Dissection of paracrine/autocrine interplay in lung tumor microenvironment mimicking cancer cell-monocyte co-culture models reveals proteins that promote inflammation and metastasis.

BACKGROUND: Tumor cell-monocyte interactions play crucial roles in shaping up the pro-tumorigenic phenotype and functional output of tumor-associated macrophages. Within the tumor microenvironment, such heterotypic cell-cell interactions are known to occur via secretory proteins. Secretory proteins establish a diabolic liaison between tumor cells and monocytes, leading to their recruitment, subsequent polarization and consequent tumor progression. METHODS: We co-cultured model lung adenocarcinoma cell line A549 with model monocytes, THP-1 to delineate the interactions between them. The levels of prototypical pro-inflammatory cytokines like TNF-𝛼, IL-6 and anti-inflammatory cytokines like IL-10 were measured by ELISA. Migration, invasion and attachment independence of lung cancer cells was assessed by wound healing, transwell invasion and colony formation assays respectively. The status of EMT was evaluated by immunofluorescence. Identification of secretory proteins differentially expressed in monocultures and co-culture was carried out using SILAC LC-MS/MS. Various insilico tools like Cytoscape, Reacfoam, CHAT and Kaplan-Meier plotter were utilized for association studies, pathway analysis, functional classification, cancer hallmark relevance and predicting the prognostic potential of the candidate secretory proteins respectively. RESULTS: Co-culture of A549 and THP-1 cells in 1:10 ratio showed early release of prototypical pro-inflammatory cytokines TNF-𝛼 and IL-6, however anti-inflammatory cytokine, IL-10 was observed to be released at the highest time point. The conditioned medium obtained from this co-culture ratio promoted the migration, invasion and colony formation as well as the EMT of A549 cells. Co-culturing of A549 with THP-1 cells modulated the secretion of proteins involved in cell proliferation, migration, invasion, EMT, inflammation, angiogenesis and inhibition of apoptosis. Among these proteins Versican, Tetranectin, IGFBP2, TUBB4B, C2 and IFI30 were found to correlate with the inflammatory and pro-metastatic milieu observed in our experimental setup. Furthermore, dysregulated expression of these proteins was found to be associated with poor prognosis and negative disease outcomes in lung adenocarcinoma compared to other cancer types. Pharmacological interventions targeting these proteins may serve as useful therapeutic approaches in lung adenocarcinoma. CONCLUSION: In this study, we have demonstrated that the lung cancer cell-monocyte cross-talk modulates the secretion of IFI30, RNH1, CLEC3B, VCAN, IGFBP2, C2 and TUBB4B favoring tumor growth and metastasis.

Implications of VEGF gene sequence variations and its expression in recurrent pregnancy loss.

RESEARCH QUESTION: What is the association between VEGF gene sequence variants and its mRNA expression in recurrent pregnancy loss (RPL)? Vascular endothelial growth factor (VEGF) has a prominent role in pregnancy and affects pregnancy outcome. The association of VEGF gene 1154G>A, 634G>C and 583C>T polymorphic variations with cases of RPL and full-term fertile women as controls was investigated. DESIGN: Two hundred women with RPL and 240 women healthy controls were included. The restriction fragment length polymorphism method was used for genotyping and quantitative real-time polymerase chain reaction was used for analysis of mRNA expression. RESULTS: In VEGF 1154G>A, significant differences were found in homozygous AA genotype between case and control participants. The variant allele A frequency was significantly more abundant in RPL cases (0.41) than controls (0.19) (P < 0.0001). Only RPL cases with the multi-generation family history of miscarriages and those without any history showed significant differences of combined genotype GA+AA (P < 0.0001). In VEGF 634 G>C, CC genotype and allele C showed significantly increased frequency in RPL cases compared with healthy controls (P < 0.0001). The association between VEGF-1154 G>A SNP and VEGF-A mRNA expression levels was significant in RPL cases (P = 0.004). Also in VEGF-583 C>T, CT genotypes were seen significantly associated with cases (P = 0.003). The heterozygous genotype GA was significantly (P = 0.03) associated with upregulation and downregulation of VEGF mRNA, whereas the homozygous variant genotype AA only leads to low expression levels of VEGF mRNA in patients with RPL. CONCLUSIONS: All the variants of VEGF play a vital role in an increased susceptibility to RPL. Also, VEGF-1154, AA genotypes are associated with its altered low mRNA expression in women with RPL and seem to affect pregnancy outcome.

Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis.

Down syndrome arises from chromosomal non-disjunction during gametogenesis, resulting in an additional chromosome. This anomaly presents with intellectual impairment, growth limitations, and distinct facial features. Positive correlation exists between maternal age, particularly in advanced cases, and the global annual incidence is over 200,000 cases. Early interventions, including first and second-trimester screenings, have improved DS diagnosis and care. The manifestations of Down syndrome result from complex interactions between genetic factors linked to various health concerns. To explore recent advancements in Down syndrome research, we focus on the integration of artificial intelligence (AI) and machine learning (ML) technologies for improved diagnosis and management. Recent developments leverage AI and ML algorithms to detect subtle Down syndrome indicators across various data sources, including biological markers, facial traits, and medical images. These technologies offer potential enhancements in accuracy, particularly in cases complicated by cognitive impairments. Integration of AI and ML in Down syndrome diagnosis signifies a significant advancement in medical science. These tools hold promise for early detection, personalized treatment, and a deeper comprehension of the complex interplay between genetics and environmental factors. This review provides a comprehensive overview of neurodevelopmental and cognitive profiles, comorbidities, diagnosis, and management within the Down syndrome context. The utilization of AI and ML represents a transformative step toward enhancing early identification and tailored interventions for individuals with Down syndrome, ultimately improving their quality of life.