Targeting the gut and tumor microbiome in cancer resistance.

Therapy resistance represents a significant challenge in oncology, occurring in various therapeutic approaches. Recently, animal models and an increasing set of clinical trials highlight the crucial impact of the gut and tumor microbiome on treatment response. The intestinal microbiome contributes to cancer initiation, progression, and formation of distant metastasis. In addition, tumor-associated microbiota is considered a critical player in influencing tumor microenvironment and regulating local immune processes. Intriguingly, numerous studies have successfully identified pathogens within the gut and tumor microbiome that might be linked to a poor response to different therapeutic modalities. The unfavorable microbial composition with the presence of specific microbes participates in cancer resistance and progression via several mechanisms, including upregulation of oncogenic pathways, macrophage polarization reprogramming, metabolism of chemotherapeutic compounds, autophagy pathway modulation, enhanced DNA damage repair, inactivation of a pro-apoptotic cascade, and bacterial secretion of extracellular vesicles, promoting the processes in the metastatic cascade. Targeted elimination of specific intratumoral bacteria appears to enhance treatment response. However, broad-spectrum antibiotic pre-treatment is mostly connected to reduced efficacy due to gut dysbiosis and lower diversity. Mounting evidence supports the potential of microbiota modulation by probiotics and fecal microbiota transplantation to improve intestinal dysbiosis and increase microbial diversity, leading to enhanced treatment efficacy while mitigating adverse effects. In this context, further research concerning the identification of clinically relevant microbiome signatures followed by microbiota-targeted strategies presents a promising approach to overcoming immunotherapy and chemotherapy resistance in refractory patients, improving their outcomes.

The liquid-liquid phase separation signature predicts the prognosis and immunotherapy response in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common and fatal malignancy characterized by poor patient prognosis and treatment outcome. The process of liquid-liquid phase separation in tumour cells alters the dysfunction of biomolecular condensation in tumour cells, which affects tumour progression and treatment. We downloaded the data of HCC samples from TCGA database and GEO database, and used a machine learning method to build a new liquid-liquid phase separation index (LLPSI) by liquid-liquid phase separation related genes. The LLPSI-related column line Figure was constructed to provide a quantitative tool for clinical practice. HCC patients were divided into high and low LLPSI groups based on LLPSI, and clinical features, tumour immune microenvironment, chemotherapeutic response, and immunotherapeutic response were systematically analysed. LLPSI, which consists of five liquid-liquid phase separation-associated genes (MAPT, WDR62, PLK1, CDCA8 and TOP2A), is a reliable predictor of survival in patients with HCC and has been validated in multiple external datasets. We found that the high LLPSI group showed higher levels of immune cell infiltration and better response to immunotherapy compared to the low LLPSI group, and LLPSI can also be used for prognostic prediction in various cancers other than HCC. In vitro experiments verified that knockdown of MAPT could inhibit the proliferation and migration of HCC. The LLPSI identified in this study can accurately assess the prognosis of patients with HCC and identify patient populations that will benefit from immunotherapy, providing valuable insights into the clinical management of HCC.

Integration of mult-omics and nucleotide metabolism reprogramming signature analysis reveals gastric cancer immunological and prognostic features.

BACKGROUND: Gastric cancer is a frequent and lethal solid tumor that has a poor prognosis and treatment result. Reprogramming of nucleotide metabolism is a characteristic of cancer development and progression. METHODS: We used a variety of machine learning techniques to create a novel nucleotide metabolism-related index (NMRI) using gastric cancer sample data obtained from the TCGA and GEO databases. This index is based on genes associated to nucleotide metabolism. Gastric cancer patients were categorized into high and low NMRI groups based on NMRI results. The clinical features, tumor immune microenvironment, response to chemotherapy, and response to immunotherapy were then thoroughly examined. In vitro experiments were then used to confirm the biological role of SERPINE1 in gastric cancer. RESULTS: The four nucleotide metabolism-related genes that make up NMRI (GAMT, ORC1, CNGB3, and SERPINE1) were verified in an external dataset and are a valid predictor of prognosis for patients with gastric cancer. The high NMRI group was more responsive to immunotherapy and had greater levels of immune cell infiltration than the low NMRI group. The proliferation and migration of stomach cancer was shown to be decreased by SERPINE1 knockdown in vitro. CONCLUSIONS: This study's NMRI can reliably predict a patient's prognosis for stomach cancer and pinpoint the patient group that will benefit from immunotherapy, offering important new information on the clinical treatment of stomach cancer.

