Expression Of Immune Checkpoint Genes Research Articles

Evaluation of the genomic and immune profiles of patients with lung adenosquamous carcinoma (LUAS) and association of response to treatment.

8567 Background: LUAS is a rare subset of non-small cell lung cancer (NSCLC) comprising up to 4% of cases, characterized by at least 10% of both adenocarcinoma and squamous components. Limited studies exist on LUAS regarding the genomic/immune landscape and response to immunotherapy (IO). Methods: NSCLC samples (n = 35404) underwent NextGen Sequencing of DNA (592 genes or whole exome)/RNA (whole transcriptome) at Caris Life Sciences. PD-L1 expression was assessed using IHC (22c3; positive: TPS >1%). High tumor mutational burden (TMB-H) was defined as ≥10 mut/MB. Cell infiltration in the tumor microenvironment was estimated by quanTIseq. Gene expression profiles were analyzed for transcriptomic signatures (IFN-γ) predictive of IO response. Real-world overall survival (rwOS) was obtained from insurance claims data, calculated from time of biopsy to last contact. Time on treatment (TOT) was calculated from the first pembrolizumab (pembro) treatment to the last. Mann-Whitney U and X2/Fisher-Exact tests were applied where appropriate, with p-values adjusted ( p < .05). Results: Among NSCLC samples, 1.0% (n=374) were LUAS, 26.5% (n = 9365) were squamous (SCC) and 72.5% (n = 25665) were adenocarcinoma (AC). LUAS exhibited actionable alterations in 29% (n = 110/374) of cases, with MET exon 14 skipping alteration ( METex14) significantly higher in LUAS compared to SCC or AC (9.02% vs 1.21% vs 2.61%, p < 0.01). BRAF V600E (0.54% vs 0.13%, p = 0.041) and ALK-Fusion (1.13% vs 0.12%, p = 0.0145) were higher in LUAS compared to SCC, while PIK3CA mutation was higher in LUAS compared to AC (10.8% vs 4.1%, p <0.01). Notably, LUAS exhibited higher prevalence of KRAS, targetable EGFRand STK11 mutations compared to SCC but lower compared to AC ( KRAS G12C: 8.63% vs 1.37% vs 13.24%, p <0.01; EGFR: 13.1% vs 1.12% vs 17.53%, p<0.01; STK11: 6.97% vs 1.83% vs 17.22%, p<0.01). LUAS exhibited significantly higher PD-L1+ (65.5% vs 59.8% vs 53.7%, p <0.01), along with elevated expression of immune checkpoint genes and the IFN-γ signature compared to AC and SCC. Moreover, LUAS exhibited significantly higher neutrophils, CD8 T cells, M2 macrophages and Tregs compared to SCC. LUAS showed improved rwOS compared to SCC (HR 0.792, p<0.01), with no difference noted in the rwOS between LUAS and AC (HR = 0.98, p = 0.81). While PD-L1+ was not associated with TOT on pembro in LUAS, high TMB showed improved TOT on pembro in LUAS. Conclusions: This study provides a comprehensive genomic landscape of LUAS, highlighting actionable alterations in ~ 29% of cases. LUAS exhibits distinct molecular and clinical features compared to AC and SCC, with enrichment in actionable METex14, emphasizing the need for NGS testing. Although PD-L1 status may not significantly impact TOT in LUAS, it remains relevant in other NSCLC subtypes on IO and high TMB emerges as a potential biomarker for favorable TOT on pembro in LUAS, warranting further investigation.

Unravelling the metabolic landscape of cutaneous melanoma: Insights from single-cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism.

This manuscript presents a comprehensive investigation into the role of lactate metabolism-related genes as potential prognostic markers in skin cutaneous melanoma (SKCM). Bulk-transcriptome data from The Cancer Genome Atlas (TCGA) and GSE19234, GSE22153, and GSE65904 cohorts from GEO database were processed and harmonized to mitigate batch effects. Lactate metabolism scores were assigned to individual cells using the 'AUCell' package. Weighted Co-expression Network Analysis (WGCNA) was employed to identify gene modules correlated with lactate metabolism. Machine learning algorithms were applied to construct a prognostic model, and its performance was evaluated in multiple cohorts. Immune correlation, mutation analysis, and enrichment analysis were conducted to further characterize the prognostic model's biological implications. Finally, the function of key gene NDUFS7 was verified by cell experiments. Machine learning resulted in an optimal prognostic model, demonstrating significant prognostic value across various cohorts. In the different cohorts, the high-risk group showed a poor prognosis. Immune analysis indicated differences in immune cell infiltration and checkpoint gene expression between risk groups. Mutation analysis identified genes with high mutation loads in SKCM. Enrichment analysis unveiled enriched pathways and biological processes in high-risk SKCM patients. NDUFS7 was found to be a hub gene in the protein-protein interaction network. After the expression of NDUFS7 was reduced by siRNA knockdown, CCK-8, colony formation, transwell and wound healing tests showed that the activity, proliferation and migration of A375 and WM115 cell lines were significantly decreased. This study offers insights into the prognostic significance of lactate metabolism-related genes in SKCM.

