Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade

Construction and significance of a breast cancer prognostic model based on cuproptosis-related genotyping and lncRNAs

BACKGROUND/PURPOSE

Cuproptosis may play a significant role in breast cancer (BC). We aimed to investigate the prognostic impact of cuproptosis-related lncRNAs in BC.

METHODS

Consensus clustering analysis categorized TCGA-BRCA samples into 3 clusters, followed by survival and immune analyses of the 3 clusters. LASSO-COX analysis was performed on cuproptosis-related lncRNAs differentially expressed in BC to construct a BC prognostic model. Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) enrichment, immune, and drug prediction analyses were performed on the high-risk and low-risk groups. Cell experiments were conducted to analyze the results of drug prediction and two cuproptosis-related lncRNAs (AC104211.1 and LINC01863).

RESULTS

Significant differences were observed in survival outcomes and immune infiltration levels among the three clusters (p < 0.05). The validation of the model showed significant differences in survival outcomes between the high-risk and low-risk groups in both the training and validation sets (p < 0.05). Differential mRNAs between the two groups were significantly enriched in the Neuroactive ligand-receptor interaction and cAMP signaling pathway. Additionally, significant differences were found in immune infiltration levels, human leukocyte antigen (HLA) expression, Immunophenoscore (IPS) scores, and Tumor Immune Dysfunction and Exclusion (TIDE) scores between the two groups (p < 0.05). Drug prediction and corresponding cell experimental results showed that Trametinib, 5-fluorouracil, and AICAR significantly inhibited the viability of MCF-7 cells (p < 0.05). AC104211.1 and LINC01863 were found to impact the proliferation of BC cells.

CONCLUSION

The risk-scoring model obtained in this study may serve as a robust prognostic biomarker, potentially aiding in clinical decision-making for BC patients.

5-Methylcytosine methylation predicts cervical cancer prognosis, shaping immune cell infiltration

BackgroundEpigenetics, encompassing DNA and RNA modifications, has emerged as a prominent area of research in the post-genomic era. Numerous studies have elucidated the impact of epigenetics on tumor regulation. However, the methylation patterns of 5-methylcytosine in cervical cancer as well as its role in the tumor microenvironment and immunotherapy remain poorly understood.MethodsUtilizing a comprehensive dataset encompassing samples from 306 patients with cervical cancer from The Cancer Genome Atlas and Gene Expression Omnibus repositories, we conducted an in-depth analysis to evaluate the potential association between the modification patterns of 5-methylcytosine and the infiltration of cells within the tumor microenvironment, taking into account 11 regulators of 5-methylcytosine modification. Subsequently, we employed stepwise regression and Least Absolute Shrinkage and Selection Operator Cox regression to quantify 5-methylcytosine modification patterns in patients with cervical squamous cell carcinoma and endocervical adenocarcinoma, yielding the 5-methylcytosine score. Our study explored the link between the 5-methylcytosine score and clinical characteristics as well as prognostic outcomes in patients with cervical squamous cell carcinoma and endocervical adenocarcinoma.ResultsA comprehensive analysis of 306 patients with cervical cancer revealed two distinct 5-methylcytosine modification patterns, henceforth labeled as 5-methylcytosine clusters A and B. These clusters exhibited distinct immunological profiles and biological attributes, with 5-methylcytosine cluster A exhibiting a higher degree of immune cell infiltration. Utilizing univariate Cox regression analysis, we identified 367 genes regulated by 5-methylcytosine that were significantly correlated with patient prognosis. This analysis further stratified the samples into three distinct genomic subtypes. Survival analyses indicated that patients belonging to gene cluster C exhibited more favorable survival outcomes than those belonging to gene clusters A and B. Intriguingly, most 5-methylcytosine regulatory factors had higher expression levels in gene cluster B than in gene cluster A. Gene set enrichment analysis of a single sample revealed elevated immune cell infiltration within gene cluster B, indicating a stronger immune response in this cluster. The 5-methylcytosine score feature was utilized to determine the 5-methylcytosine modification pattern in cervical cancer, revealing that patients with low 5-methylcytosine scores exhibited better survival rates, whereas those with high scores had increased mutation frequencies and better treatment responses.ConclusionsThis research underscores the key role of 5-methylcytosine modification patterns in cervical cancer. Analysis of these patterns will deepen our understanding of the cervical cancer tumor microenvironment, paving the way for the development of more refined and effective immunotherapy strategies.

Identification and Validation of Gastric Adenocarcinoma Prognosis Features Based on Neutrophil-Related Genes

PURPOSE

The aim of this study was to investigate the effect of neutrophil-related genes (NRGs) on prognosis and tumor microenvironment (TME) of patients with gastric adenocarcinoma (GA), to provide a new reference for prognosis evaluation and related mechanism research of GA.

METHODS

The gene expression data and clinical information of patients with GA were collected from The Cancer Genome Atlas database. NRG data are from the literature. Differential NRGs were obtained by difference analysis and regression analysis for the construction of the prognostic model, which was validated using the GSE84426 data set. The independent prognostic effect of risk score was analyzed by constructing a nomogram. The single-sample gene set enrichment analysis and CIBERSORT methods were used to evaluate differences in TME between a high-risk group (HRG) and a low-risk group (LRG) and to evaluate the differences in response to immunotherapy and sensitivity to different drugs in high and low risk score groups.

RESULTS

We established a prognostic model on the basis of seven NRGs (NHLRC3, PTPRJ, RTEL1, ST6GALNAC2, HRNR, HP, and MCEMP1) and validated its predictive value. Multivariable Cox regression analysis further demonstrated that the model remained an independent prognostic factor for overall survival, and a nomogram was constructed for clinical practice. Differential analysis of immune cell infiltration levels showed that macrophages, mast cells, and neutrophils were highly infiltrated in HRG compared with LRG. Compared with HRG, LRG was more sensitive to immunotherapy and more sensitive to candidates such as axitinib, cisplatin, and ulixertinib.

CONCLUSION

In summary, on the basis of expression levels of NRGs, a new prognostic model was established. NHLRC3, PTPRJ, RTEL1, ST6GALNAC2, HRNR, HP, and MCEMP1 were valid candidate biomarkers that may help personalize prognostic predictions and serve as references for clinical studies.

Comprehensive Analysis of GPSM2: From Pan-Cancer Analysis to Experimental Validation

G-protein signalling modulator 2 (GPSM2) plays an important role in maintaining cell polarisation and regulating the cell cycle; however, a systematic and comprehensive analysis of GPSM2 in cancer is still lacking. Using extensive multi-omics data, we explored the pan-cancer expression levels of GPSM2 from multiple perspectives and its association with prognosis, diagnosis, tumour stemness, immune-related genes, immune cell infiltration, genomic instability, and response to immunotherapy. We also elucidated the potential pan-cancer biological functions of GPSM2 using gene set enrichment analysis (GSEA) and searched for targeted drugs that affect GPSM2 expression using connectivity map analysis. To elucidate the effect of GPSM2 on colon cancer, we evaluated its effect on the biological behaviour of two colon cancer cell lines. In this study, GPSM2 was systematically analysed and shown to have satisfactory performance in disease diagnosis and prognostic prediction of various cancers. G-protein signalling modulator 2 plays an important role in the genesis and development of various tumours and is a potential tumour diagnostic and prognostic biomarker as well as an anti-cancer therapeutic target.

KRT14 is a promising prognostic biomarker of breast cancer related to immune infiltration

BACKGROUND

Breast cancer (BC) is the leading cancer among women globally, which has the highest incidence and mortality rate in over a hundred countries. This study was intended to discover a new prognostic biomarker, facilitating personalized treatment approaches.

METHODS

RNA sequencing data from The Cancer Genome Atlas database and Gene Expression Omnibus database were utilized to download to evaluate expression levels and prognostic significance of Keratin 14 (KRT14). Methylation of KRT14 was also assessed. The CIBERSORT and single-sample gene set enrichment analysis algorithms were applied to explore the connection between KRT14 and the tumor microenvironment. Primary drugs' sensitivity and potential small molecule therapeutic compounds were analyzed through the "pRRophetic" R package and the Connectivity Map. The prognostic value of KRT14 was additionally corroborated through a comparison of protein levels in peritumoral and cancerous tissues via immunohistochemistry. Moreover, an immune-related prognostic model based on KRT14 was designed to enhance the prediction accuracy for the prognosis of BC patients.

RESULTS

The study found that KRT14 expression was generally downregulated in BC, correlating strongly with poor prognosis. Compared to normal tissues, the methylation level of KRT14 was higher in BC tissues. Lower expression of KRT14 was linked to decreased anti-tumoral immune cells infiltration and increased immunosuppressive cells infiltration. Sensitivity to various key therapeutic drugs was lower in groups with diminished KRT14 expression. In addition, several potential anti-BC small molecule compounds were identified. The model designed in this study significantly enhanced the predictive capability for BC patients compared to predictions based solely on KRT14 expression levels.

CONCLUSION

Overall, KRT14 was closely correlated with the prognosis in BC, making it a reliable biomarker.

ATP13A2 as a prognostic biomarker and its correlation with immune infiltration in cervical cancer: A retrospective study

While the oncogene ATP13A2 is reportedly involved in colorectal cancer, its role in cervical cancer (CC) has yet to be fully characterized. In this study, we investigated ATP13A2 as a potential prognostic biomarker of CC. To this end, we compared CC tissues with normal tissues to identify differentially expressed genes, identifying ATP13A2 as a potential marker of CC. Elevated ATP13A2 expression levels were identified in CC samples compared to noncancerous samples across various data sets, with further immunohistochemical validation. Functional enrichment analysis revealed that ATP13A2 plays an essential role in the CXCL12-activated CXCR4 signalling pathway and chemotaxis regulation, which may alter immune infiltration. Notably, increased ATP13A2 levels were associated with poor overall survival. Furthermore, multiple clinical characteristics were significantly associated with ATP13A2 expression. Additionally, tumour bacterial infiltration was assessed using weighted co-expression network analysis, revealing a relationship between ATP13A2 expression and bacteria in the CC tumour microenvironment. Our results suggest that ATP13A2 is a promising diagnostic and prognostic marker for CC. However, further large-scale studies are needed to fully elucidate the mechanisms underlying the involvement of ATP13A2 in CC.

iMLGAM: Integrated Machine Learning and Genetic Algorithm-driven Multiomics analysis for pan-cancer immunotherapy response prediction

To address the substantial variability in immune checkpoint blockade (ICB) therapy effectiveness, we developed an innovative R package called integrated Machine Learning and Genetic Algorithm-driven Multiomics analysis (iMLGAM), which establishes a comprehensive scoring system for predicting treatment outcomes through advanced multi-omics data integration. Our research demonstrates that iMLGAM scores exhibit superior predictive performance across independent cohorts, with lower scores correlating significantly with enhanced therapeutic responses and outperforming existing clinical biomarkers. Detailed analysis revealed that tumors with low iMLGAM scores display distinctive immune microenvironment characteristics, including increased immune cell infiltration and amplified antitumor immune responses. Critically, through clustered regularly interspaced short palindromic repeats screening, we identified Centrosomal Protein 55 (CEP55) as a key molecule modulating tumor immune evasion, mechanistically confirming its role in regulating T cell-mediated antitumor immune responses. These findings not only validate iMLGAM as a powerful prognostic tool but also propose CEP55 as a promising therapeutic target, offering novel strategies to enhance ICB treatment efficacy. The iMLGAM package is freely available on GitHub (https://github.com/Yelab1994/iMLGAM), providing researchers with an innovative approach to personalized cancer immunotherapy prediction.

