Checkpoint Gene Expression Research Articles

A Novel Immune-Related Gene Signature to Identify the Tumor Microenvironment and Prognose Disease Among Patients With Oral Squamous Cell Carcinoma Patients Using ssGSEA: A Bioinformatics and Biological Validation Study.

Oral squamous cell carcinoma (OSCC) is the most invasive oral malignancy in adults and is associated with a poor prognosis. Accurate prognostic models are urgently needed, however, knowledge of the probable mechanisms behind OSCC tumorigenesis and prognosis remain limited. The clinical importance of the interplay between the immune system and tumor microenvironment has become increasingly evident. This study explored immune-related alterations at the multi-omics level to extract accurate prognostic markers linked to the immune response and presents a more accurate landscape of the immune genomic map during OSCC. The Cancer Genome Atlas (TCGA) OSCC cohort (n = 329) was used to detect the immune infiltration pattern of OSCC and categorize patients into two immunity groups using single-sample gene set enrichment analysis (ssGSEA) and hierarchical clustering analysis. Multiple strategies, including lasso regression (LASSO), Cox proportional hazards regression, and principal component analysis (PCA) were used to screen clinically significant signatures and identify an incorporated prognosis model with robust discriminative power on the survival status of both the training and testing set. We identified two OSCC subtypes based on immunological characteristics: Immunity-high and immunity low, and verified that the categorization was accurate and repeatable. Immunity_ high cluster with a higher immunological and stromal score. 1047 differential genes (DEGs) integrate with immune genes to obtain 319 immue-related DEGs. A robust model with five signatures for OSCC patient prognosis was established. The GEO cohort (n = 97) were used to validate the risk model’s predictive value. The low-risk group had a better overall survival (OS) than the high-risk group. Significant prognostic potential for OSCC patients was found using ROC analysis and immune checkpoint gene expression was lower in the low-risk group. We also investigated at the therapeutic sensitivity of a number of frequently used chemotherapeutic drugs in patients with various risk factors. The underlying biological behavior of the OSCC cell line was preliminarily validated. This study characterizes a reliable marker of OSCC disease progression and provides a new potential target for immunotherapy against this disease.

Comprehensive Analysis of a Ferroptosis-Related lncRNA Signature for Predicting Prognosis and Immune Landscape in Osteosarcoma

Research on the implications of ferroptosis in tumors has increased rapidly in the last decades. There are evidences that ferroptosis is involved in several aspects of cancer biology, including tumor progression, metastasis, immunomodulation, and therapeutic response. Nonetheless, the interaction between ferroptosis-related lncRNAs (FRLs) and the osteosarcoma immune microenvironment is poorly understood. In this study, a risk model composed of FRLs was developed using univariate and LASSO Cox regression analyses. On the basis of this model, FRL scores were calculated to systematically explore the role of the model in predicting the prognosis and immune characteristics of osteosarcoma patients. Survival analysis showed that osteosarcoma samples with lower FRL-score had better overall survival. After predicting the abundance of immune cells in osteosarcoma microenvironment by single-sample gene-set enrichment analysis (ssGSEA) and ESTIMATE analysis, we found that the FRL-score could distinguish immune function, immune score, stromal score, tumor purity, and tumor infiltration of immune cells in different osteosarcoma patients. In addition, FRL-score was also associated with immune checkpoint gene expression and half-maximal inhibitory concentration of chemotherapeutic agents. Finally, we confirmed that knockdown of RPARP-AS1 suppressed the malignant activity of osteosarcoma cells in vitro experiments. In general, the FRL-based prognostic signature could promote our understanding of the immune microenvironment characteristics of osteosarcoma and guide more effective treatment regimens.

Co-stimulatory molecule clusters correlate with survival, immune infiltration, and tumor mutation burden in non-small cell lung cancer

We aimed to identify co-stimulatory molecule clusters and explore their associations with immune infiltration and tumor mutation burden (TMB) in non-small cell lung cancer (NSCLC), using a systematic analysis based on RNA-seq and mutation data in TCGA database. Twelve immune-related prognostic co-stimulatory molecules were screened. Based on the expression of these molecules, all samples were divided into four clusters by Consensus clustering analysis. Clusters 2 and 4 were associated with worse survival and favorable prognosis, respectively. The proportion of patients with different age, Pathologic T and Pathologic stage among these four clusters showed significant differences. Cluster 4 was associated with a higher proportion of patients with pathological N0 and T1–2, as well as early tumors (stages I–II). Additionally, these clusters significantly differed in terms of immune infiltration of memory B cells, regulatory T cells, M2 macrophages, and activated memory CD4+ T cells, as well as the expression of six checkpoint genes. Worse survival was associated with a high rather than a low TMB; among the clusters, clusters 4 and 2 had the lowest and highest TMB, respectively. In conclusion, four co-stimulatory molecule clusters identified in NSCLC significantly differed in terms of survival, tumor stage, immune response, and TMB. Highlights Four co-stimulatory molecule clusters were identified that correlated differently with survival. Feature genes of four clusters were enriched in different functions. Four clusters significantly differed in terms of tumor stage, immune infiltration, and checkpoint gene expression. Worse survival was associated with high, compared with low TMB. Survival was better for cluster 4 than other clusters, and TMB was low.

