such as NSE/CYFRA21-1. CONCLUSIONS: Biomarkers lacked the sensitivity and specificity for independent differential diagnosis or histological subtyping. However, the observed patterns in biomarker levels associated with specific histological subtypes suggest potential utility in a multi-biomarker approach or in conjunction with other diagnostic tools. This insight could guide future research to improve diagnostic accuracy and personalized treatment strategies in lung cancer.

Biomarkers are crucial for identifying, managing, and predicting outcomes in lung cancer, though they lack accuracy in differentiating histological subtypes.CEA and CYFRA21-1 were the most accurate biomarkers for distinguishing between malignant and benign pathology.Pro-GRP and NSE levels were elevated in SCLC compared to NSCLC. Pro-GRP alone had poor predictive capacity for differentiating NSCLC from SCLC, but combining it with CEA and CYFRA21-1 improved diagnostic performance.Patterns in biomarker levels suggest that a multi-biomarker approach, especially when combined with other diagnostic tools, could improve diagnostic accuracy.

Subtype-WGME enables whole-genome-wide multi-omics cancer subtyping.

We present an innovative strategy for integrating whole-genome-wide multi-omics data, which facilitates adaptive amalgamation by leveraging hidden layer features derived from high-dimensional omics data through a multi-task encoder. Empirical evaluations on eight benchmark cancer datasets substantiated that our proposed framework outstripped the comparative algorithms in cancer subtyping, delivering superior subtyping outcomes. Building upon these subtyping results, we establish a robust pipeline for identifying whole-genome-wide biomarkers, unearthing 195 significant biomarkers. Furthermore, we conduct an exhaustive analysis to assess the importance of each omic and non-coding region features at the whole-genome-wide level during cancer subtyping. Our investigation shows that both omics and non-coding region features substantially impact cancer development and survival prognosis. This study emphasizes the potential and practical implications of integrating genome-wide data in cancer research, demonstrating the potency of comprehensive genomic characterization. Additionally, our findings offer insightful perspectives for multi-omics analysis employing deep learning methodologies.

Influenza surveillance in pigs: balancing act between broad diagnostic coverage and specific virus characterization.

BACKGROUND: Monitoring of infectious diseases on swine farms requires a high diagnostic sensitivity and specificity of the test system. Moreover, particularly in cases of swine influenza A virus (swIAV) it is desirable to include characterization of the virus as precisely as possible. This is indispensable for strategies concerning prophylaxis of swIAV and furthermore, to meet the requirements of a purposeful monitoring of newly emerging swIAV strains in terms of vaccine design and public health. Within the present cross-sectional study, we compared the diagnostic value of group samples (wipes of surfaces with direct contact to mouth/nose, dust wipes, udder skin wipes, oral fluids) to individual samples (nasal swabs, tracheobronchial swabs) for both swIAV identification and characterization. Sampling included different stages of pig production on 25 sow farms with attached nursery considered as enzootically infected with swIAV. Firstly, samples were analyzed for IAV genome and subsequently samples with Ct-values < 32 were subtyped by multiplex RT-qPCR. RESULTS: Nasal swabs of suckling piglets and nursery pigs resulted in a higher odds to detect swIAV (p < 0.001) and to identify swIAV subtypes by RT-qPCR (p < 0.05) compared to nasal swabs of sows. In suckling piglets, significant higher rates of swIAV detection could be observed for nasal swabs (p = 0.007) and sow udder skin wipes (p = 0.036) compared to contact wipes. In the nursery, group sampling specimens were significantly more often swIAV positive compared to individual samples (p < 0.01), with exception of the comparison between contact wipes and nasal swabs (p = 0.181). However, in general nasal swabs were more likely to have Ct-value < 32 and thus, to be suitable for subtyping by RT-qPCR compared to dust wipes, contact wipes, udder skin wipes and tracheobronchial swabs (p < 0.05). Interestingly, different subtypes were found in different age groups as well as in different specimens in the same holding. CONCLUSION: Although population-based specimens are highly effective for swIAV monitoring, nasal swabs are still the preferable sampling material for the surveillance of on-farm circulating strains due to significantly higher virus loads. Remarkably, sampling strategies should incorporate suckling piglets and different age groups within the nursery to cover as many as possible of the on-farm circulating strains.

Molecular classification and biomarkers of outcome with immunotherapy in extensive-stage small-cell lung cancer: analyses of the CASPIAN phase 3 study.

BACKGROUND: We explored potential predictive biomarkers of immunotherapy response in patients with extensive-stage small-cell lung cancer (ES-SCLC) treated with durvalumab (D) + tremelimumab (T) + etoposide-platinum (EP), D + EP, or EP in the randomized phase 3 CASPIAN trial. METHODS: 805 treatment-naïve patients with ES-SCLC were randomized (1:1:1) to receive D + T + EP, D + EP, or EP. The primary endpoint was overall survival (OS). Patients were required to provide an archived tumor tissue block (or ≥ 15 newly cut unstained slides) at screening, if these samples existed. After assessment for programmed cell death ligand-1 expression and tissue tumor mutational burden, residual tissue was used for additional molecular profiling including by RNA sequencing and immunohistochemistry. RESULTS: In 182 patients with transcriptional molecular subtyping, OS with D ± T + EP was numerically highest in the SCLC-inflamed subtype (n = 10, median 24.0 months). Patients derived benefit from immunotherapy across subtypes; thus, additional biomarkers were investigated. OS benefit with D ± T + EP versus EP was greater with high versus low CD8A expression/CD8 cell density by immunohistochemistry, but with no additional benefit with D + T + EP versus D + EP. OS benefit with D + T + EP versus D + EP was associated with high expression of CD4 (median 25.9 vs. 11.4 months) and antigen-presenting and processing machinery (25.9 vs. 14.6 months) and MHC I and II (23.6 vs. 17.3 months) gene signatures, and with higher MHC I expression by immunohistochemistry. CONCLUSIONS: These findings demonstrate the tumor microenvironment is important in mediating better outcomes with D ± T + EP in ES-SCLC, with canonical immune markers associated with hypothesized immunotherapy mechanisms of action defining patient subsets that respond to D ± T. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03043872.