A Cuproptosis-Related gene Signature as a Prognostic Biomarker in Thyroid Cancer Based on Transcriptomics

Thyroid cancer (THCA) is the most prevalent endocrine tumor, and its incidence continues to increase every year. However, the processes underlying the aggressive progression of thyroid cancer are unknown. We concentrated on the prognostic and biological importance of thyroid cancer cuproptosis-related genes in this investigation. Genomic and clinical data were obtained from the UCSC XENA website, and cuproptosis-related genes were obtained from the FerrDb website. We performed differential expression analysis and Cox regression analysis to identify possible predictive targets associated with thyroid cancer prognosis. To assess the role of CDKN2A in thyroid cancer and the ability to predict prognosis on the basis of the CDKN2A expression level, we performed immunohistochemical staining, survival analysis, immunological analysis, functional analysis, and clinical analysis with respect to CDKN2A gene expression. CDKN2A expression levels were found to be inversely correlated with thyroid cancer prognosis. Higher levels of CDKN2A expression were associated with higher T, N, and clinicopathological stage and more residual tumor cells. Through univariate and multivariate Cox regression analyses, the CDKN2A expression level was shown to be linked with thyroid cancer patients' overall survival (OS). Moreover, we discovered that CDKN2A expression was linked to a dysfunctional tumor immune microenvironment. The study shows that CDKN2A, a cuproptosis-related gene, can be used as a prognostic marker for thyroid cancer.

DYNC2H1 mutation as a potential predictive biomarker for immune checkpoint inhibitor efficacy in NSCLC and melanoma

Dynein cytoplasmic 2 heavy chain 1 (DYNC2H1) is reported to play a potential role in cancer immunotherapy. However, the association between DYNC2H1 mutation and the clinical benefit of immunotherapy in non-small cell lung cancer (NSCLC) and melanoma remains to be elucidated. We collected data from three public immune checkpoint inhibitor (ICI)-treated NSCLC cohorts (n = 137 in total) and seven ICI-treated melanoma cohorts (n = 418 in total) to explore the potential of DYNC2H1 mutation as a predictive biomarker. The clinical outcomes, including the objective response rate (ORR) and progression-free survival (PFS), of patients with DYNC2H1 mutations are significantly better than those of patients with wild-type DYNC2H1. Multivariate Cox regression analysis confirmed that DYNC2H1 mutation was an independent predictive factor for ICI efficacy in NSCLC and melanoma. In addition, DYNC2H1 mutation exhibited no prognostic value for NSCLC or melanoma. Tumour mutational burden (TMB) and tumour neoantigen burden (TNB) were significantly higher in patients with DYNC2H1 mutation than in those with wild-type DYNC2H1 in both NSCLC and melanoma cohort. The analysis of immune-related genes and immune cell enrichment revealed an association between DYNC2H1 mutation and increased immune infiltration, revealing a potential mechanism underlying the predictive role of DYNC2H1 mutation in immunotherapy efficacy. In conclusion, DYNC2H1 mutation serves as a predictive biomarker of ICI efficacy in NSCLC and melanoma.

An In-Silico Study to Identify Relevant Biomarkers in Sepsis Applying Integrated Bulk RNA Sequencing and Single-Cell RNA Sequencing Analyses

This study aims to discover sepsis-related biomarkers via in-silico analyses. The single-cell sequencing RNA (sc-RNA) data and metabolism-related genes are obtained from public databases and previous studies, respectively. Cell subpopulations are identified and annotated, followed by performing single-sample geneset enrichment analysis (ssGSEA and identification of differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) is applied to classify specific gene modules, and the key module is subjected to immune infiltration analysis. The communication between the subclusters of monocytes is visualized. Five cell subpopulations (subcluster C1-5) containing a relatively higher percentage of monocytes are identified, with subcluster C4 having the lowest enrichment score of metabolism-related genes. Genes with a higher expression in the subclusters are enriched for antigen processing and presentation of exogenous antigen, lymphocyte differentiation, and leukocyte activation. Subcluster C5 affected other subclusters through galectin 9 (LGALS9)-CD45 and LGALS9-CD44, while other subclusters affected subcluster C5 through MIF-(CD74+C-X-C motif chemokine receptor 4 (CXCR4)) and MIF-(CD74+CD44). Six genes (F-Box Protein 4, FBXO4; Forkhead Box K1, FOXK1; MSH2 with MutS Homolog 2, MSH2; Nop-7-associated 2, NSA2; Transmembrane Protein 128, TMEM128; and SBDS) are determined as the hub genes for sepsis. The 6 hub genes are positively correlated with, among others, monocytes and NK cells, but negatively correlated with neutrophils. This study identifies accurate biomarkers for sepsis, contributing to the diagnosis and treatment of the disease.

Integrating single-cell sequencing and machine learning to uncover the role of mitophagy in subtyping and prognosis of esophageal cancer

Globally, esophageal cancer stands as a prominent contributor to cancer-related fatalities, distinguished by its poor prognosis. Mitophagy has a significant impact on the process of cancer progression. This study investigated the prognostic significance of mitophagy-related genes (MRGs) in esophageal carcinoma (ESCA) to elucidate molecular subtypes. By analyzing RNA-seq data from The Cancer Genome Atlas (TCGA), 6451 differentially expressed genes (DEGs) were identified. Cox regression analysis narrowed this list to 14 MRGs with potential prognostic implications. ESCA patients were classified into two distinct subtypes (C1 and C2) based on these genes. Furthermore, leveraging the differentially expressed genes between Cluster 1 and Cluster 2, ESCA patients were classified into two novel subtypes (CA and CB). Importantly, patients in C2 and CA subtypes exhibited inferior prognosis compared to those in C1 and CB (p < 0.05). Functional enrichments and immune microenvironments varied significantly among these subtypes, with C1 and CB demonstrating higher immune checkpoint expression levels. Employing machine learning algorithms like LASSO regression, Random Forest and XGBoost, alongside multivariate COX regression analysis, two core genes: HSPD1 and MAP1LC3B were identified. A prognostic model based on these genes was developed and validated in two external cohorts. Additionally, single-cell sequencing analysis provided novel insights into esophageal cancer microenvironment heterogeneity. Through Coremine database screening, Icaritin emerged as a potential therapeutic candidate to potentially improve esophageal cancer prognosis. Molecular docking results indicated favorable binding efficacies of Icaritin with HSPD1 and MAP1LC3B, contributing to the understanding of the underlying molecular mechanisms of esophageal cancer and offering therapeutic avenues.

Spatial profiling of ANO7 in prostate tissue: links to AR-signalling-associated lipid metabolism and inflammation

Prostate cancer (PrCa) is highly prevalent in the Western world. Currently, however, there are many unmet needs in PrCa care, for example in distinguishing between clinically significant and indolent cases in early phases of the disease. ANO7 is a prostate-specific gene associated with PrCa risk and prognosis, but its exact function in the prostate remains unclear. This study investigates the role of ANO7 in benign prostatic epithelium using spatial transcriptomics by examining differences between ANO7-expressing and non-expressing epithelial regions and their corresponding stromal compartments. A total of 18,676 protein-coding genes were assessed from prostatectomy samples collected from patients with localised prostate cancer. In the collected sample cohort, ANO7 exhibited a distinct, heterogeneous, on-off epithelial expression pattern, enabling an in-depth analysis of ANO7-dependent processes. ANO7-positive epithelium was predominantly enriched with luminal epithelial cells and a specific NK cell subtype, CD56bright. In contrast, ANO7-negative regions were characterised by enrichment of club cells, inflammation, and features of proliferative inflammatory atrophy. Gene-set enrichment analysis revealed that ANO7 expression is associated with androgen receptor (AR) signalling and lipid metabolism. A detailed analysis of differentially expressed genes identified an ANO7- signature, which consisted of genes co-expressed with ANO7 in luminal cells, that demonstrated high consistency in bulk RNA-sequencing (RNA-seq) data. The ANO7-signature was enriched for AR-regulated genes, which highlighted lipid metabolism processes, particularly arachidonic acid metabolism, as a key metabolic feature of the ANO7-positive epithelium. Furthermore, the ANO7-signature demonstrated clinical significance in low-grade PrCa, correlating with a better response to therapy. In summary, these results highlight the potential role of ANO7 in regulating lipid metabolism associated with androgen signalling in benign luminal cells and low-grade cancer, reinforcing the hypothesis that ANO7 functions as a tumour suppressor. © 2025 The Pathological Society of Great Britain and Ireland.

RNA methylation of CD47 mediates tumor immunosuppression in EGFR-TKI resistant NSCLC

BACKGROUND

Although immune checkpoint inhibitors (ICIs) have been successfully utilized in patients with non-small cell lung cancer (NSCLC), EGFR-mutated patients didn't benefit from ICIs. The underlying mechanisms for the poor efficacy of this subgroup remain unclear.

METHODS

CD8+T cells cytotoxicity, DCs phagocytosis and immunofluorescence assay were applied to examine the immunosuppressive microenvironment of NSCLC. m6A RNA immunoprecipitation, luciferase assay and immunohistochemistry were used to explore the relationship between CD47 and ALKBH5 in EGFR-TKI resistant NSCLC. Autochthonous EGFR-driven lung tumor mouse model and PDXs were performed to explore the therapeutic potential of CD47 antibody and EGFR-TKI combination.

RESULTS

We found that EGFR-TKI resistance promoted a more immunosuppressive tumor microenvironment and inhibited anti-tumor functions of CD8+ T cells. Mechanistically, the m6A eraser ALKBH5 was inhibited in EGFR-TKI resistant NSCLC, which subsequently upregulates CD47 by catalyzing m6A demethylation and causes immunosuppression. Combined treatment with EGFR-TKI and inhibitors of CD47 enhances antitumor immunity and EGFR-TKI efficacy in vivo.