P392: PHARMACOLOGICAL INHIBITION OF SYK CONFERS ANTI-PROLIFERATIVE AND NOVEL ANTI-TUMOR IMMUNE RESPONSES IN AML

Background: Spleen tyrosine kinase (SYK) acts as a key integrator of signals from cell surface receptors containing an immunoreceptor tyrosine-based activation motif to boost cellular proliferation. In acute myeloid leukemia (AML), SYK serves as a relay to an oncogenic transcriptional regulatory network linked to NPM1, HOXA9 and MEIS1. The selective, orally bioavailable SYK inhibitor entospletinib (ENTO) has demonstrated clinical activity and tolerability in HOXA9/MEIS1-driven AML. ENTO is currently being investigated in a global Phase 3 trial, AGILITY (NCT05020665), in combination with intensive induction/consolidation chemotherapy in patients (pts) with treatment-naive NPM1-mutated (NPM1m) AML. Lanraplenib (LANRA) is a next-generation SYK inhibitor with similar potency and selectivity to ENTO but with more favorable pharmacologic properties that is currently being evaluated in combination with gilteritinib in pts with relapsed or refractory FLT3-mutated AML (NCT05028751). Aims: To investigate the effects of ENTO and LANRA in T-cell responses in AML. Methods: All pt-derived bone marrow (BM) and peripheral blood samples were obtained in accordance with IRB (OHSU#004422) approval. Results from gene expression profiling studies (RNA sequence [RNA-seq]) in bone marrow/peripheral blood mononuclear cells (MNCs) derived from 152 pts with AML, for which matching NPM1 status and ex vivo sensitivity to ENTO was available, previously published by Tyner et al (2018), were reanalyzed using trimmed mean of M values normalization and differential expression using edgeR. ENTO/LANRA sensitivity was assessed ex vivo after 72 hours in culture at 37°C by MTS assay. T-cell functional assays were performed by culturing MNCs with anti-CD3 and treating with ENTO or LANRA, singly and in combination with anti-PD1. CD3+ T-cell proliferation as measured by Ki67 expression and pSYK activation were assessed by quantitative single-cell immunofluorescence microscopy. Six formalin-fixed, paraffin-embedded BM biopsies from newly diagnosed AML pts were assessed for spatial expression of mRNA using a digital spatial profiling method. Results: In AML, mutations in NPM1 with co-expression of HOXA9/MEIS1 at baseline predicted anti-proliferative activity to ENTO in ex vivo drug sensitivity studies. Accordingly, treatment of leukemic cells with either ENTO or LANRA inhibited SYK auto-phosphorylation in a dose-dependent manner. Pathway analysis of archival RNA-seq data from pts enrolled in the Leukemia and Lymphoma Society BEAT AML Master Protocol (NCT03013998 [BAML-16-001-S6]) revealed gene expression signatures at baseline associated with the observed sensitivity to ENTO, including significant enrichment in the expression of genes associated with leukemogenesis, myeloid differentiation and immune regulation. This was supported by T-cell functional studies, which demonstrated that ENTO and LANRA, singly as well as in combination with anti-PD1, could restore T-cell proliferation in primary AML pt samples that exhibited suppression. Lastly, Cancer Transcriptome Atlas analysis on archival BM biopsies obtained from these AML pts showed a strong correlation between immune checkpoint response and gene expression associated with immune pathway signaling. Summary/Conclusion: Inhibition of SYK is a promising therapeutic approach in NPM1m AML. Our studies illustrate that ENTO and LANRA may restore T-cell proliferation in a subset of AML pts with dysfunctional T-cell responses, suggesting a novel mechanism of action. Additional studies are required to fully understand the mechanism of SYK-mediated antitumor immune responses in AML.

The Role of P4HA1 in Multiple Cancer Types and its Potential as a Target in Renal Cell Carcinoma.

Background: Prolyl 4-hydroxylase subunit alpha 1 (P4HA1) provides the majority of the catalytic site of the active P4H enzyme. Emerging evidence has revealed that P4HA1 participates in the initiation and development of several malignant tumors. However, a pan-cancer analysis of P4HA1 has not been performed. Methods: In this study, we carried out an in-depth analysis of the expression patterns and prognostic value of P4HA1 using the datasets of The Cancer Genome Atlas (TCGA) and Kaplan–Meier Plotter. Genomic and epigenetic alterations of P4HA1 and the correlation of P4HA1 with DNA methylation in different cancers were also analyzed across multiple databases. In addition, the purity-adjusted partial Spearman’s correlation test was utilized to evaluate the correlation between P4HA1 expression and immune cell infiltration. We also further explored the biological function and mechanism of P4HA1 in renal cell carcinoma (RCC). Results: We characterized the expression profiles and prognostic values of P4HA1 in multiple cancer types. P4HA1 expression was increased in clear cell renal cell carcinoma (RCC) compared to adjacent normal tissues, and P4HA1 positively correlated with the overall survival (OS) and disease-free survival (DFS) in papillary RCC. In addition, a positive correlation between P4HA1 expression and immune cell infiltration was observed in clear cell RCC. We also identified a strong correlation between P4HA1 expression and immune checkpoint gene expression, microsatellite instability, and tumor mutation burden in chromophobe RCC. Finally, the results of in vitro experiments verified that overexpression of P4HA1 promoted the proliferation, migration, invasion, and epithelial–mesenchymal transition of RCC cells. Conclusion: Overall, our study has suggested that P4HA1 might play a significant role in tumorigenesis in RCC and may be a prognostic biomarker and therapeutic target for several malignant tumors, including RCC.

