Expression Of Immune Checkpoint Genes Research Articles

Identifying an Inversin as a Novel Prognostic Marker in Patients with Clear-Cell Renal Cell Carcinoma

Precision medicine is a developing trend in oncology, and it includes the prognosis and treatment of advanced-stage ccRCC. New predictive factors and therapeutic targets for this disease are steadily needed. The aim of this study was to explore the tumor expression of inversin as a potential prognostic factor and/or therapeutic target in ccRCC. We compared the expression of inversin between primary ccRCC and normal renal tissues by using immunohistochemistry and rtPCR in our cohort, and we also analyzed publicly available data from the TCGA-KIRC cohort. We found that the expression of inversin was significantly lower in primary tumor tissue, in comparison to solid normal tissue. Data from the KIRC study confirmed that a lower INVS expression level in ccRCC was significantly related with the overall and disease-specific survival, as well as with a shorter progression-free interval (p < 0.05). Four out of ten inversin interactome partners were significantly related with the overall and disease-specific survival in ccRCC. A lower expression of ANKS6 was a negative survival predictor, while a higher expression of NPHP3, DVL1, or DVL3 was related with a lower survival. The expression of INVS and its interactome partners in ccRCC was correlated with the differentiation of the tumor and metastasis. The expression of INVS and its partners was also correlated with tumor leukocyte infiltration and the expression of immune checkpoint genes. The results of this study point to inversin and a distinguished group of its interactome partners as potential prognostic factors in ccRCC, with their predominant involvement in the modulation of the inflammatory infiltration of the tumor microenvironment and a strong relationship with the metastatic potential of the tumor.

Comprehensive Analysis and Verification of the Prognostic Significance of Cuproptosis-Related Genes in Colon Adenocarcinoma.

Colon adenocarcinoma (COAD) is a frequently occurring and lethal cancer. Cuproptosis is an emerging type of cell death, and the underlying pathways involved in this process in COAD remain poorly understood. Transcriptomic and clinical data for COAD patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We investigated alterations in DNA and chromatin of cuproptosis-related genes (CRGs) in COAD. In order to identify predictive differentially expressed genes (DEGs) and various molecular subtypes, we used consensus cluster analysis. Through univariate, multivariate, and Lasso Cox regression analyses, four CRGs were identified. A risk prognostic model for cuproptosis characteristics was constructed based on four CRGs. This study also examined the association between the risk score and the tumor microenvironment (TME), the immune landscape, and drug sensitivity. We distinguished two unique molecular subtypes using consensus clustering analysis. We discovered that the clinical characteristics, prognosis, and TME cell infiltration characteristics of patients with multilayer CRG subtypes were all connected. The internal and external evaluations of the predicted accuracy of the prognostic model built using data derived from a cuproptosis risk score were completed at the same time. A nomogram and a clinical pathological analysis make it more useful in the field of medicine. A significant rise in immunosuppressive cells was observed in the high cuproptosis risk score group, with a correlation identified between the cuproptosis risk score and immune cell infiltration. Despite generally poor prognoses, the patients with a high cuproptosis risk but low tumor mutation burden (TMB), cancer stem cell (CSC) index, or microsatellite instability (MSI) may still benefit from immunotherapy. Furthermore, the cuproptosis risk score positively correlated with immune checkpoint gene expression. Analyzing the potential sensitivity to medications could aid in the development of clinical chemotherapy regimens and decision-making. CRGs are the subject of our in-depth study, which exposed an array of regulatory mechanisms impacting TME. In addition, we performed additional data mining into clinical features, prognosis effectiveness, and possible treatment medications. COAD's molecular pathways will be better understood, leading to more precise treatment options.

Integration of bioinformatics and cellular experiments unveils the role of SYT12 in gastric cancer

ObjectiveThis study employs integrated bioinformatics analysis and in vitro cellular experiments to elucidate the role of Synaptotagmin-12 (SYT12) in the progression of gastric cancer.MethodsWe utilized databases and platforms such as Xiantao Academic Tools, UALCAN, Kaplan-Meier plotter analysis, and The Cancer Genome Atlas (TCGA) to extract datasets on SYT12 in gastric cancer. We analyzed the relationship between SYT12 expression and the clinicopathological features, prognosis, diagnosis, and immune infiltration of stomach adenocarcinoma (STAD) patients. Verification was conducted using samples from 31 gastric cancer patients. Additionally, in vitro cellular experiments were performed to determine the role and potential mechanisms of SYT12 in the malignant behavior of gastric cancer cells.ResultsComprehensive bioinformatics analysis indicated that SYT12 is highly expressed in most cancers and is associated with promoter DeoxyriboNucleic Acid (DNA) methylation levels. SYT12 expression correlated with clinicopathological features, immune cell infiltration, immune checkpoint gene expression, and poor prognosis in STAD patients. In vitro experiments suggest that SYT12 may promote the proliferation and migration of gastric cancer cells by inducing epithelial-mesenchymal transition (EMT).ConclusionsThis study highlights the significant role of SYT12 in gastric cancer, suggesting its potential as a new target for early diagnosis, treatment, immunological, and prognostic evaluation in gastric cancer, offering new insights for precision medicine in this disease.

