Immunotherapy Response In Patients Research Articles

Development and validation of a novel ferroptosis‑related lncRNA prognostic signature for pancreatic adenocarcinoma.

Long non‑coding RNAs (lncRNAs) serve a pivotal role in the regulation of cancer cell ferroptosis. However, the prognostic value of ferroptosis‑related lncRNAs in pancreatic adenocarcinoma (PAAD) largely remains unclear. We aimed at constructing a lncRNA‑based signature to improve the prognosis prediction of PAAD. In the present study, the transcriptome profiling data and clinical information of patients with PAAD were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Gene Consortium (ICGC) databases. Univariate Cox regression analysis of the TCGA cohort demonstrated that 26 ferroptosis‑related lncRNAs had significant prognostic value for PAAD (all P<0.01). Least absolute shrinkage and selection operator regression and multivariate Cox proportional hazards regression analyses were performed to construct a prognostic ferroptosis‑related lncRNA signature (FRLS) comprising nine ferroptosis‑related lncRNAs. The efficacy of this FRLS was verified in the training (TCGA) and validation (ICGC) cohorts. Based on the risk model, high risk scores were significantly correlated with poor overall survival (OS) (hazard ratio, 1.314; 95% confidence interval, 1.218‑1.418; P<0.001). The receiver operating characteristic curves and principal component analysis further demonstrated the robust prognostic ability of the FRLS. Furthermore, a nomogram with favorable predictive efficacy for the prediction of OS was constructed based on the FRLS and clinical features. Gene set enrichment analysis demonstrated that the genes in the FRLS participated in a number of cancer‑associated immunoregulatory pathways. Importantly, it was demonstrated that immune infiltration and response to cancer immunotherapy differed significantly between the high and low‑risk groups according to the FRLS. In conclusion, the risk signature based on the FRLS has potential for the clinical prediction of prognosis and immunotherapy response in patients with PAAD.

Prognostic implication and immunotherapy response prediction of a ubiquitination-related gene signature in breast cancer.

Breast cancer (BC) is one of the most common tumor types and has poor outcomes. In this study, a ubiquitination-related prognostic signature was constructed, and its association with immunotherapy response in BC was explored. A list of ubiquitination-related genes was obtained from the molecular signatures database, and a ubiquitination-related gene signature was obtained by least absolute shrinkage and selection operator Cox regression. The genes, TCN1, DIRAS3, and IZUMO4, had significant influence on BC outcomes. Patients were categorized into two clusters-a high-risk group with poor survival and a low-risk group with greater chances of controlling BC progression. Univariate and multivariate Cox regression analyses revealed that the risk signature was an independent prognostic factor for BC. Gene set enrichment analysis suggested that the high-risk group was enriched in cell cycle and DNA replication pathways. The risk score was positively linked to the tumor microenvironment and negatively correlated with the immunotherapy response. The IC50 values for rapamycin were higher in the low-risk group, whereas those for axitinib, AZD6244, erlotinib, GDC0941, GSK650394, GSK269962A, lapatinib, and PD0325901 were higher in the high-risk group. Therefore, the ubiquitination-related signature is considered a promising tool for predicting a BC patient's immunotherapy response.

Comprehensive Characterization of RNA-Binding Proteins in Colon Adenocarcinoma Identifies a Novel Prognostic Signature for Predicting Clinical Outcomes and Immunotherapy Responses Based on Machine Learning.

RNA-binding proteins (RBPs) are crucial factors that function in the posttranscriptional modification process and are significant in cancer. This research aimed for a multigene signature to predict the prognosis and immunotherapy response of patients with colon adenocarcinoma (COAD) based on the expression profile of RNA-binding proteins (RBPs). COAD samples retrieved from the TCGA and GEO datasets were utilized for a training dataset and a validation dataset. Totally, 14 shared RBP genes with prognostic significance were identified. Non-negative matrix factorization clusters defined by these RBPs could stratify COAD patients into two molecular subtypes. Cox regression analysis and identification of 8-gene signature categorized COAD patients into high- and low-risk populations with significantly different prognosis and immunotherapy responses. Our prediction signature was superior to another five well-established prediction models. A nomogram was generated to quantificationally predict the overall survival (OS) rate, validated by calibration curves. Our findings also indicated that high-risk populations possessed an enhanced immune evasion capacity and low-risk populations might benefit immunotherapy, especially for the joint combination of PD-1 and CTLA4 immunosuppressants. DHX15 and LARS2 were detected with significantly different expressions in both datasets, which were further confirmed by qRTPCR and immunohistochemical staining. Our observations supported an eight-RBP-related signature that could be applied for survival prediction and immunotherapy response of patients with COAD.

