Signature Model Research Articles

Intestinal subtype as a biomarker of response to neoadjuvant immunochemotherapy in locally advanced gastric adenocarcinoma: insights from a prospective phase II trial.

Neoadjuvant immunochemotherapy (NAIC) markedly induces pathologic regression in locally advanced gastric adenocarcinoma (GAC). However, specific biomarkers are still lacking to effectively identify the beneficiary patients for NAIC. A prospective, single-arm, phase II study was conducted to treat locally advanced GAC with NAIC (NCT05515796). Correlation between clinicopathological characteristics and neoadjuvant efficacy was investigated. Bulk RNA-seq data from 104 samples (from 75 patients in two independent cohorts) and scRNA-seq data from 105 treatment-naïve GACs were comprehensively analyzed to decipher the association of epithelial and microenvironmental characteristics and clinical responses. The pre-specified primary endpoints were achieved: pathological complete regression (pathCR) rate was 30%, major pathological regression (MPR) rate was 43%, while the regimen was well tolerated. Analysis of baseline clinical-pathological parameters revealed the intestinal subtype of Lauren's classification as a key feature stratifying patients with increased sensitivity to NAIC. Mechanistically, an increased pool of DNA damage repair (DDR)-active cancer cells and enrichment of CLEC9A+ dendritic cells (DCs) in the tumor microenvironment were associated with enhanced responsiveness of the intestinal subtype GAC to NAIC. More importantly, an intestinal-subtype specific signature model was constructed by the machine learning algorithm NaiveBayes via integrating the transcriptomic features of both DDR-active cancer cells and CLEC9A+ DCs, which accurately predicted the efficacy of NAIC in multiple independent GAC cohorts. Intestinal subtype is a histological biomarker of enhanced sensitivity of GAC to NAIC. The Intestinal-subtype specific signature model is applicable to guide NAIC for patients with locally advanced GAC.

Comprehensive multi-omics integration uncovers mitochondrial gene signatures for prognosis and personalized therapy in lung adenocarcinoma

The therapeutic efficacy of lung adenocarcinoma (LUAD), the most prevalent histological subtype of primary lung cancer, remains inadequate, with accurate prognostic assessment posing significant challenges. This study sought to elucidate the prognostic significance of mitochondrial-related genes in LUAD through an integrative multi-omics approach, aimed at developing personalized therapeutic strategies. Utilizing transcriptomic and single-cell RNA sequencing (scRNA-seq) data, alongside clinical information from publicly available databases, we first applied dimensionality reduction and clustering techniques to the LUAD single-cell dataset, focusing on the subclassification of fibroblasts, epithelial cells, and T cells. Mitochondrial-related prognostic genes were subsequently identified using TCGA-LUAD data, and LUAD cases were stratified into distinct molecular subtypes through consensus clustering, allowing for the exploration of gene expression profiles and clinical feature distributions across subtypes. By leveraging an ensemble of machine learning algorithms, we developed an Artificial Intelligence-Derived Prognostic Signature (AIDPS) model based on mitochondrial-related genes and validated its prognostic accuracy across multiple independent datasets. The AIDPS model demonstrated robust predictive power for LUAD patient outcomes, revealing significant differences in responses to immunotherapy and chemotherapy, as well as survival outcomes between risk groups. Furthermore, we conducted comprehensive analyses of tumor mutation burden (TMB), immune microenvironment characteristics, and genome-wide association study (GWAS) data, providing additional insights into the mechanistic roles of mitochondrial-related genes in LUAD pathogenesis. This study not only offers a novel approach to improving prognostic assessments in LUAD but also establishes a strong foundation for the development of personalized therapeutic interventions.

A computed tomography‑based radio‑clinical model for the prediction of microvascular invasion in gastric cancer.

The objective of the present study was to build and validate a radio-clinical model integrating radiological features and clinical characteristics based on information available before surgery for prediction of microvascular invasion (MI) in gastric cancer. The retrospective study included a cohort of 534 patients (n=374 for the training set and n=160 for the test set) who were diagnosed with gastric cancer. All patients underwent contrast-enhanced computed tomography within one month before surgery. The focal area was mapped by ITK-SNAP. Radiomics features were extracted from portal venous phase CT images. Principal component analysis was used to reduce dimensionality, maximum relevance minimum redundancy, and least absolute shrinkage and selection operator to screen features most associated with MI. The radiomics signature was subsequently computed based on the coefficient weight assigned to it. The independent risk factors for MI of gastric cancer were determined using univariate analysis and multivariate logistic regression analysis. Univariate logistic regression analysis was used to assess the association between clinical characteristics and MI status. A radio-clinical model was constructed by employing multi-variable logistic regression analysis, incorporating radiomic features with clinical characteristics. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and calibration curves were employed for the analysis and evaluation of the model's performance. The radiomics signature model had moderate recognition ability, with an area under ROC curve (AUC) of 0.77 for the training set and 0.73 for the test set. The radio-clinical model, consisting of rad-score and clinical features, could well discriminate the training set and test set (AUC=0.88 and 0.80, respectively). The calibration curves and DCA further validated the favorable fit and clinical applicability of the radio-clinical model. In conclusion, the radio-clinical model combining the radiomics signature and clinical characteristics may be used to individually predict MI in gastric cancer to aid in the development of a clinical treatment strategy.