Pan-cancer analysis predict that FAT1 is a therapeutic target and immunotherapy biomarker for multiple cancer types including non-small cell lung cancer.

FAT1, a substantial transmembrane protein, plays a pivotal role in cellular adhesion and cell signaling. Numerous studies have documented frequent alterations in FAT1 across various cancer types, with its aberrant expression being linked to unfavorable survival rates and tumor progression. In the present investigation, we employed bioinformatic analyses, as well as in vitro and in vivo experiments to elucidate the functional significance of FAT1 in pan-cancer, with a primary focus on lung cancer. Our findings unveiled FAT1 overexpression in diverse cancer types, including lung cancer, concomitant with its association with an unfavorable prognosis. Furthermore, FAT1 is intricately involved in immune-related pathways and demonstrates a strong correlation with the expression of immune checkpoint genes. The suppression of FAT1 in lung cancer cells results in reduced cell proliferation, migration, and invasion. These collective findings suggest that FAT1 has potential utility both as a biomarker and as a therapeutic target for lung cancer.

A novel subtype based on driver methylation–transcription in lung adenocarcinoma

AimsTo identify driver methylation genes and a novel subtype of lung adenocarcinoma (LUAD) by multi-omics and elucidate its molecular features and clinical significance.MethodsWe collected LUAD patients from public databases, and identified driver methylation genes (DMGs) by MethSig and MethylMix algrothms. And novel driver methylation multi-omics subtypes were identified by similarity network fusion (SNF). Furthermore, the prognosis, tumor microenvironment (TME), molecular features and therapy efficiency among subtypes were comprehensively evaluated.Results147 overlapped driver methylation were identified and validated. By integrating the mRNA expression and methylation of DMGs using SNF, four distinct patterns, termed as S1-S4, were characterized by differences in prognosis, biological features, and TME. The S2 subtype showed unfavorable prognosis. By comparing the characteristics of the DMGs subtypes with the traditional subtypes, S3 was concentrated in proximal-inflammatory (PI) subtype, and S4 was consisted of terminal respiratory unit (TRU) subtype and PI subtype. By analyzing TME and epithelial mesenchymal transition (EMT) features, increased immune infiltration and higher expression of immune checkpoint genes were found in S3 and S4. While S4 showed higher EMT score and expression of EMT associated genes, indicating S4 may not be as immunosensitive as the S3. Additionally, S3 had lower TIDE and higher IPS score, indicating its increased sensitivity to immunotherapy.ConclusionThe driver methylation-related subtypes of LUAD demonstrate prognostic predictive ability that could help inform treatment response and provide complementary information to the existing subtypes.

Identification of Molecular Subtypes Associated with PCD in Esophageal Squamous Cell Carcinoma and Analysis of Immune Microenvironment.

Esophageal squamous cell carcinoma (ESCC) is a highly fatal malignancy with increasing incidence, and programmed cell death (PCD) plays an important role in homeostasis. This study aimed to explore the ESCC of heterogeneity based on the PCD signatures for the diagnosis and treatment of patients. The clinical information and RNA-seq data of patients with ESCC and the PCD-related genes set were used to identify PCD signatures.The "limma" package was used to identify the differentially expressed genes (DEGs). "Clusterprofiler" package was used for function enrichment analysis, and the "ConsensusClusterPlus" package was performed for consensus clustering. Finally, the "GSVA" package and the Cibersort algorithm were used for the immune infiltration analysis. We performed differential expression analysis between ESCC and normal samples and identified 1659 DEGs, of which 124 DEGs were PCD genes. Then, the patients were divided into cluster1 and cluster2 based on the expression of 124 PCD genes. There was a significant difference in immune infiltration between the two clusters. The patients in cluster 1 had a higher immune score and more CD56dim natural killer cells, monocytes, activated CD4 T cells, eosinophil, and activated B cells infiltration, while cluster2 had a higher stromal score, more immune regulation, and immune checkpoint genes expression. We identified two clusters based on PCD gene expression and characterized their tumor microenvironment and immune checkpoint difference. Our findings may provide some new insight into the treatment of ESCC.

