Immune Infiltration Analysis with the CIBERSORT Method in Lung Cancer

Dual roles of LncRNA RNA143598: a biomarker for rheumatoid arthritis and its implications in cancer

OBJECTIVE

Rheumatoid arthritis (RA) is a chronic autoimmune disease with complex pathological mechanisms, including immune system dysregulation and chronic inflammation. Recent studies indicate that long non-coding RNAs (LncRNAs) play key roles in immune regulation and have been implicated in the pathogenesis of multiple diseases, including RA and various types of cancer. Understanding the involvement of LncRNAs in RA and their potential transcriptional effects in cancer could provide novel insights into disease mechanisms and therapeutic targets.

METHODS

Using the GSE94519 dataset, we analyzed serum LncRNA profiles from RA patients and healthy controls. Differential expression genes (DEGs) were identified using GEO2R, and findings were validated via quantitative polymerase chain reaction (qPCR) in 39 RA and 53 healthy samples. Receiver operating characteristic (ROC) analysis was performed to evaluate diagnostic performance. A pan-cancer analysis of MTRNR2L1 was conducted using TCGA data, with immune infiltration assessed via ssGSEA.

RESULTS

RNA143598 was significantly upregulated in RA patients, and qPCR confirmed its diagnostic potential (AUC = 0.77). Pan cancer analysis shows that MTRNR2L1 is highly expressed in glioblastoma (GBM) and lowly expressed in head and neck squamous cell carcinoma (HNSC), with high GBM expression linked to poor prognosis. Immune infiltration analysis showed MTRNR2L1 correlated with CD8 + T cells, macrophages, and dendritic cells in GBM.

CONCLUSION

RNA143598 is a promising RA biomarker, and its transcription gene MTRNR2L1 demonstrates potential in cancer prognosis and immune regulation. These findings provide a foundation for future research on targeted therapies for RA and cancer. Key Points • RNA143598 is identified as a significant biomarker for diagnosing rheumatoid arthritis (RA), showing promise for clinical application. • Quantitative PCR validation demonstrates the diagnostic potential of RNA143598, with an area under the curve (AUC) of 0.77. • MTRNR2L1, which is RNA143598 transcribed gene, exhibits differential expression in different cancer types, with high levels associated with poor prognosis in glioblastoma (GBM). • Immune infiltration analysis links MTRNR2L1 expression to the presence of CD8 + T cells, macrophages, and dendritic cells, suggesting its role in immune regulation in GBM.

Advancing personalized, predictive, and preventive medicine in bladder cancer: a multi-omics and machine learning approach for novel prognostic modeling, immune profiling, and therapeutic target discovery

Objective

This study aimed to identify and analyze immunogenic cell death (ICD)-related multi-omics features in bladder cancer (BLCA) using single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data. By integrating these datasets, we sought to construct a prognostic signature (ICDRS) and explore its clinical and biological implications, including its association with immune cell infiltration, tumor microenvironment (TME), and drug sensitivity.

Methods

Publicly available datasets from TCGA and GEO, including scRNA-seq (GSE222315, 9 samples) and bulk RNA-seq (TCGA-BLCA, 403 samples; GSE13507, 160 samples), were analyzed. Single-cell data were processed using Seurat, and ICD scores were calculated using single-sample gene set enrichment analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA) identified ICD-related modules, and machine learning algorithms (Lasso, Ridge, CoxBoost) were employed to construct the ICDRS. Survival analysis, immune infiltration, pathway enrichment, and drug sensitivity were evaluated to validate the model.

Results

The ICDRS, based on eight key genes (IL32, AHNAK, ANXA5, FN1, GSN, CNN3, FXYD3, CTSS), effectively stratified BLCA patients into high- and low-risk groups with significant differences in overall survival (OS, P < 0.001). High ICDRS scores were associated with immune-suppressive TME, including increased infiltration of T cells CD4 memory resting (P = 0.02) and macrophages M0/M1/M2 (P = 0.01). Pathway enrichment revealed correlations with cholesterol homeostasis, epithelial-mesenchymal transition (EMT), and KRAS signaling. Drug sensitivity analysis showed high-risk groups were resistant to Cisplatin (P = 0.003), Mitomycin C (P = 0.01), and Paclitaxel (P = 0.004), with IC50 values significantly higher than low-risk groups.

Conclusion

The ICDRS serves as a robust prognostic biomarker for BLCA, offering insights into tumor immune evasion mechanisms and potential therapeutic targets. Its integration with clinical features enhances personalized treatment strategies, highlighting the importance of ICD in BLCA immunotherapy and precision medicine. The model's predictive accuracy and biological relevance were validated across multiple datasets, underscoring its potential for clinical application.