The vasculogenic mimicry related signature predicts the prognosis and immunotherapy response in renal clear cell carcinoma.

BACKGROUND: Clear cell carcinoma of the kidney is a common urological malignancy characterized by poor patient prognosis and treatment outcomes. Modulation of vasculogenic mimicry in tumor cells alters the tumor microenvironment and the influx of tumor-infiltrating lymphocytes, and the combination of its inducers and immune checkpoint inhibitors plays a synergistic role in enhancing antitumor effects. METHODS: We downloaded the data from renal clear cell carcinoma samples and vasculogenic mimicry-related genes to establish a new vasculogenic mimicry-related index (VMRI) using a machine learning approach. Based on VMRI, patients with renal clear cell carcinoma were divided into high VMRI and low VMRI groups, and patients' prognosis, clinical features, tumor immune microenvironment, chemotherapeutic response, and immunotherapeutic response were systematically analyzed. Finally, the function of CDH5 was explored in renal clear cell carcinoma cells. RESULTS: VMRI can be used for prognostic and immunotherapy efficacy prediction in a variety of cancers, which consists of four vasculogenic mimicry-related genes (CDH5, MMP9, MAPK1, and MMP13), is a reliable predictor of survival and grade in patients with clear cell carcinoma of the kidney and has been validated in multiple external datasets. We found that the high VMRI group presented higher levels of immune cell infiltration, which was validated by pathological sections. We performed molecular docking prediction of vasculogenic mimicry core target proteins and identified natural small molecule drugs with the highest affinity for the target protein. Knockdown of CDH5 inhibited the proliferation and migration of renal clear cell carcinoma. CONCLUSIONS: The VMRI identified in this study allows for accurate prognosis assessment of patients with renal clear cell carcinoma and identification of patient populations that will benefit from immunotherapy, providing valuable insights for future precision treatment of patients with renal clear cell carcinoma.

Unraveling the immunogenic cell death pathways in gastric adenocarcinoma: A multi-omics study.

BACKGROUND: Gastric cancer (GC) is a prevalent malignant tumor of the gastrointestinal (GI) system. However, the lack of reliable biomarkers has made its diagnosis, prognosis, and treatment challenging. Immunogenic cell death (ICD) is a type of programmed cell death that is strongly related to the immune system. However, its function in GC requires further investigation. METHOD: We used multi-omics and multi-angle approaches to comprehensively explore the prognostic features of ICD in patients with stomach adenocarcinoma (STAD). At the single-cell level, we screened genes associated with ICD at the transcriptome level, selected prognostic genes related to ICD using weighted gene co-expression network analysis (WGCNA) and machine learning, and constructed a prognostic model. In addition, we constructed nomograms that incorporated pertinent clinical features and provided effective tools for prognostic prediction in clinical settings. We also investigated the sensitivity of the risk subgroups to both immunotherapy and drugs. Finally, in addition to quantitative real-time polymerase chain reaction, immunofluorescence was used to validate the expression of ICD-linked genes. RESULTS: Based on single-cell and transcriptome WGCNA analyses, we identified 34 ICD-related genes, of which 11 were related to prognosis. We established a prognostic model using the least absolute shrinkage and selection operator (LASSO) algorithm and identified dissimilarities in overall survival (OS) and progression-free survival (PFS) in risk subgroups. The nomograms associated with the ICD-related signature (ICDRS) demonstrated a good predictive value for clinical applications. Moreover, we detected changes in the tumor microenvironment (TME), including biological functions, mutation landscapes, and immune cell infiltration, between the high- and low-risk groups. CONCLUSION: We constructed an ICD-related prognostic model that incorporated features related to cell death. This model can serve as a useful tool for predicting the prognosis of GC, targeted prevention, and personalized medicine.

Epigenetic Modification-Associated Molecular Classification of Gastric Cancer.

Epigenetic modification is involved in tumorigenesis and cancer progression. We developed an epigenetic modification-associated molecular classification of gastric cancer (GC) to identify signature genes that accurately predict prognosis and the efficacy of immunotherapy. Least absolute shrinkage and selection operator and multivariate Cox regression analysis were conducted to develop an epigenetic modification-associated molecular classification. We investigated the significance of PIP4P2, an independent prognostic factor of the classification system, in predicting the prognosis and immunotherapy efficacy of patients with GC. The epigenetic modification-associated molecular classification was highly associated with the clinicopathological characteristics of patients and the existing classification of GC. PIP4P2 was highly expressed in GC tissue and tumor-associated macrophages. High PIP4P2 expression in GC tissue-induced tumor progression by activating PI3K/AKT signal transduction had a negative impact on immunotherapy efficacy. High expression of PIP4P2 in macrophages was correlated with poor prognosis in patients with GC. PIP4P2 is an independent unfavorable prognostic factor of epigenetic modification-associated molecular classification, is involved in tumorigenic progression, and is essential for assessing the prognosis and immunotherapy efficacy of GC.

