Subtype Analysis Research Articles

Deciphering the SOX4/MAPK1 regulatory axis: a phosphoproteomic insight into IQGAP1 phosphorylation and pancreatic Cancer progression

ObjectiveThis study aims to elucidate the functional role of IQGAP1 phosphorylation modification mediated by the SOX4/MAPK1 regulatory axis in developing pancreatic cancer through phosphoproteomics analysis.MethodsProteomics and phosphoproteomics data of pancreatic cancer were obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Differential analysis, kinase-substrate enrichment analysis (KSEA), and independent prognosis analysis were performed on these datasets. Subtype analysis of pancreatic cancer patients was conducted based on the expression of prognostic-related proteins, and the prognosis of different subtypes was evaluated through prognosis analysis. Differential analysis of proteins in different subtypes was performed to identify differential proteins in the high-risk subtype. Clinical correlation analysis was conducted based on the expression of prognostic-related proteins, pancreatic cancer typing results, and clinical characteristics in the pancreatic cancer proteomics dataset. Functional pathway enrichment analysis was performed using GSEA/GO/KEGG, and most module proteins correlated with pancreatic cancer were selected using WGCNA analysis. In cell experiments, pancreatic cancer cells were grouped, and the expression levels of SOX4, MAPK1, and the phosphorylation level of IQGAP1 were detected by RT-qPCR and Western blot experiments. The effect of SOX4 on MAPK1 promoter transcriptional activity was assessed using a dual-luciferase assay, and the enrichment of SOX4 on the MAPK1 promoter was examined using a ChIP assay. The proliferation, migration, and invasion functions of grouped pancreatic cancer cells were assessed using CCK-8, colony formation, and Transwell assays. In animal experiments, the impact of SOX4 on tumor growth and metastasis through the regulation of MAPK1-IQGAP1 phosphorylation modification was studied by constructing subcutaneous and orthotopic pancreatic cancer xenograft models, as well as a liver metastasis model in nude mice.ResultsPhosphoproteomics and proteomics data analysis revealed that the kinase MAPK1 may play an important role in pancreatic cancer progression by promoting IQGAP1 phosphorylation modification. Proteomics analysis classified pancreatic cancer patients into two subtypes, C1 and C2, where the high-risk C2 subtype was associated with poor prognosis, malignant tumor typing, and enriched tumor-related pathways. SOX4 may promote the occurrence of the high-risk C2 subtype of pancreatic cancer by regulating MAPK1-IQGAP1 phosphorylation modification. In vitro cell experiments confirmed that SOX4 promoted IQGAP1 phosphorylation modification by activating MAPK1 transcription while silencing SOX4 inhibited the proliferation, migration, and invasion of pancreatic cancer cells by reducing the phosphorylation level of MAPK1-IQGAP1. In vivo, animal experiments further confirmed that silencing SOX4 suppressed the growth and metastasis of pancreatic cancer by reducing the phosphorylation level of MAPK1-IQGAP1.ConclusionThe findings of this study suggest that SOX4 promotes the phosphorylation modification of IQGAP1 by activating MAPK1 transcription, thereby facilitating the growth and metastasis of pancreatic cancer.

Key factor screening in mouse NASH model using single-cell sequencing combined with machine learning

AimsTo identify and analyze genes closely related to the progression of nonalcoholic steatohepatitis (NASH) by employing a combination of single-cell RNA sequencing and machine-learning algorithms. Main methodsSingle-cell RNA sequencing (scRNA-seq) analysis was performed to find the cell population with the most significant differences between the Chow and NASH groups. This approach was used to validate the developmental trajectory of this cell population and investigate changes in cellular communication and important signaling pathways among these cells. Subsequently, high dimensional Weighted Gene Co-expression Network Analysis (hdWGCNA) was used to find the key modules in NASH. Machine learning analyses were performed to further identify core genes. Deep learning techniques were applied to elucidate the correlation between core genes and immune cells. The accuracy of this correlation was further confirmed using deep learning techniques, specifically Convolutional Neural Networks. Key findingsBy comparing scRNA-seq data between the Chow and NASH groups, we have observed a notable distinction existing in the Kupffer cell population. Signaling interactions between hepatic macrophages and other cells were significantly heightened in the NASH group. Through subsequent analysis of macrophage subtypes and key modules, we identified 150 genes tightly associated with NASH. Finally, we highlighted the 16 most significant core genes using multiple iterations of machine learning. Furthermore, we pointed out the close relationship between core genes and immune cells. SignificancesUsing scRNA-seq analysis and machine learning, we can distinguish NASH-related genes from large genetic datasets, providing theoretical support in finding potential targets for the development of novel therapies.

Multi omics analysis of mitophagy subtypes and integration of machine learning for predicting immunotherapy responses in head and neck squamous cell carcinoma.

