A novel artificial intelligence network to assess the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized gastrointestinal cancer treatment, yet the absence of reliable biomarkers hampers precise patient response prediction. Methods: We developed and validated a genomic mutation signature (GMS) employing a novel artificial intelligence network to forecast the prognosis of gastrointestinal cancer patients undergoing ICIs therapy. Subsequently, we explored the underlying immune landscapes across different subtypes using multiomics data. Finally, UMI-77 was pinpointed through the analysis of drug sensitization data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. The sensitivity of UMI-77 to the AGS and MKN45 cell lines was evaluated using the cell counting kit-8 (CCK8) assay and the plate clone formation assay. Results: Using the artificial intelligence network, we developed the GMS that independently predicts the prognosis of gastrointestinal cancer patients. The GMS demonstrated consistent performance across three public cohorts and exhibited high sensitivity and specificity for 6, 12, and 24-month overall survival (OS) in receiver operating characteristic (ROC) curve analysis. It outperformed conventional clinical and molecular features. Low-risk samples showed a higher presence of cytolytic immune cells and enhanced immunogenic potential compared to high-risk samples. Additionally, we identified the small molecule compound UMI-77. The half-maximal inhibitory concentration (IC50) of UMI-77 was inversely related to the GMS. Notably, the AGS cell line, classified as high-risk, displayed greater sensitivity to UMI-77, whereas the MKN45 cell line, classified as low-risk, showed less sensitivity. Conclusion: The GMS developed here can reliably predict survival benefit for gastrointestinal cancer patients on ICIs therapy.

Single-cell immune landscape of measurable residual disease in acute myeloid leukemia.

Measurable residual disease (MRD) is a powerful prognostic factor of relapse in acute myeloid leukemia (AML). We applied the single-cell RNA sequencing to bone marrow (BM) samples from patients with (n=20) and without (n=12) MRD after allogeneic hematopoietic stem cell transplantation. A comprehensive immune landscape with 184,231 cells was created. Compared with CD8+ T cells enriched in the MRD-negative group (MRD-_CD8), those enriched in the MRD-positive group (MRD+_CD8) showed lower expression levels of cytotoxicity-related genes. Three monocyte clusters (i.e., MRD+_M) and three B-cell clusters (i.e., MRD+_B) were enriched in the MRD-positive group. Conversion from an MRD-positive state to an MRD-negative state was accompanied by an increase in MRD-_CD8 clusters and vice versa. MRD-enriched cell clusters employed the macrophage migration inhibitory factor pathway to regulate MRD-_CD8 clusters. These findings revealed the characteristics of the immune cell landscape in MRD positivity, which will allow for a better understanding of the immune mechanisms for MRD conversion.

The cross-talk between the macro and micro-environment in precursor lesions of pancreatic cancer leads to new and promising circulating biomarkers.

Pancreatic cancer (PC) is a clinically challenging tumor to combat due to its advanced stage at diagnosis as well as its resistance to currently available therapies. The absence of early symptoms and known detectable biomarkers renders this disease incredibly difficult to detect/manage. Recent advances in the understanding of PC biology have highlighted the importance of cancer-immune cell interactions, not only in the tumor micro-environment but also in distant systemic sites, like the bone marrow, spleen and circulating immune cells, the so-called macro-environment. The response of the macro-environment is emerging as a determining factor in tumor development by contributing to the formation of an increasingly immunogenic micro-environment promoting tumor homeostasis and progression. We will summarize the key events associated with the feedback loop between the tumor immune micro-environment (TIME) and the tumor immune macroenvironment (TIMaE) in pancreatic precancerous lesions along with how it regulates disease development and progression. In addition, liquid biopsy biomarkers capable of diagnosing PC at an early stage of onset will also be discussed. A clearer understanding of the early crosstalk between micro-environment and macro-environment could contribute to identifying new molecular therapeutic targets and biomarkers, consequently improving early PC diagnosis and treatment.

Immune landscape of renal cell carcinoma with metastasis to the pancreas.