Identification and Validation of the Prognostic Panel in Clear Cell Renal Cell Carcinoma Based on Resting Mast Cells for Prediction of Distant Metastasis and Immunotherapy Response.

Clear cell renal cell carcinoma (ccRCC) has a high metastatic rate, and its incidence and mortality are still rising. The aim of this study was to identify the key tumor-infiltrating immune cells (TIICs) affecting the distant metastasis and prognosis of patients with ccRCC and to construct a relevant prognostic panel to predict immunotherapy response. Based on ccRCC bulk RNA sequencing data, resting mast cells (RMCs) were screened and verified using the CIBERSORT algorithm, survival analysis, and expression analysis. Distant metastasis-associated genes were identified using single-cell RNA sequencing data. Subsequently, a three-gene (CFB, PPP1R18, and TOM1L1) panel with superior distant metastatic and prognostic performance was established and validated, which stratified patients into high- and low-risk groups. The high-risk group exhibited lower infiltration of RMCs, higher tumor mutation burden (TMB), and worse prognosis. Therapeutically, the high-risk group was more sensitive to anti-PD-1 and anti-CTLA-4 immunotherapy, whereas the low-risk group displayed a better response to anti-PD-L1 immunotherapy. Furthermore, two immune clusters revealing distinct immune, clinical, and prognosis heterogeneity were distinguished. Immunohistochemistry of ccRCC samples verified the expression patterns of the three key genes. Collectively, the prognostic panel based on RMCs is able to predict distant metastasis and immunotherapy response in patients with ccRCC, providing new insight for the treatment of advanced ccRCC.

ICD-related risk model predicts the prognosis and immunotherapy response of patients with liver cancer.

Immunogenic cell death (ICD) is a novel cell death mechanism that activates and regulates the immune system against cancer. However, its prognostic value in liver cancer remains unclear. Here, several algorithms such as correlation analysis, Cox regression analysis, and Lasso regression analysis were carried out to evaluate the prognostic value of ICD-related genes in patients with liver cancer. Three ICD-related prognostic genes, the prion protein gene (PRNP), dynamin 1-like gene (DNM1L), and caspase-8 (CASP8), were identified and used to construct a risk signature. Patients with liver cancer were categorized into high- and low-risk groups using the ICD-related signature. Subsequently, a multivariate regression analysis revealed that the signature was an independent risk factor in liver cancer [hazard ratio (HR) = 6.839; 95% confidence interval (CI) = 1.625-78.785]. Patient survival was also predicted using the risk model, with area under the curve values of 0.75, 0.70, and 0.69 for 1-, 3-, and 5-year survival, respectively. Finally, a prognostic nomogram containing the clinical characteristics and risk scores of patients was constructed. The constructed ICD-related signature could serve as a prognostic and immunotherapeutic biomarker in liver cancer.

Integrative analysis identified two subtypes and a taurine-related signature to predict the prognosis and efficacy of immunotherapy in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent subtypes of primary liver cancer, with high mortality and poor prognosis. Immunotherapy has revolutionized treatment strategies for many cancers. However, only a subset of patients with HCC achieve satisfactory benefits from immunotherapy. Therefore, a reliable biomarker that could predict the prognosis and immunotherapy response in patients with HCC is urgently needed. Taurine plays an important role in many physiological processes. However, its participation in the occurrence and progression of liver cancer and regulation of the composition and function of various components of the immune microenvironment remains elusive. In this study, we identified and validated two heterogeneous subtypes of HCC with different taurine metabolic profiles, presenting distinct genomic features, clinicopathological characteristics, and immune landscapes, using multiple bulk transcriptome datasets. Subsequently, we constructed a risk model based on genes related to taurine metabolism to assess the prognosis, immune cell infiltration, immunotherapy response, and drug sensitivity of patients with HCC. The risk model was validated using several independent external cohorts and showed a robust predictive performance. In addition, we evaluated the expression patterns of taurine metabolism-related genes in the tumor microenvironment and the heterogeneity of taurine metabolism among cancer cells using a single-cell transcriptome. In conclusion, our study provides insights into the important role played by taurine metabolism in tumor progression and immune regulation. Furthermore, the risk model can serve as a biomarker to assess patient prognosis and immunotherapy response, potentially helping clinicians make more precise and personalized clinical decisions.

