Immune Cell Infiltration Levels Research Articles

Potential role of POFUT1 as a prognostic predictor in low-grade gliomas: Immune microenvironment insights from a pan-cancer analysis

The POFUT1 gene, known to be up-regulated in various tumor tissues and associated with tumor biology, has yet to be explored for its potential role in immune response regulation and tumor immune microenvironment. The normalized pan-cancer dataset (TCGA Pan-Cancer) was downloaded from the UCSC database, followed by analysis of POFUT1 expression in various tumors and functional enrichment analysis. The correlation between POFUT1 expression levels and patient prognosis was assessed. GSEA of POFUT1 based on low-grade glioma (LGG) samples and immune infiltration analyses of LGG and glioblastoma (GBM) were conducted. The correlation between POFUT1 expression levels and infiltration levels of 22 immune cells in LGG and GBM was examined, as well as the correlation between immune cell infiltration levels and LGG patient prognosis. Additionally, the relationship between POFUT1 expression levels and characteristic gene expression of identified immune cells was evaluated. Lastly, external dataset validation was performed using the integrated CGGA dataset. Significant differences were observed in POFUT1 expression levels across 20 tumor types. High POFUT1 expression correlated with poor prognosis in GBMLGG, and LGG patients. Enrichment analysis and GSEA of POFUT1 in LGG demonstrated involvement in tumor-related and immune-related pathways. A positive correlation was identified between POFUT1 expression levels and infiltration levels of resting memory CD4+ T cells, as well as M2 macrophages or M2-like TAMs in the LGG immune microenvironment, potentially contributing to poor prognosis. External dataset validation revealed a positive correlation between M2 macrophages or M2-like TAMs and POFUT1 expression levels in LGG, and a negative correlation with LGG patient prognosis. POFUT1's negative impact on LGG prognosis may result from its influence on M2 macrophage and M2-like TAM infiltration levels within the immune microenvironment. This suggests its potential as a prognostic predictor and therapeutic target for LGG.

Genomic and transcriptomic profiling of combined small-cell lung cancer through microdissection: unveiling the transformational pathway of mixed subtype.

Combined small-cell lung carcinoma (cSCLC) represents a rare subtype of SCLC, the mechanisms governing the evolution of cancer genomes and their impact on the tumor immune microenvironment (TIME) within distinct components of cSCLC remain elusive. Here, we conducted whole-exome and RNA sequencing on 32 samples from 16 cSCLC cases. We found striking similarities between two components of cSCLC-LCC/LCNEC (SCLC combined with large-cell carcinoma/neuroendocrine) in terms of tumor mutation burden (TMB), tumor neoantigen burden (TNB), clonality structure, chromosomal instability (CIN), and low levels of immune cell infiltration. In contrast, the two components of cSCLC-ADC/SCC (SCLC combined with adenocarcinoma/squamous-cell carcinoma) exhibited a high level of tumor heterogeneity. Our investigation revealed that cSCLC originated from a monoclonal source, with two potential transformation modes: from SCLC to SCC (mode 1) and from ADC to SCLC (mode 2). Therefore, cSCLC might represent an intermediate state, potentially evolving into another histological tumor morphology through interactions between tumor and TIME surrounding it. Intriguingly, RB1 inactivation emerged as a factor influencing TIME heterogeneity in cSCLC, possibly through neoantigen depletion. Together, these findings delved into the clonal origin and TIME heterogeneity of different components in cSCLC, shedding new light on the evolutionary processes underlying this enigmatic subtype.

Identification and verification of inflammatory biomarkers for primary Sjögren’s syndrome

IntroductionPrimary Sjögren’s syndrome (pSS) is an autoimmune disease characterized by inflammatory infiltration, and dysfunction of the salivary and lacrimal glands. This research aimed to explore the disease pathogenesis and improve the diagnosis and treatment of pSS by mining inflammation-associated biomarkers.MethodsFive pSS-related datasets were retrieved from the Gene Expression Omnibus (GEO) database. Inflammation-associated biomarkers were determined by the least absolute shrinkage and selection operator (LASSO) and support vector machines recursive feature elimination (SVM-RFE). Single sample gene set enrichment analysis (ssGSEA) was implemented to profile the infiltration levels of immune cells. Real-time quantitative PCR (RT-qPCR) verified the expression of biomarkers in clinical samples.ResultsFour genes (LY6E, EIF2AK2, IL15, and CXCL10) were screened as inflammation-associated biomarkers in pSS, the predictive performance of which were determined among three pSS-related datasets (AUC > 0.7). Functional enrichment results suggested that the biomarkers were involved in immune and inflammation-related pathways. Immune infiltration analysis revealed that biomarkers were notably connected with type 2 T helper cells, regulatory T cells which were significantly expressed between pSS and control. TESTOSTERONE and CYCLOSPORINE were predicted to take effect by targeting CXCL10 and IL15 in pSS, respectively.ConclusionFour inflammation-associated biomarkers (LY6E, EIF2AK2, IL15, and CXCL10) were explored, and the underlying regulatory mechanisms and targeted drugs associated with these biomarkers were preliminarily investigated according to a series of bioinformatics methods based on the online datasets of pSS, which provided a reference for understanding the pathogenesis of pSS.Key Points• Inflammation-associated biomarkers (LY6E, EIF2AK2, IL15, and CXCL10) were firstly identified in Sjögren’s syndrome based on LASSO and SVM-RFE analyses.• CXCL10, EIF2AK2 and LY6E were prominently positively correlated with immature B cells, while IL15 were significantly negatively correlated with memory B cells in Sjögren’s syndrome.• LY6E, EIF2AK2, IL15, and CXCL10 were significantly more highly expressed in clinical Sjögren’s syndrome samples compared to healthy control samples, which was consistent with the analysis results of the GEO database. •LY6E, EIF2AK2, IL15, and CXCL10 might be used as the biomarkers for the treatment and diagnosis of Sjögren’s syndrome.