INTRODUCTION: Clear cell Renal Cell Carcinoma (ccRCC) has a poor prognosis once metastatic. However, certain metastatic sites have been reported to have a different impact on the patient prognosis. For example, patients with pancreatic metastases have a much more favorable prognosis than those with metastases to other organs. The biological basis for this observation remains poorly understood. The aim of this study was to characterize the immune landscape of pancreatic metastases and the corresponding primary tumors in order to identify possible immunological features that correlate with disease biology. PATIENTS AND METHODS: A detailed assessment of immune cell populations was performed using a total of 1,700 microscopic images from ccRCCs from 11 patients, their corresponding pancreatic metastases and ccRCCs from 10 patients without pancreatic metastases. Tumor specimens were stained for CD45, CD8, CD163 and FOXP3 and the densities of the respective immune cells were assessed semiquantitatively in the intratumoral and extratumoral compartment. Multispectral imaging was performed in selected tumors. RESULTS: We found that pancreatic metastases show the lowest intratumoral infiltration with CD8+ cytotoxic T lymphocytes of all tumor specimens analyzed. The frequency of CD8+ lymphocytes was on 1.9 fold lower in pancreatic metastases (median density 8.3 cells per field of view [FOV] = 1.23 mm2) when compared to the corresponding primary tumor (15.6 cells per FOV, P = 0.0002) and more than 3-fold lower when compared to ccRCCs without pancreatic metastases (27.2 cells per FOV, P = 0.0012). There was also a significantly reduced intratumoral infiltration with immunosuppressive FOXP3+ lymphocytes in pancreatic metastases (2.6 cells per FOV, P = 0.009) and corresponding primary tumors (2 cells per FOV, P = 0.028) when compared to ccRCCs without pancreatic metastases (5.6 cells per FOV). CONCLUSIONS: In this proof-of-concept study, we show that pancreatic metastases of ccRCC present with unique immunological features including a low intratumoral density of CD8+ and FOXP3+ lymphocytes. The low counts of CD8+ and FOXP3+ lymphocytes may reflect less aggressive features of ccRCC with pancreatic metastasis that may result in a more favorable patient prognosis.

Understanding the heterogeneity in liver hepatocellular carcinoma with a special focus on malignant cell through single-cell analysis.

INTRODUCTION: Hepatocellular carcinoma (HCC) is the most common form of liver cancer globally and remains a major cause of cancer-related deaths. HCC exhibits significant intra-tumoral and interpatient heterogeneity, impacting treatment efficacy and patient prognosis. METHODS: We acquired transcriptome data from the TCGA and ICGC databases, as well as liver cancer chip data from the GEO database, and processed the data for subsequent analysis. We also obtained single cell data from the GEO database and performed data analysis using the Seurat package. To further investigate epithelial cell subgroups and their copy number variations, we used the Seurat workflow for subgroup classification and the InferCNV software for CNV analysis, utilizing endothelial cells as a reference. Pseudo-time analysis and transcription factor analysis of epithelial cells were performed using the monocle2 and SCENIC software, respectively. To assess intercellular communication, we employed the CellChat package to identify potential ligand-receptor interactions. We also analyzed gene expression differences and conducted enrichment analysis using the limma and clusterProfiler packages. Additionally, we established tumor-related risk characteristics using Cox analysis and Lasso regression, and predicted immunotherapy response using various datasets. RESULTS: The samples were classified into 23 clusters, with malignant epithelial cells being the majority. Trajectory analysis revealed the differentiation states of the malignant epithelial cells, with cluster 1 being in the terminal state. Functional analysis revealed higher aggressiveness and epithelial-mesenchymal transition (EMT) scores in cluster 1, indicating a higher propensity for metastasis. RBP4+ tumor cells were highly enriched with hypoxia process and intensive cell-to-cell communication. A prognostic model was established, and immune infiltration analysis showed increased infiltration in the high-risk group. TP53 demonstrated significant differences in mutation rate between the two risk groups. Validation analysis confirmed the up-regulation of model genes, including AKR1B10, ARL6IP4, ATP6V0B, and BSG in tumor tissues. CONCLUSION: A prognostic model was established based on HCC malignant cell associated gene signature, displaying decent prognosis guiding effectiveness in the multiple cohorts. The study provided comprehensive insights into the heterogeneity and potential therapeutic targets of LIHC.