Prognostic Model Construction of Disulfidptosis-Related Genes and Targeted Anticancer Drug Research in Pancreatic Cancer

Pancreatic cancer stands as one of the most lethal malignancies, characterized by delayed diagnosis, high mortality rates, limited treatment efficacy, and poor prognosis. Disulfidptosis, a recently unveiled modality of cell demise induced by disulfide stress, has emerged as a critical player intricately associated with the onset and progression of various cancer types. It has emerged as a promising candidate biomarker for cancer diagnosis, prognosis assessment, and treatment strategies. In this study, we have effectively established a prognostic risk model for pancreatic cancer by incorporating multiple differentially expressed long non-coding RNAs (DElncRNAs) closely linked to disulfide-driven cell death. Our investigation delved into the nuanced relationship between the DElncRNA-based predictive model for disulfide-driven cell death and the therapeutic responses to anticancer agents. Our findings illuminate that the high-risk subgroup exhibits heightened susceptibility to the small molecule compound AZD1208, positioning it as a prospective therapeutic agent for pancreatic cancer. Finally, we have elucidated the underlying mechanistic potential of AZD1208 in ameliorating pancreatic cancer through its targeted inhibition of the peroxisome proliferator-activated receptor-γ (PPARG) protein, employing an array of comprehensive analytical methods, including molecular docking and molecular dynamics (MD) simulations. This study explores disulfidptosis-related genes, paving the way for the development of targeted therapies for pancreatic cancer and emphasizing their significance in the field of oncology. Furthermore, through computational biology approaches, the drug AZD1208 was identified as a potential treatment targeting the PPARG protein for pancreatic cancer. This discovery opens new avenues for exploring targets and screening drugs for pancreatic cancer.

Comprehensive profiling of T-cell exhaustion signatures and establishment of a prognostic model in lung adenocarcinoma through integrated RNA-sequencing analysis

BackgroundT-cell exhaustion (TEX) in the tumor microenvironment causes immunotherapy resistance and poor prognosis.ObjectiveWe used bioinformatics to identify crucial TEX genes associated with the molecular classification and risk stratification of lung adenocarcinoma (LUAD).MethodsBulk RNA sequencing data of patients with LUAD were acquired from open sources. LUAD samples exhibited abnormal TEX gene expression, compared with normal samples. TEX gene-based prognostic signature was established and validated in both TCGA and GSE50081 datasets. Immune correlation and risk group-related functional analyses were also performed.ResultsEight optimized TEX genes were identified using the LASSO algorithm: ERG, BTK, IKZF3, DCC, EML4, MET, LATS2, and LOX. Several crucial Kyoto encyclopedia of genes and genomes (KEGG) pathways were identified, such as T-cell receptor signaling, toll-like receptor signaling, leukocytes trans-endothelial migration, Fcγ R-mediated phagocytosis, and GnRH signaling. Eight TEX gene-based risk score models were established and validated. Patients with high-risk scores had worse prognosis (P < 0.001). A nomogram model comprising three independent clinical factors showed good predictive efficacy for survival rate in patients with LUAD. Correlation analysis revealed that the TEX signature significantly correlated with immune cell infiltration, tumor purity, stromal cells, estimate, and immunophenotype score.ConclusionTEX-derived risk score is a promising and effective prognostic factor that is closely correlated with the immune microenvironment and estimated score. TEX signature may be a useful clinical diagnostic tool for evaluating pre-immune efficacy in patients with LUAD.

Exploring the association between rheumatoid arthritis and non-small cell lung cancer risk: a transcriptomic and drug target-based analysis

BACKGROUND

Non-small cell lung cancer (NSCLC) is a common subtype of lung cancer that has received considerable attention for its potential association with rheumatoid arthritis (RA). However, current understanding of the relationship between RA and NSCLC risk remains limited and in-depth studies of molecular mechanisms are lacking.

METHODS

We obtained transcriptomic data of NSCLC from the Gene Expression Omnibus (GEO) database and performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of differential genes. We then used Mendelian randomisation (MR) analysis to explore the causal relationship between RA and NSCLC, but the results showed no direct causal relationship between RA and NSCLC. In light of this finding, we shifted our research focus to investigate the effect of RA therapeutics on NSCLC risk. A drug-targeted MR analysis of drugs available for the treatment of RA was performed by searching for drugs that target NSCLC differential genes associated with RA.

RESULTS

We found that several of the drugs corresponding to NSCLC differential genes associated with RA are used to treat RA. By drug-targeted MR analysis of drugs, we found that some drugs do have an effect on the risk of developing NSCLC, increasing the risk of developing NSCLC.