Mitophagy serves as a critical mechanism for tumor cell death, significantly impacting the progression of tumors and their treatment approaches. There are significant challenges in treating patients with head and neck squamous cell carcinoma, underscoring the importance of identifying new targets for therapy. The function of mitophagy in head and neck squamous carcinoma remains uncertain, thus investigating its impact on patient outcomes and immunotherapeutic responses is especially crucial. We initially analyzed the differential expression, prognostic value, intergene correlations, copy number variations, and mutation frequencies of mitophagy-related genes at the pan-cancer level. Through unsupervised clustering, we divided head and neck squamous carcinoma into three subtypes with distinct prognoses, identified the signaling pathway features of each subtype using ssGSEA, and characterized subtype B as having features of an immune desert using various immune infiltration calculation methods. Using multi-omics data, we identified the genomic variation characteristics, mutated gene pathway features, and drug sensitivity features of the mitophagy subtypes. Utilizing a combination of 10 machine learning algorithms, we have developed a prognostic scoring model called Mitophagy Subgroup Risk Score (MSRS), which is used to predict patient survival and the response to immune checkpoint blockade therapy. Simultaneously, we applied MSRS to single-cell analysis to explore intercellular communication. Through laboratory experiments, we validated the biological function of SLC26A9, one of the genes in the risk model. In summary, we have explored the significant role of mitophagy in head and neck tumors through multi-omics data, providing new directions for clinical treatment.

SPDYC serves as a prognostic biomarker related to lipid metabolism and the immune microenvironment in breast cancer.

Breast cancer remains the most common malignant carcinoma among women globally and is resistant to several therapeutic agents. There is a need for novel targets to improve the prognosis of patients with breast cancer. Bioinformatics analyses were conducted to explore potentially relevant prognostic genes in breast cancer using The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) databases. Gene subtypes were categorized by machine learning algorithms. The machine learning-related breast cancer (MLBC) score was evaluated through principal component analysis (PCA) of clinical patients' pathological statuses and subtypes. Immune cell infiltration was analyzed using the xCell and CIBERSORT algorithms. Kyoto Encyclopedia of Genes and Genomes enrichment analysis elucidated regulatory pathways related to speedy/RINGO cell cycle regulator family member C (SPDYC) in breast cancer. The biological functions and lipid metabolic status of breast cancer cell lines were validated via quantitative real-time polymerase chain reaction (RT‒qPCR) assays, western blotting, CCK-8 assays, PI‒Annexin V fluorescence staining, transwell assays, wound healing assays, and Oil Red O staining. Key differentially expressed genes (DEGs) in breast cancer from the TCGA and GEO databases were screened and utilized to establishthe MLBC score. Moreover, the MLBC score we established was negatively correlated with poor prognosis in breast cancer patients. Furthermore, the impacts of SPDYC on the tumor immune microenvironment and lipid metabolism in breast cancer were revealed and validated. SPDYC is closely related to activated dendritic cells and macrophages and is simultaneously correlated with the immune checkpoints CD47, cytotoxic T lymphocyte antigen-4 (CTLA-4), and poliovirus receptor (PVR). SPDYC strongly correlated with C-C motif chemokine ligand 7 (CCL7), a chemokine that influences breast cancer patient prognosis. A significant relationship was discovered between key genes involved in lipid metabolism and SPDYC, such as ELOVL fatty acid elongase 2 (ELOVL2), malic enzyme 1 (ME1), and squalene epoxidase (SQLE). Potent inhibitors targeting SPDYC in breast cancer were also discovered, including JNK inhibitor VIII, AICAR, and JW-7-52-1. Downregulation of SPDYC expression in vitro decreased proliferation, increased the apoptotic rate, decreased migration, and reduced lipid droplets. SPDYC possibly influences the tumor immune microenvironment and regulates lipid metabolism in breast cancer. Hence, this study identified SPDYC as a pivotal biomarker for developing therapeutic strategies for breast cancer.

Prognostic and predictive value of examined lymph node count in stage III colorectal cancer: a population based study