A novel CD8+ T cell-related gene signature for predicting the prognosis and immunotherapy efficacy in bladder cancer.

OBJECTIVE: To identify CD8+ T cell-related molecular clusters and establish a novel gene signature for predicting the prognosis and efficacy of immunotherapy in bladder cancer (BCa). METHODS: Transcriptome and clinical data of BCa samples were obtained from the Cancer Genome Atlas (TCGA) and GEO databases. The CD8+ T cell-related genes were screened through the CIBERSORT algorithm and correlation analysis. Consensus clustering analysis was utilized to identified CD8+ T cell-related molecular clusters. A novel CD8+ T cell-related prognostic model was developed using univariate Cox regression analysis and Lasso regression analysis. Internal and external validations were performed and the validity of the model was validated in a real-world cohort. Finally, preliminary experimental verifications were carried out to verify the biological functions of SH2D2A in bladder cancer. RESULTS: A total of 52 CD8+ T cell-related prognostic genes were screened and two molecular clusters with notably diverse immune cell infiltration, prognosis and clinical features were developed. Then, a novel CD8+ T cell-related prognostic model was constructed. The patients with high-risk scores exhibited a significantly worse overall survival in training, test, whole TCGA and validating cohort. The AUC was 0.766, 0.725, 0.739 and 0.658 in the four cohorts sequentially. Subgroup analysis suggested that the novel prognostic model has a robust clinical application for selecting high-risk patients. Finally, we confirmed that patients in the low-risk group might benefit more from immunotherapy or chemotherapy, and validated the prognostic model in a real-world immunotherapy cohort. Preliminary experiment showed that SH2D2A was capable of attenuating proliferation, migration and invasion of BCa cells. CONCLUSIONS: CD8+ T cell-related molecular clusters were successfully identified. Besides, a novel CD8+ T cell-related prognostic model with an excellent predictive performance in predicting survival rates and immunotherapy efficacy of BCa was developed.

Identification and validation of an immune-relevant risk signature predicting survival outcome and immune infiltration in uveal melanoma.

PURPOSE: The current study aimed to reveal a novel immune-related signature to evaluate immune infiltration status and the survival outcome for patients with uveal melanoma (UM). METHODS: Based on 80 UM samples from the Cancer Genome Atlas, the transcriptome gene expression and clinical characteristics were analyzed to identify immune-related genes that contributed most to prognosis based on LASSO Cox regression. By combining the gene expression level with the corresponding regression coefficient, a risk score was calculated and all patients were divided into high- and low-risk groups. Survival, tumor-infiltrating immune cell abundance, dysregulated signaling pathways, immunophenoscore and tumor mutation burden were compared between two groups. Validation of the risk signature was performed in GSE22138 and GSE44295 cohort. For evaluating the immunotherapy efficacy, 348 advanced urothelial cancer patients treated with immune checkpoint inhibitor (ICI) were used for external validation. RESULTS: Nine immune-related prognostic genes were identified under the LASSO Cox regression in the TCGA cohort; they are ACKR2, AREG, CCL5, CLEC11A, IGKV1-33, IL36B, NROB1, TRAV8-4 and TRBV28. Better prognosis, elevated immune cell infiltration, decreased immune-suppressive cell infiltration, immune response-related pathways and higher immunophenoscore were found in low-risk patients, with better ICI treatment response rate. CONCLUSION: The identified immune risk signature was demonstrated to be associated with the favorable immune infiltration, prognosis and immunotherapeutic efficacy, which may provide clues for survival evaluation and immune treatment.

An aging-related signature predicts favorable outcome and immunogenicity in lung adenocarcinoma.

