Immune Infiltration Status Research Articles

Enhanced tumoricidal activity of PD-1 antibody-secreting c-Met CAR-T cells against pancreatic cancer cells

To construct c-Met CAR-T cells secreting PD-1 antibodies to reduce immune inhibitory effect of tumor cells and enhance the efficacy of CAR-T cell therapy against pancreatic cancer. Kaplan-Meier Plotter, GEPIA, and Timer 2.0 bioinformatics databases were used to analyze c-Met expression in pancreatic cancer and its correlation with survival and immune infiltration status. In clinical samples of pancreatic cancer and pancreatic cancer Aspc-1 cells, c-Met and PD-L1 expressions were detected using immunohistochemistry or flow cytometry. Using gene editing technology, PD-1 secretory antibodies and HIS tags were linked to second-generation c-Met CAR molecules to construct PD-1/c-Met CAR plasmids, which were then packaged into lentiviruses for infection of activated T cells. The positive rate and cell subset distribution of CAR-T cells were analyzed with flow cytometry, and secretory PD-1 antibodies in cell supernatants were detected using Western blotting. The target cell killing efficiency and proliferative activity of the modified CAR-T cells were evaluated after activation, and cytokine secretion was analyzed using ELISA. The expression of c-Met was significantly higher in pancreatic cancer than in normal tissues, and its expression level was negatively correlated with the patients' survival and positively correlated with immune cell infiltration. The clinical samples of pancreatic cancer tissues expressed significantly higher levels of c-Met and PD-L1 than the adjacent tissues, and 90.7% and 57.7% of Aspc-1 cells were positive for c-Met and PD-L1, respectively. The constructed PD-1/c-Met CAR-T cells were capable of secreting PD-1 antibodies and showed a significantly higher killing efficiency against tumor cells than c-Met CAR-T cells at an effector-to-target ratio of 20: 1, with also a higher proliferative activity after target cell stimulation and higher levels of IL-2 and TNF-α secretin. PD-1/c-Met CAR-T cells have higher killing efficiency against pancreatic cancer cells with also higher proliferative activity than c-Met CAR-T cells.

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.

Sialylation-associated long non-coding RNA signature predicts the prognosis, tumor microenvironment, and immunotherapy and chemotherapy options in uterine corpus endometrial carcinoma

BackgroundSialylation in uterine corpus endometrial carcinoma (UCEC) differs significantly from apoptotic and ferroptosis pathways. It plays a crucial role in cancer progression and immune response modulation. Exploring how sialylation affects tumor behavior and its link with long non-coding RNAs (lncRNAs) may provide new insights into UCEC prognosis and treatment.MethodsWe obtained RNA transcriptome, clinical, and mutation data of UCEC samples from the TCGA database. Our approach involved developing a risk model based on the co-expression patterns of sialylation genes and lncRNAs. Prognostic lncRNAs were identified through Cox regression and further refined using LASSO analysis. To understand the biological functions and pathways of model-associated differentially expressed genes (MADEGs), we conducted enrichment analyses. We also assessed the immune infiltration status of MADEGs using eight different algorithms, which helped in evaluating the potential for immunotherapy. Additionally, we validated the expression of these lncRNAs in UCEC using cell lines and clinical samples.ResultsWe developed a UCEC risk model using five sialylation-related lncRNAs (AC004884.2, AC026202.2, LINC01579, LINC00942, SLC16A1-AS1). This model, confirmed through Cox analysis and clinical evaluation, effectively predicted patient outcomes. Survival data analysis across entire cohort, as well as within training and test groups, indicated better survival in low-risk UCEC patients. Enrichment analyses linked MADEGs to sialylation functions and cancer pathways. High-risk patients showed increased responsiveness to immune checkpoint inhibitors (ICIs), as indicated by immunological assessments. Subgroup C2 patients showed superior outcomes and a robust response to immunotherapy and chemotherapy. Notably, LINC01579, LINC00942, and SLC16A1-AS1 were significantly overexpressed in UCEC clinical tumor samples as well as in Ishikawa and HEC-1-B cell lines, compared to the normal groups.ConclusionsThis lncRNA signature associated with sialylation could guide prognosis, enhance the understanding of molecular mechanisms, and inform treatment strategies in UCEC. It highlights the potential for the use of ICIs and chemotherapy.

The m5C/m6A/m7G-related non-apoptotic regulatory cell death genes for the prediction of the prognosis and immune infiltration status in hepatocellular carcinoma.