TM-Score predicts immunotherapy efficacy and improves the performance of the machine learning prognostic model in gastric cancer

Immunotherapy is becoming increasingly important, but the overall response rate is relatively low in the treatment of gastric cancer (GC). The application of tumor mutational burden (TMB) in predicting immunotherapy efficacy in GC patients is limited and controversial, emphasizing the importance of optimizing TMB-based patient selection. By combining TMB and major histocompatibility complex (MHC) related hub genes, we established a novel TM-Score. This score showed superior performance for immunotherapeutic selection (AUC = 0.808) compared to TMB, MSI status, and EBV status. Additionally, it predicted the prognosis of GC patients. Subsequently, a machine learning model adjusted by the TM-Score further improved the accuracy of survival prediction (AUC > 0.8). Meanwhile, we found that GC patients with low TM-Score had a higher mutation frequency, higher expression of HLA genes and immune checkpoint genes, and higher infiltration of CD8+ T cells, CD4+ helper T cells, and M1 macrophages. This suggests that TM-Score is significantly associated with tumor immunogenicity and tumor immune environment. Notably, based on the RNA-seq and scRNA-seq, it was found that AKAP5, a key component gene of TM-Score, is involved in anti-tumor immunity by promoting the infiltration of CD4+ T cells, NK cells, and myeloid cells. Additionally, siAKAP5 significantly reduced MHC-II mRNA expression in the GC cell line. In addition, our immunohistochemistry assays confirmed a positive correlation between AKAP5 and human leukocyte antigen (HLA) expression. Furthermore, AKAP5 levels were higher in patients with longer survival and those who responded to immunotherapy in GC, indicating its potential value in predicting prognosis and immunotherapy outcomes. In conclusion, TM-Score, as an optimization of TMB, is a more precise biomarker for predicting the immunotherapy efficacy of the GC population. Additionally, AKAP5 shows promise as a therapeutic target for GC.

Abstract PO4-24-01: Comprehensive Molecular and Immunological Characterization of Invasive Ductal Triple-Negative Breast Cancer

Abstract Background: Triple-negative breast cancer (TNBC) is a heterogeneous disease and characterized by poor outcomes with a lack of targeted therapies. A comprehensive analysis of the molecular and immune landscape of invasive ductal TNBC can help improve our understanding of TNBC biology and identify novel targets and/or pathways for better management of this disease. Here, we characterized the molecular and immune signature of invasive ductal (ID) TNBC. Methods: 13,036 BC samples (ID TNBC, n=392; ID non-TNBC, n=927) were analyzed by NGS (592, NextSeq; WES, NovaSeq), WTS (NovaSeq) (Caris Life Sciences, Phoenix, AZ). A total of 10 pro-apoptotic (BAX, BAK1, BID, BAD, BIK, BCL2L11, BMF, HRK, PMAIP1, BBC3) and 6 anti-apoptotic (BCL2, BCL2L1, BCL2L2, MCL1, BCL2A1, BCL2L10) BCL2 family genes were analyzed. Immune cell fractions were calculated by deconvolution of WTS: Quantiseq. Real world overall survival (OS) and treatment-associated survival was extracted from insurance claims and calculated from tissue collection or treatment start to last contact using Kaplan-Meier estimates. Statistical significance was determined using chi-square and Mann-Whitney U test with p-values adjusted for multiple comparisons (q < 0.05). Results: ID TNBC had a higher frequency of TP53, RB1, NF1, PIK3R1, NOTCH1, CREBBP, BRCA1, FANCI, and HRAS mutations (Table 1), NOTCH2 (4.3% vs 0.47%), EGFR (3.4% vs 0%), NFIB (4% vs 0.6%), MYB (3.5% vs 0.5%), CCNE1 (4.4% vs 1.1%) and AKT2 (2.4% vs 0.3%) copy number alterations, and NOTCH2 (1.6% VS 0.2%) fusion (all p < 0.05) compared to invasive ductal non-TNBC. Analysis of inferred immune cell infiltrates showed that ID TNBC had increased infiltration of Tregs (2% vs 1.7%), DC (3.2% vs 2.4%), CD8 T cells (0.5% vs 0.1%), and M1 macrophages (M) (3.4% vs 2.9%), but decreased infiltration of B cells (4.5% vs 6%) and M2M (2.9% vs 4.5%) (all p < 0.05). ID TNBC had increased T cell inflamed score (23 vs -9, p < 0.05), IFNy score (-0.24 vs -0.34, p < 0.05), MHC class I genes (HLA-A, HLA-B, TAP1, TAP2, FC: 1.1-1.5, all p < 0.05), immune checkpoint genes (CD274, PDCD1, CTLA4, PD-L2, FOXP3, IDO, FC: 1.1-2, all p < 0.05) and PD-L1 protein expression (SP142: 49.8% vs. 28.4%; 22c3: 41.7% vs. 16.8%, all p < 0.05). ID TNBC had differential expression of BCL2 family genes (upregulation: BAK1, BID, MCL1, BCL2A1, BCL2L10, FC: 1.1-2.0; downregulation: BAD, BIK, BBC3, BCL2, BCL2L1, BCL2L2, FC: 1.2-2.8, all p < 0.05) compared to invasive ductal non-TNBC. ID TNBC was associated with worse OS compared to ID non-TNBC (mOS: 24.5 vs 54.6 months; HR: 0.5, 95% CI 0.47-0.57; p < 0.00001) but trend towards better survival with pembrolizumab (mOS: 29.4 vs 8.8 month; HR: 0.67, 95% CI 0.28-1.5; p=0.3) treatment. No significant OS difference was noted for by high vs. low pro and anti-apoptotic BCL2 genes except for anti-apoptotic BCL2A1 (higher expression associated with better OS (mOS: 5.7 months, HR: 0.78, 95% CI 0.6-0.99; p=0.047). Conclusion: These data indicate that ID TNBC is associated with distinct mutational profile compared to non-TNBC, has increased T cell inflamed score, IFNy score, MHC class I and immune checkpoint gene expression, and differential immune cell infiltration. Interestingly, TNBC was associated with increased M1 and decreased M2M. There is evidence to suggest that transcriptomically defined M1 high tumors are clinically aggressive and have worse OS in TNBC. BCL2 family genes are not prognostic for TNBC outcomes except BCL2A1, which needs further prospective validation. Table 1: Mutation frequency in invasive ductal TNBC and invasive ductal non-TNBC Citation Format: Pooja Advani, Sachin Deshmukh, Sharon Wu, Jacob Andring, Joanne Xiu, Alex Farrell, Jose Leone, Priya Jayachandran, Stephanie Graff, Matthew Oberley, George Sledge Jr, Asher Chanan-Khan. Comprehensive Molecular and Immunological Characterization of Invasive Ductal Triple-Negative Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-24-01.