A novel mitochondrial-related lncRNA signature mediated prediction of overall survival, immune landscape, and the chemotherapeutic outcomes for bladder cancer patients.

OBJECTIVE: To develop a prognostic risk model for Bladder Cancer (BLCA) based on mitochondrial-related long non-coding RNAs (lncRNAs). METHODS: Transcriptome and clinical data of BLCA patients were retrieved from the TCGA database. Mitochondrial-related lncRNAs with independent prognostic significance were screened to develop a prognostic risk model. Patients were categorized into high- and low-risk groups using the model. Various methods including Kaplan-Meier (KM) analysis, ROC curve analysis, Gene Set Enrichment Analysis (GSEA), immune analysis, and chemotherapy drug analysis were used to verify and evaluate the model. RESULTS: A mitochondrial-associated lncRNA prognostic risk model with independent prognostic significance was developed. High-risk group (HRG) patients exhibited significantly shorter survival periods compared to low-risk group (LRG) patients (P < 0.01). The risk score from the model was an independent predictor of BLCA prognosis, correlating with tumor grade, pathological stage, and lymph node metastasis (P < 0.05). The HRG showed significant positive correlations with high expressions of immune checkpoints (CTLA4, LAG3, PD-1, TIGIT, PD-L1, PD-L2, and TIM-3) and lower IC50 for chemotherapy drugs (cisplatin, docetaxel, paclitaxel, methotrexate, and vinblastine) (P < 0.001). CONCLUSIONS: The mitochondrial-related lncRNA-based prognostic risk model effectively predicts BLCA prognosis and can guide individualized treatment for BLCA patients.

A predictive radiotranscriptomics model based on DCE-MRI for tumor immune landscape and immunotherapy in cholangiocarcinoma.

This study aims to determine the predictive role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived radiomic model in tumor immune profiling and immunotherapy for cholangiocarcinoma. To perform radiomic analysis, immune related subgroup clustering was first performed by single sample gene set enrichment analysis (ssGSEA). Second, a total of 806 radiomic features for each phase of DCE-MRI were extracted by utilizing the Python package Pyradiomics. Then, a predictive radiomic signature model was constructed after a three-step features reduction and selection, and receiver operating characteristic (ROC) curve was employed to evaluate the performance of this model. In the end, an independent testing cohort involving cholangiocarcinoma patients with anti-PD-1 Sintilimab treatment after surgery was used to verify the potential application of the established radiomic model in immunotherapy for cholangiocarcinoma. Two distinct immune related subgroups were classified using ssGSEA based on transcriptome sequencing. For radiomic analysis, a total of 10 predictive radiomic features were finally identified to establish a radiomic signature model for immune landscape classification. Regarding to the predictive performance, the mean AUC of ROC curves was 0.80 in the training/validation cohort. For the independent testing cohort, the individual predictive probability by radiomic model and the corresponding immune score derived from ssGSEA was significantly correlated. In conclusion, radiomic signature model based on DCE-MRI was capable of predicting the immune landscape of chalangiocarcinoma. Consequently, a potentially clinical application of this developed radiomic model to guide immunotherapy for cholangiocarcinoma was suggested.

Monitoring melanoma patients on treatment reveals a distinct macrophage population driving targeted therapy resistance.

Resistance to targeted therapy remains a major clinical challenge in melanoma. To uncover resistance mechanisms, we perform single-cell RNA sequencing on fine-needle aspirates from resistant and responding tumors of patients undergoing BRAFi/MEKi treatment. Among the genes most prominently expressed in resistant tumors is POSTN, predicted to signal to a macrophage population associated with targeted therapy resistance (TTR). Accordingly, tumors from patients with fast disease progression after therapy exhibit high POSTN expression levels and high numbers of TTR macrophages. POSTN polarizes human macrophages toward a TTR phenotype and promotes resistance to targeted therapy in a melanoma mouse model, which is associated with a phenotype change in intratumoral macrophages. Finally, polarized TTR macrophages directly protect human melanoma cells from MEKi-induced killing via CD44 receptor expression on melanoma cells. Thus, interfering with the protective activity of TTR macrophages may offer a strategy to overcome resistance to targeted therapy in melanoma.