BackgroundThe role of tumor-draining lymph nodes in the progression of malignant tumors, including stage III colorectal cancer (CRC), is critical. However, the prognostic and predictive value of the number of examined lymph nodes (ELNs) are not fully understood.MethodsThis population-based study retrospectively analyzed data from 106,843 patients with stage III CRC who underwent surgical treatment and registered in three databases from 2004 to 2021. The Surveillance, Epidemiology, and End Results (SEER) cohort was divided using into training and test cohorts at a ratio of 3:2. We employed restricted cubic spline (RCS) curves to explore nonlinear relationships between overall survival (OS) and ELNs counts and performed Cox regression to evaluate hazard ratios across different ELNs count subtypes. Additional validation cohorts were utilized from the First Affiliated Hospital, Sun Yat-sen University and The Cancer Genome Atlas (TCGA) under the same criteria. Outcomes measured included OS, cancer-specific survival (CSS), and progression-free survival (PFS). Molecular analyses involved differential gene expression using the “limma” package and immune profiling through CIBERSORT. Tissue microarray slides and multiplex immunofluorescence (MIF) were used to assess protein expression and immune cell infiltration.ResultsPatients with higher ELNs counts (≥ 17) demonstrated significantly better long-term survival outcomes across all cohorts. Enhanced OS, CSS, and PFS were notably evident in the LN-ELN group compared to those with fewer ELNs. Cox regression models underscored the prognostic value of higher ELNs counts across different patient subgroups by age, sex, tumor differentiation, and TNM stages. Subtype analysis based on ELNs count revealed a marked survival benefit in patients treated with adjuvant chemotherapy in the medium and large ELNs counts (≥ 12), whereas those with fewer ELNs showed negligible benefits. RNA sequencing and MIF indicated elevated immune activation in the LN-ELN group, characterized by increased CD3+, CD4+, and CD8 + T cells within the tumor microenvironment.ConclusionsThe number of ELNs independently predicts survival and the immunological landscape at the tumor site in stage III CRC, underscoring its dual prognostic and predictive value.

No causal relationship between glucose and inflammatory bowel disease: a bidirectional two-sample mendelian randomization study

BackgroundAssociation between glucose and inflammatory bowel disease (IBD) was found in previous observational studies and in cohort studies. However, it is not clear whether these associations reflect causality. Thus, this study investigated whether there is such a causal relation between elevated glucose and IBD, Crohn’s disease (CD) and ulcerative colitis (UC).MethodsWe performed a two-sample Mendelian Randomization (MR) with the independent genetic instruments identified from the largest available genome-wide association study (GWAS) for IBD (5,673 cases; 213,119 controls) and its main subtypes, CD and UC. Summarized data for glucose which included 200,622 cases and glycemic traits including HbA1c and type 2 diabetes(T2DM) were obtained from different GWAS studies. Primary and secondary analyses were conducted by preferentially using the radial inverse-variance weighted (IVW) approach. A number of other meta-analysis approach and sensitivity analyses were carried out to assess the robustness of the results.ResultsWe did not find a causal effect of genetically predicted glucose on IBD as a whole (OR 0.858; 95% CI 0.649–1.135; P = 0.286). In subtype analyses glucose was also suggestively not associated with Crohn’s disease (OR 0.22; 95% CI 0.04-1.00; P = 0.05) and ulcerative colitis (OR 0.940; 95% CI 0.628–1.407; P = 0.762). In the other direction, IBD and its subtypes were not related to glucose and glycemic traits.ConclusionsThis MR study is not providing any evidence for a causal relationship between genetically predicted elevated glucose and IBD as well as it’s subtypes UC and CD. Regarding the other direction, no causal associations could be found. Future studies with robust genetic instruments are needed to confirm this conclusion.

Outcome-guided Bayesian clustering for disease subtype discovery using high-dimensional transcriptomic data

Due to the tremendous heterogeneity of disease manifestations, many complex diseases that were once thought to be single diseases are now considered to have disease subtypes. Disease subtyping analysis, that is the identification of subgroups of patients with similar characteristics, is the first step to accomplish precision medicine. With the advancement of high-throughput technologies, omics data offers unprecedented opportunity to reveal disease subtypes. As a result, unsupervised clustering analysis has been widely used for this purpose. Though promising, the subtypes obtained from traditional quantitative approaches may not always be clinically meaningful (i.e. correlate with clinical outcomes). On the other hand, the collection of rich clinical data in modern epidemiology studies has the great potential to facilitate the disease subtyping process via omics data and to discovery clinically meaningful disease subtypes. Thus, we developed an outcome-guided Bayesian clustering (GuidedBayesianClustering) method to fully integrate the clinical data and the high-dimensional omics data. A Gaussian mixed model framework was applied to perform sample clustering; a spike-and-slab prior was utilized to perform gene selection; a mixture model prior was employed to incorporate the guidance from a clinical outcome variable; and a decision framework was adopted to infer the false discovery rate of the selected genes. We deployed conjugate priors to facilitate efficient Gibbs sampling. Our proposed full Bayesian method is capable of simultaneously (i) obtaining sample clustering (disease subtype discovery); (ii) performing feature selection (select genes related to the disease subtype); and (iii) utilizing clinical outcome variable to guide the disease subtype discovery. The superior performance of the GuidedBayesianClustering was demonstrated through simulations and applications of breast cancer expression data and Alzheimer's disease. An R package has been made publicly available on GitHub to improve the applicability of our method.

Definition and analysis of gray matter atrophy subtypes in mild cognitive impairment based on data-driven methods.