Identification of a pyroptosis-immune-related lncRNA signature for prognostic and immune landscape prediction in bladder cancer patients

PurposeIndividualized medicine has become increasingly important in bladder cancer treatment, whereas useful biomarkers for prognostic prediction are still lacking. The current study, therefore, constructed a novel risk model based on pyroptosis- and immune-related long noncoding RNAs (Pyro-Imm lncRNAs) to evaluate the potential prognosis of bladder cancer.MethodsCorresponding data of bladder cancer patients were downloaded from the Cancer Genome Atlas (TCGA) database. The univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis were employed to establish a predictive signature, which was evaluated by receiver operator characteristic (ROC) analysis and Kaplan–Meier analysis. Furthermore, the immune infiltration, immune checkpoints, and responses to chemotherapeutic drugs were analyzed with this model.ResultsThree Pyro-Imm lncRNAs (MAFG-DT, AC024060.1, AC116914.2) were finally identified. Patients in the low-risk group demonstrated a significant survival advantage. The area under the ROC curve (AUC) at 1, 3, and 5 years was 0.694, 0.709, and 0.736 respectively in the entire cohort. KEGG and GO analyses showed that the Wnt pathway plays a crucial role in the high-risk group. The risk score was significantly related to the degree of infiltration of different immune cells, the expression of multiple immune checkpoint genes, and the sensitivity of various chemotherapeutic drugs.ConclusionThis novel signature provides a theoretical basis for cancer immunology and chemotherapy, which might help develop individualized therapy.

Genomic Profiling of Rare Undifferentiated Sarcomatoid Subtypes of Pancreatic Carcinomas: In Search of Therapeutic Targets

PURPOSE The highly aggressive undifferentiated sarcomatoid carcinoma (USC) subtype of pancreatic ductal adenocarcinoma (PDAC) remains poorly characterized because of its rarity. Previous case reports suggest that immune checkpoint inhibitors could be a promising treatment strategy, but the prevalence of established predictive biomarkers of response is largely unknown. The objective of this study was to leverage comprehensive genomic profiling of USC PDAC tumors to determine the prevalence of biomarkers associated with potential response to targeted therapies. METHODS USC tumors (n = 20) underwent central pathology review by a board-certified gastrointestinal pathologist to confirm the diagnosis. These samples were compared with non-USC PDAC tumors (N = 5,562). Retrospective analysis of DNA and RNA next-generation sequencing data was performed. RESULTS USC PDACs were more frequently PD-L1+ by immunohistochemistry than non-USC PDAC (63% v 16%, respectively, P < .001). Furthermore, USC PDAC had an increase in neutrophils (8.99% v 5.55%, P = .005) and dendritic cells (1.08% v 0.00%, q = 0.022) and an increased expression of PDCD1LG2 (4.6% v 1.3%, q = 0.001), PDCD1 (2.0% v 0.8%, q = 0.060), and HAVCR2 (45.9% v 21.7%, q = 0.107) than non-USC PDAC. Similar to non-USC PDAC, KRAS was the most commonly mutated gene (86% v 90%, respectively, P = 1). CONCLUSION To our knowledge, this work represents the largest molecular analysis of USC tumors to date and showed an increased expression of immune checkpoint genes in USC tumors. These findings provide evidence for further investigation into immune checkpoint inhibitors in USC tumors.