CONCLUSIONS

Collectively, our findings reveal the possible underlying mechanism for poor immune response of ICIs in EGFR-TKI resistant NSCLC and provide preclinical evidence that targeted therapy combined with innate immune checkpoint blockade may provide synergistic effects in NSCLC treatment.

ATF3 Within the Interferon Signaling Pathway: A Potential Biomarker for Predicting Pathological Response to Neoadjuvant Chemoimmunotherapy

BACKGROUND

Neoadjuvant chemoimmunotherapy has achieved high downstaging and pathologic response rates in nonsmall-cell lung cancer (NSCLC), but outcomes vary significantly. Early identification of beneficiaries remains a challenge.

METHODS

This study analyzed baseline transcriptomic data from 24 NSCLC patients (9 major pathological response [MPR], 15 nonmajor pathological response [NMPR]) treated with neoadjuvant chemoimmunotherapy, sourced from the GEO database. Molecular analyses and immune infiltration analyses were performed using pathologic response as an endpoint. After identifying the interferon signaling subset NeoIGS, we analyzed the relationship between NeoIGS and immune scores, immune cell infiltration, and immunotherapy efficacy. A key gene in NeoIGS was screened by reveiver operating characteristic curve (ROC) analysis. Subsequently, the expression of the key gene was assessed by immunohistochemistry in 53 NSCLC patients receiving neoadjuvant chemoimmunotherapy.

RESULTS

Interferon signaling pathway expression and CD8+ T-cell infiltration were higher in the MPR group. NeoIGS predicted pathological response to neoadjuvant chemoimmunotherapy (AUC = 0.926) and also demonstrated predictive value in the ICIs monotherapy cohort. IPS and TIDE scores also confirmed NeoIGS's association with immunotherapy in the TCGA NSCLC dataset. Furthermore, patients with higher NeoIGS scores had more immune cell infiltration and increased expression of ICI targets. ROC analysis identified ATF3 as NeoIGS's key gene. In the clinical cohort, ATF3 outperformed PD-L1 in predicting pathologic response, with a 90.0% MPR rate in the high-expression group.

CONCLUSION

We established that a subset of interferon signaling pathways, NeoIGS, is closely associated with immunotherapy. Among them, ATF3 is the most critical gene that accurately predicts pathological remission in neoadjuvant chemoimmunotherapy.

Intratumoral and peritumoral PET/CT-based radiomics for non-invasively and dynamically predicting immunotherapy response in NSCLC

BACKGROUND

We aimed to develop a machine learning model based on intratumoral and peritumoral 18F-FDG PET/CT radiomics to non-invasively and dynamically predict the response to immunotherapy in non-small cell lung cancer (NSCLC).

METHODS

This retrospective study included 296 NSCLC patients, including a training cohort (N = 183), a testing cohort (N = 78), and a TCIA radiogenomic cohort (N = 35). The extreme gradient boosting algorithm was employed to develop the radiomic models.

RESULTS

The COMB-Radscore, which was developed by combining radiomic features from PET, CT, and PET/CT images, had the most satisfactory predictive performance with AUC (ROC) 0.894 and 0.819 in the training and testing cohorts, respectively. Survival analysis has demonstrated that COMB-Radscore is an independent prognostic factor for progression-free survival and overall survival. Moreover, COMB-Radscore demonstrates excellent dynamic predictive performance, with an AUC (ROC) of 0.857, enabling the earlier detection of potential disease progression in patients compared to radiological evaluation solely relying on tumor size. Further radiogenomic analysis showed that the COMB-Radscore was associated with infiltration abundance and functional status of CD8 + T cells.

CONCLUSIONS

The radiomic model holds promise as a precise, personalized, and dynamic decision support tool for the treatment of NSCLC patients.

Prognostic implications and therapeutic opportunities related to CAF subtypes in CMS4 colorectal cancer: insights from single-cell and bulk transcriptomics

Cancer-associated fibroblasts (CAFs) significantly influence tumor progression and therapeutic resistance in colorectal cancer (CRC). However, the distributions and functions of CAF subpopulations vary across the four consensus molecular subtypes (CMSs) of CRC. This study performed single-cell RNA and bulk RNA sequencing and revealed that myofibroblast-like CAFs (myCAFs), tumor-like CAFs (tCAFs), inflammatory CAFs (iCAFs), CXCL14+CAFs, and MT+CAFs are notably enriched in CMS4 compared with other CMSs of CRC. Multiplex immunohistochemistry was used to validate the distribution of CAF subtypes in patients with different CMSs. Prognosis-related CAF subtypes were identified, leading to the selection of four key genes (COL3A1, COL1A2, GEM, and TMEM47). Through machine learning, we developed a CAF poor-prognosis gene (CAFPRG) model to predict outcomes of patients with CMS4. High levels of CAFPRGs were identified as independent poor-risk factors for prognosis (p < 0.001). Tumors with elevated CAFPRGs exhibited increased infiltration of immune-suppressive cells and resistance to chemotherapy. The expression of these key genes was confirmed to be significantly higher in CAFs than in normal fibroblasts (NFs). Therefore, CAFPRGs may be valuable for precisely predicting patient survival and may present potential therapeutic opportunities for CMS4 CRC.

Identification of a long non-coding RNA signature associated with cuproptosis for prognosis and immunotherapy response prediction in patients with lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD), the most common histotype of lung cancer, exhibits high heterogeneity due to molecular variations. Cuproptosis is a newly discovered type of cell death that is linked to copper metabolism and long non-coding RNAs (lncRNAs) may play a significant role in this process. We conducted a comprehensive analysis of lncRNA related to cuproptosis and identified a CRLscore to predict the prognosis and immune landscape for LUAD patients.

METHODS

The LUAD patient cohort obtained from TCGA database was divided into training and validation sets. A range of statistical methods were employed to identify lncRNAs associated with cuproptosis. Multivariate Cox regression was then utilized to develop the CRLscore, which was further used to construct and evaluate a nomogram. Additionally, we investigated the biological functions, gene mutations, and immune landscape.

RESULTS

A CRLscore, comprising six cuproptosis-related lncRNAs, was developed to stratify patients into high- and low-risk groups. The CRLscore demonstrated its ability to independently predict prognosis in both the training set and the validation set. Utilizing the CRLscore, we constructed a nomogram that exhibited favorable predictive efficiency. Furthermore, the cuproptosis-related lncRNAs exhibited associations with important signaling pathways such as p53 signaling, MYC Targets V1, and G2M Checkpoint. Notably, the CRLscore displayed substantial differences in somatic mutations and immune landscape. Finally, qRT-PCR results showed the significant differential expression of five cuproptosis-related lncRNAs between LUAD and normal cells.

CONCLUSION

The CRLscore could serve as a potential prognostic indicator and may predict the response to immunotherapy in LUAD patients.

Characterization of key genes and immune cell infiltration associated with endometriosis through integrating bioinformatics and experimental analyses

BACKGROUNDS

Endometriosis (EM) is the most common gynecological disease in women of childbearing age. This study aims to identify key genes and screen drugs that may contribute to EM treatment.

METHODS

The differentially expressed genes (DEGs) were identified using limma analysis in the GSE11691 dataset. The protein-protein network (PPI) was constructed. Four machine learning methods, including LASSO, SVM-RFE, random forest, and Boruta, were applied to identify the key genes associated with EM. Flow cytometry, wound healing, and migration assays were applied to assess the cell functions of APLNR on hEM15A. The immune cell infiltration of each sample in EM was calculated using a single-sample gene set enrichment analysis (ssGSEA) algorithm. The potential drugs were screened using the Connectivity Map (CMAP) database, based on the DEGs. Finally, the expression levels of the three genes were further validated in the GSE23339 dataset.

RESULTS

One hundred thirty-seven down-regulated genes and 304 up-regulated genes were identified. We identified three key genes associated with EM: APLNR, HLA-DPA1, and AP1S2. The ssGSEA analysis results indicated that these genes play an important role in the development of EM. Moreover, EM immune cell infiltration was tightly associated with these three genes. Finally, several molecular compounds targeting EM were screened with the connectivity map (CMAP) database. ShAPLNR decreased the cell viability of hEM15A, increased the number of apoptotic cells, and significantly decreased the proportion of callus through APLNR in vitro studies.

DISCUSSION

Three genes (APLNR, HLA-DPA1, and AP1S2) may serve as novel therapeutic targets for diagnosing and treating patients with EM.

Single-cell sequencing uncovers a high ESM1-expression endothelial cell subpopulation associated with bladder cancer progression and the immunosuppressive microenvironment

Despite remarkable advancements in therapeutic strategies, a considerable proportion of patients with bladder cancer (BC) still experience disease progression and unfavorable prognosis. The heterogeneity and biological functions of tumor endothelial cells (ECs) during BC progression remain poorly understood. We collected scRNA-seq data from BC samples and identified two EC subpopulations through hierarchical clustering analysis. The activity of signaling pathways in distinct EC subpopulations was assessed utilizing AUCell analysis. Gene regulatory networks (GRN) were constructed and analyzed for different EC subpopulations using the pySCENIC algorithm. Additionally, we investigated the association between the abundance of EC subpopulations and both clinical prognosis and immune cell infiltration. The biological effects of ESM1 protein on BC cells were further validated through EdU and Transwell assays. We analyzed 7,519 CD45-negative single cells from BC tissues and discerned two distinct EC subpopulations. The two subpopulations were characterized by high expression of ESM1 (S1 ECs) and CXCL2 (S2 ECs), respectively. In S1 ECs, we observed significant activation of signaling pathways involved in tumor promotion, including angiogenesis and cell proliferation. Additionally, our GRN analysis uncovered notable differences in transcription factor activity between S1 and S2 ECs. Moreover, ESM1 protein promoted proliferation and migration of BC cells. Patients with higher abundance of the S1 EC subpopulation exhibited more unfavorable clinical outcomes and increased infiltration of inhibitory immune cells. Our findings elucidate the transcriptional profiles and biological roles of the high ESM1-expression endothelial cell subpopulation in BC. This subpopulation is associated with poor prognosis and immunosuppressive tumor microenvironment. Accordingly, targeting endothelial cells with high ESM1 expression may offer a novel therapeutic strategy for patients with BC.