CONCLUSION

This study employed transcriptomic analysis and MR of drug targets to elucidate the potential correlation between RA and the risk of developing NSCLC. The identification of NSCLC differentially expressed genes associated with RA and their drug targets has provided new perspectives for an in-depth understanding of the pathogenesis of NSCLC. Furthermore, an additional immune infiltration analysis demonstrated that, in NSCLC tissues, the infiltration levels of specific immune cell subpopulations, including regulatory T cells (Tregs), activated natural killer cells (NK cells) and unpolarised macrophages (M0), exhibited notable differences. These findings emphasise the significant role that immune cell interactions between RA and NSCLC may play in disease progression. Furthermore, through the analysis of validation histology, we have further confirmed the potential role of differential genes associated with RA in the development of NSCLC. The expression levels of these genes demonstrated significant differences in NSCLC samples, providing a basis for possible future therapeutic targets and biomarkers.

ERBB3-related gene PBX1 is associated with prognosis in patients with HER2-positive breast cancer

BACKGROUND

HER2-positive breast cancer (BC) is a subtype of breast cancer. Increased ERBB3 expression has been implicated as a potential cause of resistance to other HER-targeted therapies. Our study aimed to screen and validate prognostic markers associated with ERBB3 expression by bioinformatics and affecting the prognosis of HER2 staging.

METHODS

Analyzing differences in ERBB3-related groups. ERBB3 expression-related differentially expressed genes (DEGs) were identified and intersected with survival status-related DEGs to obtain intersected genes. Three algorithms, LASSO, RandomForest and XGBoost were combined to identify the signature genes. we construct risk models and generate ROC curves for prediction. Furthermore, we delve into the immunological traits, correlations, and expression patterns of signature genes by conducting a comprehensive analysis that encompasses immune infiltration analysis, correlation analysis, and differential expression analysis.

RESULTS

Significant variability in ERBB3 expression and prognosis in high and low ERBB3 expression groups. Twenty-five candidate DEGs were identified by intersecting ERBB3-related DEGs with survival-related DEGs. Utilizing three distinct machine learning algorithms, we identified three signature genes-PBX1, IGHM, and CXCL13-that exhibited significant diagnostic value within the diagnostic model. In addition, the risk model had better prognostic and predictive effects, and the immune infiltration analysis showed that IGHM, CXCL13 might affect the proliferation of BC cells through immune cells. Functional studies demonstrated that interference with PBX1 inhibited the proliferation, migration, and epithelial-mesenchymal transition process of HER2-positive BC cells.

CONCLUSION

PBX1, IGHM and CXCL13 are associated with the expression level of the ERBB3 and are prognostic markers for HER2-positive in BC, which may play an important role in the development and progression of BC.

Mitochondrial-related drug resistance lncRNAs as prognostic biomarkers in laryngeal squamous cell carcinoma

Laryngeal squamous cell carcinoma (LSCC) is a common malignant tumor of the head and neck that significantly impacts patients' quality of life, with chemotherapy resistance notably affecting prognosis. This study aims to identify prognostic biomarkers to optimize treatment strategies for LSCC. Using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), combined with mitochondrial gene database analysis, we identified mitochondrial lncRNAs associated with drug resistance genes. Key long non-coding RNAs (lncRNAs) were selected through univariate Cox regression and Lasso regression, and a multivariate Cox regression model was constructed to predict prognosis. We further analyzed the differences in immune function and biological pathway enrichment between high- and low-risk groups, developed a nomogram, and compared drug sensitivity. Results showed that the prognostic model based on seven mitochondrial lncRNAs could serve as an independent prognostic factor, with Area Under the Curve (AUC) values of 0.746, 0.827, and 0.771 at 1, 3, and 5 years, respectively, outperforming some existing models, demonstrating high predictive performance. Significant differences were observed in immune function and drug sensitivity between the high- and low-risk groups. The risk prediction model incorporating seven drug resistance-related mitochondrial lncRNAs can accurately and independently predict the prognosis of LSCC patients.

Potential mechanisms for predicting comorbidity between multiple myeloma and femoral head necrosis based on multiple bioinformatics

OBJECTIVE

This study aims to utilize multiple bioinformatics tools to elucidate the potential mechanisms underlying the comorbidity of Multiple Myeloma (MM) and Osteonecrosis of the Femoral Head (ONFH).

METHOD

High-throughput microarray datasets for MM and ONFH were retrieved from the GEO database, followed by separate preprocessing. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to construct co-expression networks within the MM datasets, further identifying modules and genes associated with MM clinical characteristics. Potential comorbid genes were enriched and analyzed using pathway and network analysis tools, and key genes for MM and ONFH comorbidity were preliminarily screened using Cytoscape. The gene expression capabilities and performance were validated using two disease-related datasets, and we evaluated the differences and consistencies in the immune microenvironment between the two diseases.