5-methylcytosine/N6-methyladenosine/N7-methylguanosine (m5C/m6A/m7G)-related genes play a critical role in tumor occurrence and progression, and non-apoptotic regulatory cell death (NARCD) is closely linked to tumor development and immunity. However, the role of m5C/m6A/m7G-related NARCD genes in hepatocellular carcinoma (HCC) remains unclear. We used m5C/m6A/m7G-related NARCD genes to construct a prognostic model of HCC for prognostic prediction and clinical treatment of patients. We obtained transcriptome data for HCC from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Using the least absolute shrinkage and selection operator (LASSO) regression, we identified m5C/m6A/m7G-related NARCD genes and constructed a prognostic model through multivariate Cox regression. Model performance was assessed using Kaplan-Meier and receiver operating characteristic (ROC) curves, with external validation using the ICGC. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were used to study differentially expressed genes between high- and low-risk groups. We also examined immune cell infiltration, drug response, and cell communication between tumor cells and immune cells in high-risk groups. We identified 140 m5C/m6A/m7G-related NARCD genes, using five of them to build the prognostic model. Functional enrichment analysis revealed enrichment in tumor and immune-related pathways for risk genes. The high-risk group displayed increased immune cell infiltration and better responses to immune checkpoint inhibitors (ICIs). High-risk patients were more responsive to cisplatin, doxorubicin, and mitomycin C, while low-risk patients were more sensitive to erlotinib. Cell communication analysis indicated that high-risk tumor cells used insulin-like growth factor (IGF) and macrophage migration inhibitory factor (MIF) signaling pathways to send signals to immune cells and received signals through the bone morphogenetic protein (BMP) and lymphotoxin-related inducible ligand (LIGHT) pathways. We have developed a prognostic model with m5C/m6A/m7G-related NARCD genes to predict the prognosis of HCC patients. This model can offer insights into the effectiveness of immunotherapy and chemotherapy for HCC patients.

Development of Chromatin Regulator-related Molecular Subtypes and a Signature to Predict Prognosis and Immunotherapeutic Response in Head and Neck Squamous Cell Carcinoma.

Chromatin regulators (CRs) serve as indispensable factors in tumor biological processes by influencing tumorigenesis and the immune microenvironment and have been identified in head and neck squamous cell carcinoma (HNSCC). Hence, CR-related genes (CRRGs) are considered potential biomarkers for predicting prognosis and immune infiltration in HNSCC. In this study, we established a novel signature for predicting the prognosis and immunotherapeutic response of HSNCC. A total of 870 CRRGs were obtained according to previous studies. Subsequently, patients in the TCGA-HNSC cohort were divided into different clusters based on the expression of prognostic CRRGs. Kaplan‒Meier (K‒M) survival analysis was conducted to compare the prognosis in clusters, and the CIBERSORT and ssGSEA methods assessed the immune infiltration status. In addition, the differences in immunotherapeutic responses were determined based on the TICA database. Furthermore, the differentially expressed CRRGs between clusters were identified, and the predictive signature was established according to the results of univariate Cox, least absolute shrinkage and selection operator regression analysis, and multivariate Cox. The predictive effects of the risk model were evaluated according to the area under the receiver operating characteristic (ROC) curve (AUC) in both the training and external test cohorts. A nomogram was established, and survival comparisons, functional enrichment analyses, and immune infiltration status and clinical treatment assessments were performed. In addition, the hub gene network and related analysis were conducted with the Cytohubba application. Based on the expression of prognostic CRRGs, patients were divided into two clusters, in which Cluster 1 exhibited a better prognosis, more enriched immune infiltration, and a better immunotherapeutic response but exhibited chemotherapy sensitivity. The AUC values of the 1-, 3- and 5- year ROC curves for the risk model were 0.673, 0.732, and 0.692, respectively, as well as 0.645, 0.608, and 0.623 for the test set. In addition, patients in the low-risk group exhibited more immune cell enrichment and immune function activation, as well as a better immunotherapy response. The hub gene network indicated ACTN2 as the core gene differentially expressed between the two risk groups. We identified molecular subtypes and established a novel predictive signature based on CRRGs. This effective CRRS system can possibly provide a novel research direction for exploring the correlation between CRs and HNSCC and requires further experimental validation.

Construction of m7G RNA modification-related prognostic model and prediction of immune therapy response in hepatocellular carcinoma.