SPC25 Functions as a Prognostic-Related Biomarker, and Its High Expression Correlates with Tumor Immune Infiltration and UCEC Progression.

Most tumor tissues expressed spindle pole body component 25 (SPC25), one of the four subunits of the NDC80 complex, at greater levels compared to surrounding normal tissues. According to earlier researches, this subunit strongly encouraged tumor cell proliferation and tumor growth, which resulted in worse prognoses in patients with hepatocellular, breast, lung, and prostate cancer. Precisely because SPC25's role in uterine corpus endometrial carcinoma (UCEC) is understudied, we chose to concentrate on UCEC for gaining a more scientific and thorough understanding of SPC25. Along with examining SPC25's differential expression, prognostic significance, and biological function in UCEC, our research sought to clarify the underlying mechanism by which SPC25 influences the course of UCEC and patient prognosis from the viewpoints of methylation and immune infiltration. We observed differential expression of SPC25 gene in different clinicopathological features of UCEC and identified SPC25 as a hazard factor for poorer overall survival (OS), disease-specific survival (DSS), and progress free interval (PFI) in UCEC, particularly in its multiple clinical subtypes. In addition, we also discovered that SPC25 and its co-expressed genes mostly engaged in biological processes and signal transduction routes linked to cell cycle and cell division in UCEC. After investigating SPC25's methylation status, we discovered that patients with UCEC had elevated SPC25 expression and a poor prognosis due to hypomethylation of CpG sites in the SPC25 gene sequence. Finally, we investigated SPC25's potential role in immunotherapy and discovered that SPC25 might alter the major immune cell infiltration levels in the tumor microenvironment (TME) by regulating the expression of immunoregulatory molecules and chemokines, which would be beneficial for SPC25 to control the progression of UCEC. In conclusion, SPC25 was a useful predictive biomarker as well as a possible therapeutic target for UCEC.

Construction of an Oxidative Stress Risk Model to Analyze the Correlation Between Liver Cancer and Tumor Immunity.

Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally. Despite advancements in immunotherapy, the prognosis for patients with HCC continues to be poor. As oxidative stress plays a significant role in the onset and progression of various diseases, including metabolism-related HCC, comprehending its mechanism in HCC is critical for effective diagnosis and treatment. This study utilized the TCGA dataset and a collection of oxidative stress genes to determine the expression of oxidative stress-related genes in HCC and their association with overall survival using diverse bioinformatics methods. A novel prognostic risk model was developed, and the TCGA cohort was divided into high-risk and low-risk groups based on each tumor sample's risk score. Levels of immune cell infiltration and the expression of immune checkpoint-related genes in different risk subgroups were analyzed to investigate the potential link between tumor immunity and oxidative stress-related features. The expression of model genes in actual samples was validated through immunohistochemistry, and their mRNA and protein expression levels were measured in cell cultures. Four oxidative stress-related genes (EZH2, ANKZF1, G6PD, and HMOX1) were identified and utilized to create a predictive risk model for HCC patient overall survival, which was subsequently validated in an independent cohort. A significant correlation was found between the expression of these prognostic genes and the infiltration of tumor immune cells. Elevated expression of EZH2, ANKZF1, G6PD, and HMOX1 was observed in both HCC tissues and cell lines. The combined assessment of EZH2, ANKZF1, G6PD, and HMOX1 gene expression can serve as a model to evaluate the risk of oxidative stress in HCC. Furthermore, there is a notable correlation between the expression of these risk model genes and tumor immunity.

