Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide, characterized by high incidence and poor survival rates. Glycosylation, a fundamental post-translational modification, influences protein stability, signaling, and tumor progression, with aberrations implicated in immune evasion and metastasis. This study investigates the role of glycosylation-related genes (Glycosylation-RGs) in CRC using machine learning and bioinformatics. Data from The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB) were analyzed to identify 67 differentially expressed Glycosylation-RGs. These genes were used to classify CRC patients into two subgroups with distinct survival outcomes, highlighting their prognostic value. Weighted gene coexpression network analysis (WGCNA) revealed key modules associated with CRC traits, including pathways like glycan biosynthesis and PI3K–Akt signaling. A machine-learning-based prognostic model demonstrated strong predictive performance, stratifying patients into high- and low-risk groups with significant survival differences. Additionally, the model revealed correlations between risk scores and immune cell infiltration, providing insights into the tumor immune microenvironment. Drug sensitivity analysis identified potential therapeutic agents, including Trametinib, SCH772984, and Oxaliplatin, showing differential efficacy between risk groups. These findings enhance our understanding of glycosylation in CRC, identifying it as a critical factor in disease progression and a promising target for future therapeutic strategies.

Patterns of disease and therapeutic responses vary widely among patients with autoimmune glomerulonephritis. This study introduces groundbreaking personalized infrared (IR)-based diagnostics for real-time monitoring of disease status and treatment responses in lupus nephritis (LN). We have established a relative absorption difference (RAD) equation to assess characteristic spectral indices based on the temporal peak heights (PHs) of two characteristic serum absorption bands: ν₁ as the target signal and ν₂ as the PH reference for the ν₁ absorption band, measured at each dehydration time (t) during dehydration. The RAD gap (Ψ), defined as the difference in the RAD values between the initial and final stages of serum dehydration, enables the measurement of serum levels of IgG glycosylation (ν₁ (1030 cm⁻¹), ν₂ (1171 cm⁻¹)), serum lactate (ν₁ (1021 cm⁻¹), ν₂ (1171 cm⁻¹)), serum hydrophobicity (ν₁ (2930 cm⁻¹), ν₂ (2960 cm⁻¹)), serum hydrophilicity (ν₁ (1550 cm⁻¹), ν₂ (1650 cm⁻¹)), and albumin (ν₁ (1400 cm⁻¹), ν₂ (1450 cm⁻¹)). Furthermore, this IR-based assay incorporates an innovative algorithm and our proprietary iPath software (ver. 1.0), which calculates the prognosis prediction function (PPF, Φ) from the RAD gaps of five spectral markers and correlates these with conventional clinical renal biomarkers. We propose that this algorithm-assisted, IR-based approach can augment the patient-centric care of LN patients, particularly by focusing on changes in serum IgG glycosylation.

Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide, charac-terized by high incidence and poor survival rates. Glycosylation, a fundamental post-translational modification, influences protein stability, signaling, and tumor pro-gression, with aberrations implicated in immune evasion and metastasis. This study investigates the role of glycosylation-related genes (Glycosylation-RGs) in CRC using machine learning and bioinformatics. Data from The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB) were analyzed to identify 67 differentially expressed Glycosylation-RGs. These genes were used to classify CRC patients into two subgroups with distinct survival outcomes, highlighting their prognostic value. Weighted Gene Coexpression Network Analysis (WGCNA) revealed key modules as-sociated with CRC traits, including pathways like glycan biosynthesis and PI3K-Akt signaling. A machine learning-based prognostic model demonstrated strong predictive performance, stratifying patients into high- and low-risk groups with significant sur-vival differences. Additionally, the model revealed correlations between risk scores and immune cell infiltration, providing insights into the tumor immune microenvironment. Drug sensitivity analysis identified potential therapeutic agents, including Trametinib, SCH772984, and Oxaliplatin, showing differential efficacy between risk groups. These findings enhance our understanding of glycosylation in CRC, identifying it as a critical factor in disease progression and a promising target for future therapeutic strategies.

Background Cytokines are central regulators of inflammation and immune responses within the tumor microenvironment and have been implicated in cancer progression and prognosis. However, the prognostic value of coordinated cytokine-related transcriptional programs across cancer types has not been systematically explored. Methods Pan-cancer transcriptomic and clinical data were analyzed to construct a cytokine-related prognostic signature using LASSO Cox regression. Patients were stratified into high-risk and low-risk groups based on the derived risk score. Prognostic performance was evaluated in training and test cohorts, and biological relevance was assessed through survival analyses and pathway-level investigations. Results A 16-gene cytokine-related signature was established that consistently stratified patients into distinct prognostic groups across multiple cancer types. High cytokine-related risk scores were significantly associated with unfavorable survival outcomes and were linked to enhanced cell cycle activity, epithelial-mesenchymal transition, and extracellular matrix remodeling. Integration of the risk score with clinical variables improved individualized survival prediction. Immunohistochemical analyses further confirmed increased protein expression of representative risk-associated genes, including PANX1 and FRMD8, in multiple tumor tissues compared with corresponding normal tissues. Conclusions The cytokine-related prognostic signature captures key inflammatory and immune-related programs underlying tumor aggressiveness and provides a robust tool for pan-cancer risk stratification with potential clinical utility.

Cytokines are central regulators of inflammation and immune responses within the tumor microenvironment and have been implicated in cancer progression and prognosis. However, the prognostic value of coordinated cytokine-related transcriptional programs across cancer types has not been systematically explored. Pan-cancer transcriptomic and clinical data were analyzed to construct a cytokine-related prognostic signature using least absolute shrinkage and selection operator (LASSO) Cox regression. Patients were stratified into high-risk and low-risk groups based on the derived risk score. Prognostic performance was evaluated in training and test cohorts, and biological relevance was assessed through survival analyses and pathway-level investigations. A 16-gene cytokine-related signature was established that consistently stratified patients into distinct prognostic groups across multiple cancer types. High cytokine-related risk scores were significantly associated with unfavorable survival outcomes and were linked to enhanced cell cycle activity, epithelial-mesenchymal transition, and extracellular matrix remodeling. Integration of the risk score with clinical variables improved individualized survival prediction. Immunohistochemical analyses further confirmed increased protein expression of representative risk-associated genes, including pannexin 1 (PANX1) and FERM domain containing 8 (FRMD8), in multiple tumor tissues compared with corresponding normal tissues. The cytokine-related prognostic signature captures key inflammatory and immune-related programs underlying tumor aggressiveness and provides a robust tool for pan-cancer risk stratification with potential clinical utility.

Galectin-3 (LGALS3), a β-galactoside-binding lectin, plays a pivotal role in regulating physiological and pathological processes in hepatocellular car