Importance of CD8 Tex cell-associated gene signatures in the prognosis and immunology of osteosarcoma

As a highly aggressive bone malignancy, osteosarcoma poses a significant therapeutic challenge, especially in the setting of metastasis or recurrence. This study aimed to investigate the potential of CD8-Tex cell-associated genes as prognostic biomarkers to reveal the immunogenomic profile of osteosarcoma and guide therapeutic decisions. mRNA expression data and clinical details of osteosarcoma patients were obtained from the TCGA database (TARGET-OS dataset). The GSE21257 dataset (from the GEO database) was used as an external validation set to provide additional information on osteosarcoma specimens. 84 samples from the TARGET-OS dataset were used as the training set, and 53 samples from the GSE21257 dataset served as the external validation cohort. Univariate Cox regression analysis was utilized to identify CD8 Tex cell genes associated with prognosis. The LASSO algorithm was performed for 1000 iterations to select the best subset to form the CD8 Tex cell gene signature (TRS). Final genes were identified using the multivariate Cox regression model of the LASSO algorithm. Risk scores were calculated to categorize patients into high- and low-risk groups, and clinical differences were explored by Kaplan–Meier survival analysis to assess model performance. Prediction maps were constructed to estimate 1-, 3-, and 5 year survival rates for osteosarcoma patients, including risk scores for CD8 Texcell gene markers and clinicopathologic factors. The ssGSEA algorithm was used to assess the differences in immune function between TRS-defined high- and low-risk groups. TME and immune cell infiltration were further assessed using the ESTIMATE and CIBERSORT algorithms. To explore the relationship between immune checkpoint gene expression levels and the two risk-defined groups. A CD8 Tex cell-associated gene signature was extracted from the TISCH database and prognostic markers including two genes were developed. The high-risk group showed lower survival, and model performance was validated by ROC curves and C-index. Predictive plots were constructed to demonstrate survival estimates, combining CD8 Tex cell gene markers and clinical factors. This study provides valuable insights into the molecular and immune characteristics of osteosarcoma and offers potential avenues for advances in therapeutic approaches.

Predictive Model to Identify the Long Time Survivor in Patients with Glioblastoma: A Cohort Study Integrating Machine Learning Algorithms.

We aimed to develop and validate a predictive model for identifying long-term survivors (LTS) among glioblastoma (GB) patients, defined as those with an overall survival (OS) of more than 3 years. A total of 293 GB patients from CGGA and 169 from TCGA database were assigned to training and validation cohort, respectively. The differences in expression of immune checkpoint genes (ICGs) and immune infiltration landscape were compared between LTS and short time survivor (STS) (OS<1.5 years). The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were used to identify the genes differentially expressed between LTS and STS. Three different machine learning algorithms were employed to select the predictive genes from the overlapping region of DEGs and WGCNA to construct the nomogram. The comparison between LTS and STS revealed that STS exhibited an immune-resistant status, with higher expression of ICGs (P<0.05) and greater infiltration of immune suppression cells compared to LTS (P<0.05). Four genes, namely, OSMR, FMOD, CXCL14, and TIMP1, were identified and incorporated into the nomogram, which possessed good potential in predicting LTS probability among GB patients both in the training (C-index, 0.791; 0.772-0.817) and validation cohort (C-index, 0.770; 0.751-0.806). STS was found to be more likely to exhibit an immune-cold phenotype. The identified predictive genes were used to construct the nomogram with potential to identify LTS among GB patients.

Comprehensive analysis of macrophage-related genes in prostate cancer by integrated analysis of single-cell and bulk RNA sequencing.

Macrophages, as essential components of the tumor immune microenvironment (TIME), could promote growth and invasion in many cancers. However, the role of macrophages in tumor microenvironment (TME) and immunotherapy in PCa is largely unexplored at present. Here, we investigated the roles of macrophage-related genes in molecular stratification, prognosis, TME, and immunotherapeutic response in PCa. Public databases provided single-cell RNA sequencing (scRNA-seq) and bulk RNAseq data. Using the Seurat R package, scRNA-seq data was processed and macrophage clusters were identified automatically and manually. Using the CellChat R package, intercellular communication analysis revealed that tumor-associated macrophages (TAMs) interact with other cells in the PCa TME primarily through MIF - (CD74+CXCR4) and MIF - (CD74+CD44) ligand-receptor pairs. We constructed coexpression networks of macrophages using the WGCNA to identify macrophage-related genes. Using the R package ConsensusClusterPlus, unsupervised hierarchical clustering analysis identified two distinct macrophage-associated subtypes, which have significantly different pathway activation status, TIME, and immunotherapeutic efficacy. Next, an 8-gene macrophage-related risk signature (MRS) was established through the LASSO Cox regression analysis with 10-fold cross-validation, and the performance of the MRS was validated in eight external PCa cohorts. The high-risk group had more active immune-related functions, more infiltrating immune cells, higher HLA and immune checkpoint gene expression, higher immune scores, and lower TIDE scores. Finally, the NCF4 gene has been identified as the hub gene in MRS using the "mgeneSim" function.