RESULTS

Our screening identified 418 immune-related comorbid genes, showing consistent biological processes in ribosome synthesis, particularly protein synthesis across both diseases. Key genes were further identified through Protein-Protein Interaction (PPI) networks, and their performance was validated in a validation cohort, with Receiver Operating Characteristic (ROC) curve areas exceeding 0.8. The immune microenvironment analysis highlighted consistent plasma cell infiltration correlated with disease comorbidity, suggesting potential immune targets for future therapies.

CONCLUSION

MM and ONFH share common pathogenic genes that mediate changes in signaling pathways and immune cell dynamics, potentially influencing the comorbidity and progression of these diseases. Key genes identified, such as RPS19, RPL35, RPL24, RPL36, and EIF3G, along with plasma cell infiltration, may serve as central mechanisms in the development of both diseases. This study offers insights and references for further research into targeted treatments for these conditions.

Elucidating common biomarkers and pathways of osteoporosis and aortic valve calcification: insights into new therapeutic targets

BACKGROUND

Osteoporosis and aortic valve calcification, prevalent in the elderly, have unclear common mechanisms. This study aims to uncover them through bioinformatics analysis.

METHODS

Microarray data from GEO was analyzed for osteoporosis and aortic valve calcification. Differential expression analysis identified co-expressed genes. SVM-RFE and random forest selected key genes. GO and KEGG enrichment analyses were performed. Immunoinfiltration and GSEA analyses were subsequently performed. NetworkAnalyst analyzed microRNAs/TFs. HERB predicted drugs, and molecular docking assessed targeting potential.

RESULTS

Thirteen genes linked to osteoporosis and aortic valve calcification were identified. TNFSF11, KYNU, and HLA-DMB emerged as key genes. miRNAs, TFs, and drug predictions offered therapeutic insights. Molecular docking suggested 17-beta-estradiol and vitamin D3 as potential treatments.

CONCLUSION

The study clarifies shared mechanisms of osteoporosis and aortic valve calcification, identifies biomarkers, and highlights TNFSF11, KYNU, and HLA-DMB. It also suggests 17-beta-estradiol and vitamin D3 as potential effective treatments.

Predicting biomarkers in laryngeal squamous cell carcinoma based on the cytokine-cytokine receptor interaction pathway

Objective

To analyze and validate differential genes in the cytokine-cytokine receptor interaction CCRI pathway in laryngeal squamous cell carcinoma (LSCC) using bioinformatics and Mendelian randomization (MR) to find potential biomarkers for LSCC.

Methods

Five sets of LSCC-related gene chips were downloaded from the GEO database, and four sets of combined datasets were randomly selected as the test set and one set as the validation set to screen for differential genes in the CCRI pathway; two-way Mendelian randomization was performed to analyze the causal relationship between cytokine receptor as the exposure factor and LSCC as the outcome variable; and the causal relationship was analyzed by DGIdb, Miranda, miRDB, miRWalk, TargetScan, spongeScan, and TISIDB databases to analyze the relationship between differential genes and drugs, immune cell infiltration, and mRNA-miNA-lncRNA interactions.

Results

A total of 7 differentially expressed genes CD27, CXCL2, CXCL9, INHBA, IL6, CXCL11, and TNFRSF17 were screened for enrichment in the CCRI signaling pathway; MR analysis showed that the CCRI receptor was a risk factor for LSCC (IVW: OR = 1.629, 95 % CI:1.060-2.504, P = 0.026); Seven differential genes were correlated with drugs, immune cells and mRNA-miNA-lncRNA, respectively; the CCRI differential gene expression analysis in the validation set was consistent with the test set results.

Conclusion

This study provided CCRI differential gene expression by bioinformatics, and MR analysis demonstrated that cytokine receptors are risk factors for LSCC, providing new ideas for the pathogenesis and therapeutic targets of LSCC.

Exploring the interaction between immune cells in the prostate cancer microenvironment combining weighted correlation gene network analysis and single-cell sequencing: An integrated bioinformatics analysis

BACKGROUND

The rise of treatment resistance and variability across malignant profiles has made precision oncology an imperative in today's medical landscape. Prostate cancer is a prevalent form of cancer in males, characterized by significant diversity in both genomic and clinical characteristics. The tumor microenvironment consists of stroma, tumor cells, and various immune cells. The stromal components and tumor cells engage in mutual communication and facilitate the development of a low-oxygen and pro-cancer milieu by producing cytokines and activating pro-inflammatory signaling pathways.

METHODS

In order to discover new genes associated with tumor cells that interact and facilitate a hypoxic environment in prostate cancer, we conducted a cutting-edge bioinformatics investigation. This included analyzing high-throughput genomic datasets obtained from the cancer genome atlas (TCGA).