A5 DEVELOPMENT OF A PRE-CLINICAL MOUSE MODEL TO INVESTIGATE ADVERSE FOOD REACTIONS IN INTESTINAL INFLAMMATION

Abstract Background Patients with chronic intestinal conditions, including inflammatory bowel disease (IBD), experience diverse food-related adverse reactions. However,, the precise mechanisms of food intolerances in IBD remains unclear. Emerging evidence suggests that altered diet-microbiota interactions can contribute to the development of IBD. Previously we shown that intestinal microbiota plays an important role in the development of food intolerances. We hypothesize that microbial alterations in IBD patients facilitate adverse reactions to foods. Aims To establish a preclinical mouse model to study adverse food reactions in intestinal inflammation. Methods Eight-week-old C57BL/6 mice were treated with 3% dextran sodium sulfate (DSS) for 5 days, then divided into three groups. The first group was sensitized to dairy protein (3% casein and 3% whey), using 4 oral administrations of cholera toxin over 2 weeks, without any further diet intervention. The second group was also sensitized to casein and whey, then after 1 week of recovery, the mice were subsequently place on a chow containing casein and whey. Finally, the third group was subjected to diet intervention without sensitization. The same experiment was repeated in a different subset of C57BL/6 mice which included a second cycle of DSS (1.5%) during the dietary interventions. Markers of intestinal inflammation (disease activity index (DAI), histological damage, cell infiltration and pro-inflammatory genes expression (NanoString)) and intestinal microbiota were analysed after 1 week of the diet intervention. Results Mice previously sensitized on the dairy diet exhibited an enhanced activation of immune cells after intestinal inflammation. While the histological damage score did not reveal significant differences among experimental groups, the sensitized group under dairy intervention exhibited an elevated level of immune cell infiltration, increasing the polymorphonuclear, CD3+ and mast cells. Gene expression also revealed that dairy induced the expression of inflammatory genes, such as C6, Arg1, Tnf, Il6, Cxcl9 and Cxcl10. When animals are subjected to a second DSS-cycle, dairy worsen colitis in mice previously sensitized, as shown by higher DAI compared to group with control diet and dairy diet without sensitization. Conclusions In the context of intestinal inflammation, dairy protein leads to immune activation characterized by an increase in polymorphonuclear cells, mast cells and lymphocytes, as well as worsening colitis. Future studies using this model will provide valuable insights into the role of intestinal microbiota in food intolerances associated with intestinal inflammation. Funding Agencies CCC

BCLAF1 binds SPOP to stabilize PD-L1 and promotes the development and immune escape of hepatocellular carcinoma

Interaction between programmed death-1 (PD-1) ligand 1 (PD-L1) on tumor cells and PD-1 on T cells allows tumor cells to evade T cell-mediated immune surveillance. Strategies targeting PD-1/PD-L1 have shown clinical benefits in a variety of cancers. However, limited response rates in hepatocellular carcinoma (HCC) have prompted us to investigate the molecular regulation of PD-L1. Here, we identify B cell lymphoma-2-associated transcription factor 1 (BCLAF1) as a key PD-L1 regulator in HCC. Specifically, BCLAF1 interacts with SPOP, an E3 ligase that mediates the ubiquitination and degradation of PD-L1, thereby competitively inhibiting SPOP-PD-L1 interaction and subsequent ubiquitination and degradation of PD-L1. Furthermore, we determined an SPOP-binding consensus (SBC) motif mediating the BCLAF1-SPOP interaction on BCLAF1 protein and mutation of BCLAF1-SBC motif disrupts the regulation of the SPOP-PD-L1 axis. In addition, BCLAF1 expression was positively correlated with PD-L1 expression and negatively correlated with biomarkers of T cell activation, including CD3 and CD8, as well as with the level of immune cell infiltration in HCC tissues. Besides, BCLAF1 depletion leads to a significant reduction of PD-L1 expression in vitro, and this reduction of PD-L1 promoted T cell-mediated cytotoxicity. Notably, overexpression of BCLAF1 sensitized tumor cells to checkpoint therapy in an in vitro HCC cells-Jurkat cells co-culture model, whereas BCLAF1-SBC mutant decreased tumor cell sensitivity to checkpoint therapy, suggesting that BCLAF1 and its SBC motif serve as a novel therapeutic target for enhancing anti-tumor immunity in HCC.

Prognostic Biomarkers for Hepatocellular Carcinoma Based on Serine and Glycine Metabolism-related Genes.

Targeted therapy and immunotherapy have emerged as treatment options for hepatocellular carcinoma (HCC) in recent years. The significance of serine and glycine metabolism in various cancers is widely acknowledged. This study aims to investigate their correlation with the prognosis and tumor immune microenvironment (TIME) of HCC. Based on the public database, different subtypes were identified by cluster analysis, and the prognostic model was constructed through regression analysis. The gene expression omnibus (GEO) data set was used as the validation set to verify the performance of the model. The survival curve evaluated prognostic ability. CIBERSORT was used to evaluate the level of immune cell infiltration, and maftools analyzed the mutations. DsigDB screened small molecule compounds related to prognostic genes. HCC was found to have two distinct subtypes. Subsequently, we constructed a risk score prognostic model through regression analysis based on serine and glycine metabolism-related genes (SGMGs). A nomogram was constructed based on risk scores and other clinical factors. HCC patients with a higher risk score showed a poor prognosis, and there were significant differences in immune cell infiltration between the high- and low-risk groups. In addition, three potential drugs associated with prognostic genes, streptozocin, norfloxacin, and hydrocotarnine, were identified. This study investigated the expression patterns of SGMGs and their relationship with tumor characteristics, resulting in the development of a novel model for predicting the prognosis of HCC patients. The study provides a reference for clinical prognosis prediction and treatment of HCC patients.