Aging has been demonstrated to play vital roles in the prognosis and treatment efficacy of cancers, including lung adenocarcinoma (LUAD). This novel study aimed to construct an aging-related risk signature to evaluate the prognosis and immunogenicity of LUAD. Transcriptomic profiles and clinical information were collected from a total of 2518 LUAD patients from 12 independent cohorts. The risk signature was developed by combining specific gene expression with the corresponding regression coefficients. One cohort treated with the immune checkpoint inhibitor (ICI) was also used. Subsequently, a risk signature was developed based on 21 aging-related genes. LUAD patients with low-risk scores exhibited improved survival outcomes in both the discovery and validation cohorts. Further immunology analysis revealed elevated lymphocyte infiltration, decreased infiltration of immune-suppressive cells, immune response-related pathways, and favorable ICI predictor enrichment in the low-risk subgroup. Genomic mutation exploration indicated the enhanced mutation burden and higher mutation rates in significantly driver genes of TP53, KEAP1, SMARCA4, and RBM10 were enriched in patients with a low-risk signature. In the immunotherapeutic cohort, it was observed that low-risk aging scores were markedly associated with prolonged ICI prognosis. Overall, the estimated aging signature proved capable of evaluating the prognosis, tumor microenvironment, and immunogenicity, which further provided clues for tailoring prognosis prediction and immunotherapy strategies, apart from promoting individualized treatment plans for LUAD patients.

Construction of a predictive model for immunotherapy efficacy in lung squamous cell carcinoma based on the degree of tumor-infiltrating immune cells and molecular typing.

BACKGROUND: To construct a predictive model of immunotherapy efficacy for patients with lung squamous cell carcinoma (LUSC) based on the degree of tumor-infiltrating immune cells (TIIC) in the tumor microenvironment (TME). METHODS: The data of 501 patients with LUSC in the TCGA database were used as a training set, and grouped using non-negative matrix factorization (NMF) based on the degree of TIIC assessed by single-sample gene set enrichment analysis (GSEA). Two data sets (GSE126044 and GSE135222) were used as validation sets. Genes screened for modeling by least absolute shrinkage and selection operator (LASSO) regression and used to construct a model based on immunophenotyping score (IPTS). RNA extraction and qPCR were performed to validate the prognostic value of IPTS in our independent LUSC cohort. The receiver operating characteristic (ROC) curve was constructed to determine the predictive value of the immune efficacy. Kaplan-Meier survival curve analysis was performed to evaluate the prognostic predictive ability. Correlation analysis and enrichment analysis were used to explore the potential mechanism of IPTS molecular typing involved in predicting the immunotherapy efficacy for patients with LUSC. RESULTS: The training set was divided into a low immune cell infiltration type (C1) and a high immune cell infiltration type (C2) by NMF typing, and the IPTS molecular typing based on the 17-gene model could replace the results of the NMF typing. The area under the ROC curve (AUC) was 0.82. In both validation sets, the IPTS of patients who responded to immunotherapy were significantly higher than those who did not respond to immunotherapy (P = 0.0032 and P = 0.0451), whereas the AUC was 0.95 (95% CI = 1.00-0.84) and 0.77 (95% CI = 0.58-0.96), respectively. In our independent cohort, we validated its ability to predict the response to cancer immunotherapy, for the AUC was 0.88 (95% CI = 1.00-0.66). GSEA suggested that the high IPTS group was mainly involved in immune-related signaling pathways. CONCLUSIONS: IPTS molecular typing based on the degree of TIIC in the TME could well predict the efficacy of immunotherapy in patients with LUSC with a certain prognostic value.

Coordinated IgG2 and IgE responses as a marker of allergen immunotherapy efficacy.

BACKGROUND: IgG2 responses are associated with repeated antigen exposure and display highly mutated variable domains. A recent study highlighted a role of IgG2+ memory B cells and allergen-specific IgG2 levels after a 3rd consecutive pre-seasonal sublingual allergen immunotherapy (AIT) with grass pollen tablet. Herein, we aim to explore changes in allergen-specific IgG2 in individuals undergoing house dust mite immunotherapy (HDM-AIT) and explore whether the interrelationship with other humoral responses (i.e., IgG4 and IgE) may discriminate between high and low responders. METHODS: Levels of serum Dermatophagoides pteronyssinus and Dermatophagoides farinae-specific IgG2, IgG4, and IgE antibodies were measured by ELISA or ImmunoCap in a sub-group of individuals enrolled in a randomized, double-blind, placebo-controlled, sublingual AIT study evaluating the safety and efficacy of a 300 IR HDM tablet. RESULTS: After 1-year sublingual AIT, HDM-specific serum IgG2 responses increase mostly in high versus low responders and are distinctive according to the clinical benefit. Higher correlation between HDM-specific IgG2, IgE, and/or IgG4 responses is seen in subjects benefiting the most from HDM-AIT as indicated by changes in Average Total Combined Scores. More strikingly, statistically significant correlation between HDM-specific IgG2 and IgE responses is only observed in individuals stratified as high responders. CONCLUSIONS: We provide evidence for coordinated serum immune responses upon AIT in HDM-allergic subjects exhibiting high clinical benefit when compared with low responders. Assessing HDM-specific IgE, IgG2, and IgG4 in serum could be used as follow-up combined markers to support decision as to AIT continuation and/or adaptation.