Identification of immune-related gene signature model for predicting lung cancer survival and response to immunotherapy.

Studies have shown that immune-related genes play a crucial role in tumor development and treatment. However, the specific roles and potential value of these genes in lung cancer patients are still not fully understood. Therefore, this study aims to establish a novel risk model based on immune-related genes for evaluating the prognostic risk and response to immune therapy in lung cancer patients. Gene expression and clinical data of lung cancer patients were retrieved from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, while immune-related genes were obtained from the ImmPort database. A risk signature model was developed using univariate Cox analysis and LASSO regression analysis. The prognostic value of the model and its response to immunotherapy were analyzed by survival analysis, immune infiltration analysis and immunotherapy response analysis. We have developed a risk signature model based on eight key immune-related genes, which can classify patients into high-risk and low-risk groups. The prognosis of the high-risk group was significantly lower than that of the low-risk group and was validated in multiple GEO datasets. The mutation frequency was lower in the low-risk group compared to the high-risk group (TP53: 55% vs 65%; TTN: 52% vs 60%; CSMD3: 34% vs 45%). Futhermore, CD274 expression was lower in the low-risk patients, and the high-risk patients in the IMvigor210 cohort had lower survival. Immune infiltration analyses showed that the high-risk group was negatively correlated with the infiltration level of B cells, CD4+ T cells, and NK cells. Importantly, patients in the low-risk group exhibit significantly lower TIDE scores, suggesting that they are more responsive to immunotherapy. Our study has established a novel and robust immune-related gene risk model that can assist in evaluating the prognostic risk and immune therapy response of lung cancer patients.

T cell exhaustion methylation signature drives differential immune responses in glioblastoma.

Methylation-related signatures play crucial roles in tumorigenesis and progression. However, their roles in the immune response in primary glioblastoma (GBM) remains unclear. We analyzed the differential expression of specific members of T cell exhaustion-related pathways in GBM from the perspective of T cell exhaustion. We further screened for significantly negatively correlated methylation sites as candidate methylation markers for T cell exhaustion. Using consensus clustering, we divided the samples into two categories with significant differences in overall survival (OS). We then performed univariate and multivariate Cox regression analyses to construct the T Cell Exhaustion Methylation (TEXM) signature. Finally, we confirmed that this signature served as an independent prognostic factor, and further characterized it in terms of drug resistance and immunotherapy. We identified 95 significantly differentially expressed T cell exhaustion-related genes and 51 methylation markers associated with T cell exhaustion. The cancer samples were classified according to methylation site markers, thus indicating two subtypes with significant differences in OS: subtype A and subtype B. Tumor scores, stromal scores, tumor purity, and ESTIMATE scores all showed significant differences between subtypes (P < 0.05). Univariate Cox regression analysis identified five methylation sites significantly associated with OS, and multivariate Cox regression analysis was used to construct the TEXM signature model by using these five methylation sites. Significant differences in OS were found between the groups with high and low TEXM signature scores, on the basis of calculation of the TEXM signature scores of tumor samples and using the median score to divide them into high and low score groups. Survival analysis revealed that the high score group had poorer OS and DFS than the low score group in the validation set. Notably, we observed a significant difference in drug sensitivity between the high and low TEXM signature score groups, with the high score group showing higher drug resistance and poorer prognosis. The tumor immune state, as predicted with Tracking Tumor Immunophenotype (TIP), revealed significant differences in antitumor immune scores between the high and low TEXM signature score groups. Finally, we identified 43 significantly differentially regulated metabolism-associated biological processes. The epigenetic methylation-related TEXM signature plays a key role in driving differential immune responses in GBM.