An exosome-related long non-coding RNAs risk model could predict survival outcomes in patients with breast cancer

Breast cancer (BC) is one of the most frequent malignancies among women worldwide. Accumulating evidence indicates that long non-coding RNA (lncRNA) may affect BC progression. Exosomes, a class of small membrane vesicles, have been reported to promote tumor progression through transporting proteins, mRNAs, lncRNAs and some other small molecules. However, the interaction between exosome-related lncRNAs and the microenvironment of malignancies is unclear. Hence, we proceeded to investigate the relationship between exosome-related lncRNAs and BC microenvironment. 121 exosome-associated genes were extracted from ExoBCD database. Then, the Pearson analysis was used to screened out the exosome-related lncRNAs. After that, 15 exosome-related differentially expressed lncRNAs were identified by the correlation with BC prognosis. According to the sum of the expression of these 15 lncRNAs, extracted from The Cancer Genome Atlas, and the regression coefficients, an exosome-related lncRNAs signature was developed by using Cox regression analysis. With the median risk score of the training set, the patients in training and validation sets were separated to low-risk group and high-risk group. Subsequently, the lncRNA–mRNA co-expression network was constructed. The distinct enrichment pathways were compared among the different risk groups by using the R package clusterProfiler. The ESTIMATE method and ssGESA database were adopted to study the ESTIMATE Score and immune cell infiltration. Eventually, the expression of immune checkpoint associated genes, microsatellite instable and the immunophenoscore were further analyzed between different risk groups. Different risk groups exhibited different prognosis, with lower survival rate in the high-risk group. The differentially expressed genes between the different risk groups were enriched in biological processes pathways as well as immune responses. BC patients in high-risk group were identified with lower scores of ESTIMATE scores. Subsequently, we noticed that the infiltrating levels of aDCs, B cells, CD8+ T cells, iDCs, DCs, Neutrophils, macrophages, NK cells, pDCs, Tfh, T helper cells, TIL and Tregs were obvious elevated with the decreased risk score in training and validation cohorts. And some immune signatures were significantly activated with the decreased risk score in both cohorts. Eventually, the exosome-associated lncRNAs risk model was demonstrated to accurately predict immunotherapy response in patients with BC. The results of our study suggest that exosome-related lncRNAs risk model has close relationship with prognosis and immune cells infiltration in BC patients. These findings could make a great contribution to improving BC immunotherapy.

Integrated investigation of the prognostic role of HLA LOH in advanced lung cancer patients with immunotherapy.

Although multiple studies have shown that loss of heterozygosity (LOH) at the human leukocyte antigen (HLA) locus is one of the mechanisms of immune escape, the effect of HLA LOH on the immunotherapy response of patients is still unclear. Based on the data of 425 Chinese lung cancer patients, the genomic characteristics with different HLA LOH statuses were analyzed. The driver genes mutation frequency, oncogenic signaling pathways mutation frequency, tumor mutational burden (TMB) and chromosomal instability (CIN) score in the HLA LOH high group was significantly higher than in the HLA LOH negative group. Transcriptome analyses revealed that pre-existing immunologically active tumor microenvironment (TME) was associated with HLA LOH negative patients. Non-small cell lung cancer (NSCLC) patients, especially for lung squamous cell carcinomas (LUSC), with HLA LOH negative have a longer survival period than those with HLA LOH. In addition, the combination of HLA LOH with TMB or programmed cell death-Ligand 1 (PD-L1) expression can further distinguish responders from non-responders. Furthermore, a comprehensive predictive model including HLA LOH status, TMB, PD-L1 expression and CD8+ T cells was constructed and exhibited a higher predictive value, which may improve clinical decision-making.