Machine learning based intratumor heterogeneity related signature for prognosis and drug sensitivity in breast cancer

Intratumor heterogeneity (ITH) is involved in tumor evolution and drug resistance. Drug sensitivity shows discrepancy in different breast cancer (BRCA) patients due to ITH. The genes mediating ITH in BRCA and their role in predicting prognosis and drug sensitivity is not yet elucidated. An ITH-related signature (IRS) was built by ten methods-based integrative machine learning programs using TCGA, METABRIC and five GEO datasets. Several indicating scores were employed to evaluate the correlation between IRS score and immune microenvironment. The biological role of PINK1 was investigated using CCK-8 assay. The optimal prognostic signature for BRCA cases was the IRS developed using StepCox(both) + Enet(alpha = 0.9) method, which had the highest average C-index of 0.79. IRS acted as a prognostic biomarker and showed good performance in predicting the prognosis of BRCA patients. Lower IRS score indicated high levels of immuno-activated cells, higher TMB score, higher PD1&CTLA4 immunophenoscore, lower ITH score, lower TIDE score and lower tumor escape score in BRCA. The gene set scores correlated with glycolysis, angiogenesis, NOTCH signaling and hypoxia were higher in BRCA with high IRS score. PINK1 knockdown significantly inhibited the proliferation of BRCA cells. Our study developed a novel IRS for BRCA, which could predict the clinical outcome and immunotherapy benefits of BRCA patients.

ERCC3 serves as a prognostic biomarker for hepatocellular carcinoma and positively regulates cell proliferation and migration

BACKGROUND

ERCC3, a crucial component of the nucleotide excision repair pathway, is implicated in the development and progression of various cancers and is a potential indicator of poor prognosis. However, the expression and function of ERCC3 in hepatocellular carcinoma (HCC) remain unclear. This study aimed to investigate the expression of ERCC3 in HCC tissues and its clinical significance, focusing on elucidating its potential mechanisms and therapeutic value in immunotherapy.

METHODS

The differential expression and genetic variation characteristics of ERCC3 across various cancers were evaluated using the TCGA database. The expression and prognostic value of ERCC3 in HCC were analyzed by integrating TCGA, GEO, and ICGC datasets. Independent prognostic value of ERCC3 expression levels in HCC was assessed using Cox regression analysis, Kaplan-Meier survival analysis, receiver operating characteristic curves, and nomograms. Pathway association scores were determined using ssGSEA to reveal the biological functions of ERCC3 in HCC and its potential clinical efficacy in immunotherapy. Stable transient cell lines were established by infecting HepG2 cells with lentivirus overexpressing ERCC3. The effects of ERCC3 on HCC cell biological phenotypes were evaluated using RTCA, wound healing, and Transwell assays. Cell cycle distribution and apoptosis were detected by flow cytometry. Transcriptome sequencing was performed to explore the impact of ERCC3 overexpression on the expression of signaling pathway-related genes in HCC.

RESULTS

The study revealed that ERCC3 is aberrantly expressed in various tumors, with significantly higher mRNA and protein levels in HCC tissues compared to normal tissues. High ERCC3 expression was significantly correlated with poor survival outcomes in HCC patients. Multivariate Cox regression analysis revealed that ERCC3 expression level is an independent prognostic factor for overall survival (P = 0.014). Gene sets associated with the high ERCC3 group were significantly involved in multiple immune pathways and tumor progression-related pathways, and ERCC3 expression was significantly correlated with immune checkpoints in HCC. Overexpression of ERCC3 promoted the proliferation and migration of HCC cells and influenced cell cycle progression. Transcriptome sequencing analysis indicated that ERCC3 overexpression regulated the proliferation of HCC cells, participated in multiple pro-inflammatory pathways, induced the formation of an inflammatory tumor microenvironment, and promoted HCC progression.

CONCLUSION

This study is the first to reveal the association between high ERCC3 expression and poor prognosis in HCC and to elucidate its immunomodulatory role in HCC. Unlike previous studies, we found that ERCC3 promotes HCC progression by regulating the inflammatory microenvironment and immune checkpoints. These findings establish a novel theoretical foundation for the development of targeted immunotherapies for HCC and provide new insights into the molecular mechanisms underlying ERCC3's role in HCC.

Immunogenomic cancer evolution: A framework to understand cancer immunosuppression

The process of tumor development involves tumor cells eluding detection and suppression of immune responses, which can cause decreased tumor cell antigenicity, expression of immunosuppressive molecules, and immunosuppressive cell recruitment to the tumor microenvironment (TME). Immunologically and genomically integrated analysis (immunogenomic analysis) of patient specimens has revealed that oncogenic aberrant signaling is involved in both carcinogenesis and immune evasion. In noninflamed cancers such as epidermal growth factor receptor (EGFR)-mutated lung cancers, genetic abnormalities in cancer cells contribute to the formation of an immunosuppressive TME by recruiting immunosuppressive cells, which cannot be fully explained by the cancer immunoediting hypothesis. This review summarizes the latest findings regarding the links between cancer genetic abnormalities and immunosuppression causing clinical resistance to immunotherapy. We propose the concepts of immunogenomic cancer evolution, in which cancer cell genomic evolution shapes the immunosuppressive TME, and immunogenomic precision medicine, in which cancer immunotherapy can be combined with molecularly targeted reagents that modulate the immunosuppressive TME.

Identification of exosome-related SERPINB1 as a novel predictor for tumor immune microenvironment and clinical outcomes in ovarian cancer

BACKGROUND

With a high global incidence of over three million new cases in 2020 and a high mortality of over two million fatalities, ovarian cancer is one of the most common malignant tumors in gynecology. Exosomes can control the immunological condition of the tumor microenvironment (TME) by participating in intercellular interactions. Therefore, we aimed to construct an exosome-related prognostic model to predict the clinical outcomes of ovarian cancer patients.

METHODS

In this research, expression patterns of exosome-related genes were examined in multiple single-cell RNA-sequencing and bulk RNA-sequencing datasets. In addition, a novel exosome-related prognostic model was established by the least absolute shrinkage and selection operator (LASSO) regression method. Then, the correlations between risk score and immunological characteristics of the TME were explored. Moreover, SERPINB1, a gene in the prognostic signature, was further analyzed to reveal its value as a novel biomarker.

RESULTS

In the current study, combined with single-cell and bulk omics datasets, we constructed an exosome-related prognostic model of four genes (LGALS3BP, SAT1, SERPINB1, and SH3BGRL3). Moreover, the risk score was associated with worse overall survival (OS) in ovarian cancer patients. Further analysis found that patients with high-risk score tended to shape a desert TME with hardly infiltration of immune cells. Then, SERPINB1, positively correlated with the favorable OS and negatively with the risk score, was chosen as the representative biomarker of the model. Moreover, SERPINB1 was positively correlated with the infiltration of immune subpopulations in both public and in-house cohort. In addition, the high-resolution analysis found that SERPINB1+ tumor cells communicated with microenvironment cells frequently, further explaining the potential reason for shaping an inflamed TME.

CONCLUSION

To sum up, we established a novel exosome-related prognostic model (LGALS3BP, SAT1, SERPINB1, and SH3BGRL3) to predict the prognosis of patients with ovarian cancer and identify the immunological characteristics of the TME. In addition, SERPINB1 was identified as a promising biomarker for prognostic prediction in ovarian cancer.

Construction and evaluation of a diagnostic model for Alzheimer's disease based on mitophagy-related genes

Alzheimer's disease (AD) is the most common cause of dementia. Mitophagy fulfills crucial functions in neurodegenerative disorders and neuronal survival but the relationship between mitophagy and AD is unclear. Mitophagy correlation scores between AD samples and control samples were calculated using single-sample GSEA (ssGSEA) based on two datasets from gene expression omnibus (GEO) database. Mitophagy-related genes (MRGs) and differentially expressed genes (DEGs) in AD screened by WGCNA and "limma" package were intersected to take common genes. These overlapping genes were further compressed and used for diagnostic modeling by adopting the recursive feature elimination (RFE) and LASSO analysis. The reliability of the diagnostic model was verified based on the receiver operating characteristic (ROC) curve. Then, a transcription factor (TF)-mRNA regulatory network of these key genes was established. Lastly, ssGSEA was employed to examine the relationship between the identified genes and cellular pathways and immune cell infiltration. AD samples had notably lower mitophagy correlation scores than control samples. A total of 12 MRGs in the module with the greatest mitophagy connection with AD patients were identified. Functional enrichment analysis revealed that the DEGs were significantly enriched in synaptic function-related pathways. Based on GSE122063, a diagnostic prediction model was created and validated using two mitophagy-related genes (YWHAZ and NDE1), showing an area under ROC curve (AUC) greater than 0.7. This confirmed that the diagnostic model had a high predictive value. The TF-mRNA network showed that four TFs, namely, FOXC1, FOXL1, HOXA5 and GATA2, were regulated by both YWHAZ and NDE1 genes. Immune infiltration analysis revealed that NDE1 promoted the infiltration of most immune cells, while YWHAZ mainly inhibited the infiltration of most immune cells. The current findings improved our understanding of mitophagy in AD, contributing to future research and treatment development in AD.

Dendritic cell-derived MYD88 potentiates as a biomarker for immune regulation in hepatocellular carcinoma and may predict a better immunological result

Introduction

MYD88 (myeloid differentiation primary response 88) is a key adaptor protein mediate immune responses, primarily through Toll-like receptors (TLRs) and interleukin-1 receptor (IL-1R) signaling. The TLR/MYD88 pathway plays a critical role in dendritic cells (DC) maturation and function, contributing to the body's innate immunity. Recent studies have further highlighted MYD88's pivotal role in intrinsic immunity and its regulatory influence on the tumor microenvironment (TME) in hepatocellular carcinoma (HCC). The expression of MYD88 in DCs and its regulatory role in the TME have gained increasing attention.

Methods

RNA-sequencing data retrieved from the TCGA and GEO databases were utilized for both the training and validation of our signature. Single-cell RNA transcriptome data from GEO were analyzed to investigate the correlation among subclusters of T cells, myeloid cells, and dendritic cells (DCs) within the HCC tumor microenvironment (TME). A combination of bioinformatics and machine learning approaches was employed to perform statistical analyses.Additionally, flow cytometry was conducted to quantify T cell subtypes and assess biomarker expression in DCs. A BALB/c-derived xenograft mouse model was established to evaluate the functional role of MyD88 in tumor progression and immunotherapy response. Furthermore, immunohistochemical (IHC) staining was performed to reassess the biological effects of MyD88 in HCC patients undergoing immune checkpoint inhibitor (ICI) therapy.

Results

Our pan-cancer data analysis further highlights the significant impact of MYD88 on clinical outcomes in HCC. Analysis of TCGA and GEO databases confirms that MYD88 serves as a key signaling molecule in DCs, reinforcing its critical role in immune regulation. Our in vitro experiments demonstrates that MyD88 modulates T cell function through DCs. In vivo, H22 tumor cells exhibited accelerated growth in MyD88 knockout mice and a reduced response to anti-PD-1 treatment, whereas wild-type mice showed the opposite trend.