Identification of an immune-related genes signature in lung adenocarcinoma to predict survival and response to immune checkpoint inhibitors

BackgroundAlthough advances in immune checkpoint inhibitor (ICI) research have provided a new treatment approach for lung adenocarcinoma (LUAD) patients, their survival is still unsatisfactory, and there are issues in the era of response prediction to immunotherapy.MethodsUsing bioinformatics methods, a prognostic signature was constructed, and its predictive ability was validated both in the internal and external datasets (GSE68465). We also explored the tumor-infiltrating immune cells, mutation profiles, and immunophenoscore (IPS) in the low-and high-risk groups.ResultsAs far as we are aware, this is the first study which introduces a novel prognostic signature model using BIRC5, CBLC, S100P, SHC3, ANOS1, VIPR1, LGR4, PGC, and IGKV4.1. According to multivariate analysis, the 9-immune-related genes (IRGs) signature provided an independent prognostic factor for the overall survival (OS). The low-risk group had better OS, and the tumor mutation burden (TMB) was significantly lower in this group. Moreover, the risk scores were negatively associated with the tumor-infiltrating immune cells, like CD8+ T cells, macrophages, dendritic cells, and NK cells. In addition, the IPS were significantly higher in the low-risk group as they had higher gene expression of immune checkpoints, suggesting that ICIs could be a promising treatment option for low-risk LUAD patients.ConclusionThe combination of these 9-IRGs not only could efficiently predict overall survival of LUAD patients but also show a powerful association with the expression of immune checkpoints and response to ICIs based on IPS; hoping this model paves the way for better stratification and management of patients in clinical practice.

Bulk integrated single-cell-spatial transcriptomics reveals the impact of preoperative chemotherapy on cancer-associated fibroblasts and tumor cells in colorectal cancer, and construction of related predictive models using machine learning

BackgroundPreoperative chemotherapy (PC) is an important component of Colorectal cancer (CRC) treatment, but its effects on the biological functions of fibroblasts and epithelial cells in CRC are unclear. MethodsThis study utilized bulk, single-cell, and spatial transcriptomic sequencing data from 22 independent cohorts of CRC. Through bioinformatics analysis and in vitro experiments, the research investigated the impact of PC on fibroblast and epithelial cells in CRC. Subpopulations associated with PC and CRC prognosis were identified, and a predictive model was constructed using machine learning. ResultsPC significantly attenuated the pathways related to tumor progression in fibroblasts and epithelial cells. NOTCH3 + Fibroblast (NOTCH3 + Fib), TNNT1 + Epithelial (TNNT1 + Epi), and HSPA1A + Epithelial (HSPA1A + Epi) subpopulations were identified in the adjacent spatial region and were associated with poor prognosis in CRC. PC effectively diminished the presence of these subpopulations, concurrently inhibiting pathway activity and intercellular crosstalk. A risk signature model, named the Preoperative Chemotherapy Risk Signature Model (PCRSM), was constructed using machine learning. PCRSM emerged as an independent prognostic indicator for CRC, impacting both overall survival (OS) and recurrence-free survival (RFS), surpassing the performance of 89 previously published CRC risk signatures. Additionally, patients with a high PCRSM risk score showed sensitivity to fluorouracil-based adjuvant chemotherapy (FOLFOX) but resistance to single chemotherapy drugs (such as Bevacizumab and Oxaliplatin). Furthermore, this study predicted that patients with high PCRSM were resistant to anti-PD1therapy. ConclusionIn conclusion, this study identified three cell subpopulations (NOTCH3 + Fib, TNNT1 + Epi, and HSPA1A + Epi) associated with PC, which can be targeted to improve the prognosis of CRC patients. The PCRSM model shows promise in enhancing the survival and treatment of CRC patients.

Development of biomarker signatures associated with anoikis to predict prognosis in patients with esophageal cancer: An observational study.