An individual-based model to explore the impact of psychological stress on immune infiltration into tumour spheroids

In recent in vitro experiments on co-culture between breast tumour spheroids and activated immune cells, it was observed that the introduction of the stress hormone cortisol resulted in a decreased immune cell infiltration into the spheroids. Moreover, the presence of cortisol deregulated the normal levels of the pro- and anti-inflammatory cytokines IFN-γ and IL-10. We present an individual-based model to explore the interaction dynamics between tumour and immune cells under psychological stress conditions. With our model, we explore the processes underlying the emergence of different levels of immune infiltration, with particular focus on the biological mechanisms regulated by IFN-γ and IL-10. The set-up of numerical simulations is defined to mimic the scenarios considered in the experimental study. Similarly to the experimental quantitative analysis, we compute a score that quantifies the level of immune cell infiltration into the tumour. The results of numerical simulations indicate that the motility of immune cells, their capability to infiltrate through tumour cells, their growth rate and the interplay between these cell parameters can affect the level of immune cell infiltration in different ways. Ultimately, numerical simulations of this model support a deeper understanding of the impact of biological stress-induced mechanisms on immune infiltration.

Exploring the potential link between MitoEVs and the immune microenvironment of periodontitis based on machine learning and bioinformatics methods.

Periodontitis is a chronic inflammatory condition triggered by immune system malfunction. Mitochondrial extracellular vesicles (MitoEVs) are a group of highly heterogeneous extracellular vesicles (EVs) enriched in mitochondrial fractions. The objective of this research was to examine the correlation between MitoEVs and the immune microenvironment of periodontitis. Data from MitoCarta 3.0, GeneCards, and GEO databases were utilized to identify differentially expressed MitoEV-related genes (MERGs) and conduct functional enrichment and pathway analyses. The random forest and LASSO algorithms were employed to identify hub MERGs. Infiltration levels of immune cells in periodontitis and healthy groups were estimated using the CIBERSORT algorithm, and phenotypic subgroups of periodontitis based on hub MERG expression levels were explored using a consensus clustering method. A total of 44 differentially expressed MERGs were identified. The random forest and LASSO algorithms identified 9 hub MERGs (BCL2L11, GLDC, CYP24A1, COQ2, MTPAP, NIPSNAP3A, FAM162A, MYO19, and NDUFS1). ROC curve analysis showed that the hub gene and logistic regression model presented excellent diagnostic and discriminating abilities. Immune infiltration and consensus clustering analysis indicated that hub MERGs were highly correlated with various types of immune cells, and there were significant differences in immune cells and hub MERGs among different periodontitis subtypes. The periodontitis classification model based on MERGs shows excellent performance and can offer novel perspectives into the pathogenesis of periodontitis. The high correlation between MERGs and various immune cells and the significant differences between immune cells and MERGs in different periodontitis subtypes can clarify the regulatory roles of MitoEVs in the immune microenvironment of periodontitis. Future research should focus on elucidating the functional mechanisms of hub MERGs and exploring potential therapeutic interventions based on these findings.

Magnetic resonance imaging-based radiomics was used to evaluate the level of prognosis-related immune cell infiltration in breast cancer tumor microenvironment.

The tumor immune microenvironment is a valuable source of information for predicting prognosis in breast cancer (BRCA) patients. To identify immune cells associated with BRCA patient prognosis from the Cancer Genetic Atlas (TCGA), we established an MRI-based radiomics model for evaluating the degree of immune cell infiltration in breast cancer patients. CIBERSORT was utilized to evaluate the degree of infiltration of 22 immune cell types in breast cancer patients from the TCGA database, and both univariate and multivariate Cox regressions were employed to determine the prognostic significance of immune cell infiltration levels in BRCA patients. We identified independent prognostic factors for BRCA patients. Additionally, we obtained imaging features from the Cancer Imaging Archive (TCIA) database for 73 patients who underwent preoperative MRI procedures, and used the Least Absolute Shrinkage and Selection Operator (LASSO) to select the best imaging features for constructing an MRI-based radiomics model for evaluating immune cell infiltration levels in breast cancer patients. According to the results of Cox regression analysis, M2 macrophages were identified as an independent prognostic factor for BRCA patients (HR = 32.288, 95% CI: 3.100-357.478). A total of nine significant features were selected to calculate the radiomics-based score. We established an intratumoral model with AUCs (95% CI) of 0.662 (0.495-0.802) and 0.678 (0.438-0.901) in the training and testing cohorts, respectively. Additionally, a peritumoral model was created with AUCs (95% CI) of 0.826 (0.710-0.924) and 0.752 (0.525-0.957), and a combined model was established with AUCs (95% CI) of 0.843 (0.723-0.938) and 0.744 (0.491-0.965). The peritumoral model demonstrated the highest diagnostic efficacy, with an accuracy, sensitivity, and specificity of 0.773, 0.727, and 0.818, respectively, in its testing cohort. The MRI-based radiomics model has the potential to evaluate the degree of immune cell infiltration in breast cancer patients, offering a non-invasive imaging biomarker for assessing the tumor microenvironment in this disease.