Discussion

These findings underscore the critical role of MYD88 in DC function, suggesting its potential as a biomarker for immunoregulation in HCC. By shaping the TME, MYD88 not only regulates the immune response in HCC but also influences patient clinical outcomes. Both ex vivo and in vivo experiments further validate that MYD88 impacts DC functionality, contributing to variations in HCC progression.

AMPK-dependent Parkin activation suppresses macrophage antigen presentation to promote tumor progression

The constrained cross-talk between myeloid cells and T cells in the tumor immune microenvironment (TIME) restricts cancer immunotherapy efficacy, whereas the underlying mechanism remains elusive. Parkin, an E3 ubiquitin ligase renowned for mitochondrial quality control, has emerged as a regulator of immune response. Here, we show that both systemic and macrophage-specific ablations of Parkin in mice lead to attenuated tumor progression and prolonged mouse survival. By single-cell RNA-seq and flow cytometry, we demonstrate that Parkin deficiency reshapes the TIME through activating both innate and adaptive immunities to control tumor progression and recurrence. Mechanistically, Parkin activation by AMP-activated protein kinase rather than PTEN-induced kinase 1 mediated major histocompatibility complex I down-regulation on macrophages via Autophagy related 5-dependent autophagy. Furthermore, Parkin deletion synergizes with immune checkpoint blockade treatment and Park2-/- signature aids in predicting the prognosis of patients with solid tumor. Our findings uncover Parkin's involvement in suppressing macrophage antigen presentation for coordinating the cross-talk between macrophages and T cells.

Identifying periphery biomarkers of first-episode drug-naïve patients with schizophrenia using machine-learning-based strategies

Schizophrenia is a complex mental disorder. Accurate diagnosis and classification of schizophrenia has always been a major challenge in clinic due to the lack of biomarkers. Therefore, identifying molecular biomarkers, particularly in the peripheral blood, is of great significance. This study aimed to identify immune-related molecular biomarkers of schizophrenia in peripheral blood. Eighty-four Peripheral blood leukocytes of first-episode drug-naïve (FEDN) patients with schizophrenia and 97 healthy controls were collected and examined using high-throughput RNA-sequencing. Differentially-expressed genes (DEGs) were analysed. Weighted correlation network analysis (WGCNA) was employed to identify schizophrenia-associated module genes. The CIBERSORT algorithm was adopted to analyse immune cell proportions. Then, machine-learning algorithms including random forest, LASSO, and SVM-RFE were employed to screen immune-related predictive genes of schizophrenia. The RNA-seq analyses revealed 734 DEGs. Further machine-learning-based bioinformatic analyses screened out three immune-related predictive genes of schizophrenia (FOSB, NUP43, and H3C1), all of which were correlated with neutrophils and natural killer cells resting. Lastly, external GEO datasets were used to verify the performance of the machine-learning models with these predictive genes. In conclusion, by analysing the peripheral mRNA expression profiles of FEDN patients with schizophrenia, this study identified three predictive genes that could be potential molecular biomarkers for schizophrenia.

Prediction and analysis of tumor infiltrating lymphocytes across 28 cancers by TILScout using deep learning

The density of tumor-infiltrating lymphocytes (TILs) serves as a valuable indicator for predicting anti-tumor responses, but its broad impact across various types of cancers remains underexplored. We introduce TILScout, a pan-cancer deep-learning approach to compute patch-level TIL scores from whole slide images (WSIs). TILScout achieved accuracies of 0.9787 and 0.9628, and AUCs of 0.9988 and 0.9934 in classifying WSI patches into three categories-TIL-positive, TIL-negative, and other/necrotic-on validation and independent test sets, respectively, surpassing previous studies. The biological significance of TILScout-derived TIL scores across 28 cancers was validated through comprehensive functional and correlational analyses. A consistent decrease in TIL scores with an increase in cancer stage provides direct evidence that the lower TIL content may stimulate cancer progression. Additionally, TIL scores correlated with immune checkpoint gene expression and genomic variation in common cancer driver genes. Our comprehensive pan-cancer survey highlights the critical prognostic significance of TILs within the tumor microenvironment.

TGF-β-mediated activation of fibroblasts in cervical cancer: implications for tumor microenvironment and prognosis

Background

Cervical cancer (CC) is a prevalent female malignancy strongly influenced by the tumor microenvironment (TME). This study focuses on the role of TGF-β signaling in cancer-associated fibroblasts (CAFs) and its interaction with immune cells, aiming to elucidate its impact on CC progression.

Methods

The TME of CC patients was analyzed using scRNA-seq data and we identified the major cell types in the TME with a focus on the activation of the TGF-β signaling pathway in fibroblasts. Gene modules related to the TGF-β signaling pathway were identified by Weighted correlation network analysis (WGCNA). Using The Cancer Genome Atlas Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) dataset, a prognostic gene model was constructed by univariate Cox, LASSO Cox and multivariate Cox regression analyses. For cellular validation, the mRNA level of prognostic model-related genes was tested via quantitative real-time real-time polymerase chain reaction (PCR). Thereafter, the following assays, including cell counting kit-8, scratch and wound healing assays, were applied to assess the viability, migration and invasion of CC cells.

Results

Analysis at single-cell resolution identified nine major cell types in the TME, and significant activation of the TGF-β signaling pathway in fibroblasts was correlated with tumor proliferation and differentiation. Strong TGF-β signaling communication between fibroblasts and macrophages and NK/T cells suggested a crucial role in the shaping of the immunosuppressive microenvironment. WGCNA analysis identified gene modules significantly associated with the TGF-β signaling pathway. The prognostic model constructed based on three genes, ITGA5, SHF and SNRPN, demonstrated good predictive ability in multiple datasets, validating its potential for clinical application. Meanwhile, the cellular validation assays have revealed the higher expression of ITGA5 and SNRPN and lower expression of SHF in CC cells. Further, ITGA5 knockdown suppressed the viability, migration and invasion of CC cells.

Conclusion

This study confirmed the important role of the TGF-β signaling pathway in CC, especially in fibroblasts on tumor microenvironment and tumor progression. The current model could effectively evaluate the prognosis of CC, providing a theoretical foundation for developing CC therapies according to the TGF-β signaling pathway. The present results provide new perspectives for further research on the pathological mechanisms and clinical management of CC.

Unveiling a Novel Glioblastoma Deep Molecular Profiling: Insight into the Cancer Cell Differentiation-Related Mechanisms

BACKGROUND

the sophisticated cellular heterogeneity of cell populations in glioblastoma (GBM) has been a key factor influencing tumor progression and response to therapy. The lack of more precise stratification based on cellular differentiation status poses a great challenge to therapeutic strategies.

MATERIALS AND METHODS

harnessing the bulk multiomics and single-nucleus RNA sequencing data available from the National Center for Biotechnology Information (NCBI) and The Cancer Genome Atlas (TCGA) Program repositories, we developed a novel and accurate GBM risk classification using an ensemble consensus clustering approach based on the junction of prognosis and trajectory analysis. Comprehensive cluster labeling and multiomics data characterization were also performed.

RESULTS

a novel GBM stratification model was constructed using 45 malignant cell fate genes: (a) energy metabolism-enhanced-type GBM; (b) invasion-enhanced-type GBM; (c) invasion-attenuated-type GBM; and (d) glycolysis-dominant energy metabolism-enhanced-type GBM. The biological plausibility of the model was verified through a range of comprehensive analyses of multiomics data, showing that cases with invasion-attenuated-type were the best prognosis and energy metabolism-enhanced-type the poorest.

CONCLUSIONS

the study has uncovered GBM complex cellular heterogeneity and a differentiated hierarchy of cell populations underlying tumorigenesis. This precise stratification system provided implications for further studies of individual therapies.

LTF as a Potential Predictive Biomarker for Durable Benefit From First-Line Chemo-Immunotherapy in Small Cell Lung Cancer

At present, only a limited fraction of patients with extensive-stage small cell lung cancer (ES-SCLC) achieve a sustained response to immune checkpoint blockade (ICB) therapy. The factors that drive therapeutic efficacy remain poorly delineated, and the field is devoid of reliable predictive biomarkers to guide personalized treatment decisions. Therefore, we conducted RNA sequencing of tumor samples from 21 patients prior to treatment to identify expression patterns associated with lasting benefit and used weighted gene co-expression network analysis (WGCNA) to identify key genes associated with favorable outcomes of chemotherapeutic immunotherapy. Multiplex immunofluorescence (mIF) quantification and reanalysis of publicly available datasets were used to validate the hub gene's association with the immune microenvironment and immunotherapy efficacy. The functional significance of the hub gene was further investigated in cellular models. We found that the durable clinical benefit (DCB) group exhibited significantly elevated levels of inflammation and interferon response compared to the no-durable benefit (NDB) group, alongside a notably lower proportion of Tregs and distinct metabolic features. Lactotransferrin (LTF) was identified as a hub gene associated with durable therapeutic benefits in chemo-immunotherapy. By further analysis, we proved that LTF acts as a tumor suppressor in small cell lung cancer, impacting cell proliferation, migration, and invasiveness. It also inhibits lipid metabolism in these cells. Elevated LTF expression is linked to better chemo-immunotherapy outcomes, suggesting its potential as a predictive biomarker for first-line treatment response in ES-SCLC.

Novel Identification of CD74 as a Biomarker for Diagnosing and Prognosing Sepsis Patients

Purpose

Sepsis, a life-threatening inflammatory condition due to an imbalanced response to infections, has been a major concern. Necroptosis, a newly discovered programmed cell death form, plays a crucial role in various inflammatory diseases. Our study aims to identify necroptosis - related genes (NRGs) and explore their potential for sepsis diagnosis.

Patients and methods

We used weighted gene co-expression network analysis to identify gene modules associated with sepsis. Cox regression and Kaplan-Meier methods were employed to assess the diagnostic and prognostic value of these genes. Single-cell and immune infiltration analyses were carried out to explore the immune environment in sepsis. Plasma CD74 protein levels were quantified in our samples, and relevant clinical data from electronic patient records were analyzed for correlation.

Results

CD74 was identified through the intersection of the hub genes of sepsis and NRGs related modules. Septic patients had lower CD74 expression compared to healthy controls. The CD74-based diagnostic model showed better performance in the training dataset (AUC, 0.79 [95% CI, 0.75-0.84]), was cross-validated in external datasets, and demonstrated better performances than other published diagnostic models. Pathway analysis and single-cell profiling supported further exploration of CD74-related inflammation and immune response in sepsis.