Anoikis, a form of programmed cell death linked to cancer, has garnered significant research attention. Esophageal cancer (ESCA) ranks among the most prevalent malignant tumors and represents a major global health concern. To ascertain whether anoikis-related genes (ARGs) can accurately predict ESCA prognosis, we evaluated the predictive value and molecular mechanisms of ARGs in ESCA and constructed an optimal model for prognostic prediction. Using the Cancer Genome Atlas (TCGA)-ESCA database, we identified ARGs with differences in ESCA. ARG signatures were generated using Cox regression. A predictive nomogram model was developed to forecast ARG signatures and patient outcomes in ESCA. Gene set enrichment analysis (GSEA) was employed to uncover potential biological pathways associated with ARG signatures. Estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) and cell-type identification by estimating relative subsets of RNA transcripts analyses were used to assess differences in the immune microenvironment of the ARG signature model. Based on ARGs, the patients with ESCA were divided into high and low groups, and the sensitivity of patients to drugs in the database of genomics of drug sensitivity in cancer was analyzed. Finally, the correlation between drug sensitivity and risk score was then evaluated based on the ARG signatures. Prognostic relevance was significantly linked to the ARG profiles of 5 genes: MYB binding protein 1a (MYBBP1A), plasminogen activator, urokinase (PLAU), budding uninhibited by benzimidazoles 3, HOX transcript antisense RNA, and euchromatic histone-lysine methyltransferase 2 (EHMT2). Using the risk score as an independent prognostic factor combined with clinicopathological features, the nomogram accurately predicted the overall survival (OS) of individual patients with ESCA. Gene ontology (GO) enrichment analysis indicated that the primary molecular roles included histone methyltransferase function, binding to C2H2 zinc finger domains, and histone-lysine N-methyltransferase activity. GSEA revealed that the high-risk cohort was connected to cytokine-cytokine receptor interaction, graft-versus-host disease, and hematopoietic cell lineage, whereas the low-risk cohort was related to arachidonic acid metabolism, drug metabolism via cytochrome P450 and fatty acid metabolism. Drug sensitivity tests showed that 16 drugs were positively correlated, and 3 drugs were negatively correlated with ARG characteristic scores. Our study developed 5 ARG signatures as biomarkers for patients with ESCA, providing an important reference for the individualized treatment of this disease.

Deciphering the prognostic and therapeutic significance of BAG1 and BAG2 for predicting distinct survival outcome and effects on liposarcoma

Liposarcoma (LPS) is the second most common kind of soft tissue sarcoma, and a heterogeneous malignant tumor derived from adipose tissue. Up to now, the prognostic value of BAG1 or BAG2 in LPS has not been defined yet. Expression profiling data of LPS patients were collected from TCGA and GEO database. Survival curves were plotted to verify the outcome differences of patients based on BAG1 or BAG2 expression. Univariate and multivariate Cox regression models were used to analyze the prognostic ability of BAG1 or BAG2. Chaperone’s regulators BAG1 and BAG2 were identified as prognostic biomarkers for LPS patients, which exhibited distinct expression patterns and survival outcome prediction performances. Patients with high BAG2 expression and/or low BAG1 expression had worse prognosis. Enrichment analysis showed that BAG1 was involved in negative regulation of TGF-β signaling. Low expression of BAG1 was associated with high abundance of regulatory T cells (Tregs). The 2-gene signature model further confirmed the improved risk assessment performance of BAG1 and BAG2: high risk patients displayed poor prognosis. BAG1 and BAG2 are supposed to be potential prognostic biomarkers for LPS and have impacts on liposarcomagenesis and immune infiltration in distinctive manners, which may function as potential therapy targets (BAG1 agonists/BAG2 inhibitors) for LPS.

Identification and validation of a novel innate lymphoid cell-based signature to predict prognosis and immune response in liver cancer by integrated single-cell RNA analysis and bulk RNA sequencing.

Innate lymphoid cells (ILCs) exert tumor suppressive and tumor promoting effects. However, the prognostic significance of ILC-associated genes remains unclear in hepatocellular carcinoma (HCC). Hence, the aim of this research was to develop an innovative predictive risk classification system using bioinformatics examination. We explored the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases to gather data pertaining to HCC and its clinical details. Significantly different ILC-associated genes were investigated by Seurat analysis. The number of signaling interactions of ILCs with other cells was discovered by CellPhoneDB analysis. ClusterProfiler and Metascape were utilized to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on ILC genes. In order to identify potential ILC predictors, we utilized univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses, subsequently validating these predictors in TCGA and GEO groups. The multi-omics ILC signature model's clinical predictive capabilities, along with drug sensitivity and immune factor relations, were assessed using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and pRRophetic. We investigated the possible molecular pathways in our predictive ILC signature through the utilization of gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). Five key genes were screened out by constructing a competing endogenous RNA (ceRNA) network using Cytoscape and their values in clinical indexes were demonstrated. Immunohistochemistry (IHC) in HCC cases confirmed the expression of these genes. ILC cell subsets were identified, and cell-cell communication analysis revealed that the signaling pathways involving ILC cell subsets were mostly abundant in the HCC microenvironment. Subsequently, 270 marker genes of the ILC clusters were subjected to GO and KEGG enrichment analysis. Furthermore, a total of 58 prognostically relevant genes were screened as features for prognostic prediction models. Next, the models were validated and clinically evaluated (P values of Kaplan-Meier survival curves below 0.05). Five key genes (C2, STK4, CALM1, IL7R, and RORA) were further screened by multi omics analysis of immune cell and factor and drug sensitivity and correlation analysis of tumor regulatory genes in liver cancer. Furthermore, the potential clinical value of the 5 key genes was confirmed in HCC patients. Finally, the IHC results confirmed the expression of C2, STK4, CALM1, IL7R, and RORA in HCC. Our experimental results provided preliminary evidence supporting the oncogenic roles of STK4 and CALM1, as well as the tumor-suppressive roles of C2, RORA, and IL7R in HCC. A novel prognostic feature of ILC potentially involved in HCC was discovered. It showed high values in predicting patient overall survival (OS) as well as good differences in immunity and drug sensitivity. Therefore, targeting these ILC signatures may be a potential effective approach in HCC treatment.