Development and verification of a novel risk model related to ubiquitination linked with prognosis and therapeutic response in clear cell renal cell carcinoma

Increasing evidence highlights the important role of ubiquitination in cancer. The objective of our study is to establish a reliable marker for predicting clinical outcomes and treatment responses in patients with clear cell renal cell carcinoma (ccRCC) using genes related to ubiquitination (URGs). The URGs subtypes were identified using consensus clustering based on TCGA-KIRC, and a signature containing the prognostic differentially expressed genes of the subtypes was determined using LASSO and Cox regression analysis. To demonstrate the strength of the signature, verification analyses were performed on both E-MTAB-1980 and TCGA-KIRC test datasets. We developed a nomogram to enhance the effectiveness of our predictive tool. Risk genes expression was determined through RT-qPCR. Six genes were combined to create the URGs signature, which had a highly correlated with patient prognosis in patients with ccRCC. A nomogram was developed based on the URGs signature and clinicopathological characteristics. We found that the predictive power was substantially greater than the other individual predictors. Moreover, the study on the immune microenvironment revealed significant variations in the levels of immune cells and the expression of immune checkpoint genes among the groups categorized as high-risk and low-risk. Furthermore, it was found that immunotherapy yielded better outcomes in cohorts with low risk. The URGs signature might serve as a novel and powerful prognosis biomarker and offer a momentous reference for individualized treatment for patients in ccRCC.

Immune regulation and prognostic prediction model establishment and validation of PSMB6 in lung adenocarcinoma.

Lung cancer is one of the most common malignant tumors, and patients are often diagnosed at an advanced stage, posing a substantial risk to human health, so it is crucial to establish a model to forecast the prognosis of patients with lung cancer. Recent research has indicated that proteasome 20S subunit 6 (PSMB6) may be closely associated with anti-apoptotic pathways, and proliferation transduction signals in tumor cells of different tumors. However, the precise role of PSMB6 in the immunoregulatory processes within lung adenocarcinoma (LUAD) is yet to be elucidated. By analyzing the TCGA database, we discovered a positive correlation between the expression of PSMB6 and tumor growth trends, and lung adenocarcinoma patients with elevated PSMB6 expression levels had a worse prognosis. Our findings suggest a close correlation between PSMB6 expression levels, immune cell infiltration and immune checkpoint gene expression, which suggests that PSMB6 may become a new independent prognostic indicator. In addition, we developed a prognostic model of PSMB6-regulated immune infiltration-associated genes by analyzing the link between PSMB6 and the immune microenvironment. This model can not only predict the prognosis of lung adenocarcinoma but also forecasts the patient's reaction to immunotherapy. The validity of this research outcome has been confirmed by the GSE31210 and IMvigor210 cohorts. Analysis of the Kaplan-Meier Plotter database indicates that individuals with elevated levels of PSMB6 expression exhibit a poorer prognosis. Additionally, in vitro experiments demonstrated that knockdown of PSMB6 inhibits the proliferation, migration, and invasion of lung adenocarcinoma cells while promoting their apoptosis. Overall, our findings suggest that PSMB6 could remarkably influence the management and treatment of lung adenocarcinoma, opening new avenues for targeted immunotherapeutic strategies.

Prognostic implications of metabolism-related genes in acute myeloid leukemia.