Conclusion

This study presents the first quantitative assessment of human plasma CD74 in sepsis patients. CD74 levels were significantly lower in the sepsis cohort. CD74 warrants further exploration as a potential prognostic and therapeutic target for sepsis.

Integrated bulk and single-cell transcriptomic analysis unveiled a novel cuproptosis-related lipid metabolism gene molecular pattern and a risk index for predicting prognosis and antitumor drug sensitivity in breast cancer

Breast cancer is the second most prevalent malignant tumor worldwide and is highly heterogeneous. Cuproptosis, a newly identified form of cell death, is intimately connected to lipid metabolism. This study investigated breast cancer heterogeneity through the lens of cuproptosis-related lipid metabolism genes (CLMGs), with the goal of predicting patient prognosis, immunotherapy efficacy, and sensitivity to anticancer drugs. By utilizing transcriptomic data from The Cancer Genome Atlas (TCGA) for breast cancer, we identified 682 CLMGs and applied the nonnegative matrix factorization (NMF) method to categorize breast cancer patients into four distinct clusters: cluster 1, ''immune-cold and stroma-poor''; cluster 2, ''immune-infiltrated''; cluster 3, ''stroma-rich''; and cluster 4, ''moderate infiltration''. We subsequently developed a risk model based on CLMGs that incorporates ACSL1, ATP2B4, ATP7B, ENPP6, HSPH1, PIP4K2C, SRD5A3, and ULBP1. This model demonstrated excellent prognostic predictive performance in both the internal (testing and entire sets) and external (GSE20685 and Kaplan-Meier Plotter sets) validation sets. High-risk patients presented lower expression levels of immune checkpoint-related genes and lower immunophenoscores (IPSs), whereas low-risk patients presented higher CD8+ T-cell infiltration levels and IPSs. Furthermore, the risk index was positively correlated with tumor cell stemness and could predict sensitivity to anticancer drugs. We also confirmed that SRD5A3 was highly expressed in breast cancer and participated in promoting the proliferation and migration of breast cancer cells. In conclusion, the results of this study provide new insights and strategies for assessing prognosis and implementing precision treatment for breast cancer through the lens of CLMGs.

Programmed cell death-related prognostic genes mediate dysregulation of the immune microenvironment in triple-negative breast cancer

Background

Programmed Cell death (PCD) encompasses a spectrum of genetically regulated cell death processes and plays a double-edged sword role in neoplastic progression and therapeutic resistance of Triple-Negative Breast Cancer(TNBC)through the tumor microenvironment (TME). However, the specific mechanisms by which PCD mediates microenvironmental dysregulation remain elusive.

Methods

Analyzing nine samples of TNBC through single-cell RNA sequencing (scRNA-seq), this study employed nonnegative matrix factorization (NMF) to assess genes associated with 13 PCD modes. Single-cell regulatory network inference and clustering (SCENIC), Monocle, CellChat, and scMetabolism were used for pseudotime analysis, intercellular communication mapping, determination of transcription factor activities (TFs), and immune infiltration of PCD-related cell clusters in TME. A robust prognostic model and drug resistance analysis were constructed using gene set enrichment analysis (GSEA), Kaplan-Meier survival analysis, and multivariable Cox regression. Finally, hub genes and critical PCD-related cell clusters were validated in the clinical breast cancer samples and the TNBC model mice.

Results

This investigation demonstrated that PCD significantly modulated the functional and phenotypic diversity of fibroblasts, macrophages, T cells, and B cells in the TME of TNBC. Furthermore, this study revealed that PCD-regulated CEBPB-positive cancer-associated fibroblast (CAF) populations are a key determinant of the TNBC immune Microenvironment heterogeneity and poor prognosis. Notably, CellChat analysis unveiled diverse and extensive interactions between PCD-related cell clusters and tumor immune cells, highlighting the CEBPB+ CAF subtype as a signaling ligand communicated with other immune cell clusters through the Midkine (MDK)-Nucleolin (NCL) signaling axis. Moreover, the TIDE analysis verified that CEBPB+ CAF is a predictor of poor prognosis in Immunotherapy. The ex vivo analyses of tumor specimens from both TNBC patients and syngeneic murine models were performed by quantitative reverse-transcription PCR (qRT-PCR), immunoblotting, immunohistochemical staining, and multiplexed immunofluorescence co-localization assays. They confirmed differential expression of the PCD-related prognostic genes and the presence of CEBPB+ CAFs.

Conclusion

In summary, our study provides a comprehensive molecular framework to understand the role of PCD-mediated TME dysregulation in TNBC pathogenesis. This study also offers new insights into the underlying mechanisms of immune therapy resistance in TNBC and identifies promising therapeutic targets for enhancing treatment efficacy and patient outcomes.

Multi-omics analysis revealed the novel role of NQO1 in microenvironment, prognosis and immunotherapy of hepatocellular carcinoma

NAD(P)H dehydrogenase quinone 1 (NQO1) is overexpressed in various cancers and is strongly associated with an immunosuppressive microenvironment and poor prognosis. In this study, we explored the role of NQO1 in the microenvironment, prognosis and immunotherapy of Hepatocellular carcinoma (HCC) using multi-omics analysis and machine learning. The results revealed that NQO1 was significantly overexpressed in HCC cells. NQO1+HCC cells were correlated with poor prognosis and facilitated tumor-associated macrophages (TAMs) polarization to M2 macrophages. We identified core NQO1-related genes (NRGs) and developed the NRGs-related risk-scores in hepatocellular carcinoma (NRSHC). The comprehensive nomogram integrating NRSHC, age, and pathological tumor-node-metastasis (pTNM) Stage achieved an area under the curve (AUC) above 0.7, demonstrating its accuracy in predicting survival outcomes and immunotherapy responses of HCC patients. High-risk patients exhibited worse prognoses but greater sensitivity to immunotherapy. Additionally, a web-based prediction tool was designed to enhance clinical utility. In conclusion, NQO1 may play a critical role in M2 polarization and accelerates HCC progression. The NRSHC model and accompanying tools offer valuable insights for personalized HCC treatment.

Identification and validation of key biomarkers associated with immune and oxidative stress for preeclampsia by WGCNA and machine learning

Background

Preeclampsia (PE), a major obstetric disorder marked by dysfunction in both placental and maternal vascular systems, continues to pose critical challenges in global maternal healthcare. This multisystem pregnancy complication contributes significantly to adverse perinatal outcomes and remains a leading cause of pregnancy-related morbidity worldwide. However, the available treatment options at present remain restricted. Our investigation employs an integrative bioinformatics approach to elucidate critical molecular signatures linked to the interplay between immunological dysregulation and oxidative stress mechanisms in PE pathogenesis.

Methods

In this study, we sourced the dataset from the GEO database with the aim of pinpointing differentially expressed genes (DEGs) between PE samples and control samples. Genes associated with oxidative stress were procured from the Genecards database. Next, we employed a comprehensive approach. This involved integrating WGCNA, GO and KEGG pathway analyses, constructing PPI networks, applying machine learning algorithms, performing gene GSEA, and conducting immune infiltration analysis to identify the key hub genes related to oxidative stress. Diagnostic potential of candidate biomarkers was quantitatively assessed through ROC curve modeling. Additionally, we constructed a miRNA - gene regulatory network for the identified diagnostic genes and predicted potential candidate drugs. In the final step, we validated the significant hub gene using independent external datasets, the hypoxia model of the HTR-8/SVneo cell line, and human placental tissue samples.

Results

At last, leptin (LEP) was identified as a core gene through screening and was found to be upregulated. The results of quantitative real-time polymerase chain reaction (qRT -PCR) and immunohistochemistry validation were consistent with those obtained from the datasets. KEGG analysis revealed that LEP was significantly enriched in "allograft rejection," "antigen processing," "ECM receptor interaction" and "graft versus host disease." GO analysis revealed that LEP was involved in biological processes such as "antigen processing and presentation," "peptide antigen assembly with MHC protein complex," "complex of collagen trimers," "MHC class II protein complex" and "mitochondrial protein containing complex." Moreover, immune cell analysis indicated that T follicular helper cells, plasmacytoid dendritic cells, neutrophils, and activated dendritic cells were positively correlated with LEP expression, whereas γδT cells, eosinophils, and central memory CD4+ T cells showed a negative correlation. These findings suggest that LEP influences the immune microenvironment of PE through its interaction with arious immune cells. In addition, 28 miRNAs and 15 drugs were predicted to target LEP. Finally, the overexpression of LEP was verified using independent external datasets, the hypoxia model of the HTR-8/SVneo cell line, and human placental tissue.

Conclusion

Through an integrated analytical framework employing WGCNA coupled with three distinct machine learning-driven phenotypic classification models, we discovered a pivotal regulatory gene. This gene has the potential to act as a novel diagnostic biomarker for PE. Moreover, it can be considered as a promising target for drug development related to PE. Notably, it shows a strong correlation with the immune microenvironment, suggesting its crucial role in the complex pathophysiological processes underlying PE.

Identification of regulator gene and pathway in myocardial ischemia-reperfusion injury: a bioinformatics and biological validation study

BACKGROUND

Acute myocardial infarction (AMI) is the primary cause of cardiac mortality worldwide. However, myocardial ischemia-reperfusion injury (MIRI) following reperfusion therapy is common in AMI, causing myocardial damage and affecting the patient's prognosis. Presently, there are no effective treatments available for MIRI.

METHODS

We performed a comprehensive bioinformatics analysis using three GEO datasets on differentially expressed genes, including gene ontology (GO), pathway enrichment analyses, and protein-protein interaction (PPI) network analysis. Cytoscape and LASSO methods were employed to identify novel regulator genes for ischemia-reperfusion (I/R). Notably, gene S100A9 was identified as a potential regulator of I/R. Additionally, clinical sample datasets were analyzed to prove the expression and mechanism of S100A9 and its down genes in I/R. The correlation of S100A9 with cardiac events was also examined to enhance the reliability of our results.

RESULTS

We identified 135 differential genes between the peripheral blood of 47 controls and 92 I/R patients. S100A9 was distinguished as a novel regulator gene of I/R with diagnostic potential. RT-qPCR test demonstrated significant upregulation of S100A9 in I/R. We also verified that S100A9 expression strongly correlates with left ventricular ejection fraction (LVEF) and MIRI.