The Function, Role and Process of DDX58 in Heart Failure and Human Cancers.

BackgroundHeart failure (HF) is the most common outcome of cardiovascular disease, and an increasing number of patients with heart failure die from noncardiac causes, such as cancer. Epidemiological data suggest that ischemic cardiomyopathy–induced HF (ischemic HF) may be associated with an increased incidence of cancer. This study aimed to investigate the possible mechanisms of the association between ischemic HF and cancer, as well as potential therapeutic targets.MethodsWeighted gene co-expression network analysis was performed to analyze the correlations between phenotypes and gene modules using immune cells as phenotypes. Differential analysis was then performed to screen differentially expressed genes (DEGs) in ischemic HF and normal control samples. The macrophage-related Brown module was identified as the key module, and immune-related DEGs were obtained by taking the intersection of the Brown module, DEGs, and immune-related genes using a Venn diagram. DDX58 was identified as the key gene using a protein–protein interaction network and expression analyses and validated using immunohistochemistry. Kaplan–Meier survival analysis was performed to analyze the correlation between DDX58 expression and tumor prognosis. Spearman correlation analysis was performed to assess the correlation between DDX58 expression and immune cell infiltration.ResultsDDX58 was identified as a key immune-related gene associated with ischemic HF and was highly expressed in most cancer types. The survival analysis revealed a significant negative correlation between high DDX58 expression and prognosis in multiple tumor types. Moreover, DDX58 expression was significantly associated with immune cell infiltration and immune checkpoint gene expression in many cancer types.ConclusionDDX58 is a key immune-related gene in ischemic HF and may play a crucial role in the relationship between ischemic HF and cancer. Pan-cancer analysis suggests that DDX58 is a promising clinical prognostic marker for most cancers and may be a therapeutic target for cancer patients and ischemic HF patients at an increased risk of cancer.

Four-Pyroptosis Gene-Based Nomogram as a Novel Strategy for Predicting the Effect of Immunotherapy in Hepatocellular Carcinoma.

Background Immunotherapy has been considered as a promising cancer treatment for hepatocellular carcinoma (HCC). However, due to the particular immune environment of the liver, identifying patients who could benefit from immunotherapy is critical in clinical practice. Methods The pyroptosis gene expression database of 54 candidates from The Cancer Genome Atlas (TCGA) were collected to discover the critical prognostic-related pyroptosis genes. A novel pyroptosis gene model was established to calculate the risk score. Kaplan–Meier analysis and receiver operating characteristic curve (ROC) were used to verify its predictive ability. The International Cancer Genome Consortium (ICGC) data was collected as external validation data to verify the model's accuracy. We employed multiple bioinformatics tools and algorithms to evaluate the tumor immune microenvironment (TIME) and the response to immunotherapy. Results Our study found that most pyroptosis genes were expressed differently in normal and tumor tissues and that their expression was associated with the prognosis. Then, a precise four-pyroptosis gene model was generated. The one-year area under the curves (AUCs) among the training, internal, and external validation patients were 0.901, 0.727, and 0.671, respectively. An analysis of survival data revealed that individuals had a worse prognosis than patients with low risk. The analysis of TIME revealed that the low-risk group had more antitumor cells, fewer immunosuppressive cells, stronger immune function, less immune checkpoint gene expression, and better immunotherapy response than the high-risk group. Immunophenoscore (IPS) analysis also demonstrated that the low-risk score was related to superior immune checkpoint inhibitors therapy. Conclusion A nomogram based on the four-pyroptosis gene signature was a novel tool to predict the effectiveness of immunotherapy for HCC. Therefore, individualized treatment targeting the pyroptosis genes may influence TIME and play an essential role in improving the prognosis in HCC patients.

Metabolic pathway-based molecular subtyping of colon cancer reveals clinical immunotherapy potential and prognosis.