Acute myeloid leukemia(AML) is a diverse malignancy with a prognosis that varies, being especially unfavorable in older patients and those with high-risk characteristics. Metabolic reprogramming has become a significant factor in AML development , presenting new opportunities for prognostic assessment and therapeutic intervention. Metabolism-related differentially expressed genes (mDEGs) were identified by integrating KEGG metabolic gene lists with AML gene expression data from GSE63270. Using TCGA data, we performed consensus clustering and survival analysis to investigate the prognostic significance of mDEGs. A metabolic risk model was constructed using LASSO Cox reg ression and enhanced by a nomogram incorporated clinical characteristics. The model was validated through receiver operating characteristic (ROC) curves and survival statistics. Gene network analysis was conducted to identify critical prognostic factors. The tumor immune microenvironment was evaluated using CIBERSORT and ESTIMATE algorithms, followed by correlation analysis between immune checkpoint gene expression and risk scores. Drug sensitivity predictions and in vitro assays were performed to explore the effects of mDEGs on cell proliferation and chemoresistance. An 11-gene metabolic prognostic model was established and validated. High-risk patients had worse overall survival in both training and validation cohorts (p < 0.05). The risk score was an independent prognostic factor. High-risk patients showed increased immune cell infiltration and potential response to checkpoint inhibitors but decreased drug sensitivity. The model correlated with sensitivity to drugs such as venetoclax. Carbonic anhydrase 13 (CA13) was identified as a key gene related to prognosis and doxorubicin resistance. Knocking down CA13 reduced proliferation and increased cell death with doxorubicin treatment. A novel metabolic gene signature was developed to stratify risk and predict prognosis in AML, serving as an independent prognostic factor. CA13 was identified as a potential therapeutic target. This study provides new insights into the prognostic and therapeutic implications of metabolic genes in AML.

Comprehensive transcriptome and scRNA-seq analyses uncover the expression and underlying mechanism of SYNJ2 in papillary thyroid carcinoma.

Synaptojanin 2 (SYNJ2) has crucial role in various tumors, but its role in papillary thyroid carcinoma (PTC) remains unexplored. This study first detected SYNJ2 protein expression in PTC using immunohistochemistry method and further assessed SYNJ2 mRNA expression through mRNA chip and RNA sequencing data and its association with clinical characteristics. Additionally, KEGG, GSVA, and GSEA analyses were conducted to investigate potential biological functions, while single-cell RNA sequencing data were used to explore SYNJ2's underlying mechanisms in PTC. Meanwhile, immune infiltration status in different SYNJ2 expression groups were analyzed. Besides, we investigated the immune checkpoint gene expression and implemented drug sensitivity analysis. Results indicated that SYNJ2 is highly expressed in PTC (SMD = 0.66 [95% CI: 0.17-1.15]) and could distinguish between PTC and non-PTC tissues (AUC = 0.74 [0.70-0.78]). Furthermore, the study identified 134 intersecting genes of DEGs and CEGs, mainly enriched in the angiogenesis and epithelial-mesenchymal transition (EMT) pathways. Subsequent analysis showed the above pathways were activated in PTC epithelial cells. PTC patients with high SYNJ2 expression showed higher sensitivity to the six common drugs. Summarily, SYNJ2 may promote PTC progression through angiogenesis and EMT pathways. High SYNJ2 expression is associated with better response to immunotherapy and chemotherapy.

Exploring and validating the necroptotic gene regulation and related lncRNA mechanisms in colon adenocarcinoma based on multi-dimensional data.

Necroptosis is intimately associated with the initiation and progression of colon adenocarcinoma (COAD). However, studies on necroptosis-related genes (NRGs) and the regulating long non-coding RNAs (NRGlncRNAs) in the context of COAD are limited. We retrieved the cancer genome atlas (TCGA) to collect datasets of NRGs and NRGlncRNAs on COAD patients. The risk model constructed using Cox and least absolute shrinkage and selection operator (LASSO) regression was then employed to identify NRGs and NRGlncRNAs with prognostic significance. Subsequently, we validated the results using gene expression omnibus (GEO) datasets from different populations, conducted Mendelian randomization (MR) analysis to explore the potential causal relationships between prognostic NRGs and COAD, and conducted cell experiments to verify the expression of prognostic NRGlncRNAs in COAD. Furthermore, we explored potential pathways and regulatory mechanisms of these prognostic NRGlncRNAs and NRGs in COAD through enrichment analysis, immune cell correlation analysis, tumor microenvironment analysis, immune checkpoint analysis, tumor sample clustering, and so on. We identified eight NRGlncRNAs (AC245100.5, AP001619.1, LINC01614, AC010463.3, AL162595.1, ITGB1-DT, LINC01857, and LINC00513) used for constructing the prognostic model and nine prognostic NRGs (AXL, BACH2, CFLAR, CYLD, IPMK, MAP3K7, ATRX, BRAF, and OTULIN) with regulatory relationships with them, and their validation was performed using GEO and GWAS datasets, as well as cell experiments, which showed largely consistent results. These prognostic NRGlncRNAs and NRGs modulate various biological functions, including immune inflammatory response, oxidative stress, immune escape, telomere regulation, and cytokine response, influencing the development of COAD. Additionally, stratified analysis of the high-risk and low-risk groups based on the prognostic model revealed elevated expression of immune cells, increased expression of tumor microenvironment cells, and upregulation of immune checkpoint gene expression in the high-risk group. Finally, through cluster analysis, we identified tumor subtypes, and the results of cluster analysis were essentially consistent with the analysis between risk groups. The prognostic NGRlncRNAs and NRGs identified in our study serve as prognostic indicators and potential therapeutic targets for COAD, providing a theoretical basis for the clinical diagnosis and treatment of COAD and offering guidance for further research.