Mild cognitive impairment (MCI) is an important stage in Alzheimer's disease (AD) research, focusing on early pathogenic factors and mechanisms. Examining MCI patient subtypes and identifying their cognitive and neuropathological patterns as the disease progresses can enhance our understanding of the heterogeneous disease progression in the early stages of AD. However, few studies have thoroughly analyzed the subtypes of MCI, such as the cortical atrophy, and disease development characteristics of each subtype. In this study, 396 individuals with MCI, 228 cognitive normal (CN) participants, and 192 AD patients were selected from ADNI database, and a semi-supervised mixture expert algorithm (MOE) with multiple classification boundaries was constructed to define AD subtypes. Moreover, the subtypes of MCI were obtained by using the multivariate linear boundary mapping of support vector machine (SVM). Then, the gray matter atrophy regions and severity of each MCI subtype were analyzed and the features of each subtype in demography, pathology, cognition, and disease progression were explored combining the longitudinal data collected for 2 years and analyzed important factors that cause conversion of MCI were analyzed. Three MCI subtypes were defined by MOE algorithm, and the three subtypes exhibited their own features in cortical atrophy. Nearly one-third of patients diagnosed with MCI have almost no significant difference in cerebral cortex from the normal aging population, and their conversion rate to AD are the lowest. The subtype characterized by severe atrophy in temporal lobe and frontal lobe have a faster decline rate in many cognitive manifestations than the subtype featured with diffuse atrophy in the whole cortex. APOE ε4 is an important factor that cause the conversion of MCI to AD. It was proved through the data-driven method that MCI collected by ADNI baseline presented different subtype features. The characteristics and disease development trajectories among subtypes can help to improve the prediction of clinical progress in the future and also provide necessary clues to solve the classification accuracy of MCI.

Evaluating disparities in receptor status, overall survival, and time to hormone therapy among women with breast cancer.

11096 Background: Breast cancer receptor status (ER/PR/HER2) is essential to guide treatment decisions. However, national race/ethnicity subtype studies rarely include Indigenous American Indian and Alaska Native (AI/AN) and Native Hawaiian and other Pacific Islander (NHPI) women. This study aims to compare across race/ethnicity: 1) hormone receptor (HR)/HER2 status, 2) Overall Survival (OS) for Triple Positive breast cancer, and 3) Time to Hormone Therapy (THT). Methods: A cohort study of women with breast cancer (Stage 1-4), age ≥18 years, diagnosed 2004-2017 was conducted with the National Cancer Database. Race was used as federally defined (White, Black, Asian, AI/AN, and NHPI) with Hispanic ethnicity. Primary endpoints were receptor status, OS, and THT (days from diagnosis to hormone therapy). Kaplan-Meier estimates and log-rank tests assessed OS. Multivariable logistic, Cox Proportional Hazard, and linear regression evaluated the likelihood of HR+/HER2+ (adjusted Odds Ratio [aOR]), death (adjusted Hazard Ratio [aHR]), and THT in days (adjusted β [aβ]), respectively, with 95% confidence intervals (95%CI). The majority non-Hispanic White population served as reference. All regressions were adjusted for sociodemographic and cancer characteristics. All HER2 analyses used a subset of women diagnosed post-2010, given inconsistent testing pre-2010. Results: 1,812,911 women were included who mostly had Stage 1 cancer (53%), from metropolitan areas (84%), with private insurance (51%), and a median age of 61 (IQR 51-71) years. Women were 81% White, 11% Black, 5% Hispanic, 2% Asian, 0.3% AI/AN, and 0.2% NHPI. NHPI women had the highest ER+ (84%) and PR+ (75%) cancers, while Asian women had the highest HER2+ (18%). Adjusted for covariates, only NHPI women were significantly more likely to have HR+ cancer (aOR=1.3, 95%CI=1.2-1.5) while Black women were less likely (aOR=0.6, 95%CI=0.6-0.6), compared to White women. Hispanic women were significantly more likely to have Triple Positive cancer (aOR=1.1, 95%CI=1.1-1.2) while Black women were less likely (aOR=0.9, 95%CI=0.9-0.9). Median follow-up was 63 (IQR 36-101) months. Among women with Triple Positive breast cancer, there were significant differences in OS (p< .0001) with 7-year survival ranging from 77% for NHPI women to 89% for Asian women. NHPI women had the highest risk of death (aHR=1.5, 95%CI=1.0-2.1), followed by AI/AN (aHR=1.5, 1.05-2.0), and Black (aHR=1.2, 95%CI=1.1-1.2). Adjusted for covariates, among women with HR+ breast cancer, longer THT occurred among Black (aβ=18 days, 95%CI=17-18), Hispanic (aβ=16 days, 95%CI=15-17), and NHPI women (aβ=14 days, 95%CI=9-18). Conclusions: NHPI women have higher HR+ rates while Black, Hispanic, and NHPI women with HR+ cancer experience longer times to hormone therapy. This is likely the first US analysis of breast cancer subtypes using federal race/ethnicity standards.