Prognostic Nutritional Index Associates with Immunotherapy Response in Patients with Metastatic Biliary Tract Cancer

Purpose: The present study aims to evaluate the effect of prognostic nutrition index (PNI) on the response and prognosis of patients with metastatic biliary tract cancer (BTC) patients treated with immunotherapy.Methods: The outcomes of 83 patients with metastatic BTC were evaluated retrospectively. Among them, 51 received immune checkpoint inhibitors (ICIs) treatment (ICIs cohort) and 32 patients received first-line chemotherapy (chemotherapy cohort). According to the optimal cutoff value of PNI, patients in ICIs cohort were divided into low PNI group (PNI < 44.30) and high PNI group (PNI≥ 44.30).Results: Patients received first-line immunotherapy-based combination antitumor therapy in ICIs cohort showed significant longer median PFS and OS contrast with first-line chemotherapy cohort. In ICIs cohort, median PFS and OS were significantly longer in the high PNI group. In addition, multivariate Cox regression analysis showed that high PNI was an independent risk factor for median PFS (hazard ratio (HR), 0.474, 95% CI, 0.246–0.910; P = 0.025) and median OS (HR, 0.229, 95% CI, 0.097–0.539; P = 0.001) in ICIs cohort, respectively. Conclusion: Our study provides preliminary evidence that immunotherapy for metastatic BTC is effective and safe. PNI was an independent prognostic indicator of median PFS and OS in patients with metastatic BTC receiving immunotherapy.

A Prognostic Model for Predicting Liver Cancer Patients Based On Immune Checkpoint Gene-Related Basement Membrane Genes, And Analyzing Immunity and Potential Drug Candidates

Abstract:&#x0D; Objective: Hepatocellular carcinoma is one of the most common malignant tumors in the world. The expression of immune checkpoint genes in tumor cells prevents the immune system from eliminating tumors. Basement membrane-related genes are genes that are closely related to human diseases obtained in the latest research.&#x0D; Methods: First, basement membrane-related genes were extracted from immune checkpoint genes, and a prognosis model of immune checkpoint-related basement membrane genes was constructed. C-index curves and ROC were drawn by survival analysis, progression-free survival analysis, and independent prognostic analysis. Curves, principal component analysis, and validation of the clinical grouping model were performed to verify its accuracy, enrichment analysis, immune analysis, and tumor mutation burden survival analysis were performed to further explore the potential functions of the model, and finally, potential drugs targeting the model were discussed.&#x0D; Results: A prognostic model for predicting the survival time of liver cancer patients was constructed, and the predictive ability of the model was verified. GO and KEGG enrichment analysis revealed differences in the functions and pathways of differential genes. Four differentially expressed immune functions were found. The top 4 genes mutated in the high and low risk groups were compared. Twenty-five drugs with significant differences in drug sensitivity between high- and low-risk groups were explored.&#x0D; Conclusion: The risk-prognostic model based on the association of basement membrane genes and immune checkpoint genes in this study may be promising for clinical prediction of prognosis and immunotherapy response in patients with liver cancer.

Identification of molecular subtypes based on liquid-liquid phase separation and cross-talk with immunological phenotype in bladder cancer.

Mounting evidence has demonstrated that an imbalance in liquid-liquid phase separation (LLPS) can induce alteration in the spatiotemporal coordination of biomolecular condensates, which plays a role in carcinogenesis and cachexia. However, the role of LLPS in the occurrence and progression of bladder cancer (BLCA) remains to be elucidated. Identifying the role of LLPS in carcinogenesis may aid in cancer therapeutics. A total of 1,351 BLCA samples from six cohorts were retrieved from publicly available databases like The Cancer Genome Atlas, Gene Expression Omnibus, and ArrayExpress. The samples were divided into three distinct clusters, and their multi-dimensional heterogeneities were explored. The LLPS patterns of all patients were determined based on the LLPS-related risk score (LLPSRS), and its multifaceted landscape was depicted and experimentally validated at the multi-omics level. Finally, a cytotoxicity-related and LLPSRS-based classifier was established to predict the patient's response to immune checkpoint blockade (ICB) treatment. Three LLPS-related subtypes were identified and validated. The differences in prognosis, tumor microenvironment (TME) features, cancer hallmarks, and certain signatures of the three LLPS-related subtypes were validated. LLPSRS was calculated, which could be used as a prognostic biomarker. A close correlation was observed between clinicopathological features, genomic variations, biological mechanisms, immune infiltration in TME, chemosensitivity, and LLPSRS. Furthermore, our classifier could effectively predict immunotherapy response in patients with BLCA. Our study identified a novel categorization of BLCA patients based on LLPS. The LLPSRS could predict the prognosis of patients and aid in designing personalized medicine. Further, our binary classifier could effectively predict patients' sensitivity to immunotherapy.