Comprehensive analysis of NOTCH pathway with tumor environment in pancreatic adenocarcinoma

Abstract Objectives Pancreatic adenocarcinoma (PAAD) ranks among the most prevalent malignant neoplasms, and multiple pathways are involved in its pathogenesis, including the NOTCH pathway. However, the variable biological functions of the pathway in PAAD are controversial. Methods RNA-seq data for PAAD was analyzed using data from The Cancer Genome Atlas and Genotype-Tissue Expression databases. Utilizing Kaplan-Meier survival curves and Cox regression analyses, we examined the prognostic significance. The tumor microenvironment and immunotherapy responses were investigated using ssGSEA, ESTIMATE, and TIDE models. Functional enrichment analysis was used to explore gene functions. Results We identified NOTCH2, JAG1, NOTCH4, and DLL3 as high-priority members of the NOTCH pathway that modulates PAAD. Elevated NOTCH2 and JAG1 levels were markedly linked to reduced overall survival (OS), while increased NOTCH4 and DLL3 levels were significantly related to extended OS. Immune analyses showed that NOTCH-based scores were closely related to the immune microenvironment. NOTCH scores were not only closely correlated with tumor-infiltrating immune cells, but also with immunologically activated and immune checkpoint gene expression. The high NOTCH score group had a higher proportion of tumor-infiltrating immune cells and had better responses to immune checkpoint inhibitor therapy. Conclusions These data indicate that NOTCH2, JAG1, NOTCH4, and DLL3 could function as efficient prognostic biomarkers and therapeutic targets in PAAD, and patients with a high NOTCH score may have a significant response to immune checkpoint inhibitor treatment.

Elevated BEAN1 expression correlates with poor prognosis, immune evasion, and chemotherapy resistance in rectal adenocarcinoma

BackgroundThe BEAN1 gene, primarily studied in neurodegenerative diseases, has been scarcely studied in the context of cancers. Our research examines BEAN1 expression specifically in rectal adenocarcinoma (READ) and its association with prognosis, immune evasion, and chemotherapy resistance.MethodsData from TCGA and GEO were analyzed to assess BEAN1 levels across various cancer types, with particular emphasis on READ. Functional enrichment, immune infiltration, and treatment response analyses were conducted, followed by validation using patient tissue samples.ResultsREAD tissues exhibited a marked increase in BEAN1 expression compared to normal tissues. Elevated BEAN1 levels were associated with reduced overall survival and increased immune suppression, characterized by elevated M2 macrophage infiltration and reduced CD8+ T cell presence. BEAN1 expression was also linked to higher immune checkpoint genes expression and resistance to immune checkpoint inhibitors and 5-fluorouracil.ConclusionThis research offers initial evidence that BEAN1 is linked to unfavorable prognosis, immune escape, and resistance to chemotherapy in READ. BEAN1 appears to be a promising new biomarker and potential therapeutic target, warranting further investigation into its potential clinical applications in improving treatment outcomes for READ patients.

Exploring the interplay between bisphenol A exposure, the immune microenvironment and hepatocellular carcinoma progression.