RNA plays an important role in tumorigenesis. Changes in RNA may cause changes in the biological function. The N7-methylguanosine (m7G) methylation modification performs an integral function in tumor progression as the most widely existed RNA modification. Hepatocellular carcinoma (HCC) is among the greatest threats to human health worldwide. Low detection rates remain the main cause of advanced disease progression. Therefore, finding significant biomarkers for prognosis prediction and immune therapy response in HCC is valuable and urgently needed. RNA expression and clinical data were acquired from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Different subtypes screening was finished by consensus cluster. Different expression was performed by R software. The results were validated by western blot (WB) methods. Genes with HCC prognostic potential were identified utilizing least absolute shrinkage and selection operator (LASSO) analyses. A prognosis model was established with the help of the risk score that we calculated. Related genes screening and protein-protein interactions (PPI) network construction were performed using the GeneMANIA database. Functional annotation was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) databases. In addition, gene set enrichment analysis (GSEA) of key genes and immune infiltration status were both done by R software. Finally, the immune infiltration was performed by cibersort method and single sample GSEA (ssGSEA) method. The response of immune therapy was validated by Tumor Immune Dysfunction and Exclusion database (TIDE) and the immune therapy cohort in GEO database. We found that two different subtypes related with m7G RNA modification and four genes associated with m7G RNA modification were differentially expressed in the TCGA-Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Additionally, to examine the value of these four genes in the HCC patients' prognoses according to the LASSO, we selected three genes, including WDR4, AGO2, and NCBP2, as prognostic related genes. Premised on the expression of these three genes, a risk score model and nomogram were constructed to provide a prediction of the HCC patients' prognoses. We performed functional annotation and created a PPI network based on the three genes (WDR4, NCBP2, and AGO2). Using R software, we performed the GSEA and immune regulation analyses. Finally, we predicted the relationship between the gene expression and the response of immune therapy. Our study suggests that high expression of m7G RNA modification subtype is related with poor prognosis and immune response. WDR4, AGO2, and NCBP2 are key regulators of m7G RNA modification which can be clinically promising biomarkers that can be used to treat HCC. In addition, our risk score model was shown to have a strong link to OS in patients with HCC.

Abstract PO5-21-05: An Endocrine Resistant-Related Gene Signature Revealing the Tumor Microenvironment to Predict the Prognosis of Hormone Receptor-Positive Breast Cancer Patients

Abstract Key words: breast cancer; hormone receptor-positive; predictive model; prognosis; endocrine therapy; drug resistance Hormone receptor positive (HR+) breast cancer is the most common type of breast cancer, accounting for approximately 70% of all breast cancer, in whom endocrine therapy plays a vital role. However, the limited effectiveness of endocrine therapy due to intricate mechanisms of drug resistance poses a significant challenge in treating certain patients. Therefore, it is imperative to identify novel indicators of endocrine resistance and develop new models for predicting recurrence-free survival (RFS) in patients with HR+ breast cancer. In this study, we successfully established an endocrine resistance-related risk model based on multicenter RNA sequencing data. This model could represent tumor microenvironment characteristics and provide valuable insights into predicting the prognosis of HR+ breast cancer patients. A seven gene risk model reflecting endocrine therapy resistance in 208 early HR+ breast cancer patients from two centers was constructed by LASSO analysis and multivariate cox regression. Based on the median value of risk score, patients were divided into high-risk group and low-risk group. Compared with the low-risk group, the high-risk group exhibited a significantly worse prognosis, which showed consistent across two external validation cohorts (TCGA-BRCA and METABRIC cohort). Time dependent ROC curve showed excellent capacity of gene model in predicting 1,3 and 5-year RFS. The IC50 analysis revealed that patients in low-risk group exhibited greater sensitivity to endocrine drugs, such as tamoxifen, fulvestrant and palbociclib, which strongly suggested that the identified gene signature can effectively differentiate between endocrine-resistant and -sensitive status. Gene-set enrichment analysis revealed regulation of cell killing, positive regulation of inflammatory response and T cell mediated cytotoxicity were enriched in low risk group, suggesting immune activation and a higher immune infiltration status. Conversely, the high-risk group demonstrated associations with collagen metabolic process, extracellular matrix structural constituent and collagen fibril organization, which were the indicative of the location and function of cancer-associated fibroblasts (CAFs). As expected, ssGSEA results revealed elevated expression levels of immune cells (e.g., natural killer cells, tumor-infiltrating lymphocytes, CD8+ T cells) and immune functions (e.g., T cell co-stimulation, inflammation-promoting, cytolytic activity) in the low-risk group. However, three diverse acknowledged methods (TIDE, EPIC and MCP-counter) consistently identified CAFs as predominantly content in the high-risk groups. Furthermore, considering that CD8+ T cell is the primary cell type responsible for exerting cytotoxic effects, we observed a negative correlation between CD8+ T cells and CAFs. This suggests that CAFs may inhibit the infiltration of CD8+ T cells, thereby weakening their cytotoxic capabilities, leading to a shorter RFS in high-risk patient groups. In conclusion, a novel gene signature constructed with 7 endocrine resistance response-related genes can be used for prognostic prediction and reflecting the tumor microenvironment status in HR+ early breast cancer. The low-risk group exhibited enhanced anti-tumor immunity, resulting in a favorable prognosis. In contrast, the high-risk group displayed pronounced infiltration of CAFs, indicating their potential to impede immune cell infiltration and impact the efficacy of endocrine drugs through the formation of physical barriers and recruitment of immunosuppressive factors. Consequently, patients in the high-risk group experienced a worse prognosis. Therefore, inhibiting these genes may be a therapeutic option for overcoming endocrine therapy resistance in HR+ breast cancer patients. Citation Format: Xiyu Kang, Jiaxiang Liu, Xin Wang. An Endocrine Resistant-Related Gene Signature Revealing the Tumor Microenvironment to Predict the Prognosis of Hormone Receptor-Positive Breast Cancer Patients [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 PO5-21-05.