Cancer-associated fibroblasts-derived lncRNA signature as a putative biomarker in breast cancer.

Long noncoding RNAs (lncRNAs) have been reported to play a key role in regulating tumor microenvironment and immunity. Cancer-associated fibroblasts (CAFs) are abundant in many tumors. However, the functional and clinical significance of lncRNAs specifically expressed in CAFs has not been fully elucidated. In this study, we identified a list of 95 CAF-specific lncRNAs (FibLnc), including HHLA3, TP53TG1, ST7-AS1, LINC00536, ZNF503-AS1, MIR22HG, and MAPT-AS1, based on immune cell transcriptome expression profiling data. Based on the Cancer Genome Atlas and Gene Expression Omnibus datasets, we found that the FibLnc score predicted differences in overall patient survival and performed well in multiple datasets. FibLnc score was associated with the clinical stage of patients with breast cancer but did not significantly correlate with the PAM50 classification. Functional analysis showed that FibLnc was positively correlated with signaling pathways associated with malignant tumor progression. In addition, FibLnc was positively correlated with tumor mutational load and could predict immunotherapy response in patients with breast cancer receiving anti-PD-1 or anti-CTLA4 therapy. Our proposed FibLnc score was able to reflect the status of the immune environment and immunotherapeutic response in breast cancer, which could help explore potential therapeutic decisions and regulatory mechanisms of CAF-specific lncRNAs.

Patient-derived organoids potentiate precision medicine in advanced clear cell renal cell carcinoma.

To investigate the role of patient-derived organoid (PDO) model in the precision medicine of advanced clear cell renal cell carcinoma (ccRCC), we retrospectively analyzed the clinical data of seven cases of ccRCC diagnosed by operation and pathology in Renji Hospital from September 2021 to September 2022. The seven patients were diagnosed with advanced ccRCC with or without remote metastasis. Cytoreductive and radical nephrectomy was performed respectively. To predict the response to immunotherapy and provide personalized medicine recommendation, a PDO model based on air-liquid interface system was established from the surgical resected tumor and subsequent drug screening was performed. Hematoxylin and eosin (H&E) staining and immunohistochemistry revealed that the PDO recapitulated the histological feature of parent tumor. Immunofluorescence staining identified that CD3+ T cells, SMA+ cancer associated fibroblasts, and CD31+ endothelial cells were preserved in PDO models. Fluorescence activated cell sorter (FACS) revealed an evidently increased ratio of CD8+/CD4+ T cells and apoptotic tumor cells in PDO treated with toripalimab than those treated with IgG4. The results showed that toripalimab is able to rescue the excessive death of CD8+ T cells by critically reversing the immune exhaustion state of ccRCC in PDO model. This research validated that PDO is a promising and faithful preclinical model for prediction of immunotherapy response in patients with ccRCC.

Seven Fatty Acid Metabolism-Related Genes as Potential Biomarkers for Predicting the Prognosis and Immunotherapy Responses in Patients with Esophageal Cancer.

Background: Esophageal cancer (ESCA) is a major cause of cancer-related mortality worldwide. Altered fatty acid metabolism is a hallmark of cancer. However, studies on the roles of fatty acid metabolism-related genes (FRGs) in ESCA remain limited. Method: We identified differentially expressed FRGs (DE-FRGs). Then, the DE-FRGs prognostic model was constructed and validated using a comprehensive analysis. Moreover, the correlation between the risk model and clinical characteristics was investigated. A nomogram for predicting survival was established and evaluated. Subsequently, the difference in tumor microenvironment (TME) was compared between two risk groups. The sensitivity of key DE-FRGs to chemotherapeutic interventions and their correlation with immune cells were investigated. Finally, DEGs between two risk groups were measured and the prognostic value of key DE-FRGs in ESCA was confirmed in other databases. Results: A prognostic model was constructed based on seven selected DEG-FRGs. TNM staging and CD8+ T cells were significantly correlated with high-risk groups. Low-risk groups exhibited more infiltrated M0 macrophages, an activation of type II interferon (IFN-γ) responses, and were found to be more suitable for immunotherapy. Seven key DE-FRGs with prognostic value were found to be considerably influenced by different chemotherapy drugs. Conclusion: A prognostic model based on seven DE-FRGs may efficiently predict patient prognosis and immunotherapy response, helping to develop individualized treatment strategies in ESCA.