Genomic and Transcriptomic Landscape of RET Wild-Type Medullary Thyroid Cancer and Potential Use of Mitogen-Activated Protein Kinase-Targeted Therapy.

About 75% of medullary thyroid cancers (MTCs) are sporadic with 45% to 70% being driven by a RET mutation. Selpercatinib is an approved treatment for RET-mutated (mut RET ) MTC; however, treatments are needed for wild-type RET MTC (wt RET ). Genomic alterations and transcriptomic signatures of wt RET MTC may reveal new therapeutic insights. We did a retrospective analysis of MTC samples submitted for DNA/RNA sequencing and programmed cell death ligand 1 expression using immunohistochemistry at a Clinical Laboratory Improvement Amendments/College of American Pathologists-certified laboratory. Tumor microenvironment immune cell fractions were estimated using RNA deconvolution (quanTIseq). Transcriptomic signatures of inflammation and MAP kinase pathway activation scores were calculated. Mann-Whitney U, chi-square, and Fisher's exact tests were applied (p values adjusted for multiple comparisons). The 160-patient cohort included 108 mut RET and 52 wt RET MTC samples. wt RET tumors frequently harbored mitogen-activated protein kinase (MAPK) pathway mutations, including HRAS (42.31%), KRAS (15.7%), NF1 (6.7%), and BRAF (2%), whereas only 1 MAPK pathway mutation ( NF1 ) was identified among mut RET MTC. Recurrent mutations seen in wt RET MTC included MGA , VHL, APC , STK11 , and NFE2L2 . Increased transcriptional activation of the MAPK pathway was observed in patients with wt RET harboring mutations in MAPK genes. Although the frequency of programmed cell death ligand 1 expression was similar in wt RET and mut RET (10.2% vs 7%, p = 0.531), wt RET tumors were more often tumor mutational burden high (7.7% vs 0%, p = 0.011), and wt RET MTC exhibited higher expression of immune checkpoint genes. We identified molecular alterations and immune-related features that distinguish wt RET from mut RET MTC. Although RET mutation drives MTC in the absence of other alterations, we showed that wt RET MTC frequently harbors MAPK pathway mutations. These findings may indicate a potential basis for MAPK-targeted therapy, possibly in combination with immuno-oncology agents for selected patients with wt RET MTC.

A prognostic model established using bile acid genes to predict the immunity and survival of patients with gastrointestinal cancer.

The metabolism of abnormal bile acids (BAs) is implicated in the initiation and development of gastrointestinal (GI) cancer. However, there was a lack of research on the molecular mechanisms of BAs metabolism in GI. Genes involved in BAs metabolism were excavated from public databases of The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, and Molecular Signatures Database (MSigDB). ConsensusClusterPlus was used to classify molecular subtypes for GI. To develop a RiskScore model for predicting GI prognosis, univariate Cox analysis was performed on the genes in protein-protein interaction (PPI) network, followed by using Lasso regression and stepwise regression to refine the model and to determine the key prognostic genes. Tumor immune microenvironment in GI patients from different risk groups was assessed using the ESTIMATE algorithm and enrichment analysis. Reverse transcription-quantitative real-time PCR (RT-qPCR), Transwell assay, and wound healing assay were carried out to validate the expression and functions of the model genes. This study defined three molecular subtypes (C1, C2, and C3). Specifically, C1 had the best prognosis, while C3 had the worst prognosis with high immune checkpoint gene expression levels and TIDE scores. We selected nine key genes (AXIN2, ATOH1, CHST13, PNMA2, GYG2, MAGEA3, SNCG, HEYL, and RASSF10) that significantly affected the prognosis of GI and used them to develop a RiskScore model accordingly. Combining the verification results from a nomogram, the prediction of the model was proven to be accurate. The high RiskScore group was significantly enriched in tumor and immune-related pathways. Compared with normal gastric mucosal epithelial cells, the mRNA levels of the nine genes were differential in the gastric cancer cells. Inhibition of PNMA2 suppressed migration and invasion of the cancer cells. We distinguished three GI molecular subtypes with different prognosis based on the genes related to BAs metabolism and developed a RiskScore model, contributing to the diagnosis and treatment of patients with GI.

Prognosis and therapeutic significance of IGF-1R-related signaling pathway gene signature in glioma.