DLX1 acts as a novel prognostic biomarker involved in immune cell infiltration and tumor progression in lung adenocarcinoma.

The biological function of distal-less homeobox 1 (DLX1) in lung adenocarcinoma (LUAD) remains unclear, despite a growing body of evidence that DLX1 is involved in the initiation and progression of various tumors. This study explored and confirmed the prognostic and immunologic roles of DLX1 in LUAD via bioinformatic analysis and cellular functional validation. MethSurv was used to analyze the DNA methylation levels of DLX1 and the prognostic value of CpG islands. DLX1 mutation rates and prognoses between patients with and without the mutated DLX1 gene were analyzed by cBioPortal. Finally, cellular functional assays were used to investigate the effect of DLX1 on LUAD cells. Our results showed that DLX1 mRNA expression was significantly upregulated in LUAD. High DLX1 expression or promoter methylation was associated with worse prognosis, which confirmed DLX1 as an independent prognostic factor in LUAD. The level of multiple immune cell infiltration was significantly associated with DLX1 expression. Genes in the high DLX1 expression group were mainly enriched in cell cycle checkpoint, DNA replication, DNA repair, Fceri-mediated MAPK activation, TP53 activity regulation, and MET activation of PTK2-regulated signaling pathways. Cellular functional assays showed that the knockdown of DLX1 inhibited the proliferation, migration, and invasion of LUAD cells. Our study identified DLX1 as a potential diagnostic and prognostic biomarker, and a promising therapeutic target in LUAD.

Abstract B026: Dissecting the pancreatic cancer microenvironment using landscape ecology

Abstract Pancreatic ductal adenocarcinoma (PDAC) is predicted to be the second cause of cancer death in the US by 2040. Five-year overall survival is only ~12% despite decades of research. The mainstay of treatment remains chemotherapy in the absence of highly recurrent, therapeutically actionable, genetic targets. For the minority of patients (~15-20%) who are candidates for curative-intent surgical resection, six months of intensive neoadjuvant chemotherapy with/without chemoradiation has become standard-of-care. However, despite evidence of radiographic, metabolic, and biochemical responses, significant viable residual disease on pathologic analysis is almost universally observed with approximately 40-60% risk of disease recurrence. Thus, there is a critical need to better understand why PDAC remains recalcitrant to current therapeutic modalities. Pathologic evaluation of PDAC reveals a complex microenvironment consisting of neoplastic cells dynamically interacting with both “normal” cellular (e.g. mesenchymal, immune) and non-cellular (e.g. collagen) stromal elements. While biologically important, experimental models and small-scale efforts to dissect the PDAC ecosystem are limited by focus on individual tumor compartments and generation of conflicting results. Consequently, there is a need for a more comprehensive understanding of the structure and dynamics of the PDAC TME through the theoretical lens of evolutionary ecology. Current therapies are not focused on the PDAC ecosystem. We hypothesize that cell-cell interactions of PDAC cells with its local niche are important for conferring treatment resistance. Thus, better understanding of said interactions could yield novel ecologically-informed therapies. Consequently, we developed a digital pathology library of approximately 1000 PDAC resection cases (whole-slide H&E images) performed at Mayo Clinic between 2013-2021. Approximately 400 cases are treatment naive while about 600 were treated with neoadjuvant therapy. While some qualitative features of pathologic analysis have prognostic value (e.g., pathologic treatment response, presence of perineural or lymphovascular invasion, extent of desmoplasia), we aim to identify prognostic spatial relationships using tools from landscape ecology. We utilized the HoVer-Net model for nuclei segmentation and classification and spatial statistics methods and landscape fragmentation analysis to understand the spatial organization within the PDAC tumor ecology. With these methodologies, we can assess the spatial context of cell interactions, habitat/niche/microenvironment formation, ecological relationships between cell populations, and the different tissue components. The different spatial statistics/metrics are used to quantify levels of immune cell infiltration, mixing, as well as the complexity and diversity of microenvironment phenotypes leading to high levels of tumor heterogeneity. Such measurements can help predict the likelihood of a PDAC tumor adaptive potential that could lead to fast evolutionary responses to therapy, progression, and poor prognosis. Citation Format: Merih D. Toruner, Luis Cisneros, Zafar Siddiqui, Chady Meroueh, Ritambhara Singh, Carlo Maley, Martin E. Fernandez-Zapico, Ryan M. Carr. Dissecting the pancreatic cancer microenvironment using landscape ecology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr B026.