RESULTS

A combination of weighted gene co-expression network analysis and single-cell sequencing has identified nine dysregulated immune hub genes (AMACR, KCNN3, MME, EGFR, FLT1, GDF15, KDR, IGF1, and KRT7) that are believed to have significant involvement in the biological pathways involved with the advancement of prostate cancer enviriment. In the prostate cancer environment, we observed the overexpression of GDF15 and KRT7 genes, as well as the downregulation of other genes. Additionally, the cBioPortal platform was used to investigate the frequency of alterations in the genes and their effects on the survival of the patients. The Kaplan-Meier survival analysis indicated that the changes in the candidate genes were associated with a reduction in the overall survival of the patients.

CONCLUSIONS

In summary, the findings indicate that studying the genes and their genomic changes may be used to develop precise treatments for prostate cancer. This approach involves early detection and targeted therapy.

Disulfidptosis-associated LncRNA signature predicts prognosis and immune response in kidney renal clear cell carcinoma

BACKGROUND

Kidney renal clear cell carcinoma (KIRC) represents a significant proportion of renal cell carcinomas and is characterized by high aggressiveness and poor prognosis despite advancements in immunotherapy. Disulfidptosis, a novel cell death pathway, has emerged as a critical mechanism in various cellular processes, including cancer. This study leverages machine learning to identify disulfidptosis-related long noncoding RNAs (DRlncRNAs) as potential prognostic biomarkers in KIRC, offering new insights into tumor pathogenesis and treatment avenues.

RESULTS

Our analysis of data from The Cancer Genome Atlas (TCGA) led to the identification of 431 DRlncRNAs correlated with disulfidptosis-related genes. Five key DRlncRNAs (SPINT1-AS1, AL161782.1, OVCH1-AS1, AC131009.3, and AC108673.3) were used to develop a prognostic model that effectively distinguished between low- and high-risk patients with significant differences in overall survival and progression-free survival. The low-risk group had a favorable prognosis associated with a protective immune microenvironment and a better response to targeted drugs. Conversely, the high-risk group displayed aggressive tumor features and poor immunotherapy outcomes. Validation through qRT‒PCR confirmed the differential expression of these DRlncRNAs in KIRC cells compared to normal kidney cells, underscoring their potential functional significance in tumor biology.

CONCLUSIONS

This study established a robust link between disulfidptosis-related lncRNAs and patient prognosis in KIRC, underscoring their potential as prognostic biomarkers and therapeutic targets. The differential expression of these lncRNAs in tumor versus normal tissue further highlights their relevance in KIRC pathogenesis. The predictive model not only enhances our understanding of KIRC biology but also provides a novel stratification tool for precision medicine approaches, improving treatment personalization and outcomes in KIRC patients.

Uncovering molecular features driving lung adenocarcinoma heterogeneity in patients who formerly smoked

BACKGROUND

An increasing proportion of lung adenocarcinoma (LUAD) occurs in patients even after they have stopped smoking. Here, we aimed to determine whether tobacco smoking induced changes across LUADs from patients who formerly smoked correspond to different biological and clinical factors.

METHODS

Random forest models (RFs) were trained utilizing a smoking associated signature developed from differentially expressed genes between LUAD patients who had never smoked (NS) or currently smoked (CS) from TCGA (n = 193) and BCCA (n = 69) cohorts. The RFs were subsequently applied to 299 and 131 formerly smoking patients from TCGA and MSKCC cohorts, respectively. FS were RF-classified as either CS-like or NS-like and associations with patient characteristics, biological features, and clinical outcomes were determined.

RESULTS

We elucidated a 123 gene signature that robustly classified NS and CS in both RNA-seq (AUC = 0.85) and microarray (AUC = 0.92) validation test sets. The RF classified 213 patients who had formerly smoked as CS-like and 86 as NS-like from the TCGA cohort. CS-like and NS-like status in formerly smoking patients correlated poorly with patient characteristics but had substantially different biological features including tumor mutational burden, number of mutations, mutagenic signatures and immune cell populations. NS-like formerly smoking patients had 17.5 months and 18.6 months longer overall survival than CS-like patients from the TCGA and MSKCC cohorts, respectively.

CONCLUSIONS

Patients who had formerly smoked with LUAD harbor heterogeneous tumor biology. These patients can be divided by smoking induced gene expression to inform prognosis and underlying biological characteristics for treatment selection.

Identification of PANoptosis-related subtypes, construction of a prognosis signature, and tumor microenvironment landscape of hepatocellular carcinoma using bioinformatic analysis and experimental verification

Background

Hepatocellular carcinoma (HCC) is one of the most lethal malignancies worldwide. PANoptosis is a recently unveiled programmed cell death pathway, Nonetheless, the precise implications of PANoptosis within the context of HCC remain incompletely elucidated.