Hepatocellular carcinoma (HCC) remains a formidable challenge in oncology, with its pathogenesis and progression influenced by myriad factors. Among them, the pervasive organic synthetic compound, bisphenol A (BPA), previously linked with various adverse health effects, has been speculated to play a role. This study endeavors to elucidate the complex interplay between BPA, the immune microenvironment of HCC, and the broader molecular landscape of this malignancy. A comprehensive analysis was undertaken using data procured from both The Cancer Genome Atlas and the Comparative Toxicogenomics Database. Rigorous differential expression analyses were executed, supplemented by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. In addition, single-sample gene set enrichment analysis, gene set enrichment analysis and gene set variation analysis were employed to reveal potential molecular links and insights. Immune infiltration patterns were delineated, and a series of in vitro experiments on HCC cells were conducted to directly assess the impact of BPA exposure. Our findings unveiled a diverse array of active immune cells and functions within HCC. Distinct correlations emerged between high-immune-related scores, established markers of the tumor microenvironment and the expression of immune checkpoint genes. A significant discovery was the identification of key genes simultaneously associated with immune-related pathways and BPA exposure. Leveraging these genes, a prognostic model was crafted, offering predictive insights into HCC patient outcomes. Intriguingly, in vitro studies suggested that BPA exposure could promote proliferation in HCC cells. This research underscores the multifaceted nature of HCC's immune microenvironment and sheds light on BPA's potential modulatory effects therein. The constructed prognostic model, if validated further, could serve as a robust tool for risk stratification in HCC, potentially guiding therapeutic strategies. Furthermore, the implications of the findings for immunotherapy are profound, suggesting new avenues for enhancing treatment efficacy. As the battle against HCC continues, understanding of environmental modulators like BPA becomes increasingly pivotal.

Leveraging diverse cellular stress patterns for predicting clinical outcomes and therapeutic responses in patients with multiple myeloma.

Tumour microenvironment harbours diverse stress factors that affect the progression of multiple myeloma (MM), and the survival of MM cells heavily relies on crucial stress pathways. However, the impact of cellular stress on clinical prognosis of MM patients remains largely unknown. This study aimed to provide a cell stress-related model for survival and treatment prediction in MM. We incorporated five cell stress patterns including heat, oxidative, hypoxic, genotoxic, and endoplasmic reticulum stresses, to develop a comprehensive cellular stress index (CSI). Then we systematically analysed the effects of CSI on survival outcomes, clinical characteristics, immune microenvironment, and treatment sensitivity in MM. Molecular subtypes were identified using consensus clustering analysis based on CSI gene profiles. Moreover, a prognostic nomogram incorporating CSI was constructed and validated to aid in personalised risk stratification. After screening from five stress models, a CSI signature containing nine genes was established by Cox regression analyses and validated in three independent datasets. High CSI was significantly correlated with cell division pathways and poor clinical prognosis. Two distinct MM subtypes were identified through unsupervised clustering, showing significant differences in prognostic outcomes. The nomogram that combined CSI with clinical features exhibited good predictive performances in both training and validation cohorts. Meanwhile, CSI was closely associated with immune cell infiltration level and immune checkpoint gene expression. Therapeutically, patients with high CSI were more sensitive to bortezomib and antimitotic agents, while their response to immunotherapy was less favourable. Furthermore, invitro experiments using cell lines and clinical samples verified the expression and function of key genes from CSI. The CSI signature could be a clinically applicable indicator of disease evaluation, demonstrating potential in predicting prognosis and guiding therapy for patients with MM.

Development of an immunogenic cell death-related lncRNAs signature for prognostic risk assessment in gastric cancer.

Immunogenic cell death (ICD) is a functionally specialized form of apoptosis induced by endoplasmic reticulum (ER) stress and is associated with a variety of cancers, including gastric cancer (GC). In recent years, long non-coding RNAs (lncRNAs) have been shown to be important mediators in the regulation of ICD. However, the specific role and prognostic value of ICD-related lncRNAs in GC remain unclear. This study aims to develop an ICD-related lncRNAs signature for prognostic risk assessment in GC. The ICD-related lncRNAs signature (ICDlncSig) of GC was constructed by univariate Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression model and multivariate Cox regression analysis, and the signature was correlated with immune infiltration. The potential response of GC patients to immunotherapy was predicted by the tumor immune dysfunction and rejection (TIDE) algorithm. In vitro functional experiments were conducted to assess the impact of lncRNAs on the proliferation, migration, and invasion capabilities of GC cells. We constructed a novel ICDlncSig and found that this signature could be used as a prognostic risk model to predict survival of GC patients by validating it in the training cohort, testing cohort and entire cohort. The robust predictive power of the signature was demonstrated by building a Nomogram based on ICDlncSig scores and clinical characteristics. Furthermore, immune cell subpopulations, expression of immune checkpoint genes, and response to chemotherapy and immunotherapy differed significantly between the high- and low-risk groups. The in vitro functional experiments revealed that AP002954.1 and AP000695.1 can promote the proliferation, migration, and invasion of GC cells. In conclusion, our ICDlncSig model has significant predictive value for the prognosis of GC patients and may provide clinical guidance for individualized immunotherapy.