A novel signature based on twelve programmed cell death patterns to predict the prognosis of lung adenocarcinoma.

Programmed cell death (PCD) plays a pivotal role in tumor initiation and progression. However, the prognostic value and clinical characteristics of PCD-related genes (PRGs) remain unclear. We collected and analyzed genes associated with twelve PCD patterns, including apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, entotic cell death, netotic cell death, parthanatos, lysosome-dependent cell death, autophagy-dependent cell death, alkaliptosis, and oxeiptosis to construct a gene signature. Our analysis identified 215 differentially expressed PRGs out of 1254 in lung adenocarcinoma (LUAD) and normal lung tissues. Subsequently, we performed univariate Cox regression analysis and identified 58 prognostic PRGs. Based on LASSO Cox regression analysis, we constructed a risk score using the expression levels of seven genes: DAPK2, DDIT4, E2F2, GAPDH, MET, PIM2, and FOXF1. Patients with lower risk scores showed earlier stages of cancer, longer survival times, and better immune infiltrations and functions. Notably, we found that knockdown of DDIT4 significantly increased apoptosis and impaired the proliferation of human LUAD cell lines. Our study proposes a PRG-based prognostic signature that sheds light on the potential role of PCD-related genes in LUAD and provides valuable insights into future therapeutic strategies.

Exploring the immune landscape and drug prediction of an M2 tumor-associated macrophage-related gene signature in EGFR-negative lung adenocarcinoma.

BACKGROUND: Improving immunotherapy efficacy for EGFR-negative lung adenocarcinoma (LUAD) patients remains a critical challenge, and the therapeutic effect of immunotherapy is largely determined by the tumor microenvironment (TME). Tumor-associated macrophages (TAMs) are the top-ranked immune infiltrating cells in the TME, and M2-TAMs exert potent roles in tumor promotion and chemotherapy resistance. An M2-TAM-based prognostic signature was constructed by integrative analysis of single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data to reveal the immune landscape and select drugs in EGFR-negative LUAD. METHODS: M2-TAM-based biomarkers were obtained from the intersection of bulk RNA-seq data and scRNA-seq data. After consensus clustering of EGFR-negative LUAD into different clusters based on M2-TAM-based genes, we compared the prognosis, clinical features, estimate scores, immune infiltration, and checkpoint genes among the clusters. Next, we combined univariate Cox and LASSO regression analyses to establish an M2-TAM-based prognostic signature. RESULTS: CCL20, HLA-DMA, HLA-DRB5, KLF4, and TMSB4X were verified as prognostic M2-like TAM-related genes by univariate Cox and LASSO regression analyses. IPS and TMB analyses revealed that the high-risk group responded better to common immunotherapy. CONCLUSION: The study shows the potential of the M2-like TAM-related gene signature in EGFR-negative LUAD, explores the immune landscape based on M2-like TAM-related genes, and predict immunotherapy response of patients with EGFR-negative LUAD, providing a new insight for individualized treatment.

Unraveling pancreatic ductal adenocarcinoma immune prognostic signature through a naive B cell gene set.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC), a leading cause of cancer mortality, has a complex pathogenesis involving various immune cells, including B cells and their subpopulations. Despite emerging research on the role of these cells within the tumor microenvironment (TME), the detailed molecular interactions with tumor-infiltrating immune cells (TIICs) are not fully understood. METHODS: We applied CIBERSORT to quantify TIICs and naive B cells, which are prognostic for PDAC. Marker genes from scRNA-seq and modular genes from weighted gene co-expression network analysis (WGCNA) were integrated to identify naive B cell-related genes. A prognostic signature was constructed utilizing ten machine-learning algorithms, with validation in external cohorts. We further assessed the immune cell diversity, ESTIMATE scores, and immune checkpoint genes (ICGs) between patient groups stratified by risk to clarify the immune landscape in PDAC. RESULTS: Our analysis identified 994 naive B cell-related genes across single-cell and bulk transcriptomes, with 247 linked to overall survival. We developed a 12-gene prognostic signature using Lasso and plsRcox algorithms, which was confirmed by 10-fold cross-validation and showed robust predictive power in training and real-world cohorts. Notably, we observed substantial differences in immune infiltration between patients with high and low risk. CONCLUSION: Our study presents a robust prognostic signature that effectively maps the complex immune interactions in PDAC, emphasizing the critical function of naive B cells and suggesting new avenues for immunotherapeutic interventions. This signature has potential clinical applications in personalizing PDAC treatment, enhancing the understanding of immune dynamics, and guiding immunotherapy strategies.