Colon cancer presents challenges to clinical diagnosis and management due to its high heterogeneity. For more efficient and convenient diagnosis and treatment of colon cancer, we are committed to characterizing the molecular features of colon cancer by pioneering a classification system based on metabolic pathways. Based on the 113 metabolic pathways and genes collected in the previous stage, we scored and filtered the metabolic pathways of each sample in the training set by ssGSEA, and obtained 16 metabolic pathways related to colon cancer recurrence. In consistent clustering of training set samples with recurrence-related metabolic pathway scores, we identified two robust molecular subtypes of colon cancer (MC1 and MC2). Furthermore, we performed multi-angle analysis on the survival differences of subtypes, metabolic characteristics, clinical characteristics, functional enrichment, immune infiltration, differences with other subtypes, stemness indices, TIDE prediction, and drug sensitivity, and finally constructed colon cancer prognostic model. The results showed that the MC1 subtype had a poor prognosis based on higher immune activity and immune checkpoint gene expression. The MC2 subtype is associated with high metabolic activity and low expression of immune checkpoint genes and a better prognosis. The MC2 subtype was more responsive to PD-L1 immunotherapy than the MC1 subclass. However, we did not observe significant differences in tumor mutational burden between the two. Two molecular subtypes of colon cancer based on metabolic pathways have distinct immune signatures. Constructing prognostic models based on subtype differential genes provides valuable reference for personalized therapy targeting unique tumor metabolic signatures.

A Novel Prognostic Signature Based on Glioma Essential Ferroptosis-Related Genes Predicts Clinical Outcomes and Indicates Treatment in Glioma.

BackgroundFerroptosis is a form of programmed cell death (PCD) that has been implicated in cancer progression, although the specific mechanism is not known. Here, we used the latest DepMap release CRISPR data to identify the essential ferroptosis-related genes (FRGs) in glioma and their role in patient outcomes.MethodsRNA-seq and clinical information on glioma cases were obtained from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). FRGs were obtained from the FerrDb database. CRISPR-screened essential genes (CSEGs) in glioma cell lines were downloaded from the DepMap portal. A series of bioinformatic and machine learning approaches were combined to establish FRG signatures to predict overall survival (OS) in glioma patients. In addition, pathways analysis was used to identify the functional roles of FRGs. Somatic mutation, immune cell infiltration, and immune checkpoint gene expression were analyzed within the risk subgroups. Finally, compounds for reversing high-risk gene signatures were predicted using the GDSC and L1000 datasets.ResultsSeven FRGs (ISCU, NFS1, MTOR, EIF2S1, HSPA5, AURKA, RPL8) were included in the model and the model was found to have good prognostic value (p < 0.001) in both training and validation groups. The risk score was found to be an independent prognostic factor and the model had good efficacy. Subgroup analysis using clinical parameters demonstrated the general applicability of the model. The nomogram indicated that the model could effectively predict 12-, 36-, and 60-months OS and progression-free interval (PFI). The results showed the presence of more aggressive phenotypes (lower numbers of IDH mutations, higher numbers of EGFR and PTEN mutations, greater infiltration of immune suppressive cells, and higher expression of immune checkpoint inhibitors) in the high-risk group. The signaling pathways enriched closely related to the cell cycle and DNA damage repair. Drug predictions showed that patients with higher risk scores may benefit from treatment with RTK pathway inhibitors, including compounds that inhibit RTKs directly or indirectly by targeting downstream PI3K or MAPK pathways.ConclusionIn summary, the proposed cancer essential FRG signature predicts survival and treatment response in glioma.

A novel endoplasmic reticulum stress-related lncRNA prognostic risk model for cutaneous melanoma.

Endoplasmic reticulum stress (ERS) and long non-coding RNAs (lncRNAs) are important in melanoma development and progression. This study aimed to explore the prognostic value of ERS-associated lncRNA profiles in cutaneous melanoma (CM). The Cancer Genome Atlas (TCGA) provides the raw data of CM. GSEA website was used to obtain ERS-related genes, and mRNA and LncRNA co-expression network were used to obtain ERS-related lncRNAs. A Lasso regression analysis was used to identify a prognostic risk model for the composition of ERS-related lncRNAs. Patients were divided into high- and low-risk groups based on the model's risk score. The researchers then compared the two groups' survival rates, immune infiltration, chemotherapeutic drug sensitivity, and immune checkpoint gene expression. Thirty-nine ERS-related lncRNAs were discovered to be prognostic. A prognostic risk model made up of ten ERS-related lncRNAs was discovered. Patients in the low-risk group had a better prognosis than those in the high-risk group. An examination of tumor microenvironment revealed that risk scores correlated with immune cell infiltration in eight cases. Dacarbazine, paclitaxel, and cisplatin, three chemotherapy drugs, were more sensitive in the low-risk group than in the high-risk group. This study identified a risk model of ten ERS-related lncRNAs that have significant prognostic value in CM and could help guide clinical treatment.

The Value of m5C-Related lncRNAs in the Prognostic Assessment and Immunotherapy of Stomach Adenocarcinoma.

