Functional experiments showed that FKBP4 knockdown inhibited proliferation, migration, and invasion of A549 and H1975 cells, supporting a potential role for FKBP4 in LUAD progression, and provided a promising tool for prognostic stratification.
Abstract
Lung adenocarcinoma (LUAD) is the most common lung cancer histological subtype. Although the unfolded protein response (UPR) has been linked to various human diseases, its role in LUAD remains unclear. To identify UPR-related genes, we applied various methods, including weighted gene co-expression network analysis, differential expression analysis, and multivariate Cox regression. Ten machine learning algorithms were used to construct a UPR-related signature (UPRRS), which was validated using multiple public LUAD datasets. The UPRRS was integrated into a nomogram used in clinical practice for prognosis prediction. We also evaluated predicted drug sensitivity patterns across different risk subgroups. We identified 33 UPR-associated hub genes. A UPRRS was developed through systematic evaluation of 101 machine-learning combinations, exhibiting stable prognostic performance across multiple cohorts. Integration of the UPRRS into a nomogram facilitated the construction of a quantitative prognostic model. Significant differences in biological processes and tumor microenvironment immune cell infiltration were observed between the high- and low-risk UPRRS groups. All five UPRRS genes (ALDH2, FKBP4, KLF4, LAIR1, SIDT2) were validated at the protein level in LUAD cell lines, and FKBP4 was further confirmed by IHC in clinical tissues. Functional experiments showed that FKBP4 knockdown inhibited proliferation, migration, and invasion of A549 and H1975 cells, supporting a potential role for FKBP4 in LUAD progression. Our UPRRS provides a promising tool for prognostic stratification and may offer additional insights into tumor immune microenvironment characterization and therapeutic response prediction in LUAD.
A ferroptosis- and lipid metabolism-related prognostic signature is developed that accurately predicts survival outcomes and immune characteristics in CRC and CRY2 was identified as a critical regulator of tumor growth.
Yu Guo, Yong-Bo Zou, Min Wang· Annals medicus· 0 citations
Public LUAD transcriptomes identified two inflammation-associated subtypes and a five-gene score comprising CHRDL1, FDCSP, CXCL13, CYP4B1, and S100P that separated survival groups and marked distinct proliferative and immune expression programs.
Xing-Chen Zhou, Zhen Le, Pengxia Song et al.· bioRxiv· 0 citations
Pancreatic ductal adenocarcinoma (PDAC) is characterized by marked molecular, cellular, and clinical heterogeneity. Chaperone-mediated autophagy (CMA) supports adaptation to metabolic and environmental stress, but its cell type-specific distribution and prognostic relevance in PDAC remain unclear.
Single-cell RNA sequencing data from GSE212966 were analyzed to characterize CMA-related transcriptional states in PDAC and adjacent non-tumor tissues. Bulk transcriptomic data from TCGA-PAAD were used for differential expression analysis, weighted gene co-expression network analysis, and molecular model development, while ICGC PACA-CA and PACA-AU served as independent validation cohorts. Multiple survival machine-learning approaches were compared to establish a CMA-related prognostic model. Hallmark pathway activity, immune infiltration, and predicted drug sensitivity were evaluated between risk groups. KRT19, the highest-weighted model gene, was selected for
in vitro
validation. In parallel, an independent single-center cohort of 468 patients was analyzed using eight survival machine-learning methods to identify clinical prognostic factors and construct a nomogram.
CMA-related transcriptional activity varied among cell types, with macrophages showing prominent scores and PDAC-derived macrophages exhibiting higher CMA scores than those from adjacent tissues. Integration of TCGA differential expression analysis and WGCNA identified 105 candidate genes. The StepCox [forward] plus random survival forest model showed favorable overall performance, with C-index values of 0.903, 0.678, and 0.733 in the TCGA, PACA-CA, and PACA-AU cohorts, respectively. High molecular risk was associated with enhanced glycolytic, proliferative, and cell cycle-related signaling, increased M0 macrophages, reduced CD8
+
T cells, and differential predicted drug sensitivity. KRT19 overexpression promoted PDAC cell proliferation, colony formation, migration, and invasion. In the single-center cohort, N stage, CA125, vascular tumor thrombus, and total bilirubin ranked highest in weighted prognostic importance. The clinical nomogram achieved AUC values of 0.661 and 0.750 for 1- and 3-year overall survival, respectively.
This study identified CMA-related cellular heterogeneity, established a molecular prognostic model that retained prognostic discrimination in two independent validation cohorts, demonstrated the functional relevance of KRT19, and developed an independent clinical prediction tool. These molecular and clinical models provide complementary perspectives on PDAC prognosis and warrant further evaluation in matched prospective cohorts.
Qing-Yan Kou, Sheng-Qian Qiao, Zhen-Yuan Liu et al.· Frontiers in Cell and Develo...· 0 citations
Esophageal Cancer: Molecular Biology/Pathology
To use bioinformatics methods to evaluate the prognostic value of Programmed Cell Death Related Genes (PCDRGs) in esophageal carcinoma (EC), and to explore the development and immune regulatory mechanisms of EC from multiple perspectives.