Immune phenotype profiling based on anatomic origin of melanoma and impact on clinical outcomes of immune checkpoint inhibitor treatment.

9569 Background: The inflamed immune phenotype is associated with a favorable response to immunotherapy in metastatic malignant melanoma. Although variations in molecular features across melanoma subtypes and differing ethnic distributions have been well reported, studies investigating immune phenotypes across multiple institutions within the context of different melanoma subtypes are scarce. We examined the association of immune phenotypes with outcomes after immunotherapy, based on subtypes of malignant melanoma. Methods: Two institutional advanced melanoma cohortsrepresenting Asian and Western populations (YUHS, n = 123; Stanford University, n = 102) were included. H&E-stained tumor slides were assessed using Lunit SCOPE IO, an AI-powered whole slide image analyzer, to define the inflamed score (IS, % of inflamed area) and the inflamed phenotype (>33.3% IS). Clinical outcomes after immune checkpoint inhibitor (anti-PD-1/PD-L1) treatment, including progression-free survival (PFS) and overall-survival (OS), were assessed. Results: In the overall cohort (n = 225), the inflamed phenotype was associated with prolonged PFS (HR = 0.634, 95% CI: 0.453 – 0.887, p = 0.007) and OS (HR = 0.655, 95% CI: 0.432 – 0.992, p = 0.044). On analysis of melanoma subtypes, we identified non-acral cutaneous melanomas (CM, n = 94), acral melanomas (AM, n = 52), mucosal melanomas (n = 43), uveal melanomas (n = 16), and melanomas of unknown primary site (n = 20), with a disproportionate enrichment of AM (37.4%) and mucosal (29.3%) melanoma cases in the YUHS cohort and CM cases (68.6%) in the Stanford cohort. The IS varied across different melanoma subtypes, with the highest IS observed in CM (median, [IQR]; 34.0, [5.8–52.2]), and significantly lower scores in AM (22.3, [7.7–36.9]), mucosal (13.6, [0–31.4]), and uveal melanoma (5.5, [0–13.6]). Among CM patients, the inflamed phenotype was significantly associated with better post-immunotherapy PFS (HR = 0.476, 95% CI: 0.274 – 0.826, p = 0.007). In contrast, the inflamed phenotype did not correlate with survival in AM patients (p = 0.58), who exhibited universally poorer PFS compared to non-AM patients (HR = 2.124, 95% CI: 1.486 - 3.005). Conclusions: AI-powered immune phenotype assessment highlights a heterogeneity of immune phenotypes across specific melanoma subtypes, as well as differential association with outcomes after immune checkpoint inhibitor treatment. While CM patients were well-stratified by immune phenotype, the predictive value of immune phenotyping was less pronounced in acral melanomas, which demonstrated poorer outcomes following immunotherapy.

Impact of hormone receptor status and tumor subtypes on clinical behavior and outcomes of breast cancer in young BRCA carriers.

504 Background: Hormone receptor expression is a known positive prognostic and predictive factor in breast cancer (BC). Among young patients harboring germline BRCA1or BRCA2 (BRCA) pathogenic variants (PVs), most of the tumors arising in BRCA2carriers express hormone receptors, while most BC cases in BRCA1 carriers do not; however, limited evidence exists on the impact of hormone receptor status on clinical behavior and outcomes in young patients harboring BRCA PVs. Methods: This is an international, multicenter, hospital-based, retrospective cohort study including young patients (≤40 years) diagnosed with invasive BC between January 2000 and December 2020, harboring germline PVs in BRCA genes. Our analysis investigates the impact of hormone receptor status on clinical behavior and outcomes of BC. The type and pattern of recurrence and survival outcomes (disease-free survival [DFS], BC specific survival [BCSS] and overall survival [OS]) were first investigated according to hormone receptors expression (positive vs. negative), and then according to BC subtype (luminal A-like vs. luminal B-like vs. triple-negative vs. HER2-positive BC). Results: From 78 centers worldwide, 4,709 BRCA carriers were included in this analysis, of whom 2,143 (45.5%) had hormone receptor-positive and 2,566 (54.5%) hormone receptor-negative BC. Patients with hormone receptor-positive BC were more likely to harbor BRCA2 PVs while less frequently had grade 3 tumors and nodal involvement. Median follow-up was 7.88 (IQR 4.47-12.61) years. The rate of distant recurrences was higher in patients with hormone receptor-positive BC (13.1% vs. 9.6%, p<.001), while the rate of second primary BC was lower (9.1% vs. 14.7%, p<.001) when compared to patients with hormone receptor-negative disease. The 8-years DFS was 65.8% in patients with hormone receptor-positive and 63.4% in those with hormone receptor-negative BC. No differences in terms of OS nor BCSS were observed. The hazard ratio of hormone receptor-positive vs. negative disease changed over time for DFS, BCSS, and OS (p<.05 for interactions of hormone receptor status and survival time). Among the 4,363 patients eligible for subtypes analysis, 612 had luminal A-like, 1,038 luminal B-like, 2,373 triple-negative and 340 HER2-positive BC. Patients with Luminal A-like BC had the worst long-term prognosis in terms of DFS compared to all the other subgroups (8-years DFS: 60.8% in luminal A-like vs. 63.5% in triple-negative vs. 65.5% in HER2-positive and 69.7% in luminal B-like subtype). Conclusions: In young BRCAPVs carriers with early BC, hormone receptor status did not appear to be a positive prognostic factor. The differences in pattern of recurrence and second primary BC among hormone receptor-positive vs. negative disease warrants consideration in counseling patients on treatment, follow-up strategies and indication for risk-reducing surgery.