Development of a Risk Model and Genotyping Patterns Based on Disulfidptosis-Related lncRNAs to Predict Prognosis and Immune Landscape in Osteosarcoma.

BACKGROUND: Osteosarcoma (OS) is the most prevalent orthopedic malignancy with a dismal prognosis. Disulfidptosis-related lncRNAs (DRLncs) may be related to the progression of OS, but their potential molecular regulatory role is still unclear. METHODS: Based on the data collected from The Cancer Genome Atlas (TCGA), we conducted correlation analysis and the univariate Cox analysis to screen prognosis-related DRLncs, followed by developing genotyping patterns and corresponding classifier. Subsequently, the survival analysis, enrichment analysis, drug sensitivity analysis and immune infiltration analysis were performed. Afterward, multivariate Cox regression was used to construct a risk model, which was further validated by the receiver operating characteristic (ROC) curve. The aberrant expression of hub DRLncs in OS was validated using the Reverse Transcription Polymerase Chain Reaction (RT-qPCR) assay. RESULTS: We identified 262 DRLncs and eleven prognosis-related DRLncs through filtering. We then constructed two distinct expression patterns of prognosis-related DRLncs and developed a classifier. We obtained 393 differentially expressed genes (DEGs) between different subtypes, which were significantly enriched in biological processes related to the extracellular matrix, integrin binding, focal adhesion, and Wnt signaling pathways. Through immune infiltration analysis, the activated CD4 memory T cells, resting natural killer (NK) cells, M1 macrophages, and resting dendritic cells (DC) were observed to exhibit different abundance in distinct subtypes. In the drug sensitivity analysis, tamoxifen showed a promising effect for drug-resistant OS. Furthermore, we identified five hub DRLncs and constructed a risk model. The RT-qPCR confirmed the aberrant expression of five hub DRLncs in OS. CONCLUSIONS: The present study identified DRLncs in OS, and conducted a comprehensive investigation of DRLncs-related expression patterns, survival status, immune landscape and drug sensitivity to reveal the difference in prognostic, pharmacological and immunological phenotype characteristics between distinct subtypes. Additionally, we developed a risk model to predict the prognosis, and constructed a genotyping classifier to predict the above phenotype characteristics in OS.

Immune cell patterns before and after neoadjuvant immune checkpoint blockade combined with chemoradiotherapy in locally advanced esophageal squamous cell carcinoma.