Integrating machine learning to construct aberrant alternative splicing event related classifiers to predict prognosis and immunotherapy response in patients with hepatocellular carcinoma.

Introduction: In hepatocellular carcinoma (HCC), alternative splicing (AS) is related to tumor invasion and progression. Methods: We used HCC data from a public database to identify AS subtypes by unsupervised clustering. Through feature analysis of different splicing subtypes and acquisition of the differential alternative splicing events (DASEs) combined with enrichment analysis, the differences in several subtypes were explored, cell function studies have also demonstrated that it plays an important role in HCC. Results: Finally, in keeping with the differences between these subtypes, DASEs identified survival-related AS times, and were used to construct risk proportional regression models. AS was found to be useful for the classification of HCC subtypes, which changed the activity of tumor-related pathways through differential splicing effects, affected the tumor microenvironment, and participated in immune reprogramming. Conclusion: In this study, we described the clinical and molecular characteristics providing a new approach for the personalized treatment of HCC patients.

Machine learning-based tumor-infiltrating immune cell-associated lncRNAs for predicting prognosis and immunotherapy response in patients with glioblastoma.

Long noncoding ribonucleic acids (RNAs; lncRNAs) have been associated with cancer immunity regulation. However, the roles of immune cell-specific lncRNAs in glioblastoma (GBM) remain largely unknown. In this study, a novel computational framework was constructed to screen the tumor-infiltrating immune cell-associated lncRNAs (TIIClnc) for developing TIIClnc signature by integratively analyzing the transcriptome data of purified immune cells, GBM cell lines and bulk GBM tissues using six machine learning algorithms. As a result, TIIClnc signature could distinguish survival outcomes of GBM patients across four independent datasets, including the Xiangya in-house dataset, and more importantly, showed superior performance than 95 previously established signatures in gliomas. TIIClnc signature was revealed to be an indicator of the infiltration level of immune cells and predicted the response outcomes of immunotherapy. The positive correlation between TIIClnc signature and CD8, PD-1 and PD-L1 was verified in the Xiangya in-house dataset. As a newly demonstrated predictive biomarker, the TIIClnc signature enabled a more precise selection of the GBM population who would benefit from immunotherapy and should be validated and applied in the near future.

Establishment of lactate-metabolism-related signature to predict prognosis and immunotherapy response in patients with colon adenocarcinoma.

The outcome of colon adenocarcinoma (COAD) patients remains dismal, and lactate metabolism has been characterized to promote tumor development and immune evasion. Based on the above background, it is worthwhile to explore novel prognostic and therapeutic biomarkers for COAD patients from the aspect of lactate metabolism. Above all, 228 available lactate-metabolism-related genes (LMRGs) were acquired, and the landscape of copy number variation and the expression difference of mRNA levels between colon normal and tumor samples were investigated among these LMRGs. Importantly, eight overall survival (OS)-involved LMRGs were then distinguished by means of univariate Cox regression analysis in both GSE40967 and TCGA-COAD data sets. Subsequently, prognostic risk scores were established, integrating seven OS-related LMRGs by LASSO Cox regression analysis in the GSE40967 set, and then verified in the TCGA-COAD cohort. From the comprehensive analyses, COAD patients with high risk had comparatively more inferior survival probability in all populations of the study, and they tended to have more severe clinicopathological features with the risk score increasing. Moreover, by integrating age, AJCC T and pathological stage, and risk score, we constructed a prognostic nomogram that demonstrated great prediction effectiveness for OS of COAD patients. Furthermore, the potential effect of various risk score on tumor immune was assessed from enrichment of immune-related pathways, tumor-infiltrating immune cells, and expression levels of immune checkpoints separately. We could draw a conclusion that COAD patients with higher lactate-metabolism-related risk scores may acquire an immunosuppressive tumor microenvironment, which subsequently led to immune escapes and poor prognoses. Conclusively, all findings in the present study illustrate a great prognostic value of the lactate-metabolism-related risk signature, providing more in-depth insights into the indispensable function of lactate metabolism in prognosis and tumor immunity of COAD.