Bulk and single-cell RNA sequencing analyses coupled with multiple machine learning to develop a glycosyltransferase associated signature in colorectal cancer

BackgroundThis study aims to identify key glycosyltransferases (GTs) in colorectal cancer (CRC) and establish a robust prognostic signature derived from GTs. MethodsUtilizing the AUCell, UCell, singscore, ssgsea, and AddModuleScore algorithms, along with correlation analysis, we redefined genes related to GTs in CRC at the single-cell RNA level. To improve risk model accuracy, univariate Cox and lasso regression were employed to discover a more clinically subset of GTs in CRC. Subsequently, the efficacy of seven machine learning algorithms for CRC prognosis was assessed, focusing on survival outcomes through nested cross-validation. The model was then validated across four independent external cohorts, exploring variations in the tumor microenvironment (TME), response to immunotherapy, mutational profiles, and pathways of each risk group. Importantly, we identified potential therapeutic agents targeting patients categorized into the high-GARS group. ResultsIn our research, we classified CRC patients into distinct subgroups, each exhibiting variations in prognosis, clinical characteristics, pathway enrichments, immune infiltration, and immune checkpoint genes expression. Additionally, we established a Glycosyltransferase-Associated Risk Signature (GARS) based on machine learning. GARS surpasses traditional clinicopathological features in both prognostic power and survival prediction accuracy, and it correlates with higher malignancy levels, providing valuable insights into CRC patients. Furthermore, we explored the association between the risk score and the efficacy of immunotherapy. ConclusionA prognostic model based on GTs was developed to forecast the response to immunotherapy, offering a novel approach to CRC management.

Neutrophil estimation and prognosis analysis based on existing lung squamous cell carcinoma datasets: the development and validation of a prognosis prediction model.

Notwithstanding the rapid developments in precision medicine in recent years, lung cancer still has a low survival rate, especially lung squamous cell cancer (LUSC). The tumor microenvironment (TME) plays an important role in the progression of lung cancer, in which high neutrophil levels are correlated with poor prognosis, potentially due to their interactions with tumor cells via pro-inflammatory cytokines and chemokines. However, the precise mechanisms of how neutrophils influence lung cancer remain unclear. This study aims to explore these mechanisms and develop a prognosis predictive model in LUSC, addressing the knowledge gap in neutrophil-related cancer pathogenesis. LUSC datasets from the Xena Hub and Gene Expression Omnibus (GEO) databases were used, comprising 473 tumor samples and 195 tumor samples, respectively. Neutrophil contents in these samples were estimated using CIBERSORT, xCell, and microenvironment cell populations (MCP) counter tools. Differentially expressed genes (DEGs) were identified using DEseq2, and a weighted gene co-expression network analysis (WGCNA) was performed to identify neutrophil-related genes. A least absolute shrinkage and selection operator (LASSO) Cox regression model was constructed for prognosis prediction, and the model's accuracy was validated using Kaplan-Meier survival curves and time-dependent receiver operating characteristic (ROC) curves. Additionally, genomic changes, immune correlations, drug sensitivity, and immunotherapy response were analyzed to further validate the model's predictive power. Neutrophil content was significantly higher in adjacent normal tissue compared to LUSC tissue (P<0.001). High neutrophil content was associated with worse overall survival (OS) (P=0.02), disease-free survival (DFS) (P=0.02), and progression-free survival (PFS) (P=0.03) using different software estimates. Nine gene modules were identified, with blue and yellow modules showing strong correlations with neutrophil prognosis (P<0.001). Eight genes were selected for the prognostic model, which accurately predicted 1-, 3-, and 5-year survival in both the training set [area under the curve (AUC) value =0.60, 0.63, 0.66, respectively] and validation set (AUC value =0.58, 0.58, 0.59, respectively), with significant prognosis differences between high- and low-risk groups (P<0.001). The model's independent prognostic factors included risk group, pathologic M stage, and tumor stage (P<0.05). A further molecular mechanism analysis revealed differences between risk groups were revealed in immune checkpoint and human leukocyte antigen (HLA) gene expression, hallmark pathways, drug sensitivity, and immunotherapy responses. This study established a risk-score model that effectively predicts the prognosis of LUSC patients and sheds light on the molecular mechanisms involved. The findings enhance the understanding of neutrophil-tumor interactions, offering potential targets for personalized treatments. However, further experimental validation and clinical studies are required to confirm these findings and address study limitations, including reliance on public databases and focus on a specific lung cancer subtype.