Major Facilitator Superfamily Domain Containing 12 Is Overexpressed in Lung Cancer and Exhibits an Oncogenic Role in Lung Adenocarcinoma Cells.

Major facilitator superfamily domain containing 12 (MFSD12) regulates lysosomal cysteine import and promotes the proliferation and survival of melanoma cells. However, the expression and function of MFSD12 in other cancers, particularly in lung cancer, remain unclear. The expression of MFSD12 across various types of cancers and corresponding control tissues was examined using TIMER. MFSD12 expression in lung adenocarcinoma (LUAD) and its correlation with distinct clinicopathological features of LUAD patients were analyzed with UALCAN. The correlation between MFSD12 expression and survival of LUAD patients was assessed using the R package, survival, and the relationship between MFSD12 expression and immune infiltration status in LUAD was investigated using CIBERSORT. In addition, MFSD12 expression was knocked down in PC9 LUAD cells and their proliferation, capacity for expansion, cell cycle, apoptosis, and migration/invasion were evaluated through CCK-8 assays, colony formation assays, 7-AAD staining, Annexin V/PI staining, and Transwell assays, respectively. The stemness of these PC9 cells was determined through Western blotting, flow cytometry, and tumor sphere formation assays. MFSD12 mRNA levels were significantly elevated in multiple types of cancers, including LUAD. MFSD12 expression was also positively correlated with cancer stage, nodal metastasis, and infiltration of various immune cells in LUAD, and high MFSD12 levels predicted poor survival among LUAD patients. Knockdown of MFSD12 in PC9 cells resulted in decreased proliferation, attenuated colony formation capacity, cell cycle arrest, elevated apoptosis, impaired migration/invasion, and reduced stemness in PC9 cells. MFSD12 is an oncogene in LUAD.

Analysis of weighted gene co-expression networks and clinical validation identify hub genes and immune cell infiltration in the endometrial cells of patients with recurrent implantation failure.

About 10% of individuals undergoing in vitro fertilization encounter recurrent implantation failure (RIF), which represents a worldwide social and economic concern. Nevertheless, the critical genes and genetic mechanisms underlying RIF are largely unknown. We first obtained three comprehensive microarray datasets "GSE58144, GSE103465 and GSE111974". The differentially expressed genes (DEGs) evaluation, enrichment analysis, as well as efficient weighted gene co-expression network analysis (WGCNA), were employed for distinguishing RIF-linked hub genes, which were tested by RT-qPCR in our 30 independent samples. Next, we studied the topography of infiltration of 22 immune cell subpopulations and the association between hub genes and immune cells in RIF using the CIBERSORT algorithm. Finally, a novel ridge plot was utilized to exhibit the potential function of core genes. The enrichment of GO/KEGG pathways reveals that Herpes simplex virus 1 infection and Salmonella infection may have an important role in RIF. After WGCNA, the intersected genes with the previous DEGs were obtained using both variance and association. Notably, the subsequent nine hub genes were finally selected: ACTL6A, BECN1, SNRPD1, POLR1B, GSK3B, PPP2CA, RBBP7, PLK4, and RFC4, based on the PPI network and three different algorithms, whose expression patterns were also verified by RT-qPCR. With in-depth analysis, we speculated that key genes mentioned above might be involved in the RIF through disturbing endometrial microflora homeostasis, impairing autophagy, and inhibiting the proliferation of endometrium. Furthermore, the current study revealed the aberrant immune infiltration patterns and emphasized that uterine NK cells (uNK) and CD4+ T cells were substantially altered in RIF endometrium. Finally, the ridge plot displayed a clear and crucial association between hub genes and other genes and key pathways. We first utilized WGCNA to identify the most potential nine hub genes which might be associated with RIF. Meanwhile, this study offers insights into the landscape of immune infiltration status to reveal the underlying immune pathogenesis of RIF. This may be a direction for the next study of RIF etiology. Further studies would be required to investigate the involved mechanisms.