Construction and validation of a prognostic signature for mucinous colonic adenocarcinoma based on N7-methylguanosine-related long non-coding RNAs.

Mucinous colonic adenocarcinoma remains a challenging disease due to its high propensity for metastasis and recurrence. N7-methylguanosine (m7G) and long non-coding RNA (lncRNA) are closely associated with the occurrence and progression of tumors. However, research on m7G-related lncRNA in mucinous colonic adenocarcinoma is lacking. Therefore, we sought to explore the prognostic impact of m7G-related lncRNAs in mucinous adenocarcinoma (MC) patients. In this study, Pearson analysis was used to identify m7G-related lncRNAs from transcriptome data in The Cancer Genome Atlas (TCGA). Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to further screen m7G-related lncRNAs and incorporate them into a prognostic signature. Based on the risk model, patients were divided into low- and high-risk groups and randomly assigned to the training set and test sets in a 6:4 ratio. Kaplan-Meier, receiver operating characteristic (ROC) curve, multivariate regression, and nomogram analyses were used to confirm the accuracy of the signature. The CIBERSORT algorithm was used to calculate the degree of immune cell infiltration (ICI). Finally, the correlation of the prognostic signature with tumor mutational burden (TMB) and immunophenotype score (IPS) was evaluated. A total of 432 m7G-related lncRNAs were identified by Pearson analysis. Univariate Cox regression, LASSO regression and survival analysis were performed to further select six m7G-related lncRNAs (P<0.05): AC254629.1, LINC01133, LINC01134, MHENCR, SMIM2-AS1, and XACT. Based on the risk model, heat maps, Kaplan-Meier curves, and ROC curves were constructed, and the results showed that there were significant differences in expression levels and survival status between the two risk groups. The area under the ROC curve (AUC) values for 3-, 5-, and 10-year survival in the training set were 0.944, 0.957, and 1.000, respectively. And in the test set were 0.964, 1.000, and 1.000, respectively. Subsequently, univariate and multivariate regression analyses of clinical characteristics and risk score were performed. The results of risk score were [hazard ratio (HR): 6.458, 95% confidence interval (CI): 2.708-15.403, P<0.001; HR: 7.280, 95% CI: 2.500-21.203, P<0.001], respectively. Using the risk score as an independent prognostic factor, the AUC of it over 3, 5, and 10 years was 0.911, 0.955, and 0.961, respectively. Calibration plots for the nomogram show that the model calibration line is very close to the ideal calibration line, indicating good calibration. The level of ICI was significantly different in the different risk groups. Survival analysis showed that, regardless of TMB risk, patients with MC and a high-risk score consistently had a poor overall survival (OS). The m7G-related lncRNA prognostic signature has potential value for the prognosis of mucinous colonic adenocarcinoma.

Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model.

Accurate survival predictions and early interventional therapy are crucial for people with clear cell renal cell carcinoma (ccRCC). In this retrospective study, we identified differentially expressed immune-related (DE-IRGs) and oncogenic (DE-OGs) genes from The Cancer Genome Atlas (TCGA) dataset to construct a prognostic risk model using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. We compared the immunogenomic characterization between the high- and low-risk patients in the TCGA and the PUCH cohort, including the immune cell infiltration level, immune score, immune checkpoint, and T-effector cell- and interferon (IFN)-γ-related gene expression. A prognostic risk model was constructed based on 9 DE-IRGs and 3 DE-OGs and validated in the training and testing TCGA datasets. The high-risk group exhibited significantly poor overall survival compared with the low-risk group in the training (P < 0.0001), testing (P = 0.016), and total (P < 0.0001) datasets. The prognostic risk model provided accurate predictive value for ccRCC prognosis in all datasets. Decision curve analysis revealed that the nomogram showed the best net benefit for the 1-, 3-, and 5-year risk predictions. Immunogenomic analyses of the TCGA and PUCH cohorts showed higher immune cell infiltration levels, immune scores, immune checkpoint, and T-effector cell- and IFN-γ-related cytotoxic gene expression in the high-risk group than in the low-risk group. The 12-gene prognostic risk model can reliably predict overall survival outcomes and is strongly associated with the tumor immune microenvironment of ccRCC.

HdWGCNA and Cellular Communication Identify Active NK Cell Subtypes in Alzheimer's Disease and Screen for Diagnostic Markers through Machine Learning.