Prognostic Hypoxia-Angiogenesis-Related Gene Signature in Hepatocellular Carcinoma, in Which HILPDA Contributes to Tumor Progression.

Objective: Hepatocellular carcinoma (HCC) is the predominant form of liver cancer. Hypoxia can be involved in HCC tumor growth, invasion and metastasis through inducing angiogenesis. Nevertheless, the assessment of the impact of hypoxia and angiogenesis on the prognosis of HCC remains inadequate. Methods: According to hypoxia-angiogenesis-related genes (HARGs) expression information and clinical data from patients within the Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort, we constructed a prognostic model (HARG-score) using bioinformatic tools. In addition to assessing the predictive ability of this prognostic model in both Liver Cancer-Riken-Japan (LIRI-JP) and GSE14520 cohorts, we analyzed the correlation between HARG-score and clinical characteristics, immune infiltration and immunotherapy efficacy. Moreover, we investigated the exact role and underlying mechanism of key HARGs through molecular experiments. Results: We constructed a 5-gene prognostic model HARG-score consisting of hypoxia-inducible lipid droplet-associated (HILPDA), erythropoietin (EPO), solute carrier family 2 member 1 (SLC2A1), proteasome subunit alpha type 7 (PSMA7) and cAMP responsive element-binding protein 1 (CREB1) through differentially expressed HARGs. The findings demonstrated that HARG-score was a good predictor of the prognosis of HCC patients from distinct cohorts and was correlated with clinical characteristics and immune infiltration. Furthermore, the HARG-score was identified as an independent prognostic factor. Lower HARG-score implied greater immunotherapy efficacy and better response. The expression and prognostic significance of these 5 genes were additionally validated in clinical data. In addition, experimental data revealed that the key gene HILPDA contributes to the progression of HCC through facilitating angiogenesis and affecting the expression of cytotoxic T-lymphocyte-associated protein 4 (CTLA4). Conclusion: HARG-score has promising applications in prognosis prediction of HCC patients, in which HILPDA may be a latent prognostic biomarker and therapeutic target, providing a foundation for further research and treatment of HCC.

The deubiquitinating enzymes-related signature predicts the prognosis and immunotherapy response in breast cancer.

BACKGROUND: Breast cancer is a prevalent disease that has a dismal prognosis for patients and a bad outlook for treatments. Ubiquitination is a reversible biological process that regulates protein production and degradation, as well as plays a vital role in protein transport, localization, and biological activity. METHODS: We obtained the breast cancer patient sample data and used a machine learning technique to create a novel index called Deubiquitinating enzyme related index (DUBRI) by gathering genes associated to deubiquitinating enzymes. Based on DUBRI, we systematically analyze patients' prognosis, clinical characteristics, tumor immune microenvironment, chemotherapy response and immunotherapy response. Finally, the function of OTUB2 was explored in breast cancer cells. RESULTS: DUBRI, which consists of five deubiquitinating enzyme genes (OTUB2, USP41, MINDY2, YOD1, and PSMD7), is a reliable predictor of survival in breast cancer patients. We found that the high DUBRI group presented higher levels of immune cell infiltration. We performed molecular docking prediction of core target proteins in deubiquitinating enzymes. In vitro experiments verified that knockdown of OTUB2 could inhibit the proliferation and migration of breast cancer. CONCLUSIONS: The DUBRI discovered in this research may effectively evaluate the outlook of breast cancer patients and identify groups of patients who would gain advantages from immunotherapy, offering vital knowledge for the future targeted treatment of breast cancer patients.

Deciphering the immunological and prognostic features of hepatocellular carcinoma through ADP-ribosylation-related genes analysis and identify potential therapeutic target ARFIP2.