Using TCGA, GSE53622 data sets, and downloaded key regulatory genes of 18 PCD patterns, combined with 10 different machine learning methods to develop a prediction model, named this model ‘Characteristics of Cell Deaths’ (CDS). Seven prognosis-related genes were screened out by the model. The correlationbetween these seven genes and EC was analyzed.
The PCDRGs prognostic model developed using the StepCox[both] + RSF method performed the best. CDS showed significant and powerful performance in predicting EC clinical outcomes and was able to serve as an independent risk factor in TCGA and GEO datasets.
This study successfully developed a novel EC PCDRGs model, which could predict the prognosis and drug treatment sensitivity of EC patients in the future based on further validation.
Si-Han Lu, Yi Zhu, Yong-Tao Han et al.· Diseases of the esophagus· 0 citations
Background Reliable biomarkers for predicting prognosis and therapeutic response in skin cutaneous melanoma (SKCM) remain limited. This study aimed to develop an intratumoral heterogeneity (ITH)-related prognostic signature for SKCM using integrative machine learning. Methods RNA sequencing (RNA-seq) data from 472 SKCM patients in The Cancer Genome Atlas (TCGA) and 214 patients in the GSE65904 cohort were analyzed. ITH scores were calculated using the DEPTH2 algorithm. Differentially expressed genes (DEGs) were identified between high- and low-ITH groups [|log2fold change (FC)| ≥1, false discovery rate (FDR) <0.05]. Based on 38 prognostic DEGs identified by univariate Cox regression, we employed an integrative framework of 101 machine learning algorithm combinations to construct prognostic models in the TCGA training cohort. The model with the highest average concordance index (C-index) was validated in the GSE65904 cohort and selected as the prognostic ITH-related signature (PIRS). Associations of the PIRS risk score with tumor mutational burden (TMB), immune cell infiltration, immune checkpoint gene expression, and drug sensitivity were systematically evaluated. Model performance was assessed using receiver operating characteristic (ROC) curves and Cox regression analyses. Results A 38-gene PIRS was constructed using the plsRcox algorithm. Patients with high PIRS risk scores exhibited significantly poorer overall survival (OS) in both the TCGA and Gene Expression Omnibus (GEO) cohorts. The PIRS was identified as an independent prognostic factor, with area under the curve (AUC) values of 0.779, 0.734, and 0.756 for 1-, 3-, and 5-year survival, respectively. High-risk samples displayed significantly lower TMB (P<0.05), reduced immune and stromal cell infiltration (P<0.001), downregulated immune function, and decreased expression of immune checkpoint genes. Additionally, high- and low-PIRS risk score groups exhibited distinct sensitivity patterns to different classes of targeted agents. Conclusions The machine learning-derived PIRS robustly predicts prognosis in SKCM patients. Its clinical application is promising for optimizing patient risk stratification and treatment decisions, though further prospective validation is warranted.
Feng-Ling Ding, Wei Tian, Sarina Bai et al.· Translational Cancer Researc...· 0 citations
Wilms tumor (WT) is the most common pediatric renal malignancy. Reliable prognostic markers are crucial for improving patient outcomes. Immune-related genes (IRGs) significantly influence tumor progression and the tumor microenvironment, yet their prognostic value in WT remains unclear. This study aimed to develop an immune-related prognostic model for WT and investigate its underlying molecular and immunological mechanisms. We analyzed RNA-seq data and clinical information from the TARGET-WT and Gene Expression Omnibus databases. Using differential expression analysis, we identified differentially expressed genes. We identified immune-related differentially expressed genes (DEIRGs) by intersecting differentially expressed genes with known IRGs. Using univariate and multivariate Cox regression along with Least Absolute Shrinkage and Selection Operator regression, we selected 4 DEIRGs and constructed a prognostic risk score model. We further analyzed the model’s molecular and immunological characteristics. Four DEIRGs (epidermal growth factor [EGF], teratocarcinoma-derived growth factor 1 [TDGF1], leukotriene B4 receptor [LTB4R], and HLA-DMB) showed significant associations with overall survival in WT patients. The risk stratification model categorized patients into high- and low-risk groups, with significantly poorer survival in the high-risk group (P < .001). Enrichment analysis revealed that the high-risk group showed enrichment in oncogenic pathways (e.g., genome instability), whereas the low-risk group demonstrated enrichment in immune defense and homeostasis pathways. The high-risk group exhibited reduced tumor microenvironment (TME) immunoreactivity, with EGF and LTB4R emerging as key regulatory factors. Both EGF and LTB4R demonstrate differential expression across multiple tumor types and correlate significantly with TME scores. The immune-related prognostic model developed in this study elucidates the regulatory roles of EGF and LTB4R in Wilms tumor progression. This model effectively stratifies patients, enables accurate prognosis prediction, facilitates individualized treatment planning, and identifies potential therapeutic targets.
Jin Chen, Guobin Yang, Zhihui Zhu et al.· Medicine· 0 citations
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