Methods

We conducted a comprehensive bioinformatics analysis to evaluate both the expression and mutation patterns of PANoptosis-related genes (PRGs). We categorized HCC into two clusters and identified differentially expressed PANoptosis-related genes (DEPRGs). Next, a PANoptosis risk model was constructed using LASSO and multivariate Cox regression analyses. The relationship between PRGs, risk genes, the risk model, and the immune microenvironment was studies. In addition, drug sensitivity between high- and low-risk groups was examined. The expression profiles of these four risk genes were elucidate by qRT-PCR or immunohistochemical (IHC). Furthermore, the effect of CTSC knock down on HCC cell behavior was verified using in vitro experiments.

Results

We constructed a prognostic signature of four DEPRGs (CTSC, CDCA8, G6PD, and CXCL9). Receiver operating characteristic curve analyses underscored the superior prognostic capacity of this signature in assessing the outcomes of HCC patients. Subsequently, patients were stratified based on their risk scores, which revealed that the low-risk group had better prognosis than those in the high-risk group. High-risk group displayed a lower Stromal Score, Immune Score, ESTIMATE score, and higher cancer stem cell content, tumor mutation burden (TMB) values. Furthermore, a correlation was noted between the risk model and the sensitivity to 56 chemotherapeutic agents, as well as immunotherapy efficacy, in patient with. These findings provide valuable guidance for personalized clinical treatment strategies. The qRT-PCR analysis revealed that upregulated expression of CTSC, CDCA8, and G6PD, whereas downregulated expression of CXCL9 in HCC compared with adjacent tumor tissue and normal liver cell lines. The knockdown of CTSC significantly reduced both HCC cell proliferation and migration.

Conclusion

Our study underscores the promise of PANoptosis-based molecular clustering and prognostic signatures in predicting patient survival and discerning the intricacies of the tumor microenvironment within the context of HCC. These insights hold the potential to advance our comprehension of the therapeutic contribution of PANoptosis plays in HCC and pave the way for generating more efficacious treatment strategies.

A senescence-related lncRNA signature predicts prognosis and reflects immune landscape in HNSCC

OBJECTIVE

Long noncoding RNAs (lncRNAs) regulate cancer cell senescence in many cancers. However, their specific involvement in head and neck squamous cell carcinoma (HNSCC) remains unclear. We are looking for an ingenious prognostic signature that utilizes senescence-related lncRNAs (SRlncRNAs) to predict prognosis and provide insights into the immune landscape in HNSCC.

MATERIALS AND METHODS

HNSCC clinical and Cellular senescence genes information were collected from The Cancer Genome Atlas and Human Aging Genomic Resources. Then we performed Cox and Lasso regression to locate SRlncRNAs related to the prognosis of HNSCC and built a predictive signature. Further, prognosis assessment, potential mechanisms, and immune status were assessed by Kaplan-Meier analysis, Gene Set Enrichment Analysis (GSEA), and CIBERSORT, respectively.

RESULTS

A prognosis prediction model based on sixteen SRlncRNAs was identified and internally validated. Then, patients with high-risk scores suffered an unfavorable overall survival (All p < 0.05). The risk score, age, and stage were independent prognostic parameters (all p < 0.001). Our model has good predictive ability (The AUC (area under the curves) 1-year = 0.707, AUC3-year = 0.748 and AUC5-year = 0.779). Subsequently, GESA revealed SRlncRNAs regulated immune responses. Patients in the high-risk group had higher tumor mutation burden and Tumor Immune Dysfunction and Exclusion but lower levels of 37 immune checkpoint genes, immune scores, and immune cells like CD8 + T cells, follicular helper T cells, and regulatory T cells.

CONCLUSIONS

A prognostic model based on SRlncRNAs is the potential target for improving immunotherapy outcomes for HNSCC.

Bioinformatic analysis reveals the clinical value of SASH3 in survival prognosis and immune infiltration of acute myelocytic leukemia (AML)

Acute myeloid leukemia (AML), a malignant clonal disease, is the most prevalent form of leukemia, and it is associated with a poor prognosis and unfavorable treatment outcomes in both pediatric and adult populations. Accordingly, enhancing anti-tumor responses using immunomodulators is a promising therapeutic strategy and a new avenue for treating AML. In this study, we used publicly available data from The Cancer Genome Atlas and Genotype-Tissue Expression databases to investigate the correlation between SAM and SH3 domain-containing 3 (SASH3) and AML, and we performed Cox regression and Kaplan-Meier analyses to assess the clinical characteristics associated with overall survival among patients with AML. Additionally, we analyzed the relationship between immune infiltration and SASH3. Compared with that in the normal group, patients with AML were characterized by significantly higher levels of SASH3 expression (P = 3.05e-34), which was strongly associated with survival outcomes. We observed a significant correlation between SASH3 expression and the expression of cancer-related genes (HCK, SYK, FYN, ITGB2, PIK3CD, FGR, PIK3R5, VAV1, LCP2, and GRB2) and pathways. Our findings in this study indicate that SASH3 plays a key role in AML development and survival outcomes and in the regulation of small GTPase-mediated signal transduction and immune-related pathways. Accordingly, targeting SASH3 may offer a promising approach for the treatment of AML and may potentially influence the progression of other cancers via multiple immune pathways.