BACKGROUND: Neoadjuvant immune checkpoint blockade (ICB) combined with chemoradiotherapy offers high pathologic complete response (pCR) rate for patients with locally advanced esophageal squamous cell carcinomas (ESCC). But the dynamic tumor immune microenvironment modulated by such neoadjuvant therapy remains unclear. PATIENTS AND METHODS: A total of 41 patients with locally advanced ESCC were recruited. All patients received neoadjuvant toripalimab combined with concurrent chemoradiotherapy. Matched pre- and post-treatment tissues were obtained for fluorescent multiplex immunohistochemistry (mIHC) and IHC analyses. The densities and spatial distributions of immune cells were determined by HALO modules. The differences of immune cell patterns before and after neoadjuvant treatment were investigated. RESULTS: In the pre-treatment tissues, more stromal CD3 + FoxP3 + Tregs and CD86+/CD163 + macrophages were observed in patients with residual tumor existed in the resected lymph nodes (pN1), compared with patients with pCR. The majority of macrophages were distributed in close proximity to tumor nest in pN1 patients. In the post-treatment tissues, pCR patients had less CD86 + cell infiltration, whereas higher CD86 + cell density was significantly associated with higher tumor regression grades (TRG) in non-pCR patients. When comparing the paired pre- and post-treatment samples, heterogeneous therapy-associated immune cell patterns were found. Upon to the treatment, CD3 + T lymphocytes were slightly increased in pCR patients, but markedly decreased in non-pCR patients. In contrast, a noticeable increase and a less obvious decrease of CD86 + cell infiltration were respectively depicted in non-pCR and pCR patients. Furthermore, opposite trends of the treatment-induced alterations of CD8 + and CD15 + cell infiltrations were observed between pN0 and pN1 patients. CONCLUSIONS: Collectively, our data demonstrate a comprehensive picture of tumor immune landscape before and after neoadjuvant ICB combined with chemoradiotherapy in ESCC. The infiltration of CD86 + macrophage may serve as an unfavorable indicator for neoadjuvant toripalimab combined with chemoradiotherapy.

Prognostic significance and immune landscape of a cell cycle progression-related risk model in bladder cancer.

BACKGROUND: A greater emphasis has been placed on the part of cell cycle progression (CCP) in cancer in recent years. Nevertheless, the precise connection between CCP-related genes and bladder cancer (BCa) has remained elusive. This study endeavors to establish and validate a reliable risk model incorporating CCP-related factors, aiming to predict both the prognosis and immune landscape of BCa. METHODS: Clinical information and RNA sequencing data were collected from the GEO and TCGA databases. Univariate and multivariate Cox regression analyses were conducted to construct a risk model associated with CCP. The performance of the model was assessed using ROC and Kaplan-Meier survival analyses. Functional enrichment analysis was employed to investigate potential cellular functions and signaling pathways. The immune landscape was characterized using CIBERSORT algorithms. Integration of the risk model with various clinical variables led to the development of a nomogram. RESULTS: To build the risk model, three CCP-related genes (RAD54B, KPNA2, and TPM1) were carefully chosen. ROC and Kaplan-Meier survival analysis confirm that our model has good performance. About immunological infiltration, the high-risk group showed decreased levels of regulatory T cells and dendritic cells coupled with increased levels of activated CD4 + memory T cells, M2 macrophages, and neutrophils. Furthermore, the nomogram showed impressive predictive power for OS at 1, 3, and 5 years. CONCLUSION: This study provides new insights into the association between the CCP-related risk model and the prognosis of BCa, as well as its impact on the immune landscape.

Novel kinase 1 regulates CD8+T cells as a potential therapeutic mechanism for idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a rare, chronic and progressively worsening lung disease that poses a significant threat to patient prognosis, with a mortality rate exceeding that of some common malignancies. Effective methods for early diagnosis and treatment remain for this condition are elusive. In our study, we used the GEO database to access second-generation sequencing data and associated clinical information from IPF patients. By utilizing bioinformatics techniques, we identified crucial disease-related genes and their biological functions, and characterized their expression patterns. Furthermore, we mapped out the immune landscape of IPF, which revealed potential roles for novel kinase 1 and CD8+T cells in disease progression and outcome. These findings can aid the development of new strategies for the clinical diagnosis and treatment of IPF.

The viral etiology of EBV-associated gastric cancers contributes to their unique pathology, clinical outcomes, treatment responses and immune landscape.

Epstein-Barr virus (EBV) is a pathogen known to cause a number of malignancies, often taking years for them to develop after primary infection. EBV-associated gastric cancer (EBVaGC) is one such malignancy, and is an immunologically, molecularly and pathologically distinct entity from EBV-negative gastric cancer (EBVnGC). In comparison with EBVnGCs, EBVaGCs overexpress a number of immune regulatory genes to help form an immunosuppressive tumor microenvironment (TME), have improved prognosis, and overall have an "immune-hot" phenotype. This review provides an overview of the histopathology, clinical features and clinical outcomes of EBVaGCs. We also summarize the differences between the TMEs of EBVaGCs and EBVnGCs, which includes significant differences in cell composition and immune infiltration. A list of available EBVaGC and EBVnGC gene expression datasets and computational tools are also provided within this review. Finally, an overview is provided of the various chemo- and immuno-therapeutics available in treating gastric cancers (GCs), with a focus on EBVaGCs.