Alzheimer's disease (AD) is a recognized complex and severe neurodegenerative disorder, presenting a significant challenge to global health. Its hallmark pathological features include the deposition of β-amyloid plaques and the formation of neurofibrillary tangles. Given this context, it becomes imperative to develop an early and accurate biomarker model for AD diagnosis, employing machine learning and bioinformatics analysis. In this study, single-cell data analysis was employed to identify cellular subtypes that exhibited significant differences between the diseased and control groups. Following the identification of NK cells, hdWGCNA analysis and cellular communication analysis were conducted to pinpoint NK cell subset with the most robust communication effects. Subsequently, three machine learning algorithms-LASSO, Random Forest, and SVM-RFE-were employed to jointly screen for NK cell subset modular genes highly associated with AD. A logistic regression diagnostic model was then designed based on these characterized genes. Additionally, a protein-protein interaction (PPI) networks of model genes was established. Furthermore, unsupervised cluster analysis was conducted to classify AD subtypes based on the model genes, followed by the analysis of immune infiltration in the different subtypes. Finally, Spearman correlation coefficient analysis was utilized to explore the correlation between model genes and immune cells, as well as inflammatory factors. We have successfully identified three genes (RPLP2, RPSA, and RPL18A) that exhibit a high association with AD. The nomogram based on these genes provides practical assistance in diagnosing and predicting patients' outcomes. The interconnected genes screened through PPI are intricately linked to ribosome metabolism and the COVID-19 pathway. Utilizing the expression of modular genes, unsupervised cluster analysis unveiled three distinct AD subtypes. Particularly noteworthy is subtype C3, characterized by high expression, which correlates with immune cell infiltration and elevated levels of inflammatory factors. Hence, it can be inferred that the establishment of an immune environment in AD patients is closely intertwined with the heightened expression of model genes. This study has not only established a valuable diagnostic model for AD patients but has also delved deeply into the pivotal role of model genes in shaping the immune environment of individuals with AD. These findings offer crucial insights into early AD diagnosis and patient management strategies.

Establishment and validation of a novel peroxisome-related gene prognostic risk model in kidney clear cell carcinoma

BackgroundKidney clear cell carcinoma (KIRC) is the most common subtype of renal cell carcinoma. Peroxisomes play a role in the regulation of tumorigenesis and cancer progression, yet the prognostic significance of peroxisome-related genes (PRGs) remains rarely studied. The study aimed to establish a novel prognostic risk model and identify potential biomarkers in KIRC.MethodsThe significant prognostic PRGs were screened through differential and Cox regression analyses, and LASSO Cox regression analysis was performed to establish a prognostic risk model in the training cohort, which was validated internally in the testing and entire cohorts, and further assessed in the GSE22541 cohort. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to explore the function and pathway differences between the high-risk and low-risk groups. The relationship between risk score and immune cell infiltration levels was evaluated in the CIBERSORT, ESTIMATE and TIMER databases. Finally, potential biomarkers were identified and validated from model genes, using immunohistochemistry.ResultsFourteen significant prognostic PRGs were identified using multiple analyses, and 9 genes (ABCD1, ACAD11, ACAT1, AGXT, DAO, EPHX2, FNDC5, HAO1, and HNGCLL1) were obtained to establish a prognostic model via LASSO Cox regression analysis. Combining the risk score with clinical factors to construct a nomogram, which provided support for personalized treatment protocols for KIRC patients. GO and KEGG analyses highlighted associations with substance metabolism, transport, and the PPAR signaling pathways. Tumor immune infiltration indicated immune suppression in the high-risk group, accompanied by higher tumor purity and the expression of 9 model genes was positively correlated with the level of immune cell infiltration. ACAT1 has superior prognostic capabilities in predicting the outcomes of KIRC patients.ConclusionsThe peroxisome-related prognostic risk model could better predict prognosis in KIRC patients.

Robust machine−learning based prognostic index using cytotoxic T lymphocyte evasion genes highlights potential therapeutic targets in colorectal cancer

BackgroundA minute fraction of patients stands to derive substantial benefits from immunotherapy, primarily attributable to immune evasion. Our objective was to formulate a predictive signature rooted in genes associated with cytotoxic T lymphocyte evasion (CERGs), with the aim of predicting outcomes and discerning immunotherapeutic response in colorectal cancer (CRC).Methods101 machine learning algorithm combinations were applied to calculate the CERGs prognostic index (CERPI) under the cross−validation framework, and patients with CRC were separated into high− and low−CERPI groups. Relationship between immune cell infiltration levels, immune−related scores, malignant phenotypes and CERPI were further analyzed. Various machine learning methods were used to identify key genes related to both patient survival and immunotherapy benefits. Expression of HOXC6, G0S2, and MX2 was evaluated and the effects of HOXC6 and G0S2 on the viability and migration of a CRC cell line were in−vitro verified.ResultsThe CERPI demonstrated robust prognostic efficacy in predicting the overall survival of CRC patients, establishing itself as an independent predictor of patient outcomes. The low−CERPI group exhibited elevated levels of immune cell infiltration and lower scores for tumor immune dysfunction and exclusion, indicative of a greater potential benefit from immunotherapy. Moreover, there was a positive correlation between CERPI levels and malignant tumor phenotypes, suggesting that heightened CERPI expression contributes to both the occurrence and progression of tumors. Thirteen key genes were identified, and their expression patterns were scrutinized through the analysis of single−cell datasets. Notably, HOXC6, G0S2, and MX2 exhibited upregulation in both CRC cell lines and tissues. Subsequent knockdown experiments targeting G0S2 and HOXC6 resulted in a significant suppression of CRC cell viability and migration.ConclusionWe developed the CERPI for effectively predicting survival and response to immunotherapy in patients, and these results may provide guidance for CRC diagnosis and precise treatment.