Uncovering a novel DNA repair-related radiosensitivity model for evaluation of radiotherapy susceptibility in uterine corpus endometrial cancer

BackgroundUterine corpus endometrial cancer (UCEC) exhibit heterogeneity in their DNA repair capacity, which can impact their response to radiotherapy. Our study aimed to identify potential DNA repair-related biomarkers for predicting radiation response in UCEC. MethodsWe conducted a thorough analysis of 497 UCEC samples obtained from TCGA database. Using LASSO-COX regression analysis, we constructed a radiosensitivity signature and subsequently divided patients into the radiosensitive (RS) and the radioresistant (RR) groups based on their radiosensitivity index. The GSVA and GSEA were performed to explore functional annotations. The CIBERSORT and ESTIMATE algorithms were utilized to investigate the immune infiltration status of the two groups. Additionally, we utilized the Tumor Immune Dysfunction and Exclusion (TIDE), Immunophenotype Score (IPS), and pRRophetic algorithms to predict the effectiveness of different treatment modalities. ResultsWe constructed a radiosensitivity index consists of four DNA repair-related genes. Patients in the RS group demonstrated significantly improved prognosis compared to patients in the RR group when treated with radiotherapy. We observed that the RS group exhibited a higher proportion of the POLE ultra-mutated subtype, while the RR group had a higher proportion of the copy number high subtype. GSVA enrichment analysis revealed that the RS group exhibited enrichment in DNA damage repair pathways. Notably, the RS group demonstrated a higher proportion of naïve B cells and follicular helper T cells, while regulatory T cells (Tregs) and memory B cells were more abundant in the RR group. Furthermore, patients in the RS-PD-L1-high subgroup exhibited enrichment in immune-related pathways and increased sensitivity to immunotherapy, which is likely to contribute to their improved prognosis. Additionally, we conducted in vitro experiments to validate the expression of radiosensitivity genes in non-radioresistant (AN3CA) and radioresistant (AN3CA/IR) endometrial cancer cells. ConclusionsIn conclusion, our research successfully constructed a radiosensitivity signature with robust predictive capacity. These findings shed light on the association between immune activation, PD-L1 expression, and the response to immunotherapy in the context of radiotherapy.

Identification of a novel inflammatory-related gene signature to evaluate the prognosis of gastric cancer patients.

Gastric cancer (GC) is a highly aggressive malignancy with a heterogeneous nature, which makes prognosis prediction and treatment determination difficult. Inflammation is now recognized as one of the hallmarks of cancer and plays an important role in the aetiology and continued growth of tumours. Inflammation also affects the prognosis of GC patients. Recent reports suggest that a number of inflammatory-related biomarkers are useful for predicting tumour prognosis. However, the importance of inflammatory-related biomarkers in predicting the prognosis of GC patients is still unclear. To investigate inflammatory-related biomarkers in predicting the prognosis of GC patients. In this study, the mRNA expression profiles and corresponding clinical information of GC patients were obtained from the Gene Expression Omnibus (GEO) database (GSE66229). An inflammatory-related gene prognostic signature model was constructed using the least absolute shrinkage and selection operator Cox regression model based on the GEO database. GC patients from the GSE26253 cohort were used for validation. Univariate and multivariate Cox analyses were used to determine the independent prognostic factors, and a prognostic nomogram was established. The calibration curve and the area under the curve based on receiver operating characteristic analysis were utilized to evaluate the predictive value of the nomogram. The decision curve analysis results were plotted to quantify and assess the clinical value of the nomogram. Gene set enrichment analysis was performed to explore the potential regulatory pathways involved. The relationship between tumour immune infiltration status and risk score was analysed via Tumour Immune Estimation Resource and CIBERSORT. Finally, we analysed the association between risk score and patient sensitivity to commonly used chemotherapy and targeted therapy agents. A prognostic model consisting of three inflammatory-related genes (MRPS17, GUF1, and PDK4) was constructed. Independent prognostic analysis revealed that the risk score was a separate prognostic factor in GC patients. According to the risk score, GC patients were stratified into high- and low-risk groups, and patients in the high-risk group had significantly worse prognoses according to age, sex, TNM stage and Lauren type. Consensus clustering identified three subtypes of inflammation that could predict GC prognosis more accurately than traditional grading and staging. Finally, the study revealed that patients in the low-risk group were more sensitive to certain drugs than were those in the high-risk group, indicating a link between inflammation-related genes and drug sensitivity. In conclusion, we established a novel three-gene prognostic signature that may be useful for predicting the prognosis and personalizing treatment decisions of GC patients.