Influence of TP53 mutation on efficacy and survival in advanced EGFR-mutant non-small cell lung cancer patients treated with third-generation EGFR tyrosine kinase inhibitors.

TP53 comutation is related to poor prognosis of non-small cell lung cancer. However, there is limited study focusing on the structural influence of TP53 mutation on third-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) treatment. We retrospectively analyzed the clinical and molecular data of patients treated with third-generation EGFR-TKIs in two independent cohorts. A total of 117 patients from the Sun Yat-sen University Cancer Center (SYSUCC) and 141 patients from the American Association for Cancer Research Project GENIE database were included. In the SYSUCC cohort, TP53 comutations were found in 59 patients (50.4%) and were associated with poor median progress-free survival (mPFS) and median overall survival (mOS). The additional subtype analysis found that TP53 mutation in the alpha-helix region had shorter mOS compared with those with TP53 mutations in other regions in the SYSUCC cohort (mOS, 12.2 vs. 21.7 months; p=0.027). Similar findings were confirmed in the GENIE cohort. Specifically, the presence of TP53 mutation in the alpha-helix region was an independent negative predictive factor for PFS [hazard ratio (HR) 2.05(1.01-4.18), p=0.048] and OS [HR 3.62(1.60-8.17), p=0.002] in the SYSUCC cohort. TP53 mutation in alpha-helix region was related to inferior clinical outcomes in patients treated with third-generation EGFR-TKIs.

Analysis of cell death-related genes to evaluate the prognostic and immunotherapeutic value in bladder cancer

To enhance therapeutic approaches, we created a distinctive pattern utilizing the cell demise indicator (CDI) to predict the effectiveness of immunotherapy in individuals with bladder carcinoma (BLCA). Hub prognostic CDIs were identified from the TCGA database using differential gene expression and survival analysis, encompassing 763 genes across 13 death modes. The subtype assessment was employed to evaluate the impact of these genes on the prognosis and immunotherapeutic outcomes in patients with BLCA. The LASSO regression method was used to identify significant CDIs, while Cox regression and nomogram analyses were conducted to explore the impact of CDIs on prognosis. CHMP4C and GSDMB were selected as the hub genes for the following research. Subsequently, These two central genes underwent further investigation to explore their association with immunotherapy, followed by an analysis of their potential regulatory network. Subtype analysis showed that these CDIs were significantly associated with the prognosis and immunotherapy of BLCA patients. The regulatory network in BLCA was evaluated through the establishment of the lncRNA XIST/NEAT1-CDIs-miR-146a-5p/miR-429 axis. Immunohistochemical analysis revealed a significant up-regulation of CHMP4C in bladder cancer tissues, which was strongly associated with an unfavorable prognosis for BLCA patients. Moreover, our findings provide compelling evidence that CHMP4C plays a pivotal role in promoting BLCA progression through the activation of the epithelial-mesenchymal transition (EMT) pathway. These findings highlight the negative impact of CHMP4C on BLCA patient prognosis, while also providing insights into the oncogenic mechanisms and immunotherapy in which CHMP4C may be involved.

Correlation between the miR-618 rs2682818 C>A polymorphism and venous malformation susceptibility.

Venous malformations are the most common congenital vascular malformations, and the incidence rate is high. Previous studies have confirmed that a variety of polymorphisms within the miRNA functional region are associated with tumor susceptibility. We examined the correlation between miR-618 rs2682818 C>A and risk of developing venous malformation in a southern Chinese population (1113 patients and 1158 controls). TaqMan genotyping of miR-618 rs2682818 C>A was conducted utilizing real-time fluorescent quantitative PCR. The miR-618 rs2682818 polymorphism was not correlated with susceptibility to venous malformation (CA/AA vs. CC: adjusted odds ratio [AOR] = 1.00, 95% confidence interval [CI] = 0.81-1.25, p = 0.994; AA vs. CC/CA: AOR = 1.10, 95% CI = 0.73-1.65, p = 0.646). Stratified analysis of different subtypes of venous malformation revealed that there was no significant difference in the rs2682818 C>A polymorphism genotypes across these subtypes. Our results indicate that miR-618 rs2682818 C>A polymorphism is not correlated with the susceptibility to venous malformation.