DCLK1 is Overexpressed and Associated with Immune Cell Infiltration in Hepatocellular Carcinoma.

Recent research has shown that Doublecortin-like kinase 1 (DCLK1) is overexpressed in different types of cancer. It has recently been described as a cancer stem cells (CSCs) marker, is associated with carcinogenesis, and positively correlates with infiltration of multiple immune cell types in some cancers. However, studies focused on assessing DCLK1 expression in HCC are limited, and the role of DCLK1 in HCC tumor immunity remains to be determined. In this study, we used a modified model of the resistant hepatocyte (MRHM) to evaluate DCLK1 expression in HCC. Furthermore, DCLK1 expression in HCC was analyzed using TIMER 2.0, UALCAN, GEPIA, GEO, and HPA web-based tools. Correlations between DCLK1 expression and clinicopathological factors in patients were analyzed using the UALCAN web-based tool. Finally, correlations between DCLK1 and immune infiltrates were investigated using the TIMER 2.0 and TISIDB web-based tools. The results showed that DCLK1 is significantly overexpressed during progression of the HCC carcinogenic process in the MRHM. DCLK1 is overexpressed in HCC according to multiple publics web-based tools, and its overexpression is associated with cancer stage. Furthermore, DCLK1 expression was correlated with infiltration levels of multiple immune cells, immunomodulatory factors, immunoinhibitors, MHC molecules, chemokines, receptors, and immune cell-specific markers. These results suggest that DCLK1 is a potential prognostic biomarker that determines cancer progression and correlates with immune cell infiltration in HCC.

Integrative bioinformatics and experimental validation of hub genetic markers in acne vulgaris: Toward personalized diagnostic and therapeutic strategies.

Acne vulgaris is a widespread chronic inflammatory dermatological condition. The precise molecular and genetic mechanisms of its pathogenesis remain incompletely understood. This research synthesizes existing databases, targeting a comprehensive exploration of core genetic markers. Gene expression datasets (GSE6475, GSE108110, and GSE53795) were retrieved from the GEO. Differentially expressed genes (DEGs) were identified using the limma package. Enrichment analyses were conducted using GSVA for pathway assessment and clusterProfiler for GO and KEGG analyses. PPI networks and immune cell infiltration were analyzed using the STRING database and ssGSEA, respectively. We investigated the correlation between hub gene biomarkers and immune cell infiltration using Spearman's rank analysis. ROC curve analysis validated the hub genes' diagnostic accuracy. miRNet, TarBase v8.0, and ChEA3 identified miRNA/transcription factor-gene interactions, while DrugBank delineated drug-gene interactions. Experiments utilized HaCaT cells stimulated with Propionibacterium acnes, treated with retinoic acid and methotrexate, and evaluated using RT-qPCR, ELISA, western blot, lentiviral transduction, CCK-8, wound-healing, and transwell assays. There were 104 genes with consistent differences across the three datasets of paired acne and normal skin. Functional analyses emphasized the significant enrichment of these DEGs in immune-related pathways. PPI network analysis pinpointed hub genes PTPRC, CXCL8, ITGB2, and MMP9 as central players in acne pathogenesis. Elevated levels of specific immune cell infiltration in acne lesions corroborated the inflammatory nature of the disease. ROC curve analysis identified the acne diagnostic potential of four hub genes. Key miRNAs, particularly hsa-mir-124-3p, and central transcription factors like TFEC were noted as significant regulators. Invitro validation using HaCaT cells confirmed the upregulation of hub genes following Propionibacterium acnes exposure, while CXCL8 knockdown reduced pro-inflammatory cytokines, cell proliferation, and migration. DrugBank insights led to the exploration of retinoic acid and methotrexate, both of which mitigated gene expression upsurge and inflammatory mediator secretion. This comprehensive study elucidated pivotal genes associated with acne pathogenesis, notably PTPRC, CXCL8, ITGB2, and MMP9. The findings underscore potential biomarkers, therapeutic targets, and the therapeutic potential of agents like retinoic acid and methotrexate. The congruence between bioinformatics and experimental validations suggests promising avenues for personalized acne treatments.