Identification of circadian rhythm-related gene classification patterns and immune infiltration analysis in heart failure based on machine learning

BackgroundCircadian rhythms play a key role in the failing heart, but the exact molecular mechanisms linking changes in the expression of circadian rhythm-related genes to heart failure (HF) remain unclear. MethodsBy intersecting differentially expressed genes (DEGs) between normal and HF samples in the Gene Expression Omnibus (GEO) database with circadian rhythm-related genes (CRGs), differentially expressed circadian rhythm-related genes (DE-CRGs) were obtained. Machine learning algorithms were used to screen for feature genes, and diagnostic models were constructed based on these feature genes. Subsequently, consensus clustering algorithms and non-negative matrix factorization (NMF) algorithms were used for clustering analysis of HF samples. On this basis, immune infiltration analysis was used to score the immune infiltration status between HF and normal samples as well as among different subclusters. Gene Set Variation Analysis (GSVA) evaluated the biological functional differences among subclusters. Results13 CRGs showed differential expression between HF patients and normal samples. Nine feature genes were obtained through cross-referencing results from four distinct machine learning algorithms. Multivariate LASSO regression and external dataset validation were performed to select five key genes with diagnostic value, including NAMPT, SERPINA3, MAPK10, NPPA, and SLC2A1. Moreover, consensus clustering analysis could divide HF patients into two distinct clusters, which exhibited different biological functions and immune characteristics. Additionally, two subgroups were distinguished using the NMF algorithm based on circadian rhythm associated differentially expressed genes. Studies on immune infiltration showed marked variances in levels of immune infiltration between these subgroups. Subgroup A had higher immune scores and more widespread immune infiltration. Finally, the Weighted Gene Co-expression Network Analysis (WGCNA) method was utilized to discern the modules that had the closest association with the two observed subgroups, and hub genes were pinpointed via protein-protein interaction (PPI) networks. GRIN2A, DLG1, ERBB4, LRRC7, and NRG1 were circadian rhythm-related hub genes closely associated with HF. ConclusionThis study provides valuable references for further elucidating the pathogenesis of HF and offers beneficial insights for targeting circadian rhythm mechanisms to regulate immune responses and energy metabolism in HF treatment. Five genes identified by us as diagnostic features could be potential targets for therapy for HF.

Development of a promising PPAR signaling pathway-related prognostic prediction model for hepatocellular carcinoma

The peroxisome proliferator-activated receptor (PPAR) signaling pathway plays a crucial role in systemic cell metabolism, energy homeostasis and immune response inhibition. However, its significance in hepatocellular carcinoma (HCC) has not been well documented. In our study, based on the RNA sequencing data of HCC, consensus clustering analyses were performed to identify PPAR signaling pathway-related molecular subtypes, each of which displaying varying survival probabilities and immune infiltration status. Following, a prognostic prediction model of HCC was developed by using the random survival forest method and Cox regression analysis. Significant difference in survival outcome, immune landscape, drug sensitivity and pathological features were observed between patients with different prognosis. Additionally, decision tree and nomogram models were adopted to optimize the prognostic prediction model. Furthermore, the robustness of the model was verified through single-cell RNA-sequencing data. Collectively, this study systematically elucidated that the PPAR signaling pathway-related prognostic model has good predictive efficacy for patients with HCC. These findings provide valuable insights for further research on personalized treatment approaches for HCC.

Overexpression of SPP1 is a prognostic indicator of immune infiltration in lung adenocarcinoma.

The extracellular phosphoprotein, secreted phosphoprotein 1 (SPP1), plays a crucial role in various tumors and regulating the immune system. This study aimed to evaluate its prognostic value and relationship to immune infiltration in lung adenocarcinoma (LUAD). In the TCGA and GEO datasets, the information on clinic and transcriptome analysis of SPP1 in non-small-cell lung cancer (NSCLC) was examined accordingly. The association of SPP1 expression with overall survival and clinicopathologic characteristics was investigated by univariate and multivariate analysis. CancerSEA database was utilized to investigate the role of SPP1 at the cellular level by single-cell analysis. Additionally, the CIBERSORT algorithm was utilized to assess the correlation among the immune cells that infiltrated. NSCLC tissues exhibited a notable rise in SPP1 expression compared with that of normal tissues. Furthermore, the overexpression of SPP1 was substantially associated with clinicopathological features and unfavorable survival outcomes in individuals with LUAD, whereas no such correlation was observed in lung squamous cell carcinoma. Immune cells that infiltrate tumors and their corresponding genes were associated with SPP1 expression levels in LUAD. SPP1 is a reliable indicator for assessing LUAD immune infiltration status and prognosis. With this approach, SPP1 can help earlier LUAD diagnosis and act as a possible immunotherapy target.