Characterizing the distinct imaging phenotypes, clinical behavior, and genetic vulnerability of brain maturational subtypes in mood disorders.

Mood disorders are characterized by great heterogeneity in clinical manifestation. Uncovering such heterogeneity using neuroimaging-based individual biomarkers, clinical behaviors, and genetic risks, might contribute to elucidating the etiology of these diseases and support precision medicine. We recruited 174 drug-naïve and drug-free patients with major depressive disorder and bipolar disorder, as well as 404 healthy controls. T1 MRI imaging data, clinical symptoms, and neurocognitive assessments, and genetics were obtained and analyzed. We applied regional gray matter volumes (GMV) and quantile normative modeling to create maturation curves, and then calculated individual deviations to identify subtypes within the patients using hierarchical clustering. We compared the between-subtype differences in GMV deviations, clinical behaviors, cell-specific transcriptomic associations, and polygenic risk scores. We also validated the GMV deviations based subtyping analysis in a replication cohort. Two subtypes emerged: subtype 1, characterized by increased GMV deviations in the frontal cortex, cognitive impairment, a higher genetic risk for Alzheimer's disease, and transcriptionally associated with Alzheimer's disease pathways, oligodendrocytes, and endothelial cells; and subtype 2, displaying globally decreased GMV deviations, more severe depressive symptoms, increased genetic vulnerability to major depressive disorder and transcriptionally related to microglia and inhibitory neurons. The distinct patterns of GMV deviations in the frontal, cingulate, and primary motor cortices between subtypes were shown to be replicable. Our current results provide vital links between MRI-derived phenotypes, spatial transcriptome, genetic vulnerability, and clinical manifestation, and uncover the heterogeneity of mood disorders in biological and behavioral terms.

Establishment of a neutrophil extracellular trap-related prognostic signature for colorectal cancer liver metastasis and expression validation of CYP4F3

Liver metastasis stands as the primary contributor to mortality among patients diagnosed with colorectal cancer (CRC). Neutrophil extracellular traps (NETs) emerge as pivotal players in the progression and metastasis of cancer, showcasing promise as prognostic biomarkers. Our objective is to formulate a predictive model grounded in genes associated with neutrophil extracellular traps and identify novel therapeutic targets for combating CRLM. We sourced gene expression profiles from the Gene Expression Omnibus (GEO) database. Neutrophil extracellular trap-related gene set was obtained from relevant literature and cross-referenced with the GEO datasets. Differentially expressed genes (DEGs) were identified through screening via the least absolute shrinkage and selection operator regression and random forest modeling, leading to the establishment of a nomogram and subtype analysis. Subsequently, a thorough analysis of the characteristic gene CYP4F3 was undertaken, and our findings were corroborated through immunohistochemical staining. We identified seven DEGs (ATG7, CTSG, CYP4F3, F3, IL1B, PDE4B, and TNF) and established nomograms for the occurrence and prognosis of CRLM. CYP4F3 is highly expressed in CRC and colorectal liver metastasis (CRLM), exhibiting a negative correlation with CRLM prognosis. It may serve as a potential therapeutic target for CRLM. A novel prognostic signature related to NETs has been developed, with CYP4F3 identified as a risk factor and potential target for CRLM.

Comparative study on the efficacy of Conbercept and Aflibercept in the treatment of neovascular age-related macular degeneration

This study compares the effectiveness of Conbercept and Aflibercept in treating neovascular age-related macular degeneration (nAMD). Conducted at the First Affiliated Hospital of Chongqing Medical University's Ophthalmology Department (May 2020–May 2023), this prospective study enrolled 159 nAMD patients. Participants were randomly divided into two groups: one receiving 0.5 mg Conbercept and the other 2 mg Aflibercept intravitreal injections. Over 12 months, the study, employing a Treat-and-Extend (T&E) regimen, assessed Best-Corrected Visual Acuity (BCVA), Central Retinal Thickness (CRT) changes and injection frequency. Of the 159 patients, 137 (149 eyes) completed the study. No significant age difference was found between the groups (P = 0.331). After 12 months, BCVA improved similarly in both groups (Conbercept: 52.8 ± 18.9, Aflibercept: 52.0 ± 19.7 letters; P = 0.820). CRT reduction was also comparable (Conbercept: 246.3 ± 82.8 µm, Aflibercept: 275.9 ± 114.3 µm; P = 0.079). Injection frequencies averaged 6.9 ± 0.7 (Conbercept) and 6.7 ± 0.7 (Aflibercept; P = 0.255). Subtype analysis revealed Type 1 MNV had higher baseline BCVA and lower CRT, with more frequent injections compared to other types. Both Conbercept and Aflibercept are clinically similar in efficacy for nAMD, with the T&E regimen proving therapeutically effective and potentially reducing patient costs. Anti-VEGF treatment efficacy varies across nAMD subtypes, indicating a potential benefit in tailored treatments for specific subtypes.Clinical trial registration number NCT05539235 (Protocol Registration and Results System).