BACKGROUND: Hepatocellular carcinoma is one of the most common malignancies, and its prognosis and treatment outcome cannot be accurately predicted. ADP-ribosylation (ADPR) is a post-translationa modification of proteins involved in protein trafficking and immune response. Therefore, it is necessary to explore the ADPR-related genes associated with the prognosis and therapeutic efficacy of hepatocellular carcinoma treatments. METHODS: We downloaded the data of hepatocellular carcinoma samples to identify ADPR-related genes as prognostic markers, and established a novel ADPR-related index (ADPRI) based on univariate and multivariate COX regression analyses. Patients' prognosis, clinical features, somatic variant, tumor immune microenvironment, chemotherapeutic response and immunotherapeutic response were systematically analyzed. Finally, the role of ARFIP2 in hepatocellular carcinoma cells was preliminarily explored in vitro. RESULTS: The ADPRI consisting of four ADPR related genes (ARL8B, ARFIP2, PARP12, ADPRHL1) was established to be a reliable predictor of survival in patients with hepatocellular carcinoma and was validated using external datasets. Compared with the low ADPRI group, the high ADPRI group presented higher levels of mutation frequency, immune infiltration and patients in high ADPRI group benefit more from immune checkpoint inhibitor treatment. In addition, we predicted some natural small molecule drugs as potential therapeutic targets for hepatocellular carcinoma. Finally, Knockdown of ARFIP2 inhibits the proliferation and migration of hepatocellular carcinoma cells by inducing the G1/S phase cell cycle arrest in HCC cells. CONCLUSIONS: The ADPRI can be used to accurately predict the prognosis and immunotherapeutic response of hepatocellular carcinoma patients and providing valuable insights for future precision treatment of patients with hepatocellular carcinoma.

Identification of a risk score model based on tertiary lymphoid structure-related genes for predicting immunotherapy efficacy in non-small cell lung cancer.

BACKGROUND: Tertiary lymphoid structures (TLSs) affect the prognosis and efficacy of immunotherapy in patients with non-small cell lung cancer (NSCLC), but the underlying mechanisms are not well understood. METHODS: TLSs were identified and categorized online from the Cancer Digital Slide Archive (CDSA). Overall survival (OS) and disease-free survival (DFS) were analyzed. GSE111414 and GSE136961 datasets were downloaded from the GEO database. GSVA, GO and KEGG were used to explore the signaling pathways. Immune cell infiltration was analyzed by xCell, ssGSEA and MCP-counter. The analysis of WGCNA, Lasso and multivariate cox regression were conducted to develop a gene risk score model based on the SU2C-MARK cohort. RESULTS: TLS-positive was a protective factor for OS according to multivariate cox regression analysis (p = 0.029). Both the TLS-positive and TLS-mature groups exhibited genes enrichment in immune activation pathways. The TLS-mature group showed more activated dendritic cell infiltration than the TLS-immature group. We screened TLS-related genes using WGCNA. Lasso and multivariate cox regression analysis were used to construct a five-genes (RGS8, RUF4, HLA-DQB2, THEMIS, and TRBV12-5) risk score model, the progression free survival (PFS) and OS of patients in the low-risk group were markedly superior to those in the high-risk group (p < 0.0001; p = 0.0015, respectively). Calibration and ROC curves indicated that the combined model with gene risk score and clinical features could predict the PFS of patients who have received immunotherapy more accurately than a single clinical factor. CONCLUSIONS: Our data suggested a pivotal role of TLSs formation in survival outcome and immunotherapy response of NSCLC patients. Tumors with mature TLS formation showed more activated immune microenvironment. In addition, the model constructed by TLS-related genes could predict the response to immunotherapy and is meaningful for clinical decision-making.

Copper-binding protein modelling by single-cell transcriptome and Bulk transcriptome to predict overall survival in lung adenocarcinoma patients.