The prognostic model and immune landscape based on cancer-associated fibroblast features for patients with locally advanced rectal cancer.

Background: This study aimed to construct a nomogram based on CAF features to predict the cancer-specific survival (CSS) rates of locally advanced rectal cancer (LARC) patients. Methods: The EPIC algorithm was employed to calculate the proportion of CAFs. based on the differentially expressed genes between the high and low CAF proportion subgroups, prognostic genes were identified via LASSO and Cox regression analyses. They were then used to construct a prognostic risk signature. Moreover, the GSE39582 and GGSE38832 datasets were used for external validation. Lastly, the level of immune infiltration was evaluated using ssGSEA, ESTIMATE, CIBERSORTx, and TIMER. Results: A higher level of CAF infiltration was associated with a worse prognosis. Additionally, the number of metastasized lymph nodes and distant metastases, as well as the level of immune infiltration were higher in the high CAF proportion subgroup. Five prognostic genes (SMOC2, TUBAL3, C2CD4A, MAP1B, BMP8A) were identified and subsequently incorporated into the prognostic risk signature to predict the 1-, 3-, and 5-year CSS rates in the training and validation sets. Differences in survival rates were also determined in the external validation cohort. Furthermore, independent prognostic factors, including TNM stage and risk score, were combined to established a nomogram. Notably, our results revealed that the proportions of macrophages and neutrophils and the levels of cytokines secreted by M2 macrophages were higher in the high-risk subgroup. Finally, the prognostic genes were significantly associated with the level of immune cell infiltration. Conclusion: Herein, a nomogram based on CAF features was developed to predict the CSS rate of LARC patients. The risk model was capable of reflecting differences in the level of immune cell infiltration.

Molecular subtype construction and prognosis model for stomach adenocarcinoma characterized by metabolism-related genes.

Background: Metabolic reprogramming is implicated in cancer progression. However, the impact of metabolism-associated genes in stomach adenocarcinomas (STAD) has not been thoroughly reviewed. Herein, we characterized metabolic transcription-correlated STAD subtypes and evaluated a metabolic RiskScore for evaluation survival. Method: Genes related to metabolism were gathered from previous study and metabolic subtypes were screened using ConsensusClusterPlus in TCGA-STAD and GSE66229 dataset. The ssGSEA, MCP-Count, ESTIMATE and CIBERSORT determined the immune infiltration. A RiskScore model was established using the WGCNA and LASSO Cox regression in the TCGA-STAD queue and verified in the GSE66229 datasets. RT-qPCR was employed to measure the mRNA expressions of genes in the model. Result: Two metabolism-related subtypes (C1 and C2) of STAD were constructed on account of the expression profiles of 113 prognostic metabolism genes with different immune outcomes and apparently distinct metabolic characteristic. The overall survival (OS) of C2 subtype was shorter than that of C1 subtype. Four metabolism-associated genes in turquoise model, which closely associated with C2 subtype, were employed to build the RiskScore (MATN3, OSBPL1A, SERPINE1, CPNE8) in TCGA-train dataset. Patients developed a poorer prognosis if they had a high RiskScore than having a low RiskScore. The promising effect of RiskScore was verified in the TCGA-test, TCGA-STAD and GSE66229 datasets. The prediction reliability of the RiskScore was validated by time-dependent receiver operating characteristic curve (ROC) and nomogram. Moreover, samples with high RiskScore had an enhanced immune status and TIDE score. Moreover, MATN3, OSBPL1A, SERPINE1 and CPNE8 mRNA levels were all elevated in SGC7901 cells. Inhibition of OSBPL1A decreased SGC7901 cells invasion numbers. Conclusion: This work provided a new perspective into heterogeneity in metabolism and its association with immune escape in STAD. RiskScore was considered to be a strong prognostic label that could help individualize the treatment of STAD patients.