Prokaryotic expression of N protein of akabane disease virus and preparation of monoclonal antibody

In order to generate monoclonal antibodies against the akabane virus (AKAV) N protein, this study employed a prokaryotic expression system to express the AKAV N protein. Following purification, BALB/c mice were immunized, and their splenocytes were fused with mouse myeloma cells (SP2/0) to produce hybridoma cells. The indirect ELISA method was used to screen for positive hybridoma cells. Two specific hybridoma cell lines targeting AKAV N protein, designated as 2C9 and 5E9, were isolated after three rounds of subcloning. Further characterization was conducted through ELISA, Western blotting, and indirect immunofluorescence assay (IFA). The results confirmed that the monoclonal antibodies specifically target AKAV N protein, exhibiting strong reactivity in IFA. Subtype analysis identified the heavy chain of the 2C9 mAb's as IgG2b and its light chain as κ-type; the 5E9 mAb's heavy chain was determined to be IgG1, with a κ-type light chain. Their ELISA titers reached 1:4 096 000. This study successfully developed two monoclonal antibodies targeting AKAV N protein, which lays a crucial foundation for advancing diagnostic methods for akabane disease prevention and control, as well as for studying the function of the AKAV N protein.

Action of m6A-related gene signatures on the prognosis and immune microenvironment of colonic adenocarcinoma

N6-methyladenosine (m6A) modification in human tumor cells exerts considerable influence on crucial processes like tumorigenesis, invasion, metastasis, and immune response. This study aims to comprehensively analyze the impact of m6A-related genes on the prognosis and immune microenvironment (IME) of colonic adenocarcinoma (COAD). Public data sources, predictive algorithms identified m6A-related genes and differential gene expression in COAD. Subtype analysis and assessment of immune cell infiltration patterns were performed using consensus clustering and the CIBERSORT algorithm. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis determined gene signatures. Independent prognostic factors were identified using univariate and multivariate Cox proportional hazards models. The findings indicate that 206 prognostic m6A-related DEGs contribute to the m6A regulatory network along with 8 m6A enzymes. Based on the expression levels of these genes, 438 COAD samples from The Cancer Genome Atlas (TCGA) were classified into 3 distinct subtypes, showing marked differences in survival prognosis, clinical characteristics, and immune cell infiltration profiles. Subtype 3 and 2 displayed reduced levels of infiltrating regulatory T cells and M0 macrophages, respectively. A six-gene signature, encompassing KLC3, SLC6A15, AQP7 JMJD7, HOXC6, and CLDN9, was identified and incorporated into a prognostic model. Validation across TCGA and GSE39582 datasets exhibited robust predictive specificity and sensitivity in determining the survival status of COAD patients. Additionally, independent prognostic factors were recognized, and a nomogram model was developed as a prognostic predictor for COAD. In conclusion, the six target genes governed by m6A mechanisms offer substantial potential in predicting COAD outcomes and provide insights into the unique IME profiles associated with various COAD subtypes.

Development of a biomarker signature associated with anoikis to predict prognosis and immunotherapy response in melanoma.

Skin cutaneous melanoma (SKCM) is malignant cancer known for its high aggressiveness and unfavorable prognosis, particularly in advanced tumors. Anoikis is a specific pattern of programmed cell death associated with tumor regeneration, migration, and metastasis. Nevertheless, limited research has been conducted to investigate the function of anoikis in SKCM. Anoikis-related genes (ARGs) were extracted from Genecards to identify SKCM subtypes and to explore the immune microenvironment between the different subtypes. Prognostic models of SKCM were developed by LASSO COX regression analysis. Subsequently, the predictive value of risk scores in SKCM and the association with immunotherapy were further explored. Finally, the expression of 6 ARGs involved in the model construction was detected by immunohistochemistry and PCR. This study identified 20 ARGs significantly associated with SKCM prognosis and performed disease subtype analysis of samples based on these genes, different subtypes exhibited significantly different clinical features and tumor immune microenvironment (TIME) landscapes. The risk score prognostic model was generated by further screening and identification of the six ARGs. The model exhibited a high degree of sensitivity and specificity to predict the prognosis of individuals with SKCM. These high- and low-risk populations showed different immune statuses and drug sensitivity. Further immunohistochemical and PCR experiments identified significant differential expression of the six ARGs in tumor and normal samples. Anoikis-based features may serve as novel prognostic biomarkers for SKCM and may provide important new insights for survival prediction and individualized treatment development.