An effective disease diagnostic model related to pyroptosis in ischemic cardiomyopathy

Pyroptosis is involved in ischemic cardiomyopathy (ICM). The study aimed to investigate the pyroptosis-related genes and clarify their diagnostic value in ICM. The bioinformatics method identified the differential pyroptosis genes between the normal control and ICM samples from online datasets. Then, protein-protein interaction (PPI) and function analysis were carried out to explore the function of these genes. Following, subtype analysis was performed using ConsensusClusterPlus, functions, immune score, stromal score, immune cell proportion and human leukocyte antigen (HLA) genes between subtypes were investigated. Moreover, optimal pyroptosis genes were selected using the least absolute shrinkage and selection operator (LASSO) analysis to construct a diagnostic model and evaluate its effectiveness using receiver operator characteristic (ROC) analysis. Twenty-one differential expressed pyroptosis genes were identified, and these genes were related to immune and pyroptosis. Subtype analysis identified two obvious subtypes: sub-1 and sub-2. And LASSO identified 13 optimal genes used to construct the diagnostic model. The diagnostic model in ICM diagnosis with the area under ROC (AUC) was 0.965. Our results suggested that pyroptosis was tightly associated with ICM.

Identification of novel gene signature for lung adenocarcinoma by machine learning to predict immunotherapy and prognosis

Background

Lung adenocarcinoma (LUAD) as a frequent type of lung cancer has a 5-year overall survival rate of lower than 20% among patients with advanced lung cancer. This study aims to construct a risk model to guide immunotherapy in LUAD patients effectively.

Materials and methods

LUAD Bulk RNA-seq data for the construction of a model, single-cell RNA sequencing (scRNA-seq) data (GSE203360) for cell cluster analysis, and microarray data (GSE31210) for validation were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used the Seurat R package to filter and process scRNA-seq data. Sample clustering was performed in the ConsensusClusterPlus R package. Differentially expressed genes (DEGs) between two groups were mined by the Limma R package. MCP-counter, CIBERSORT, ssGSEA, and ESTIMATE were employed to evaluate immune characteristics. Stepwise multivariate analysis, Univariate Cox analysis, and Lasso regression analysis were conducted to identify key prognostic genes and were used to construct the risk model. Key prognostic gene expressions were explored by RT-qPCR and Western blot assay.

Results

A total of 27 immune cell marker genes associated with prognosis were identified for subtyping LUAD samples into clusters C3, C2, and C1. C1 had the longest overall survival and highest immune infiltration among them, followed by C2 and C3. Oncogenic pathways such as VEGF, EFGR, and MAPK were more activated in C3 compared to the other two clusters. Based on the DEGs among clusters, we confirmed seven key prognostic genes including CPA3, S100P, PTTG1, LOXL2, MELTF, PKP2, and TMPRSS11E. Two risk groups defined by the seven-gene risk model presented distinct responses to immunotherapy and chemotherapy, immune infiltration, and prognosis. The mRNA and protein level of CPA3 was decreased, while the remaining six gene levels were increased in clinical tumor tissues.

Conclusion

Immune cell markers are effective in clustering LUAD samples into different subtypes, and they play important roles in regulating the immune microenvironment and cancer development. In addition, the seven-gene risk model may serve as a guide for assisting in personalized treatment in LUAD patients.

Development and validation of a MUC16 mutation-associated immune prognostic model for lung adenocarcinoma

Mucin 16 (MUC16) mutation ranks third among all common mutations in lung adenocarcinoma (LUAD), and it has a certain effect on LUAD development and prognostic outcome. This research aimed to analyze the effects of MUC16 mutation on LUAD immunophenotype regulation and determine the prognostic outcome using an immune prognostic model (IPM) built with immune-related genes. The MUC16 mutation status and mRNA expression profiles were analyzed using diverse platforms and among several LUAD patients (n = 691). An IPM was then constructed using differentially expressed immune-related genes (DEIRGs) in MUC16MUT LUAD cases, and the data were compared with those of MUC16WT LUAD cases. The IPM's performance in distinguishing high-risk cases from low-risk ones among 691 LUAD cases was verified. Additionally, a nomogram was built and applied in the clinical setting. Furthermore, a comprehensive IPM-based analysis of how MUC16 mutation affected the tumor immune microenvironment (TIME) of LUAD was performed. MUC16 mutation decreased the immune response in LUAD. As revealed by functional annotation, the DEIRGs in the IPM were most significantly enriched in the humoral immune response function and the immune system disease pathway. Moreover, high-risk cases were associated with increased proportions of immature dendritic cells, neutrophils, and B-cells; enhanced type I interferon T-cell response; and increased expression of PD-1, CTLA-4, TIM-3, and LAG3 when compared with low-risk cases. MUC16 mutation shows potent association with TIME of LUAD. The as-constructed IPM displays high sensitivity to MUC16 mutation status and can be applied to discriminate high-risk LUAD cases from low-risk ones.