Identification and verification of AK4 as a protective immune-related biomarker in adipose-derived stem cells and breast cancer.

Background: Breast cancer (BC) remains the most common cancer among women, and novel post-surgical reconstruction techniques, including autologous fat transplantation, have emerged. While Adipose-derived stem cells (ADSCs) are known to impact the viability of fat grafts, their influence on breast cancer progression remains unclear. This study aims to elucidate the genetic interplay between ADSCs and breast cancer, focusing on potential therapeutic targets. Methods: Using the GEO and TCGA databases, we pinpointed differentially expressed (DE) mRNAs, miRNAs, lncRNAs, and pseudogenes of ADSCs and BC. We performed functional enrichment analysis and constructed protein-protein interaction (PPI), RNA binding protein (RBP)-pseudogene-mRNA, and lncRNA-miRNA-transcription factor (TF)-gene networks. Our study delved into the correlation of AK4 expression with 33 different malignancies and examined its impact on prognostic outcomes across a pan-cancer cohort. Additionally, we scrutinized immune infiltration, microsatellite instability, and tumor mutational burden, and conducted single-cell analysis to further understand the implications of AK4 expression. We identified novel sample subtypes based on hub genes using the ConsensusClusterPlus package and examined their association with immune infiltration. The random forest algorithm was used to screen DE mRNAs between subtypes to validate the powerful prognostic prediction ability of the artificial neural network. Results: Our analysis identified 395 DE mRNAs, 3 DE miRNAs, 84 DE lncRNAs, and 26 DE pseudogenes associated with ADSCs and BC. Of these, 173 mRNAs were commonly regulated in both ADSCs and breast cancer, and 222 exhibited differential regulation. The PPI, RBP-pseudogene-mRNA, and lncRNA-miRNA-TF-gene networks suggested AK4 as a key regulator. Our findings support AK4 as a promising immune-related therapeutic target for a wide range of malignancies. We identified 14 characteristic genes based on the AK4-related cluster using the random forest algorithm. Our artificial neural network yielded excellent diagnostic performance in the testing cohort with AUC values of 0.994, 0.973, and 0.995, indicating its ability to distinguish between breast cancer and non-breast cancer cases. Conclusions: Our research sheds light on the dual role of ADSCs in BC at the genetic level and identifies AK4 as a key protective mRNA in breast cancer. We found that AK4 significantly predicts cancer prognosis and immunotherapy, indicating its potential as a therapeutic target.

Identification of Lung Adenocarcinoma Subtypes Based on MHC-II Gene Expression Profile and Immunological Analysis.

INTRODUCTION: Major histocompatibility complex class II molecule (MHC-II) is pivotal in anti-tumor immunity, and targeting MHC-II in tumors may help improve patient survival. But function of MHC-II in the immunotherapy and prognosis of lung adenocarcinoma (LUAD) patients has not been thoroughly studied and reported. METHODS: We selected LUAD-related MHC-II genes from public databases based on previous literature reports. We identified different subtypes according to expression differences of these genes in different LUAD samples through cluster analysis. We used R package to conduct a series of analyses on different subtypes, exploring their survival differences, gene expression differences, pathway enrichment differences, and differences in immune characteristics and immune therapy. Finally, we screened potential drugs from the cMAP database. RESULTS: We identified two MHC-II-related LUAD subtypes. Our analyses presented that patients with cluster2 subtype showed better prognosis, higher immune scores, higher levels of immune cell infiltration and immune function activation. In addition, patients with this subtype had higher immunophenoscore, lower TIDE scores, and DEPTH scores. We also identified 10 small molecule drugs, such as lenalidomide, VX-745, and tyrphostin-AG-1295. CONCLUSION: Overall, MHC-II is not only a potential biomarker for accurately distinguishing LUAD subtypes but also a predictive factor for their survival. Our study offers novel insights into understanding of impact of MHC-II in LUAD and offers a new perspective for improving the accurate classification of LUAD patients and enhancing drug treatment.