Long noncoding RNAs (lncRNAs) are closely associated with a variety of tumors, including stomach adenocarcinoma (STAD). However, the role of 5-methylcytosine- (m5C-) related lncRNAs in STAD is still uncertain. This study investigated the value of m5C-related lncRNAs in prognostic evaluation and immunotherapy of STAD. STAD transcriptome sequencing data were downloaded from The Cancer Genome Atlas (TCGA) database, and m5C-related lncRNAs were screened by Pearson correlation analysis. A prognostic m5C-related lncRNA signature (m5CRLSig) associated with STAD was established using univariate and multivariate Cox regression analysis. We constructed a prognostic risk model for STAD with six m5C-related lncRNAs. The receiver operating characteristic (ROC) curve was used to examine the predictive efficacy. Univariate and multifactorial Cox regression analysis and principal component analysis (PCA) validated m5CRLSig as an independent factor of STAD prognosis. The clinicopathological characteristics of the model showed higher risk scores for stages II-IV, grade 3, N1-3, and death status. The calibration curve of a nomogram revealed that the nomogram had an excellent predictive function for survival risk. Furthermore, the expression of six m5C-related lncRNAs in STAD and paracancerous tissues was detected by quantitative real-time PCR (qRT-PCR), which verified the feasibility of the m5CRLSig as a prognostic marker for STAD. m5C-related lncRNAs were linked to multiple immune-associated pathways, according to gene set enrichment analysis (GSEA). CIBERSORT analysis indicated that m5CRLSig was involved in immune cell infiltration. Risk score was associated with immune checkpoint gene expression, immune function scores, and chemotherapeutic drug sensitivity. Therefore, m5CRLSig can efficiently assess the prognosis of STAD patients and can be used as a biological marker for immunotherapy.

HER2 in endometrioid endometrial adenocarcinoma (E-EMCA): Defining incidence, molecular profiles, and outcomes.

5588 Background: Immunohistochemistry (IHC) for HER2 in E-EMCA is not standard of care. We aimed to establish the correlation of HER2 transcript to IHC expression in the much more frequently tested uterine serous carcinoma (USC). We applied the threshold calculated in USC to E-EMCA and compared molecular and immune profiles among HER2+ and HER2- E-EMCA tumors, which may affect response to targeted therapy. Methods: 1462 E-EMCA tumors were analyzed using next-generation sequencing (592, NextSeq; WES, NovaSeq), WTS (NovaSeq) (Caris Life Sciences, Phoenix, AZ). PD-L1 was tested by IHC (SP142, &gt;1%). Microsatellite instability (MSI) was tested by FA, IHC and NGS. TMB was measured by totaling somatic mutations per tumor (TMB-H: &gt;10 mutations/MB). LOH cut-off was &gt; 16%. HER2+ cut-off by WTS was determined by Receiver Operator Characteristic (ROC) analysis in USC tumors by comparing to HER2 IHC/CISH results using 2018 Breast Cancer ASCO/CAP Guidelines. Immune cell infiltrates were calculated by Quantiseq. Real world overall survival (OS) was extracted from insurance claims data and calculated using Kaplan-Meier survival curves for molecularly defined cohorts. Significance was determined using chi-square and Wilcoxon rank sum test and adjusted for multiple comparisons (q-value &lt; 0.05), p &lt; 0.05 but q &gt; 0.05 was considered a trend. Results: We determined a cut-off of &gt; 62.99 TPM for HER2+ with a sensitivity of 81.5%, specificity of 87.6% and AUC of 0.92 in Uterine Serous. When this cut-off is applied to E-EMCA, 76 of 1462 (5.2%) E-EMCA tumors were HER2+. HER2+ tumors had fewer mutations (mt) in PI3KR1, PTEN and CTNNB1 but higher mts in TP53 and more frequent LOH (q &lt; 0.05). HER2+ tumors had a trend towards decreased MSI-H status (22.4% vs 39.1%; p = 0.003, q = 0.058) and TMB-H (25.4% vs 41.5%; p = 0.007, q = 0.084). MSS HER2+ E-EMCA had a similar mutational profile compared to all HER2+ tumors; MSI-H HER2+ E-EMCA had a trend towards higher DDR pathway gene mts compared to MSI-H HER2- EMCA tumors. HER2+ tumors had increased Dendritic cell (3.84% vs 2.97%) but decreased Neutrophil (2.66% vs 5.20%) and T-reg (1.38% vs 2.07%) infiltration (q &lt; 0.01). HER2+ tumors had higher immune checkpoint gene expression of CD80, HAVCR2 and PDCD1LG2 (q &lt; 0.01), and increased T-cell inflamed and MAPK activation score (q &lt; 0.01). MSS HER2+ E-EMCA tumors had a similar immune profile when compared to all HER2+ tumors; MSI-H HER2+ E-EMCA tumors had increased Treg infiltration and MAPK activation score. Median OS was significantly worse for HER2+ pts compared to HER2- (64.3 vs. 23.6 months, HR: 1.93(1.32-2.80), p &lt; 0.001). Conclusions: Using a WTS cutoff from USC, we found 5% of E-EMCA are HER2+ and showed distinct molecular and immune profile compared to HER2- tumors. HER2+ confers a worse OS compared to HER2- tumors. Furthermore, HER2+ tumors demonstrate an immune hot phenotype suggesting that immunotherapy may be a potential therapeutic option.