CONCLUSION

This study confirms that S100A9 is a key regulator of I/R progression and may participate in ischemia-reperfusion injury by upregulating RAGE /NFKB-NLRP3 activation. Elevated S100A9 levels may serve as a marker for identifying high-risk MIRI patients, especially those with coronary artery no-reflow (CNR), who might benefit from targeted therapeutic interventions. Furthermore, Peripheral blood S100A9 in AMI represents a new therapeutic target for preventing MIRI.

Deep Clustering-Based Immunotherapy Prediction for Gastric Cancer mRNA Vaccine Development

Immunotherapy is becoming a promising strategy for treating diverse cancers. However, it benefits only a selected group of gastric cancer (GC) patients since they have highly heterogeneous immunosuppressive microenvironments. Thus, a more sophisticated immunological subclassification and characterization of GC patients is of great practical significance for mRNA vaccine therapy. This study aimed to find a new immunological subclassification for GC and further identify specific tumor antigens for mRNA vaccine development. First, deep autoencoder (AE)-based clustering was utilized to construct the immunological profile and to uncover four distinct immune subtypes of GC, labeled as Subtypes 1, 2, 3, and 4. Then, in silico prediction using machine learning methods was performed for accurate discrimination of new classifications with an average accuracy of 97.6%. Our results suggested significant clinicopathology, molecular, and immune differences across the four subtypes. Notably, Subtype 4 was characterized by poor prognosis, reduced tumor purity, and enhanced immune cell infiltration and activity; thus, tumor-specific antigens associated with Subtype 4 were identified, and a customized mRNA vaccine was developed using immunoinformatic tools. Finally, the influence of the tumor microenvironment (TME) on treatment efficacy was assessed, emphasizing that specific patients may benefit more from this therapeutic approach. Overall, our findings could help to provide new insights into improving the prognosis and immunotherapy of GC patients.

Huoshan Dendrobium Zengye Jiedu Formula mitigates radiation-induced oral mucositis and improves oral immune microenvironment by targeting the EGFR/PI3K/AKT pathway: evidence from network pharmacology, molecular docking, and experimental validation

Introduction

Radiation-induced oral mucositis (RIOM) manifests as mucosal ulceration, pain, and dysphagia, disrupting treatment and quality of life. Its pathogenesis involves inflammatory imbalance and immune dysregulation, driven by microbial infiltration and cytokine storms. Current therapies remain inadequate, necessitating deeper exploration of immune-microbial interactions for effective interventions.

Methods

Bioactive components of Huoshan Dendrobium Zengye Jiedu Formula (HDZJF) and RIOM-related targets were retrieved from public databases. Core therapeutic targets and pathways were systematically analyzed via protein-protein interaction (PPI) networks, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Molecular docking evaluated interactions between HDZJF components and key targets. A rat RIOM model validated HDZJF efficacy by assessing mucositis severity, inflammatory cytokines, and EGFR/PI3K/AKT pathway protein expression.

Results

A total of 102 bioactive components and 379 potential targets for RIOM were identified. GO and KEGG enrichment analyses suggest that HDZJF exerts therapeutic effects on RIOM by modulating processes such as angiogenesis, inflammation, and apoptosis through pathways like PI3K-AKT. Molecular docking confirmed strong binding affinities between HDZJF components and key targets. In vivo, HDZJF reduced inflammation, promoted mucosal healing, improved body weight, and modulated protein expression related to EGFR/PI3K/AKT.

Discussion

The findings highlight HDZJF's capacity to alleviate RIOM by targeting the EGFR/PI3K/AKT pathway, thereby suppressing inflammatory responses and apoptotic processes. These results underscore HDZJF's translational potential for RIOM treatment and justify further clinical investigation into its therapeutic utility.

Size-specific clonidine-loaded liposomes: Advancing melanoma microenvironment suppression with safety and precision

The immunosuppressive tumor microenvironment (TME) plays a crucial role in the progression and treatment resistance of melanoma. Modulating the TME is thus a key strategy for enhancing therapeutic outcomes. Previousstudies have identified clonidine (CLD), an α2-adrenergic receptor agonist, as a promising agent that enhances T lymphocyte infiltration and reduces myeloid-derived suppressor cells within the TME, thereby promoting antitumor immune responses. In this study, we discovered that CLD reshaped the melanoma immune microenvironment, facilitating T-cell activation and exerting antitumor effects. However, the high doses of CLD required for effective TME modulation pose significant toxicity concerns, limiting its clinical applicability. To address this, we employed the controllable cavitation-on-a-chip (CCC) platform to formulate CLD-loaded liposomes and optimize their size. This approach aimed to enhance the precision and efficacy of drug delivery while reducing systemic side effects. Our results demonstrated that size-specific CLD liposomes, particularly those at 50 nm, significantly improved tumor growth inhibition and immune cell infiltration within the TME. Moreover, these optimized liposomes mitigate adverse effects associated with high-dose CLD treatment. This study indicates the potential of CCC-optimized CLD liposomes as a safer and more effective melanoma therapy, highlighting the critical interplay between liposome size control and therapeutic outcomes in cancer treatment.

Identification and Experimental Validation of Biomarkers Related to MiR-125a-5p in Chronic Obstructive Pulmonary Disease

Purpose

The miR-125a-5p has been reported influence the development of lung cancer, however, the link between it and chronic obstructive pulmonary disease (COPD) is still not well understood. Hence, this study was designed to investigate the molecular pathway by which miR-125a-5p related biomarkers were involved in COPD.

Patients and Methods

The differentially expressed genes (DEGs) and module genes related to COPD in GSE100153 were screened out by differential analysis and weighted gene co-expression network analysis, respectively. Then, the target genes of miR-125a-5p obtained from miRWalk database were intersected with DEGs and module genes, followed by identification of biomarkers through SVM-RFE algorithms. Moreover, the gene set enrichment analysis, immune infiltration analysis, construction of regulatory network, single-cell analysis and Mendelian randomization (MR) analysis were performed. At last, the expression levels of the biomarkers were further validated in GSE100153 and GSE146560 as well as in qRT-PCR.

Results

A total of 10 genes were acquired by intersecting the 126 DEGs, the 3989 module genes, and 2329 target genes, of which PITHD1, CNTNAP2 and GUCD1 were identified as biomarkers. Enrichment analysis showed their roles in various cellular functions. In addition, significant associations were identified between 9 distinct cells and biomarkers. Subsequently, 5 TFs and 63 therapeutic agents were predicted as biomarkers. Moreover, GUCD1 and PITHD1 were significantly different between case and control in T cells and Alveolar cells. In COPD, GUCD1 and PITHD1 were significantly down-regulated in GSE100153 and GSE146560 datasets and confirmed by qRT-PCR.

Conclusion

In our study, PITHD1, CNTNAP2, and GUCD1 were recognized as biomarkers related to miR-125a-5p-related genes in COPD, providing new references for treatment of COPD.

TMED9: a potential therapeutic target and prognostic marker in glioma and its implications across pan-cancer contexts

Background

The escalating global cancer burden, projected to reach 35 million new cases by 2050, underscores the urgent need for innovative cancer biomarkers to improve treatment efficacy and patient outcomes. The TMED family, particularly TMED9, has garnered attention for its involvement in cancer progression; however, its comprehensive role across various cancer types remains poorly understood.

Methods

Utilizing multi-omics data, we analyzed the expression pattern, prognostic significance, genomic alterations, and immunological features of TMED9 in various cancer types. Through in vitro experiments, we paid special attention to its role in glioma, especially its correlation with glioma cell migration and invasion behavior.

Results

Our findings reveal that TMED9 is significantly overexpressed in various tumor tissues and is associated with poor prognosis in cancers such as glioblastoma and lower-grade gliomas. Genetic analysis shows TMED9 mutations predominantly in kidney renal clear cell carcinoma, with its expression linked to chromosomal instability. Immunological analysis indicates that TMED9 correlates positively with immune cell infiltration, particularly macrophages, suggesting its role in promoting tumor immunity. Furthermore, TMED9 expression was negatively correlated with tumor stemness, indicating its potential influence on chemotherapy resistance. Knockdown of TMED9 led to reduced migration and invasion in glioma cell lines.

Conclusions

Our comprehensive analysis positions TMED9 as a critical player in cancer progression and immune modulation, especially in gliomas. Elevated TMED9 expression correlates with poorer outcomes and may serve as a prognostic marker and therapeutic target. Future research should focus on elucidating TMED9's mechanistic pathways and validating its role in clinical settings to enhance glioma treatment strategies.

Identification and validation of m6A RNA methylation and ferroptosis-related biomarkers in sepsis: transcriptome combined with single-cell RNA sequencing

Background

Sepsis, a systemic inflammatory response syndrome triggered by infection, is associated with high mortality rates and an increasing global incidence. While N 6-methyladenosine (m6A) RNA methylation and ferroptosis are implicated in inflammatory diseases, their specific genes and mechanisms in sepsis remain unclear.

Methods

Transcriptomic datasets of sepsis, along with m6A-related genes (m6A-RGs) and ferroptosis-related genes (FRGs), were sourced from public databases. Differentially expressed genes (DEGs) were identified between the sepsis and control groups, and m6A-RGs were analyzed through weighted gene co-expression network analysis (WGCNA) to uncover m6A module genes. These were then intersected with DEGs and FRGs to identify candidate genes. Biomarkers were identified using two machine learning methods, receiver operating characteristic (ROC) curves, and expression validation, followed by the development of a nomogram. Further in-depth analyses of the biomarkers were performed, including functional enrichment, immune infiltration, drug prediction, and molecular docking. Single-cell analysis was conducted to identify distinct cell clusters and evaluate biomarker expression at the single-cell level. Finally, reverse transcription-quantitative PCR (RT-qPCR) was employed to validate biomarker expression in clinical samples.

Results

DPP4 and TXN were identified as key biomarkers, showing higher expression in control and sepsis samples, respectively. The nomogram incorporating these biomarkers demonstrated strong diagnostic potential. Enrichment analysis highlighted their involvement in spliceosome function and antigen processing and presentation. Differential analysis of immune cell types revealed significant correlations between biomarkers and immune cells, such as macrophages and activated dendritic cells. Drug predictions identified gambogenic acid and valacyclovir as potential treatments, which were successfully docked with the biomarkers. Single-cell analysis revealed that the biomarkers were predominantly expressed in CD4+ memory cells, and CD16+ and CD14+ monocytes. The expression of DPP4 was further validated in clinical samples.

Conclusions

DPP4 and TXN were validated as biomarkers for sepsis, with insights into immune infiltration and therapeutic potential at the single-cell level, offering novel perspectives for sepsis treatment.