Construction and validation of prognostic model for colorectal mucinous adenocarcinoma patients and identification of a new prognosis related gene FAM174B.

Mucinous adenocarcinoma (MAC) is a peculiar histological subtype of colorectal cancer (CRC) with distinct medical, disease-related, and genetic characteristics. The prognosis of MAC is generally poorer less favorable compared to non-specific adenocarcinoma (AC), but the prognostic indicator of MAC is rare. Therefore, this study aims to identify potential biomarkers and construct a prognostic model to better predict patient outcomes in MAC. We conducted differential genes expression investigation, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO)-Cox regression model using RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) to pinpoint hub genes. Then, the hub genes were used to construct a prognostic model for MAC. Kaplan-Meier survival, receiver operating characteristic (ROC), and Cox regression analysis were used to assess the prognostic utility of the model. The potential biological function of the hub gene was examined using gene set enrichment analysis (GSEA). Four hub genes, FAM174B, CREB3L1, SPDEF, and RAP1GAP, were identified between MAC and AC by differential genes expression analysis, WGCNA, and LASSO regression analysis. The prognostic signature model was constructed based on these four hub genes, which could divide MAC into low- and high-risk groups. The overall survival (OS) was notably lower in the high-risk group compared to the low-risk group (P=0.007). The area under the curves (AUCs) for 1-, 3-, and 5-year OS were 0.61 [95% confidence interval (CI): 0.73-0.49], 0.69 (95% CI: 0.76-0.63), and 0.77 (95% CI: 0.83-0.71), respectively. We also found that FAM174B expression was closely related to the OS of MAC (P=0.02). Further, the expression of FAM174B was positively correlated with MAC's Mucin type O-glycan biosynthesis. Finally, it was indicated that FAM174B was positively correlated with the critical molecules of mucus formation, MUC5AC (P=0.004, r=0.33), MUC5B (P<0.001, r=0.43), and MUC2 (P<0.001, r=0.39). We have developed and validated a four-gene prognostic model to predict the survival of MAC. Additionally, we found that FAM174B might correlate with mucin production in MAC.

Ultrasound-based Radiomics Analysis for Assessing Risk Factors Associated With Early Recurrence Following Surgical Resection of Hepatocellular Carcinoma

ObjectiveThe aim of this study was to explore the value of ultrasound-based radiomics analysis for early recurrence (ER) after surgical resection of hepatocellular carcinoma (HCC). MethodsThis retrospective study included 127 patients who underwent primary surgical resection for HCC between October 2019 and November 2021. The patients were subsequently divided into training and validation sets (7:3 ratio). All patients received preoperative routine ultrasound and contrast-enhanced ultrasound (CEUS), with postoperative pathological confirmation of HCC. Radiomics features were extracted from maximum section of two-dimensional ultrasound image. The least absolute shrinkage and selection operation (LASSO) logistic regression algorithm with 10-fold cross-validation was employed to establish ultrasonic radiomics features. Logistic regression modeling was employed to build models based on clinical and ultrasonic features (model 1, clinical-ultrasonic model), radiomics signature (model 2, ultrasonic radiomics model), and the combination (model 3, clinical-ultrasonic-radiomics model). Then a nomogram model was established to predict the risk of ER, and the application value of nomogram through internal verification was evaluated. ResultsModel 3 showed optimal diagnostic performance in both training set (AUC=0.907) and validation set (AUC=0.925), followed by the model 1 in training set (AUC=0.846) and validation set (AUC=0.855), both above two models performed better than model 2 in training set (AUC=0.751) and validation set (AUC=0.702) (p<0.05). In training set and validation set of model 3, the sensitivity were 83.3%, 77.8%, the specificity ware 95.8%, 100.0% and the C-index were 0.791, 0.778. ConclusionThe preoperative clinical-ultrasonic-radiomics model is anticipated to be a reliable tool for predicting the ER of surgical resection of HCC.

Bioinformatics approaches for studying molecular sex differences in complex diseases.