Molecular characteristics of novel immune subtypes of HCC based on lncRNAs related to immune disorders

As one of the most malignant cancers and despite various treatment breakthroughs, the prognosis of hepatocellular carcinoma (HCC) remains unsatisfactory. The immune status of the tumor microenvironment (TME) relates closely to HCC progression; however, the mechanism of immune cell infiltration in the TME remains unclear. In this study, we performed a new combination algorithm on lncRNA expression profile data from the TCGA-LIHC cohort to identify lncRNAs related to immune disorders. We identified 20 immune disorder-related lncRNAs and clustered HCC samples based on these lncRNAs. We identified four clusters with differences in immune cell infiltration and immune checkpoint gene expression. We further analyzed differences between groups 1 and 3 and found that the poor prognosis of group 3 may be due to specific and non-specific immunosuppression of the TME, upregulation of immune checkpoint pathways, and activation of tumor proliferation and migration pathways in group 3. We also developed a prognostic model and verified that it has good stability, effectiveness, and prognostic power. This study provides a basis for further exploration of the immune cell infiltration mechanism in HCC, differential HCC prognosis, and improvement of the efficacy of ICIs for the treatment of HCC.

Clinicopathological and Prognostic Value of Necroptosis-Associated lncRNA Model in Patients with Kidney Renal Clear Cell Carcinoma.

Background Necroptosis, a recently identified type of programmed necrotic cell death, is closely related to the tumorigenesis and development of cancer. However, it remains unclear whether necroptosis-associated long noncoding RNAs (lncRNAs) can be used to predict the prognosis of kidney renal clear cell carcinoma (KIRC). This work was designed to probe the possible prognostic worth of necroptosis-associated lncRNAs along with their impact on the tumor microenvironment (TME) in KIRC. Methods The Cancer Genome Atlas (TCGA) database was used to extract KIRC gene expression and clinicopathological data. Pearson correlation analysis was used to evaluate necroptosis-associated lncRNAs against 159 known necroptosis-associated genes. To define molecular subtypes, researchers used univariate Cox regression analysis and consensus clustering, as well as clinical significance, TME, and tumor immune cells in each molecular subtype. We develop the necroptosis-associated lncRNA prognostic model using univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Patients were divided into high- and low-risk groups according to prognostic model. Moreover, comprehensive analyses, including prognostic value, gene set enrichment analysis (GSEA), immune infiltration, and immune checkpoint gene expression, were performed between the two risk groups. Finally, anticancer drug sensitivity analyses were employed for assessing associations for necroptosis-associated lncRNA expression profile and anticancer drug chemosensitivity. Results Through univariate analysis, sixty-nine necroptosis-associated lncRNAs were found to have a significant relationship with KIRC prognosis. Two molecular clusters were identified, and significant differences were found with respect to clinicopathological features and prognosis. The segregation of patients into two risk groups was done by the constructed necroptosis-associated lncRNA model. The survival prognosis, clinical features, degree of immune cell infiltration, and expression of immune checkpoint genes of high-risk and low-risk groups were all shown to vary. Conclusions Our study identified a model of necroptosis-associated lncRNA signature and revealed its prognostic role in KIRC. It is expected to provide a reference for the screening of KIRC prognostic markers and the evaluation of immune response.

Melanoma Molecular Subtypes and Development of Prognostic and Immunotherapy-Related Genetic Characteristics by Ferroptosis Gene Analysis.

The dissimilarity is a major problem in clinical therapy of skin cutaneous melanoma (SKCM). Objective and reproducible classification systems may help decode SKCM heterogeneity. ConsensusClusterPlus was used to establish a stable immune molecular classification based on ferroptosis-related genes that had been acquired from FerrDb. Moreover, the prognosis, somatic mutations, immune microenvironment characteristics, functional enrichment, and clinical responsiveness to the immune checkpoint blockade of different subtypes in two independent melanin datasets were compared. Kaplan-Meier curves, univariate, multivariate, least absolute contraction, and selection operator (LASSO) Cox regression analysis were used to develop a molecular model for predicting survival, which was verified by a nomogram on the basis of independent prognostic indicators. Two molecular subtypes (C1 and C2) for SKCM were first identified according to ferroptosis-related genes; C1 showed a poor prognosis, with lower infiltration degree of immune cells and TIED score and higher homologous recombination defects, fraction altered, the number of segments, and copy number amplification and deletion. These characteristics of C2 were the opposite of C1. A ferroptosis-related prognosis risk score (FPRS) model was constructed using 6 of 463 genes with differential expression between C1 and C2. This model splits patients into low- and high-risk cohorts. There were significant differences in the infiltration and proportion of immune cells, immune checkpoint gene expression, responsiveness to immune checkpoint therapy, and sensitivity to chemotherapeutic medications between low- and high-risk cohorts. This model was an independent prognostic marker for SKCM and has a high AUC. In summary, we have identified two subtypes of SKCM with different molecular and immune characteristics on the basis of ferroptosis-related genes and further developed and verified an FPRS model, which might independently serve as a prognostic marker for SKCM.