Background: Copper and copper-binding proteins are key components of tumour progression as they play an important role in tumour invasion and migration, and abnormal accumulation of copper (Cu) may be intimately linked to with lung adenocarcinoma (LUAD). Methods: Data on lung adenocarcinoma were sourced from the Cancer Genome Atlas (TCGA) database and the National Centre for Biotechnology Information (GEO). 10x scRNA sequencing, which is from Bischoff P et al, was used for down-sequencing clustering and subgroup identification using TSNE. The genes for Copper-binding proteins (CBP) were acquired from the MSigDB database. LASSO-Cox analysis was subsequently used to construct a model for copper-binding proteins (CBPRS), which was then compared to lung adenocarcinoma models developed by others. External validation was carried out in the GSE31210 and GSE50081 cohorts. The effectiveness of immunotherapy was evaluated using the TIDE algorithm and the IMvigor210, GSE78220, and TCIA cohorts. Furthermore, differences in mutational profiles and the immune microenvironment between different risk groups were investigated. The CBPRS's key regulatory genes were screened using ROC diagnostic and KM survival curves. The differential expression of these genes was then verified by RT-qPCR. Results: The six CBP genes were identified as highly predictive of LUAD prognosis and significantly correlated with it. Multivariate analysis showed that patients in the low-risk group had a higher overall survival rate than those in the high-risk group, indicating that the model was an independent predictor of LUAD. The CBPRS demonstrated superior predictive ability compared to 11 previously published models. We constructed a column-line graph that includes CBPRS and clinical characteristics, which exhibits high predictive performance. Additionally, we observed significant differences in biological functions, mutational landscapes, and immune cell infiltration in the tumour microenvironment between the high-risk and low-risk groups. It is noteworthy that immunotherapy was also significant in both the high- and low-risk groups. These results suggest that the model has good predictive efficacy. Conclusions: The CBP model demonstrated good predictive performance, revealing characteristics of the tumour microenvironment. This provides a new method for assessing the efficacy of pre-immunisation and offers a potential strategy for future treatment of lung adenocarcinoma.

Comprehensive multi-omics analysis reveals a combination of lncRNAs that synergistically regulate glycolysis and immunotherapeutic effects in renal clear cell carcinoma.

BACKGROUND: Clear cell renal carcinoma is a common urological malignancy with poor prognosis and treatment outcomes. lncRNAs are important in metabolic reprogramming and the tumor immune microenvironment, but their role in clear cell renal carcinoma is unclear. METHODS: Renal clear cell carcinoma sample data from The Cancer Genome Atlas was used to establish a new risk profile by glycolysis-associated lncRNAs via machine learning. Risk profile-associated column-line plots were constructed to provide a quantitative tool for clinical practice. Patients with renal clear cell carcinoma were divided into high- and low-risk groups. Clinical features, tumor immune microenvironments, and immunotherapy responses were systematically analyzed. We experimentally confirmed the role of LINC01138 and LINC01605 in renal clear cell carcinoma. RESULTS: The risk profile, consisting of LUCAT1, LINC01138, LINC01605, and HOTAIR, reliably predicted survival in patients with renal clear cell carcinoma and was validated in multiple external datasets. The high-risk group presented higher levels of immune cell infiltration and better immunotherapy responses than the low-risk group. LINC01138 and LINC01605 knockdown inhibited the proliferation of renal clear cell carcinoma. CONCLUSIONS: The identified risk profiles can accurately assess the prognosis of patients with clear cell renal carcinoma and identify patient populations that would benefit from immunotherapy, providing valuable insights and therapeutic targets for the clinical management of clear cell renal carcinoma.

Identification and validation of immune-related gene signature models for predicting prognosis and immunotherapy response in hepatocellular carcinoma.

Background: This study seeks to enhance the accuracy and efficiency of clinical diagnosis and therapeutic decision-making in hepatocellular carcinoma (HCC), as well as to optimize the assessment of immunotherapy response. Methods: A training set comprising 305 HCC cases was obtained from The Cancer Genome Atlas (TCGA) database. Initially, a screening process was undertaken to identify prognostically significant immune-related genes (IRGs), followed by the application of logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods for gene modeling. Subsequently, the final model was constructed using support vector machines-recursive feature elimination (SVM-RFE). Following model evaluation, quantitative polymerase chain reaction (qPCR) was employed to examine the gene expression profiles in tissue samples obtained from our cohort of 54 patients with HCC and an independent cohort of 231 patients, and the prognostic relevance of the model was substantiated. Thereafter, the association of the model with the immune responses was examined, and its predictive value regarding the efficacy of immunotherapy was corroborated through studies involving three cohorts undergoing immunotherapy. Finally, the study uncovered the potential mechanism by which the model contributed to prognosticating HCC outcomes and assessing immunotherapy effectiveness. Results: SVM-RFE modeling was applied to develop an OS prognostic model based on six IRGs (CMTM7, HDAC1, HRAS, PSMD1, RAET1E, and TXLNA). The performance of the model was assessed by AUC values on the ROC curves, resulting in values of 0.83, 0.73, and 0.75 for the predictions at 1, 3, and 5 years, respectively. A marked difference in OS outcomes was noted when comparing the high-risk group (HRG) with the low-risk group (LRG), as demonstrated in both the initial training set (P <0.0001) and the subsequent validation cohort (P <0.0001). Additionally, the SVMRS in the HRG demonstrated a notable positive correlation with key immune checkpoint genes (CTLA-4, PD-1, and PD-L1). The results obtained from the examination of three cohorts undergoing immunotherapy affirmed the potential capability of this model in predicting immunotherapy effectiveness. Conclusions: The HCC predictive model developed in this study, comprising six genes, demonstrates a robust capability to predict the OS of patients with HCC and immunotherapy effectiveness in tumor management.