Integrated bioinformatic analyses investigate macrophage-M1-related biomarkers and tuberculosis therapeutic drugs

Tuberculosis (TB) is a common infectious disease linked to host genetics and the innate immune response. It is vital to investigate new molecular mechanisms and efficient biomarkers for Tuberculosis because the pathophysiology of the disease is still unclear, and there aren't any precise diagnostic tools. This study downloaded three blood datasets from the GEO database, two of which (GSE19435 and 83456) were used to build a weighted gene co-expression network for searching hub genes associated with macrophage M1 by the CIBERSORT and WGCNA algorithms. Furthermore, 994 differentially expressed genes (DEGs) were extracted from healthy and TB samples, four of which were associated with macrophage M1, naming RTP4, CXCL10, CD38, and IFI44. They were confirmed as upregulation in TB samples by external dataset validation (GSE34608) and quantitative real-time PCR analysis (qRT-PCR). CMap was used to predict potential therapeutic compounds for tuberculosis using 300 differentially expressed genes (150 downregulated and 150 upregulated genes), and six small molecules (RWJ-21757, phenamil, benzanthrone, TG-101348, metyrapone, and WT-161) with a higher confidence value were extracted. We used in-depth bioinformatics analysis to investigate significant macrophage M1-related genes and promising anti-Tuberculosis therapeutic compounds. However, more clinical trials were necessary to determine their effect on Tuberculosis.

TTC7B Is a Novel Prognostic-Related Biomarker in Glioma Correlating with Immune Infiltrates and Response to Oxidative Stress by Temozolomide

Background

Gliomas are one of the most prevalent malignant brain tumors. Hence, identifying biological markers for glioma is imperative. TTC7B (Tetratricopeptide Repeat Domain 7B) is a gene whose role in cancer in currently identified. To this end, we examined the TTC7B expression as well as its prognostic significance, biological roles, and immune system impacts in patients with glioma.

Methods

We evaluated the function of TTC7B in GBM and LGG through the published CGGA (Chinese Glioma Genome Atlas) and TCGA (The Cancer Genome Atlas) databases. CIBERSORT and TIMER were used to analyze the link between TTC7B and immune cells, while R was used for statistical analysis. In addition, Transwell analysis, including migration and invasion assays, was performed to identify the relationship between TTC7B and temozolomide.

Results

Low expression of TTC7B was observed in GBM and LGG. 1p/19q codeletion, IDH mutation, chemotherapy, and grade were found to have a significant correlation with TTC7B. Besides, low TTC7B expression was linked with low overall survival (OS) in both GBM and LGG. In the Cox analysis, TTC7B was found to independently function as a risk element for OS of patients with glioma. Furthermore, CIBERSORT analysis demonstrated a positive link between TTC7B and multiple immune cells, especially activated NK cells. Transwell analysis, including migration and invasion assays, revealed that temozolomide reduced the migration and invasion capacity of glioma cells and increased the expression of TTC7B.

Conclusion

In all, TTC7B could serve as a promising prognostic indicator of LGG and GBM, and is closely associated with immune infiltration and response to oxidative stress by temozolomide.

Construction of five cuproptosis-related lncRNA signature for predicting prognosis and immune activity in skin cutaneous melanoma

Cuproptosis is a newly discovered new mechanism of programmed cell death, and its unique pathway to regulate cell death is thought to have a unique role in understanding cancer progression and guiding cancer therapy. However, this regulation has not been studied in SKCM at present. In this study, data on Skin Cutaneous Melanoma (SKCM) patients were downloaded from the TCGA database. We screened the genes related to cuproptosis from the published papers and confirmed the lncRNAs related to them. We applied Univariate/multivariate and LASSO Cox regression algorithms, and finally identified 5 cuproptosis-related lncRNAs for constructing prognosis prediction models (VIM-AS1, AC012443.2, MALINC1, AL354696.2, HSD11B1-AS1). The reliability and validity test of the model indicated that the model could well distinguish the prognosis and survival of SKCM patients. Next, immune microenvironment, immunotherapy analysis, and functional enrichment analysis were also performed. In conclusion, this study is the first analysis based on cuproptosis-related lncRNAs in SKCM and aims to open up new directions for SKCM therapy.