Many complex diseases exhibit pronounced sex differences that can affect both the initial risk of developing the disease, as well as clinical disease symptoms, molecular manifestations, disease progression, and the risk of developing comorbidities. Despite this, computational studies of molecular data for complex diseases often treat sex as a confounding variable, aiming to filter out sex-specific effects rather than attempting to interpret them. A more systematic, in-depth exploration of sex-specific disease mechanisms could significantly improve our understanding of pathological and protective processes with sex-dependent profiles. This survey discusses dedicated bioinformatics approaches for the study of molecular sex differences in complex diseases. It highlights that, beyond classical statistical methods, approaches are needed that integrate prior knowledge of relevant hormone signaling interactions, gene regulatory networks, and sex linkage of genes to provide a mechanistic interpretation of sex-dependent alterations in disease. The review examines and compares the advantages, pitfalls and limitations of various conventional statistical and systems-level mechanistic analyses for this purpose, including tailored pathway and network analysis techniques. Overall, this survey highlights the potential of specialized bioinformatics techniques to systematically investigate molecular sex differences in complex diseases, to inform biomarker signature modeling, and to guide more personalized treatment approaches.

A novel computational pathology approach for identifying gene signatures prognostic of disease-free survival for papillary thyroid carcinomas

IntroductionPapillary thyroid carcinoma (PTC) is the most prevalent form of thyroid cancer, with the classical and follicular variants representing most cases. Despite generally favorable prognoses, approximately 10% of patients experience recurrence post-surgery and radioactive iodine therapy. Attempts to stratify risk of recurrence have relied on gene expression-based prognostic and predictive signatures with a focus on mutations of well-known driver genes, while hallmarks of tumor morphology have been ignored. ObjectivesWe introduce a new computational pathology approach to develop prognostic gene signatures for PTC that is informed by quantitative features of tumor and immune cell morphology. MethodsWe quantified nuclear and immune-related features of tumor morphology to develop a pathomic signature, which was then used to inform an RNA-expression signature model provides a notable advancement in risk stratification compared to both standalone and pathology-informed gene-expression signatures. ResultsThere was a 17.8% improvement in the C-index (from 0.605 to 0.783) for 123 cPTCs and 15% (from 0.576 to 0.726) for 38 fvPTCs compared to the standalone gene-expression signature. Hazard ratios also improved for cPTCs from 0.89 (0.67,0.99) to 4.43 (3.65,6.68) and fvPTC from 0.98 (0.76,1.32) to 2.28 (1.87,3.64). We validated the image-based risk model on an independent cohort of 32 cPTCs with hazard ratio 1.8 (1.534,2.167).

Prognostic signature and immunotherapeutic relevance of Focal adhesion signaling pathway-related genes in osteosarcoma

BackgroundAs the most common primary malignant bone tumor in children and adolescents, osteosarcoma currently lacks an effective clinical cure. Focal adhesion plays a crucial role in tumor invasion, migration, and drug resistance by mediating communication between the extracellular matrix and tumor cells. This study investigated the prognostic features and immunotherapeutic relevance of focal adhesion pathway-related genes in osteosarcoma to aid in the development of new therapeutic options. MethodsWe obtained mutational, transcriptomic, gene expression, and clinical data of osteosarcoma patients from the Gene Expression Omnibus (GEO) and Therapeutically Applicable Research to Generate Effective (TARGET) databases. Differentially expressed genes were screened, followed by the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. Kaplan-Meier survival analysis was performed for genes related to the focal adhesion pathway, and multivariate Cox regression analysis was employed to construct a prognostic signature model. Genes such as SIGLEC15, TIGIT, CD274, HAVCR2, PDCD1, CTLA4, and LAG3 were extracted from the TARGET and CCLE databases for osteosarcoma patients and osteosarcoma cell lines, respectively，to observe the expression of immune checkpoint-related genes. Finally, qRT-PCR was used to verify the expression of these immune checkpoint-related genes in osteosarcoma cell lines. ResultsIn our study, 376 samples were analyzed, including 369 osteosarcoma samples and 7 normal tissue samples. We identified 50 up-regulated and 28 down-regulated differentially expressed genes. Among these, 10 Candidate genes relative to focal Adhesion were selected， and CAV1, ZYX, and ITGA5 were found to have a significant prognostic role based on survival analysis of osteosarcoma samples from the TARGET database. A predictive signature model related to the focal adhesion signaling pathway was constructed using these genes, and the AUCs of the 1-year, 3-year, and 5-year ROC curves were 0. 647, 0. 712, and 0. 717, respectively. The overall survival (OS) rate of osteosarcoma patients with high-risk scores was poorer than those with low-risk scores. Then, samples were divided into two subgroups based on the expression of the three genes, revealing significant differences in the expression of certain immune checkpoint-related genes between the subgroups. Additionally, above three genes and immune checkpoint-related genes in osteosarcoma cell lines were extracted from the CCLE database, showing high expression levels in eight osteosarcoma cell lines. We observed that CD274 and PDCD1LG2 were highly expressed in some osteosarcoma cell lines. Finally, the expression of CAV1, ZYX, ITGA5, CD80, CD274, and PDCD1LG2 in osteosarcoma cell lines was verified by qRT-PCR. ConclusionsOur study validated the prognostic role of three focal adhesion pathway-related genes (ZYX, CAV1, and ITGA5) in patients with osteosarcoma and constructed a prognostic signature model associated with the focal adhesion signaling pathway. We identified significant differences in the expression of multiple immune checkpoint-related genes among subgroups defined by the three genes. Additionally, CD274 and PDCD1LG2 showed higher expression in osteosarcoma cell lines characterized by these genes. These findings may aid in the selection of effective immunotherapy for specific osteosarcoma patients.