Glioma is the most common cancer of the central nervous system with poor therapeutic response and clinical prognosis. Insulin-like growth factor 1 receptor (IGF-1R) signaling is implicated in tumor development and progression and induces apoptosis of cancer cells following functional inhibition. However, the relationship between the IGF-1R-related signaling pathway genes and glioma prognosis or immunotherapy/chemotherapy is poorly understood. LASSO-Cox regression was employed to develop a 16-gene risk signature in the TCGA-GBMLGG cohort, and all patients with glioma were divided into low-risk and high-risk subgroups. The relationships between the risk signature and the tumor immune microenvironment (TIME), immunotherapy response, and chemotherapy response were then analyzed. Immunohistochemistry was used to evaluate the HSP90B1 level in clinical glioma tissue. The gene risk signature yielded superior predictive efficacy in prognosis (5-year area under the curve: 0.875) and can therefore serve as an independent prognostic indicator in patients with glioma. The high-risk subgroup exhibited abundant immune infltration and elevated immune checkpoint gene expression within the TIME. Subsequent analysis revealed that patients in the high-risk subgroup benefited more from chemotherapy. Immunohistochemical analysis confirmed that HSP90B1 was overexpressed in glioma, with significantly higher levels observed in glioblastoma than in astrocytoma or oligodendrocytoma. The newly identified 16-gene risk signature demonstrates a robust predictive capacity for glioma prognosis and plays a pivotal role in the TIME, thereby offering valuable insights for the exploration of novel biomarkers and targeted therapeutics.

SLC7A11 inhibits ferroptosis and downregulates PD-L1 levels in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a prevalent form of lung cancer originating from lung glandular cells with low survival rates despite recent therapeutic advances due to its diverse and complex nature. Recent evidence suggests a link between ferroptosis and the effectiveness of anti-PD-L1 therapy, with potential synergistic effects. Our study comprehensively analyzed the expression patterns of ferroptosis regulators in LUAD and their association with prognosis and PD-L1 expression. Furthermore, we identified two distinct subtypes of LUAD through consensus clustering of ferroptosis regulators, revealing significant tumor heterogeneity, divergent PD-L1 expression, and varying prognoses between the subtypes. Among the selected ferroptosis regulators, SLC7A11 emerged as an independent prognostic marker for LUAD patients and exhibited a negative correlation with PD-L1 expression. Subsequent investigations revealed high expression of SLC7A11 in the LUAD population. In vitro experiments demonstrated that overexpression of SLC7A11 led to reduced PD-L1 expression and inhibited ferroptosis in A549 cells, underscoring the significant role of SLC7A11 in LUAD. Additionally, pan-cancer analyses indicated an association between SLC7A11 and the expression of immune checkpoint genes across multiple cancer types with poor prognoses. From a clinical standpoint, these findings offer a foundation for identifying and optimizing potential combination strategies to enhance the therapeutic effectiveness of immune checkpoint inhibitors and improve the prognosis of patients with LUAD.

Identification of m6A/m5C-related lncRNA signature for prediction of prognosis and immunotherapy efficacy in esophageal squamous cell carcinoma.

N6-methyladenosine (m6A) and 5-methylcytosine (m5C) RNA modifications have garnered significant attention in the field of epigenetic research due to their close association with human cancers. This study we focus on elucidating the expression patterns of m6A/m5C-related long non-coding RNAs (lncRNAs) in esophageal squamous cell carcinoma (ESCC) and assessing their prognostic significance and therapeutic potential. Transcriptomic profiles of ESCC were derived from public resources. m6A/m5C-related lncRNAs were obtained from TCGA using Spearman's correlations analysis. The m6A/m5C-lncRNAs prognostic signature was selected to construct a RiskScore model for survival prediction, and their correlation with the immune microenvironment and immunotherapy response was analyzed. A total of 606 m6A/m5C-lncRNAs were screened, and ESCC cases in the TCGA cohort were stratified into three clusters, which showed significantly distinct in various clinical features and immune landscapes. A RiskScore model comprising ten m6A/m5C-lncRNAs prognostic signature were constructed and displayed good independent prediction ability in validation datasets. Patients in the low-RiskScore group had a better prognosis, a higher abundance of immune cells (CD4 + T cell, CD4 + naive T cell, class-switched memory B cell, and Treg), and enhanced expression of most immune checkpoint genes. Importantly, patients with low-RiskScore were more cline benefit from immune checkpoint inhibitor treatment (P < 0.05). Our findings underscore the potential of RiskScore system comprising ten m6A/m5C-related lncRNAs as effective biomarkers for predicting survival outcomes, characterizing the immune landscape, and assessing response to immunotherapy in ESCC.

ZNF26-Associated Genes as Prognostic Signatures in Colorectal Cancer with Broad Therapeutic Implications.