Identification of a novel immunogenic death-associated model for predicting the immune microenvironment in lung adenocarcinoma from single-cell and Bulk transcriptomes.

Background: Studies on immunogenic death (ICD) in lung adenocarcinoma are limited, and this study aimed to determine the function of ICD in LUAD and to construct a novel ICD-based prognostic model to improve immune efficacy in lung adenocarcinoma patients. Methods: The data for lung adenocarcinoma were obtained from the Cancer Genome Atlas (TCGA) database and the National Center for Biotechnology Information (GEO). The single-cell data were obtained from Bischoff P et al. To identify subpopulations, we performed descending clustering using TSNE. We collected sets of genes related to immunogenic death from the literature and identified ICD-related genes through gene set analysis of variance (GSVA) and weighted gene correlation network analysis (WGCNA). Lung adenocarcinoma patients were classified into two types using consistency clustering. The difference between the two types was analyzed to obtain differential genes. An immunogenic death model (ICDRS) was established using LASSO-Cox analysis and compared with lung adenocarcinoma models of other individuals. External validation was performed in the GSE31210 and GSE50081 cohorts. The efficacy of immunotherapy was assessed using the TIDE algorithm and the IMvigor210, GSE78220, and TCIA cohorts. Furthermore, differences in mutational profiles and immune microenvironment between different risk groups were investigated. Subsequently, ROC diagnostic curves and KM survival curves were used to screen ICDRS key regulatory genes. Finally, RT-qPCR was used to verify the differential expression of these genes. Results: Eight ICD genes were found to be highly predictive of LUAD prognosis and significantly correlated with it. Multivariate analysis showed that patients in the low-risk group had a higher overall survival rate than those in the high-risk group, indicating that the model was an independent predictor of LUAD. Additionally, ICDRS demonstrated better predictive ability compared to 11 previously published models. Furthermore, significant differences in biological function and immune cell infiltration were observed in the tumor microenvironment between the high-risk and low-risk groups. It is noteworthy that immunotherapy was also significant in both groups. These findings suggest that the model has good predictive efficacy. Conclusions: The ICD model demonstrated good predictive performance, revealing the tumor microenvironment and providing a new method for evaluating the efficacy of pre-immunization. This offers a new strategy for future treatment of lung adenocarcinoma.

Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort.

Introduction: The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related genes also play a crucial role in cancer progression and patient prognosis. However, further studies are needed to investigate the prognostic significance of the interaction between immune-related genes and cell death in LUAD. Methods: In this study, 10 clustering algorithms were applied to perform molecular typing based on cell death-related genes, immune-related genes, methylation data and somatic mutation data. And a powerful computational framework was used to investigate the relationship between immune genes and cell death patterns in LUAD patients. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations, and we constructed an immune-associated programmed cell death model (PIGRS) using the machine learning model that exhibited the best performance. Finally, based on a series of in vitro experiments used to explore the role of PSME3 in LUAD. Results: We used 10 clustering algorithms and multi-omics data to categorize TCGA-LUAD patients into three subtypes. patients with the CS3 subtype had the best prognosis, whereas patients with the CS1 and CS2 subtypes had a poorer prognosis. PIGRS, a combination of 15 high-impact genes, showed strong prognostic performance for LUAD patients. PIGRS has a very strong prognostic efficacy compared to our collection. In conclusion, we found that PSME3 has been little studied in lung adenocarcinoma and may be a novel prognostic factor in lung adenocarcinoma. Discussion: Three LUAD subtypes with different molecular features and clinical significance were successfully identified by bioinformatic analysis, and PIGRS was constructed using a powerful machine learning framework. and investigated PSME3, which may affect apoptosis in lung adenocarcinoma cells through the PI3K/AKT/Bcl-2 signaling pathway.