An epidemiological and clinicopathological study of type 1 vs. type 2 morphological subtypes of papillary renal cell carcinoma- results from a nation-wide study covering 50 years in Iceland.

INTRODUCTION: Papillary renal cell carcinoma (pRCC) is the second most common histology of renal cell carcinoma (RCC), accounting for 10-15% of cases. Traditionally, pRCC is divided into type 1 and type 2, although this division is currently debated as a prognostic factor of survival. Our aim was to investigate the epidemiology and survival of the pRCC subtypes in a whole nation cohort of patients during a 50-year period. MATERIALS AND METHODS: A Population based retrospective study including consecutive cases of RCC in Iceland from 1971-2020. Comparisons were made between histological classifications of RCC, with emphasis on pRCC subtypes (type 1 vs. 2) for outcome estimation. Changes in RCC incidence were analyzed in 5-year intervals after age standardization. The Kaplan-Meier method and Cox regression were used for outcome analysis. RESULTS: A total of 1.725 cases were identified, with 74.4%, 2.1% and 9.2% having clear cell (ccRCC), chromophobe (chRCC), and pRCC, respectively. The age standardized incidence (ASI) of pRCC was 1.97/100.000 for males and 0.5/100.000 for females, and the proportion of pRCC increased from 3.7% to 11.5% between the first and last intervals of the study (p < 0.001). Age standardized cancer specific mortality (ASCSM) of pRCC was 0.6/100.000 and 0.19/100.000 for males and females, respectively. The annual average increase in ASI was 3.6% for type 1 pRCC, but the ASI for type 2 pRCC and ASCSM for both subtypes did not change significantly. Male to female ratio was 4.4 for type 1 pRCC and 2.3 for type 2. The average tumor size for type 1 and 2 was 58.8 and 73.7 mm, respectively. Metastasis at diagnosis was found in 8.7% in the type 1 pRCC, compared to 30.0% of patients with type 2 pRCC (p < 0.001). Estimated 5-year cancer-specific survival (CSS) were 94.4%, 80.7%, and 69.3% for chRCC, pRCC and ccRCC, respectively (p < 0.001). For the pRCC subtypes, type 1 was associated with better 5-year CSS than type 2 (86.3% vs. 66.0%, p < 0.001), although this difference was not significant after adjusting for cancer stage and grading. CONCLUSIONS: pRCC histology was slightly less common in Iceland than in other countries. Males are more than three times more likely to be diagnosed with pRCC, compared to other RCC histologies. The subtype of pRCC was not found to be an independent risk factor for worse survival, and as suggested by the most recent WHO Classification of Urinary Tumors, grade and TNM-stage seem to be the most important factors for estimation of survival for pRCC patients.

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

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

Establishing and Validating an Innovative Focal Adhesion-Linked Gene Signature for Enhanced Prognostic Assessment in Endometrial Cancer.

Studies have highlighted the significant role of focal adhesion signaling in cancer. Nevertheless, its specific involvement in the pathogenesis of endometrial cancer and its clinical significance remains uncertain. We analyzed TCGA-UCEC and GSE119041 datasets with corresponding clinical data to investigate focal adhesion-related gene expression and their clinical significance. A signature, "FA-riskScore," was developed using LASSO regression in the TCGA cohort and validated in the GSE dataset. The FA-riskScore was compared with four existing models in terms of their prediction performance. We employed univariate and multivariate Cox regression analyses towards FA-riskScore to assess its independent prognostic value. A prognostic evaluation nomogram based on our model and clinical indexes was established subsequently. Biological and immune differences between high- and low-risk groups were explored through functional enrichment, PPI network analysis, mutation mining, TME evaluation, and single-cell analysis. Sensitivity tests on commonly targeted drugs were performed on both groups, and Connectivity MAP identified potentially effective molecules for high-risk patients. qRT-PCR validated the expressions of FA-riskScore genes. FA-riskScore, based on FN1, RELN, PARVG, and PTEN, indicated a poorer prognosis for high-risk patients. Compared with published models, FA-riskScore achieved better and more stable performance. High-risk groups exhibited a more challenging TME and suppressive immune status. qRT-PCR showed differential expression in FN1, RELN, and PTEN. Connectivity MAP analysis suggested that BU-239, potassium-canrenoate, and tubocurarine are effective for high-risk patients. This study introduces a novel prognostic model for endometrial cancer and offers insights into focal adhesion's role in cancer pathogenesis.