Unveiling the Molecular Features of SCLC With a Clinical RNA Expression Panel

IntroductionThe translation of gene expression profiles of SCLC to clinical testing remains relatively unexplored. In this study, gene expression variations in SCLC were evaluated to identify potential biomarkers. MethodsRNA expression profiling was performed on 44 tumor samples from 35 patients diagnosed with SCLC using the clinically validated RNA Salah Targeted Expression Panel (RNA STEP). RNA sequencing (RNA-Seq) and immunohistochemistry were performed on two different SCLC cohorts, and correlation analyses were performed for the ASCL1, NEUROD1, POU2F3, and YAP1 genes and their corresponding proteins. RNA STEP and RNA-Seq results were evaluated for gene expression profiles and heterogeneity between SCLC primary and metastatic sites. RNA STEP gene expression profiles of independent SCLC samples (n = 35) were compared with lung adenocarcinoma (n = 160) and squamous cell carcinoma results (n = 25). ResultsThe RNA STEP results were highly correlated with RNA-Seq and immunohistochemistry results. The dominant transcription regulator by RNA STEP was ASCL1 in 74.2% of the samples, NEUROD1 in 20%, and POU2F3 in 2.9%. The ASCL1, NEUROD1, and POU2F3 gene expression profiles were heterogeneous between primary and metastatic sites. SCLCs displayed markedly high expression for targetable genes DLL3, EZH2, TERT, and RET. SCLCs were found to have relatively colder immune profiles than lung adenocarcinomas and squamous cell carcinomas, characterized by lower expression of HLA genes, immune cell, and immune checkpoint genes, except the LAG3 gene. ConclusionsClinical-grade SCLC RNA expression profiling has value for SCLC subtyping, design of clinical trials, and identification of patients for trials and potential targeted therapy.

Immune profiling of premalignant lesions in patients with Peutz-Jeghers syndrome.

Peutz-Jeghers syndrome (PJS), is a rare autosomal dominant hereditary disease characterized by an elevated risk of various cancers. Serine/Threonine Kinase 11 (STK11) gene is a major tumor suppressor crucial for immune evasion with and beyond tumorigenic cells. It has garnered increasing attention in the realm of oncology treatment, particularly in the context of immunotherapy development. This study aimed to assess the suitability of polyps obtained from individuals with PJS, resulting from germline STK11 deficiency, for immunotherapy. Additionally, we seek to identify potential shared mechanisms related to immune evasion between PJS polyps and cancers. To achieve this, we examined PJS polyps alongside familial adenomatous polyposis (FAP) and sporadic polyps. Polyps were compared among themselves and with either the paracancerous tissues or colon cancers. Pathological and gene expression profiling approaches were employed to characterize infiltrating immune cells and assess the expression of immune checkpoint genes. Our findings revealed that PJS polyps exhibited a closer resemblance to cancer tissues than other polyps in terms of their immune microenvironment. Notably, PJS polyps displayed heightened expression of the immune checkpoint gene CD80 and an accumulation of myeloid cells, particularly myeloid-derived suppressor cells (MDSCs). The findings suggest an immunobiological foundation for the increased cancer susceptibility in PJS patients, paving the way for potential immune therapy applications in this population. Furthermore, utilizing PJS as a model may facilitate the exploration of immune evasion mechanisms, benefiting both PJS and cancer patients.

Immunogenic cell death (ICD) genes predict immunotherapy response and therapeutic targets in acute myeloid leukemia (AML).

Numerous studies have demonstrated acute myeloid leukemia (AML) is one of the malignancies with high mortality worldwide. Immunogenic cell death (ICD) is a form of cell death that is specialised in that it triggers the body's immune response, particularly the adaptive immune response. Recent evidence has confirmed that pseudogenes are implicated in multiple human tumorigenesis and progression although lacking the function of coding protein. However, the roles of ICD-associated genes in AML remain largely unascertained. TCGA-AML and GSE71014 cohorts were picked out and we combined them into a merged dataset by removing the batch effect using the sva package in the R project. A consensus clustering analysis of the ICD genes in AML was performed to define subgroups. Based on the expression of 15 prognostic-related pseudogenes, we developed a prognostic model and categorized AML samples into low and high-risk groups. AML was differentiated into two subgroups (C1 and C2 clusters). Most ICD-related genes were significantly up-regulated in the C2 cluster. The single sample gene set enrichment analysis (ssGSEA) revealed that the immune cell infiltration and immune checkpoint gene expression of the C2 cluster was strongly high, suggesting that the C2 population responded well to immune checkpoint blockade (ICB) therapy and had better survival. The C1 group was sensitive to chemotherapy, including Cytarabine, Midostaurin, and Doxorubicin. On the other hand, 15 ICD-related pseudogenes were identified to be associated with AML prognosis. The receiver operator curve (ROC) analysis and nomogram manifested that our prognostic model had high accuracy in predicting survival. However, the high-risk group was sensitive to ICB therapy and chemotherapy such as Methotrexate, Cytarabine, and Axitinib while the low-risk group benefited from 5-Fluorouracil, Talazoparib, and Navitoclax therapy. In summary, we defined two subgroups relying on 33 ICD-related genes and this classification exerted a decisive role in assessing immunotherapy and chemotherapy benefit. Significantly, a prognostic signature identified by critical ICD-related pseudogene was created. The pseudogene prognostic signature had a powerful performance in predicting prognosis and therapeutic efficacy, including immunotherapy and chemotherapy to AML. Our research points out novel implications of ICD in cancer prognosis and treatment approach choice.