Identification of a combined apoptosis and hypoxia gene signature for predicting prognosis and immune infiltration in breast cancer

BackgroundBreast cancer (BC) is the most common malignant tumor worldwide. Apoptosis and hypoxia are involved in the progression of BC, but reliable biomarkers for these have not been developed. We hope to explore a gene signature that combined apoptosis and hypoxia‐related genes (AHGs) to predict BC prognosis and immune infiltration.MethodsWe collected the mRNA expression profiles and clinical data information of BC patients from The Cancer Genome Atlas database. The gene signature based on AHGs was constructed using the univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analysis. The associations between risk scores, immune infiltration, and immune checkpoint gene expression were studied using single‐sample gene set enrichment analysis. Besides, gene signature and independent clinicopathological characteristics were combined to establish a nomogram. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on the potential functions of AHGs.ResultsWe identified a 16‐AHG signature (AGPAT1, BTBD6, EIF4EBP1, ERRFI1, FAM114A1, GRIP1, IRF2, JAK1, MAP2K6, MCTS1, NFKBIA, NFKBIZ, NUP43, PGK1, RCL1, and SGCE) that could independently predict BC prognosis. The median score of the risk model divided the patients into two subgroups. By contrast, patients in the high‐risk group had poorer prognosis, less abundance of immune cell infiltration, and expression of immune checkpoint genes. The gene signature and nomogram had good predictive effects on the overall survival of BC patients. GO and KEGG analyses revealed that the differential expression of AHGs may be closely related to tumor immunity.ConclusionWe established and verified a 16‐AHG BC signature which may help predict prognosis, assess potential immunotherapy benefits, and provide inspiration for future research on the functions and mechanisms of AHGs in BC.

Transcriptome analysis of adipocytokines and their-related LncRNAs in lung adenocarcinoma revealing the association with prognosis, immune infiltration, and metabolic characteristics

ABSTRACT Lung adenocarcinoma (LUAD) is amongst the major contributors to cancer-related deaths on a global scale. Adipocytokines and long non-coding RNAs (lncRNAs) are indispensable participants in cancer. We performed a pan-cancer analysis of the mRNA expression, single nucleotide variation, copy number variation, and prognostic value of adipocytokines. LUAD samples were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Simultaneously, train, internal and external cohorts were grouped. After a stepwise screening of optimized genes through least absolute shrinkage and selection operator regression analysis, random forest algorithm,, and Cox regression analysis, an adipocytokine-related prognostic signature (ARPS) with superior performance compared with four additional well-established signatures for survival prediction was constructed. After determination of risk levels, the discrepancy of immune microenvironment, immune checkpoint gene expression, immune subtypes, and immune response in low- and high-risk cohorts were explored through multiple bioinformatics methods. Abnormal pathways underlying high- and low-risk subgroups were identified through gene set enrichment analysis (GSEA). Immune-and metabolism-related pathways that were correlated with risk score were selected through single sample GSEA. Finally, a nomogram with satisfied predictive survival probability was plotted. In summary, this study offers meaningful information for clinical treatment and scientific investigation.

Single-Cell Sequencing Identifies the Heterogeneity of CD8+ T Cells and Novel Biomarker Genes in Hepatocellular Carcinoma.

CD8+ T cells are required for the establishment of antitumor immunity, and their substantial infiltration is associated with a good prognosis. However, CD8+ T cell subsets in the tumor microenvironment may play distinct roles in tumor progression, prognosis, and immunotherapy. In this study, we used the scRNA-seq data of hepatocellular carcinoma (HCC) to reveal the heterogeneity of different CD8+ T cell subsets. The scRNA-seq data set GSE149614 was obtained from the GEO database, and the transcriptome and sample phenotypic data of TCGA-LIHC were obtained from the TCGA database. CD8+ T cell subtypes and metabolic gene sets were obtained from published reports. The data processing and analysis of CD8+ T cell groups was performed by R language. The PPI network was constructed to obtain the hub genes, and the KM survival curve of the hub genes was further plotted to determine the hub genes with differences in survival. CD8+ T cells in HCC were divided into 7 subsets, and the cytotoxic CD8 T cells 4 subset showed considerable differences between the TP53-mutant and nonmutant groups, as well as between different degrees of cirrhosis, HCC grades, stages, ages, and body weights. Cytotoxic CD8 T cells 4 differential genes were analyzed by TCGA-LIHC data and single-cell sequencing data set. 10 hub genes were found: FGA, ApoA1, ApoH, AHSG, FGB, HP, TTR, TF, HPX, and APOC3. Different subsets of CD8+ T cells were found to contribute to heterogeneous prognosis and pathway activity in HCC. Alterations in the cytotoxic and immune checkpoint gene expression during CD8+ T cell differentiation were also identified. We found that cytotoxic CD8 T cells 4 is closely associated with survival and prognosis of HCC and identified four differential genes that can be used as biological markers for survival, prognosis, and clinically relevant characteristics of HCC. Results of this study could help finding targets for immunotherapy of HCC and aid in the accelerated development of immunotherapy for HCC.