Identification and validation of palmitoylation-related biomarkers in gestational diabetes mellitus

Palmitoylation plays a crucial role in the pathophysiology of diabetes, and an increase in palmitoylation may inhibit the function of insulin receptors, thereby affecting the progression of gestational diabetes mellitus (GDM). However, its involvement in gestational diabetes mellitus (GDM) remains underexplored. This study analyzed GDM-related datasets and 30 palmitoylation-related genes (PRGs), identifying MNDA, FCGR3B, and AQP9 as significantly upregulated biomarkers in GDM samples. Consistent with the dataset analysis, reverse transcription-polymerase chain reaction (RT-qPCR) confirmed elevated AQP9 expression. Comprehensive analyses, including nomogram construction, enrichment analysis, immune infiltration assessment, molecular regulatory network generation, drug prediction, and molecular docking, were conducted. The biomarker-based nomogram demonstrated excellent predictive performance for GDM risk. MNDA, FCGR3B, and AQP9 were significantly enriched in pathways such as "Myc-targets-v1" and "TNFA signaling via NFkB." Additionally, eosinophil infiltration showed a strong positive correlation with these biomarkers. Regulatory networks involving SH3BP5-AS1-hsa-miR-182-5p-AQP9 and hsa-miR-182-5p-AQP9-ELF5 were identified, and stable binding energies were observed between the biomarkers and corresponding drugs. These findings provide promising avenues for early GDM screening and diagnosis.

Identification of the Molecular Subtype and Prognostic Characteristics of Breast Cancer Based on Tumor-Infiltrating Regulatory T Cells

Background: T regulatory cells (Tregs) are essential for preserving immune tolerance. They are present in large numbers in many tumors, hindering potentially beneficial antitumor responses. However, their predictive significance for breast cancer (BC) remains ambiguous. This study aimed to explore genes associated with Tregs and develop a prognostic signature associated with Tregs. Methods: The gene expression and clinical data on BC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The integration of CIBERSORT and weighted correlation network analysis (WGCNA) algorithms was utilized to identify modules associated with Tregs. The consensus cluster algorithm was utilized to create molecular subtypes determined by genes associated with Tregs. Then, a prognostic signature associated with Tregs was constructed and its relationship to tumor immunity and the prognosis was evaluated. Results: The blue module genes exhibited the most significant correlation with Tregs, and 1080 genes related to Tregs were acquired. A total of 93 genes from the TCGA dataset were found to have a significant impact on patient prognosis. Samples from BC were categorized into two clusters by consensus cluster analysis. The overall survival, immune checkpoint genes, molecular subtype, and biological behaviors varied significantly between these two subtypes. A 10-gene signature developed from differentially expressed genes between two subtypes demonstrated consistent prognostic accuracy in both TCGA and GEO datasets. It functioned as a standalone prognostic marker for individuals with BC. In addition, patients with low risk are more inclined to exhibit increased immune cell infiltration, TME score, and tumor mutation burden (TMB). Meanwhile, Individuals classified within the low-risk group showed better responses to immunotherapies compared to their counterparts in the high-risk group. Conclusions: The prognostic model derived from Tregs-related genes could aid in assessing the prognosis, guiding personalized treatment, and potentially enhancing the clinical outcomes for patients with BC.

A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables

Cancer is an umbrella term that includes a wide spectrum of disease severity, from those that are malignant, metastatic, and aggressive to benign lesions with very low potential for progression or death. The ability to prognosticate patient outcomes would facilitate management of various malignancies: patients whose cancer is likely to advance quickly would receive necessary treatment that is commensurate with the predicted biology of the disease. Former prognostic models based on clinical variables (age, gender, cancer stage, tumor grade, etc.), though helpful, cannot account for genetic differences, molecular etiology, tumor heterogeneity, and important host biological mechanisms. Therefore, recent prognostic models have shifted toward the integration of complementary information available in both molecular data and clinical variables to better predict patient outcomes: vital status (overall survival), metastasis (metastasis-free survival), and recurrence (progression-free survival). In this article, we review 20 survival prediction approaches that integrate multi-omics and clinical data to predict patient outcomes. We discuss their strategies for modeling survival time (continuous and discrete), the incorporation of molecular measurements and clinical variables into risk models (clinical and multi-omics data), how to cope with censored patient records, the effectiveness of data integration techniques, prediction methodologies, model validation, and assessment metrics. The goal is to inform life scientists of available resources, and to provide a complete review of important building blocks in survival prediction. At the same time, we thoroughly describe the pros and cons of each methodology, and discuss in depth the outstanding challenges that need to be addressed in future method development.

Exploring radiation resistance-related genes in pancreatic cancer and their impact on patient prognosis and treatment

Background

Pancreatic cancer is a highly lethal disease with increasing incidence worldwide. Despite surgical resection being the main curative option, only a small percentage of patients are eligible for surgery. Radiotherapy, often combined with chemotherapy, remains a critical treatment, especially for locally advanced cases. However, pancreatic cancer's aggressiveness and partial radio resistance lead to frequent local recurrence. Understanding the mechanisms of radiotherapy resistance is crucial to improving patient outcomes.

Methods

Pancreatic cancer related gene microarray data were downloaded from GEO database to analyze differentially expressed genes before and after radiotherapy using GEO2R online tool. The obtained differentially expressed genes were enriched by GO and KEGG to reveal their biological functions. Key genes were screened by univariate and multivariate Cox regression analysis, and a risk scoring model was constructed, and patients were divided into high-risk group and low-risk group. Subsequently, Kaplan-Meier survival analysis was used to compare the survival differences between the two groups of patients, further analyze the differential genes of the two groups of patients, and evaluate their sensitivity to different drugs.

Results

Our model identified 10 genes associated with overall survival (OS) in pancreatic cancer. Based on risk scores, patients were categorized into high- and low-risk groups, with significantly different survival outcomes and immune profile characteristics. High-risk patients showed increased expression of pro-inflammatory immune markers and increased sensitivity to specific chemotherapy agents, while low-risk patients had higher expression of immune checkpoints (CD274 and CTLA4), indicating potential sensitivity to targeted immunotherapies. Cross-dataset validation yielded consistent AUC values above 0.77, confirming model stability and predictive accuracy.

Conclusion

This study provides a scoring model to predict radiotherapy resistance and prognosis in pancreatic cancer, with potential clinical application for patient stratification. The identified immune profiles and drug sensitivity variations between risk groups highlight opportunities for personalized treatment strategies, contributing to improved management and survival outcomes in pancreatic cancer.

Prognostic analysis and identification of M7G immune-related genes in lung squamous cell carcinoma

Background

In recent years, the clinical application of targeted therapies and immunotherapy has significantly improved survival outcomes for patients with lung adenocarcinomas(LUAD). However, due to fewer mutations, lung squamous cell carcinomas(LUSC) shows limited efficacy with targeted and immunotherapy, resulting in a notably lower 5-year survival rate compared to lung adenocarcinoma. The m7G modification plays an important role in tumorigenesis, progression, immune evasion, and therapeutic response. This study aims to develop a novel scoring system based on m7G modification and immune status to clinically predict the prognosis of patients with LUSC and to provide new therapeutic targets.

Methods

In this study, we utilized RNA-seq data from the TCGA-LUSC database as the training set and GSE50081 from the GEO database as the validation set. Immunotherapy data were obtained from the IMMPORT database, and m7G data from previous research. Using bioinformatics, we developed a prognostic model for LUSC based on m7G pathway-related immune gene characteristics. We analyzed the correlation between the prognostic model and clinical pathological features of LUSC, as well as the model's independent prognostic capability. Subsequently, patients were divided into high-risk and low-risk groups, and we examined the differences in enriched pathways, immune cell infiltration correlations, and drug sensitivity between the two groups.

Results

The m7G immune-related genes FGA, CSF3R, and ORM1 increase the survival risk in patients with lung squamous cell carcinoma, whereas NTS exerts a protective effect. The prognostic risk model for lung squamous cell carcinoma (LUSC) based on m7G immune-related gene expression demonstrates that the overall survival of the high-risk group is significantly poorer than that of the low-risk group.

Conclusion

The risk model developed based on m7G immune-related genes can help predict the clinical prognosis of LUSC patients and guide treatment decisions.

Identification of a novel prognostic and therapeutic prediction model in clear cell renal carcinoma based on Renin-angiotensin system related genes

Background

Clear cell renal cell carcinoma is the most predominant type of renal malignancies, characterized by high aggressiveness and probability of distant metastasis. Renin angiotensin system (RAS) plays a crucial role in maintaining fluid balance within the human body, and its involvement in tumorigenesis is increasingly being uncovered, while its role in ccRCC remains unclear.

Methods

WGCNA was used to identify RAS related genes. Machine learning was applied to screen hub genes for constructing risk model, E-MTAB-1980 dataset was used for external validation. Transwell and CCK8 assays were used to investigate the impact of SLC6A19 to ccRCC cells.

Results

SLC6A19, SLC16A12 and SMIM24 were eventually screened to construct risk model and the predictive efficiency for prognosis was validated by internal and external cohorts. Moreover, the differences were found in pathway enrichment, immune cell infiltration, mutational landscapes and drug prediction between high and low risk groups. Experimental results indicated that SLC6A19 could inhibit invasion and proliferation of ccRCC cells and GSEA pinpointed that SLC6A19 was intimately correlated with fatty acid metabolism and CPT1A.

Conclusion

The risk model based on the three RAS-related genes have a robust ability to predict the prognosis and drug sensitivity of ccRCC patients, further providing a valid instruction for clinical care.

Alterations in PD-L1 succinylation shape anti-tumor immune responses in melanoma

Tumors undergo metabolic reprogramming to meet the energetic, synthetic and redox demands essential for malignancy, often characterized by increased glycolysis and lactate production. However, the role of mitochondrial metabolism in tumor immunity remains unclear. The present study integrates spatial transcriptomics, bulk transcriptomics and proteomics, revealing a strong link between the metabolite succinyl-CoA and tumor immunity as well as the efficacy of anti-programmed cell death protein-1 (PD-1) therapy in patients with melanoma. Elevated succinyl-CoA levels, through α-ketoglutarate or succinate supplementation, enhanced T cell-mediated tumor elimination, both in vitro and in vivo. Mechanistically, succinylation of the ligand of PD-1 (PD-L1) at lysine 129 led to its degradation. Increased carnitine palmitoyltransferase 1A (CPT1A), identified as a succinyltransferase for PD-L1, boosted anti-tumor activity. Preclinically, bezafibrate, a hyperlipidemia drug, upregulated CPT1A and synergized with CTLA-4 monoclonal antibody to inhibit tumor growth. Clinically, higher PD-L1 and lower CPT1A levels in tumors correlated with better anti-PD-1 therapy responses, suggesting potential biomarkers for prediction of treatment efficacy.