Glycolysis-related genes predict prognosis and indicate immune microenvironment features in gastric cancer.

Gastric cancer (GC) is a major contributor to cancer-related mortality. Glycolysis plays a pivotal role in tumor microenvironment (TME) reprogramming. In this research, the functions of glycolysis-associated genes (GRGs) were evaluated to predict the outcome and reveal the characteristics of the immune microenvironment in individuals with stomach cancer. The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) cohort provided gene expression and clinical data for gastric cancer (GC) patients, which were further authenticated using datasets sourced from the Gene Expression Omnibus (GEO). By referencing the Molecular Signatures Database (MSigDB), a total of 326 GRGs were pinpointed. The various subtypes of GC were outlined through consensus clustering, derived from the expression patterns of these GRGs. Utilizing multivariate Cox regression analysis, a multigene risk score model was formulated. Both the CIBERSORT and ESTIMATE algorithms played a pivotal role in assessing the immune microenvironment. To delve into the biological functions of the key genes, wound healing, transwell invasion, and MTT assays were conducted. Based on the expression patterns of GRGs, patients were categorized into two distinct groups: the metabolic subtype, designated as cluster A, and the immune subtype, labeled as cluster B. Patients belonging to cluster B exhibited a poorer prognosis. A prognostic risk score model, formulated upon the expression levels of six key GRGs - ME1, PLOD2, NUP50, CXCR4, SLC35A3, and SRD35A3 - emerged as a viable tool for predicting patient outcomes. The downregulation of CXCR4 notably diminished the glycolytic capacity of gastric cancer (GC) cells, alongside their migratory, invasive, and proliferative capabilities. Intriguingly, despite the adverse prognostic implications associated with both the immune subtype (cluster B) and the high-risk cohort, these groups exhibited a favorable immune microenvironment coupled with elevated expression of immune checkpoint genes. Our investigations revealed a positive correlation between high CXCR4 expression and low ME1 expression with the infiltration of CD8+ T cells, as well as an enhanced responsiveness to treatment with an anti-PD-1 immune checkpoint inhibitor. In this study, we discovered that the expression profiles of GRGs hold the potential to forecast the prognosis of gastric cancer (GC) patients, thereby possibly aiding in clinical treatment decision-making.

Construction and validation of a SASP-related prognostic signature in patients with acute myeloid leukaemia.

Acute myeloid leukaemia (AML) is a common and highly aggressive haematological malignancy in adults. Senescence-associated secretory phenotype (SASP) plays important roles in tumorigenesis and progression of tumour. However, the prognostic value of SASP in patients with AML has not been clarified. The present study aims to explore the prognostic value of SASP and develop a prognostic risk signature for AML. The RNA-sequencing data was collected from the TCGA, GTEx and TARGET databases. Subsequently, differentially expressed gene analysis, univariate Cox regression and LASSO regression were applied to identified prognostic SASP-related genes and construct a prognostic risk-scoring model. The risk score of each patient were calculated and patients were divided into high- or low-risk groups by the median risk score. This novel prognostic signature included 11 genes: G6PD, CDK4, RPS6KA1, UBC, H2BC12, KIR2DL4, HSF1, IFIT3, PIM1, RUNX3 and TRIM21. The patients with AML in the high-risk group had shorter OS, demonstrating that the risk score acted as a prognostic predictor, which was validated in the TAGET-AML dataset. Univariate and multivariate analysis revealed the risk score was an independent prognostic factor in patients with AML. Furthermore, the present study revealed that the risk score was associated with immune landscape, immune checkpoint gene expression and chemotherapeutic efficacy. In the present study, we constructed and validated a unique SASP-related prognostic model to assess therapeutic effect and prognosis in patients with AML, which might contribute to understanding the role of SASP in AML and guiding the treatment for AML.