Tumor microenvironment biomarkers predicting pathological response to neoadjuvant chemoimmunotherapy in locally advanced esophageal squamous cell carcinoma: post-hoc analysis of a single center, phase 2 study

BackgroundNeoadjuvant chemoimmunotherapy has a promising effect on locally advanced esophageal squamous cell carcinoma (ESCC). However, reliable biomarkers robustly predicting therapeutic response are still lacking.MethodsFormalin-fixed and paraffin-embedded pre-neoadjuvant chemoimmunotherapy biopsy samples...

Machine learning developed a macrophage signature for predicting prognosis, immune infiltration and immunotherapy features in head and neck squamous cell carcinoma

Macrophages played an important role in the progression and treatment of head and neck squamous cell carcinoma (HNSCC). We employed weighted gene co-expression network analysis (WGCNA) to identify macrophage-related genes (MRGs) and classify patients with HNSCC into two distinct subtypes. A macrophage-related risk signature (MRS) model, comprising nine genes: IGF2BP2, PPP1R14C, SLC7A5, KRT9, RAC2, NTN4, CTLA4, APOC1, and CYP27A1, was formulated by integrating 101 machine learning algorithm combinations. We observed lower overall survival (OS) in the high-risk group and the high-risk group showed elevated expression levels in most of the immune checkpoint and human leukocyte antigen (HLA) genes, suggesting a strong immune evasion capacity. Correspondingly, TIDE score positively correlated with risk score, implying that high-risk tumors may resist immunotherapy more effectively. At the single-cell level, we noted macrophages in the tumor microenvironment (TME) predominantly stalled in the G2/M phase, potentially hindering epithelial-mesenchymal transition and playing a crucial role in the inhibition of tumor progression. Finally, the proliferation and migration abilities of HNSCC cells significantly decreased after the expression of IGF2BP2 and SLC7A5 reduced. It also decreased migration ability of macrophages and facilitated their polarization towards the M1 direction. Our study constructed a novel MRS for HNSCC, which could serve as an indicator for predicting the prognosis, immune infiltration and immunotherapy for HNSCC patients.

Multiomics and machine learning-based analysis of pancancer pseudouridine modifications

Pseudouridine widely affects the stability and function of RNA. However, our knowledge of pseudouridine properties in tumors is incomplete. We systematically analyzed pseudouridine synthases (PUSs) expression, genomic aberrations, and prognostic features in 10907 samples from 33 tumors. We found that the pseudouridine-associated pathway was abnormal in tumors and affected patient prognosis. Dysregulation of the PUSs expression pattern may arise from copy number variation (CNV) mutations and aberrant DNA methylation. Functional enrichment analyses determined that the PUSs expression was closely associated with the MYC, E2F, and MTORC1 signaling pathways. In addition, PUSs are involved in the remodeling of the tumor microenvironment (TME) in solid tumors, such as kidney and lung cancers. Particularly in lung cancer, increased expression of PUSs is accompanied by increased immune checkpoint expression and Treg infiltration. The best signature model based on more than 112 machine learning combinations had good prognostic ability in ACC, DLBC, GBM, KICH, MESO, THYM, TGCT, and PRAD tumors, and is expected to guide immunotherapy for 19 tumor types. The model was also effective in identifying patients with tumors amenable to etoposide, camptothecin, cisplatin, or bexarotene treatment. In conclusion, our work highlights the dysregulated features of PUSs and their role in the TME and patient prognosis, providing an initial molecular basis for future exploration of pseudouridine. Studies targeting pseudouridine are expected to lead to the development of potential diagnostic strategies and the evaluation and improvement of antitumor therapies.