Cyclin-Dependent Kinase 6 Identified as the Target Protein in the Antitumor Activity of Tetrastigma hemsleyanum.

Background Tetrastigma hemsleyanum (T. hemsleyanum) is widely used as an adjuvant drug for tumor therapy but its antitumor therapeutic targets and molecular mechanisms have remained unclear. The prediction and analysis of natural products has previously used only network pharmacology methods to identify potential target proteins from public databases. In this study, we use comprehensive bioinformatics analysis and experimental verification to determine the antitumor mechanism of T. hemsleyanum.MethodsNetwork pharmacology analysis was used to predict the potential in vivo target proteins of T. hemsleyanum. The expression matrix and clinical data to perform an analysis of hub genes were collected from the TCGA and GTEx databases, specifically the analysis of expression, prognosis, tumor immune cell infiltration analysis, immune checkpoint genes, microsatellite instability, tumor mutational burden, tumor neoantigen, and immune microenvironment, which identify the roles and biological functions of the hub genes in pan-cancer. Finally, gene set enrichment analysis was used to verify the biological processes and signaling pathways involved in the pan-cancer expression profile.ResultsWe found 124 potential in vivo target proteins of T. hemsleyanum through network pharmacological analysis, and five hub genes (AKR1C1, MET, PTK2, PIK3R1, and CDK6) were then screened by protein–protein interaction (PPI) network analysis and molecular complex detection analysis (MCODE). Experimental intervention with an aqueous extract of T. hemsleyanum verified that these hub genes are the target proteins involved in the regulation of T. hemsleyanum in cells. A pan-cancer analysis then confirmed that CDK6 and MET are potential targets upon which T. hemsleyanum may exert antitumor action, especially in ACC, CESC, LGG, and PAAD. The CDK6 protein targeted by T. hemsleyanum is also involved in the immune and mutation process of pan-cancer, especially in the regulation of immune cell infiltration, immune checkpoint gene expression, microsatellite instability, tumor mutation burdens, and tumor neoantigens. Together, these analyses show that T. hemsleyanum affects tumor immune regulation and genomic stability. Finally, a gene set enrichment analysis confirmed that T. hemsleyanum regulates the cell cycle checkpoint.ConclusionsWe found that T. hemsleyanum can behave as an antitumor agent by acting as a potential cell cycle checkpoint inhibitor in CDK6-driven tumors, such as ACC, CESC, LGG, and PAAD, and that it acts as a tyrosine kinase receptor inhibitor that inhibits the expression of the proto-oncogene MET. Combined with an analysis of immune and mutation correlations in pan-cancer, we determined that T. hemsleyanum may function biologically as an immune regulator and interfere with the stability of the tumor genome, which is worthy of further study.

Identification of three immune subtypes characterized by distinct tumor immune microenvironment and therapeutic response in stomach adenocarcinoma.

In primary stomach adenocarcinoma (STAD), the tumor immune microenvironment (TIME) is important for cancer occurrence and progression; however, its clinical significance remains unclear. This study investigated the association between patient survival, TIME, and therapeutic response to STAD. Gene expression profiles of STAD cases were collected from the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus. Molecular subtypes were explored with consistent clustering methods according to 119 immune signatures and the infiltrating scores of 22 immune cells using the Multi-Omics Immuno-Oncology Biological Research algorithm. We determined IFNγ scores and immune cytolytic activity (CYT) scores on the basis of corresponding gene signatures via single-sample Gene Set Enrichment Analysis. Comparisons of survival, TIME, 10 immunity-related oncogenic pathways, immune checkpoint expression, and therapeutic response were conducted among the three subtypes. We further applied linear discriminant analysis to construct a characteristic index to classify the subtypes, and the Pearson correlation coefficient for the relationship between the index and immune checkpoint genes. Weighted Correlation Network Analysis (WGCNA) was used to mine the associated modules and specific genes. We collected gene expression profiles from 352 STAD cases in the TCGA database, 300 in GSE62254, and 344 in GSE84437. Three STAD subtypes (IS1-IS3) were established according to the TIME signatures. The IS3 subtype had the highest immune score and the best prognosis, as well as markedly increased immune T-cell CYT, Th1/IFNγ scores, and immune checkpoint gene expression, compared to the other two subtypes. It was highly similar to the PD-1 response group in the previous study samples of GSE91061. The established TIME classification index performed well in classifying subtypes and was directly proportional to immune checkpoint-related gene expression levels. WGCNA explored 6 modules and 14 genes, namely DYSF, MAN1C1, HTRA3, EMCN, RFLNB, KANK3, MAGEH1, CD93, PCAT19, FUT11, BMP1, FOSB, DCHS1, and TCF3, which were associated with the established TIME classification index and STAD patient prognosis. TIME phenotypes of STAD patients could be divided into three different molecular subtypes, which displayed different prognoses, immune features, and therapeutic responses. Our results shed new light on predicting patient outcomes and the discovery of new anti-STAD therapeutic strategies according to the TIME.