A novel PANoptosis-related long non-coding RNA index to predict prognosis, immune microenvironment and personalised treatment in hepatocellular carcinoma.

PANoptosis is involved in the interaction of apoptosis, necroptosis and pyroptosis, playing a role in programmed cell death. Moreover, long non-coding RNAs (lncRNAs) regulate the PCD. This work aims to explore the role of PANoptosis-associated lncRNAs in hepatocellular carcinoma (HCC). Co-expression analysis identified PANoptosis-associated lncRNAs in HCC. Cox and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms were utilised to filter lncRNAs and establish a PANoptosis-related lncRNA index (PANRI). Additionally, Cox, Kaplan-Meier and receiver operating characteristic (ROC) curves were utilised to systematically evaluate the PANRI. Furthermore, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE), single sample gene set enrichment analysis (ssGSEA) and immune checkpoints were performed to analyse the potential of the PANRI in differentiating different tumour immune microenvironment (TIME) populations. The consensus clustering algorithm was used to distinguish individuals with HCC having different TIME subtypes. Finally, HCC cell lines HepG2 were utilised for further validation in in vitro experiments. The PANRI differentiates patients according to risk. Notably, ESTIMATE and ssGSEA algorithms revealed a high immune infiltration status in high-risk patients. Additionally, consensus clustering divided the patients into three clusters to identify different subtypes of TIME. Moreover, in vitro results showed that siRNA-mediated silencing of AL049840.4 inhibited the viability and migration of HepG2 cells and promoted apoptosis. This is the first PANoptosis-related, lncRNA-based risk index in HCC to assess patient prognosis, TIME and response to immunotherapy. This study offers novel perspectives on the role of PANoptosis-associated lncRNAs in HCC.

Identification of CD8+ T cell-related biomarkers and immune infiltration characteristic of rheumatoid arthritis.

Rheumatoid arthritis (RA) is an autoimmune rheumatic disease, which do not respond well to current treatment partially. Therefore, further in-depth elucidation of the molecular mechanism and pathogenesis of RA is urgently needed for the diagnosis, personalized therapy and drug development. Herein, we collected 111 RA samples from Gene Expression Omnibus (GEO) database, and conducted differentially expressed genes and GESA analysis. Abnormal activation and imbalance of immune cells in RA were observed. WGCNA was utilized to explore the gene modules and CD8+ T cell-related genes (CRGs) were chosen for KEGG and GO analysis. Besides, to explore biomarkers of RA in depth, machine learning algorithms and bioinformatics analysis were used, and we identified GDF15, IGLC1, and IGHM as diagnostic markers of RA, which was confirmed by clinical samples. Next, ssGSEA algorithms were adopted to investigate the differences in immune infiltration of 23 immune cell subsets between RA and healthy control group. Finally, optimal classification analysis based on consensus clustering combined with ssGSEA algorithms were conducted. GDF15 was revealed that to be positively correlated with mast cells and type 2 T helper cells, but negatively correlated with most other immune cells. On the other hand, IGHM and IGLC1 were negatively correlated with CD56dim natural killer cells, while positively associated with other immune cells. Finally, RA samples in subtype A exhibited a higher immune infiltration status. This study could provide guidance for individualized treatment of RA patients and provide new targets for drug design.

Identification of Breast Cancer Immune-related Prognostic Characteristics in Tumor Microenvironment.

Accumulated evidence suggest that tumor microenvironment (TME) plays a crucial role in breast cancer (BRCA) progression and therapeutic effects. This study aimed to characterize immune-related BRCA subtypes in TME, and identify genes with prognostic value. RNA sequencing profiles with corresponding clinical data from The Cancer Genome Atlas (TCGA) database of BRCA patients were downloaded to evaluate immune infiltration using the single-sample gene set enrichment (ssGAEA) algorithm. Further, BRCA was clustered according to immune infiltration status by consensus clustering analysis. Using Venn analysis, differentially expressed genes (DEGs) were overlapped to obtain candidate genes. Kaplan-Meier (K-M) analysis was performed to identify prognostic genes, and the results were verified in the GEO and METABRIC datasets. RT-qPCR was conducted to detect the mRNA expression of prognostic genes. In the TCGA database, 3 immune-related BRCA subtypes were identified [cluster1 (C1), cluster2 (C2), and cluster3 (C2)]. The C2 subtype had better overall survival (OS) compared to the C1 subtype. Higher levels of immune markers and checkpoint protein were found in the C2 subtype than in others. By combining DEGs between BRCA and normal tissues, with the C1 and C2 subtypes associated with different OS, 25 BRCA candidate genes were identified. Among these, 8 genes were identified as prognostic genes for BRCA. RT-qPCR showed that the expressions of 2 genes were significantly elevated in BRCA tissues, while that of other genes were decreased. Three BRCA subtypes were identified with the immune index, which may help design advanced treatment of BRCA. The data code used for the analysis in this article was available on GitHub (https://github.com/tangzhn/BRCA1.git).