Identification of RBM15 as a prognostic biomarker in prostate cancer involving the regulation of prognostic m6A-related lncRNAs

BackgroundLong noncoding RNAs (lncRNAs) and N6-methyladenosine (m6A) modification of RNA play pivotal roles in tumorigenesis and cancer progression. However, knowledge regarding the expression patterns of m6A-related lncRNAs and their corresponding m6A regulators in prostate cancer (PCa) is limited. This study aimed to delineate the landscape of m6A-related lncRNAs, develop a predictive model, and identify the critical m6A regulators of prognostic lncRNAs in PCa.MethodsClinical and transcriptome data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA) database. Prognostic m6A-related lncRNAs were subsequently identified through Pearson correlation and univariate Cox regression analyses. The prognostic lncRNAs were clustered into two groups by consensus clustering analysis, and a risk signature model was constructed using least absolute shrinkage and selection operator (LASSO) regression analysis of the lncRNAs. This model was evaluated using survival, clinicopathological, and immunological analyses. Furthermore, based on the constructed lncRNA-m6A regulatory network and RT-qPCR results, RBM15 was identified as a critical regulator of m6A-related lncRNAs. The biological roles of RBM15 in PCa were explored through bioinformatics analysis and biological experiments.ResultsThirty-four prognostic m6A-related lncRNAs were identified and categorized into two clusters with different expression patterns and survival outcomes in PCa patients. Seven m6A lncRNAs (AC105345.1, AL354989.1, AC138028.4, AC022211.1, AC020558.2, AC004076.2, and LINC02666) were selected to construct a risk signature with robust predictive ability for overall survival and were correlated with clinicopathological characteristics and the immune microenvironment of PCa patients. Among them, LINC02666 and AC022211.1 were regulated by RBM15. In addition, RBM15 expression correlated with PCa progression, survival, and the immune response. Patients with elevated RBM15 expression were more susceptible to the drug AMG-232. Moreover, silencing RBM15 decreased the viability of PCa cells and promoted apoptosis.ConclusionRBM15 is involved in the regulation of prognostic lncRNAs in the risk signature and has a robust predictive ability for PCa, making it a promising biomarker in PCa.

Development and validation of a hypoxia- and mitochondrial dysfunction- related prognostic model based on integrated single-cell and bulk RNA sequencing analyses in gastric cancer.

Gastric cancer (GC) remains a major global health threat ranking as the fifth most prevalent cancer. Hypoxia, a characteristic feature of solid tumors, significantly contributes to the malignant progression of GC. Mitochondria are the major target of hypoxic injury that promotes mitochondrial dysfunction during the development of cancers including GC. However, the gene signature and prognostic model based on hypoxia- and mitochondrial dysfunction-related genes (HMDRGs) in the prediction of GC prognosis have not yet been established. The gene expression profile datasets of stomach cancer patients were retrieved from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Prognostic genes were selected using Least Absolute Shrinkage andSelection Operator Cox (LASSO-Cox) regression analysis to construct a prognostic model. Immune infiltration was evaluated through ESTIMATE, CIBERSORT, and ssGSEA analyses. Tumor immune dysfunction and exclusion (TIDE) and immunophenoscore (IPS) were utilized to explore implications for immunotherapy. Furthermore, in vitro experiments were conducted to validate the functional roles of HMDRGs in GC cell malignancy. In this study, five HMDRGs (ZFP36, SERPINE1, DUSP1, CAV1, and AKAP12) were identified for developing a prognostic model in GC. This model stratifies GC patients into high- and low-risk groups based on median risk scores. A nomogram predicting overall survival (OS) was constructed and showed consistent results with observed OS. Immune infiltration analysis indicated that individuals in the high-risk group tend to exhibit increased immune cell infiltration. Additionally, analysis of cancer immunotherapy responses revealed that high-risk group patients exhibit poorer responses to cancer immunotherapy compared to the low-risk group. Immunohistochemistry (IHC) staining indicated that the expression levels of HMDRGs were remarkably correlated with GC, of which, SERPINE1 displayed the most pronounced up-regulation, while ZFP36 exhibited the most notable down-regulation in GC patients. Furthermore, in vitro investigation validated that SERPINE1 and ZFP36 contribute to the malignant processes of GC cells correlated with mitochondrial dysfunction. This study presents a novel and efficient approach to evaluate GC prognosis and immunotherapy efficacy, and also provides insights into understanding the pathogenesis of GC.