Identification of tumor microenvironment-related signature for predicting prognosis and immunotherapy response in patients with bladder cancer.

The tumor microenvironment (TME) not only provides fertile soil for tumor growth and development but also widely involves immune evasion as well as the resistance towards therapeutic response. Accumulating interest has been attracted from the biological function of TME to its effects on patient outcomes and treatment efficacy. However, the relationship between the TME-related gene expression profiles and the prognosis of bladder cancer (BLCA) remains unclear. The TME-related genes expression data of BLCA were collected from The Cancer Genome Atlas (TCGA) database. NFM algorithm was used to identify the distinct molecular pattern based on the significantly different TME-related genes. LASSO regression and Cox regression analyses were conducted to identify TME-related gene markers related to the prognosis of BLCA and to establish a prognostic model. The predictive efficacy of the risk model was verified through integrated bioinformatics analyses. Herein, 10 TME-related genes (PFKFB4, P4HB, OR2B6, OCIAD2, OAS1, KCNJ15, AHNAK, RAC3, EMP1, and PRKY) were identified to construct the prognostic model. The established risk scores were able to predict outcomes at 1, 3, and 5 years with greater accuracy than previously known models. Moreover, the risk score was closely associated with immune cell infiltration and the immunoregulatory genes including T cell exhaustion markers. Notably, the predictive power of the model in immunotherapy sensitivity was verified when it was applied to patients with metastatic urothelial carcinoma (mUC) undergoing immunotherapy. In conclusion, TME risk score can function as an independent prognostic biomarker and a predictor for evaluating immunotherapy response in BLCA patients, which provides recommendations for improving patients’ response to immunotherapy and promoting personalized tumor immunotherapy in the future.

Identification of a Signature for Predicting Prognosis and Immunotherapy Response in Patients with Glioma.

Glioma is a deadly tumor that accounts for the vast majority of brain tumors. Thus, it is important to elucidate the molecular pathogenesis and potential diagnostic and prognostic biomarkers of glioma. In the present study, gene expression profiles of GSE2223 were obtained from the Gene Expression Omnibus (GEO) database. Core modules and hub genes related to glioma were identified using weighted gene coexpression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis of differentially expressed genes (DEGs). After a series of database screening tests, we identified 11 modules during glioma progression, followed by six hub genes (RAB3A, TYROBP, SYP, CAMK2A, VSIG4, and GABRA1) that can predict the prognosis of glioma and were validated in glioma tissues by qRT-PCR. The CIBERSORT algorithm was used to analyze the difference of immune cell infiltration between the glioma and control groups. Finally, Identification VSIG4 for immunotherapy response in patients with glioma demonstrating utility for immunotherapy research.

Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer

Immunotherapy is used to treat almost all patients with advanced non-small cell lung cancer (NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we show the predictive capacity of integrating medical imaging, histopathologic and genomic features to predict immunotherapy response using a cohort of 247 patients with advanced NSCLC with multimodal baseline data obtained during diagnostic clinical workup, including computed tomography scan images, digitized programmed death ligand-1 immunohistochemistry slides and known outcomes to immunotherapy. Using domain expert annotations, we developed a computational workflow to extract patient-level features and used a machine-learning approach to integrate multimodal features into a risk prediction model. Our multimodal model (area under the curve (AUC) = 0.80, 95% confidence interval (CI) 0.74–0.86) outperformed unimodal measures, including tumor mutational burden (AUC = 0.61, 95% CI 0.52–0.70) and programmed death ligand-1 immunohistochemistry score (AUC = 0.73, 95% CI 0.65–0.81). Our study therefore provides a quantitative rationale for using multimodal features to improve prediction of immunotherapy response in patients with NSCLC using expert-guided machine learning.