Matched analysis of detailed peripheral blood and tumor immune microenvironment profiles in bladder cancer.

Background: Bladder cancer and therapy responses hinge on immune profiles in the tumor microenvironment (TME) and blood, yet studies linking tumor-infiltrating immune cells to peripheral immune profiles are limited. Methods: DNA methylation cytometry quantified TME and matched peripheral blood immune cell proportions. With tumor immune profile data as the input, subjects were grouped by immune infiltration status and consensus clustering. Results: Immune hot and cold groups had different immune compositions in the TME but not in circulating blood. Two clusters of patients identified with consensus clustering had different immune compositions not only in the TME but also in blood. Conclusion: Detailed immune profiling via methylation cytometry reveals the significance of understanding tumor and systemic immune relationships in cancer patients.

The microRNA target site profile is a novel biomarker in the immunotherapy response.

MicroRNAs (miRNAs) bind on the 3' untranslated region (3'UTR) of messenger RNAs (mRNAs) and regulate mRNA expression in physiological and pathological conditions, including cancer. Thus, studies have identified miRNAs as potential biomarkers by correlating the miRNA expression with the expression of important mRNAs and/or clinical outcomes in cancers. However, tumors undergo pervasive 3'UTR shortening/lengthening events through alternative polyadenylation (APA), which varies the number of miRNA target sites in mRNA, raising the number of miRNA target sites (numTS) as another important regulatory axis of the miRNA binding effects. In this study, we developed the first statistical method, BIOMATA-APA, to identify predictive miRNAs based on numTS features. Running BIOMATA-APA on The Cancer Genome Atlas (TCGA) and independent cohort data both with immunotherapy and no immunotherapy, we demonstrated for the first time that the numTS feature 1) distinguishes different cancer types, 2) predicts tumor proliferation and immune infiltration status, 3) explains more variation in the proportion of tumor-infiltrating immune cells, 4) predicts response to immune checkpoint blockade (ICB) therapy, and 5) adds prognostic power beyond clinical and miRNA expression. To the best of our knowledge, this is the first pan-cancer study to systematically demonstrate numTS as a novel type of biomarker representing the miRNA binding effects underlying tumorigenesis and pave the way to incorporate miRNA target sites for miRNA biomarker identification. Another advantage of examining the miRNA binding effect using numTS is that it requires only RNA-Seq data, not miRNAs, thus resulting in high power in the miRNA biomarker identification.

Construction of a 5-gene prognostic signature based on oxidative stress related genes for predicting prognosis in osteosarcoma.

The understanding of the complex biological scenario of osteosarcoma will open the way to identifying new strategies for its treatment. Oxidative stress is a cancer-related biological scenario. At present, it is not clear the oxidative stress genes in affecting the prognosis and progression of osteosarcoma, the underlying mechanism as well as their impact on the classification of osteosarcoma subtypes. We selected samples and sequencing data from TARGET data set and GSE21257 data set, and downloaded oxidative stress related-genes (OSRGs) from MsigDB. Univariate Cox analysis of OSRG was conducted using TARGET data, and the prognostic OSRG was screened to conduct unsupervised clustering analysis to identify the molecular subtypes of osteosarcoma. Through least absolute shrinkage and selection operator (LASSO) regression analysis and COX regression analysis of differentially expressed genes (DEGs) between subgroups, a risk assessment system for osteosarcoma was developed. 45 prognosis-related OSRGs genes were acquired, and two molecular subtypes of osteosarcoma were clustered. C2 cluster displayed prolonged overall survival (OS) accompanied with high degree of immune infiltration and enriched immune pathways. While cell cycle related pathways were enriched in C2 cluster. Based on DEGs between subgroups and Lasso analysis, 5 hub genes (ZYX, GJA5, GAL, GRAMD1B, and CKMT2) were screened to establish a robust prognostic risk model independent of clinicopathological features. High-risk group had more patients with cancer metastasis and death as well as C1 subtype with poor prognosis. Low-risk group exhibited favorable OS and high immune infiltration status. Additionally, the risk assessment system was optimized by building decision tree and nomogram. This study defined two molecular subtypes of osteosarcoma with different prognosis and tumor immune microenvironment status based on the expression of OSRGs, and provided a new risk assessment system for the prognosis of osteosarcoma.