The identification of biomarkers correlated with colorectal cancer (CRC) prognosis holds substantial importance from both clinical and scientific perspectives. Zinc finger protein 26 (ZNF26) has not been previously investigated or documented in solid tumors; thus, further research is necessary to ascertain its prognostic value in CRC. Gene expression profiles and clinicopathological data were acquired from The Cancer Genome Atlas (TCGA) database. Subsequently, expression correlation was assessed utilizing the TCGA CRC cohort. The prognostic value of ZNF26 was evaluated through Kaplan-Meier (KM) and ROC curve analyses. Following this, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to perform enrichment analysis between high- and low-ZNF26 expression groups. The association between immune cells, immune checkpoint genes, and ZNF26 expression levels was examined. Lastly, the research findings were further validated using CRC tissue samples. The results revealed that, in comparison to healthy controls, CRC significantly reduced ZNF26 expression. Elevated ZNF26 expression was associated with poorer overall survival in CRC patients. Additionally, high ZNF26 expression exhibited an inverse relationship with the immunological score and immune checkpoint gene expression in CRC patients. The findings from the TCGA data analysis were corroborated by the PCR results obtained from CRC tissue samples. ZNF26 is markedly upregulated in colorectal cancer tissues, potentially serving as a biomarker for CRC.

Everolimus treatment enhances inhibitory immune checkpoint molecules’ expression in monocyte-derived dendritic cells

BackgroundAntigen-specific T-cell immunity is provided by dendritic cells (DCs), which are specialized antigen-presenting cells. Furthermore, they establish a link between innate and adaptive immune responses. Currently, DC modification is a new approach for the therapy of several disorders. During solid organ transplantation, Everolimus, which is a mammalian target of rapamycin (mTOR) inhibitor, was initially utilized to suppress the immune system's functionality. Due to the intervention of Everolimus in various signaling pathways in cells and its modulatory properties on the immune system, this study aims to investigate the effect of treatment with Everolimus on the maturation and expression of immune checkpoint genes in monocyte-derived DCs. MethodsTo isolate monocytes from PBMCs, the CD14 marker was used via the MACS method. Monocytes were cultured and induced to differentiate into monocyte-derived DCs by utilizing GM-CSF and IL-4 cytokines. On the fifth day, immature DCs were treated with Everolimus and incubated for 24 h. On the sixth day, the flow cytometry technique was used to investigate the effect of Everolimus on the phenotypic characteristics of DCs. In the end, the expression of immune checkpoint genes in both the Everolimus-treated and untreated DCs groups was assessed using the real-time PCR method. ResultsThe findings of this research demonstrated that the administration of Everolimus to DCs led to a notable rise in human leukocyte antigen (HLA)-DR expression and a decrease in CD11c expression. Furthermore, there was a significant increase in the expression of immune checkpoint molecules, namely CTLA-4, VISTA, PD-L1, and BTLA, in DCs treated with Everolimus. ConclusionThe findings of this study show that Everolimus can target DCs and affect their phenotype and function in order to shift them toward a partially tolerogenic state. However, additional research is required to gain a comprehensive understanding of the precise impact of Everolimus on the activation status of DCs.

Disulfidptosis-related lncRNA signature reveals immune microenvironment and novel molecular subtyping of stomach adenocarcinoma

The main challenge in treating stomach adenocarcinoma (STAD) is chemotherapy resistance, which is characterized by changes in the immune microenvironment. Disulfidptosis, a novel form of programmed cell death, is involved in STAD but its mechanism is not fully understood. Long non-coding RNAs (LncRNAs) may play a role in regulating disulfidptosis and influencing the immune microenvironment and chemotherapy resistance in STAD. This study aims to establish disulfidptosis-related lncRNA (DRL) features and explore their significance in the immune microenvironment and chemotherapy resistance in STAD patients. By analyzing RNA sequencing and clinical data from STAD patients and extracting disulfidptosis-related genes, we identified DRLs through co-expression, single-factor and multi-factor Cox regression, and Lasso regression analyses. We also investigated differences in the immune microenvironment, immune function, immune checkpoint gene expression, and chemotherapy resistance between different risk groups using various algorithms. A prognostic risk model consisting of 2 DRLs was constructed, with a strong predictive value for patient survival, outperforming other clinical-pathological factors in predicting 3-year and 5-year survival. Immune-related analysis revealed a strong positive correlation between T cell CD4+ cells and risk score across all algorithms, and higher expression of immune checkpoint genes in the high-risk group. In addition, high-risk patients showed better sensitivity to Erlotinib, Oxaliplatin, and Gefitinib. Furthermore, three novel molecular subtypes of STAD were identified based on the 2-DRLs features, with evaluation of the immune microenvironment and chemotherapy drug sensitivity for each subgroup, which holds significant implications for achieving precise treatment in STAD. Overall, our 2-DRLs prognostic model demonstrates high predictive value for patient survival in STAD, potentially providing new targets for individualized immune and chemical therapy.