Gene Signature Research Articles

Identification and validation of diagnostic markers related to immunogenic cell death and infiltration of immune cells in diabetic nephropathy

IntroductionImmunogenic cell death (ICD) is a unique cell death triggered by chemotherapy. However, studies elucidating the potential therapeutic role of ICD and the underlying mechanism in diabetic nephropathy (DN) are limited. MethodsWGCNA was conducted on the human kidney biopsy data linked to DN, analyzing gene sets associated with ICD. Gene Set Enrichment Analysis and Gene Set Variation Analysis were utilized to examine the discrepancy in biological function. We used Gene Ontology, the Kyoto Encyclopedia of Genes and Genomes, and the GeneMANIA database to investigate the function of the signature genes. An analysis using the receiver operating characteristic (ROC) was conducted to validate the diagnostic value of hub genes. Additionally, immune infiltration-related analyses were also performed. In conclusion, we examined the association between the glomerular filtration rate, serum creatinine, and hub genes. Hub genes were validated by immunohistochemistry using db/db mice kidneys. ResultsWGCNA revealed that the targets in the turquoise unit (1674 genes) exhibited the highest positive correlation with ICD. Furthermore, 4222 statistically significant DEGs were identified when comparing the DN and healthy control groups. Significantly, the KEGG pathway enrichment analysis indicated a connection between ICD and the nuclear factor-kappa B signaling pathway and the synthesis of cytokines (tumor necrosis factor superfamily). ROC analysis revealed that 16 hub genes exhibited strong discriminatory potential as biomarkers for DN. Therefore, immunohistochemical validation, with the potential involvement of chemokines (CCL11, CCR2, CCR7, CX3CR1, CXCL10, CXCL12, and CXCR5) and immune cells (CD3G, CD5, and CD247) may be crucial for the diagnosis and therapy of DN. ConclusionsDKK3, NR4A1, NR4A2, VEGFA, and DUSP1 may be associated with the development of DN. The pathogenesis of DN may specifically involve chemokines (CCL11, CCR2, CCR7, CX3CR1, CXCL10, CXCL12, and CXCR5) and immune cells (CD3G, CD5, and CD247), with LCP2 playing a significant role.

8109 Cancer-Associated Fibroblasts Correlate with Aggressive Thyroid Cancer Behavior: Insights from Four Large Patient Cohorts

Abstract Disclosure: M.A. Loberg: None. X. George: None. P.J. Courtney: None. H.C. Eric: None. A. Golding: None. B. David: None. M. Alshalalfa: Employee; Self; Veracyte, Inc.. Stock Owner; Self; Veracyte, Inc. Y. Hao: Employee; Self; Veracyte, Inc.. Stock Owner; Self; Veracyte, Inc. J.P. Klopper: Employee; Self; Veracyte, Inc.. Stock Owner; Self; Veracyte, Inc. R.T. Kloos: Employee; Self; Veracyte, Inc.. Stock Owner; Self; Veracyte, Inc.. V. Weiss: None. Thyroid tumor molecular analysis often utilizes fine-needle aspirate (FNA) samples to predict malignancy with high sensitivity. An outstanding question is if molecular sequencing can provide prognostic information to predict disease aggression, potentially informing management. Recently, the composition of the tumor microenvironment, in particular the presence of tumor-promoting fibroblasts, has been associated with aggressive thyroid cancer. However, fibroblast gene expression has not previously been analyzed in thyroid tumor FNA samples. In this work, we detect cancer-associated fibroblast (CAF) gene signatures across 4 distinct bulk RNA sequencing cohorts and confirm detection in FNA samples. Associations between CAF gene signatures and tumor characteristics were explored in: Afirma Genomic Sequencing Classifier (GSC) cohort from FNA (n=47,695), a Memorial Healthcare System (MHS) retrospective FNA cohort with pathology outcome data (n=318), TCGA papillary thyroid cancers (n=496), and a Vanderbilt University Medical Center-University of Washington (VUMC-UW) resection cohort (n=312). Published breast CAF gene sets were used to generate normal fibroblast and CAF subtype scores. Novel thyroid cancer specific CAF gene sets were generated from thyroid cancer single-cell RNA sequencing data. Scores for each gene set were calculated as the average Z-score. Across all cohorts, multiple CAF subsets were enriched in samples with BRAFV600E and papillary histology. The strongest enrichment was in an SFRP2+ CAF subset identified in thyroid cancer single-cell sequencing data. In Afirma GSC FNA samples, SFRP2+ CAFs were enriched in GSC-suspicious and Bethesda V/VI relative to GSC-benign nodules (Wilcoxon p value < 2e-[1]6 for all). In TCGA, SFRP2+ CAFs were associated with extrathyroidal extension, advanced disease stage, and aggressive histology. In the MHS cohort of 318 malignant FNA samples, SFRP2+ CAFs were significantly higher in patients with vascular invasion or lymph node metastasis (LNM) (OR:2.75 [95% CI 1.63-4.75], p=4e-5). In the VUMC-UW cohort, SFRP2+ CAFs were associated with shorter progression-free survival (PFS) (p=0.0033). In summary we created a novel thyroid cancer-specific CAF gene signature that is elevated in BRAFV600E+ and aggressive thyroid cancers across multiple large patient cohorts. We also demonstrated the ability to detect these CAF signatures in thyroid FNAs using the Afirma GSC molecular testing platform, with enrichment in suspicious and malignant samples. Amongst the CAF scores tested, thyroid-specific CAF subsets had the highest prognostic potential, and SFRP2+ CAFs, specifically, were associated with shorter PFS, tumor invasion and LNM. Together, these results highlight the potential of interrogating the thyroid tumor stroma in FNA using RNA-based assays to inform prognosis and possibly management. Presentation: 6/3/2024

25-hydroxycholesterol promotes brain cytokine production and leukocyte infiltration in a mouse model of lipopolysaccharide-induced neuroinflammation

Neuroinflammation has been implicated in the pathogenesis of several neurologic and psychiatric disorders. Microglia are key drivers of neuroinflammation and, in response to different inflammatory stimuli, overexpress a proinflammatory signature of genes. Among these, Ch25h is a gene overexpressed in brain tissue from Alzheimer’s disease as well as various mouse models of neuroinflammation. Ch25h encodes cholesterol 25-hydroxylase, an enzyme upregulated in activated microglia under conditions of neuroinflammation, that hydroxylates cholesterol to form 25-hydroxycholesterol (25HC). 25HC can be further metabolized to 7α,25-dihydroxycholesterol, which is a potent chemoattractant of leukocytes. We have previously shown that 25HC increases the production and secretion of the proinflammatory cytokine, IL-1β, by primary mouse microglia treated with lipopolysaccharide (LPS). In the present study, wildtype (WT) and Ch25h-knockout (KO) mice were peripherally administered LPS to induce an inflammatory state in the brain. In LPS-treated WT mice, Ch25h expression and 25HC levels increased in the brain relative to vehicle-treated WT mice. Among LPS-treated WT mice, females produced significantly higher levels of 25HC and showed transcriptomic changes reflecting higher levels of cytokine production and leukocyte migration than WT male mice. However, females were similar to males among LPS-treated KO mice. Ch25h-deficiency coincided with decreased microglial activation in response to systemic LPS. Proinflammatory cytokine production and intra-parenchymal infiltration of leukocytes were significantly lower in KO compared to WT mice. Amounts of IL-1β and IL-6 in the brain strongly correlated with 25HC levels. Our results suggest a proinflammatory role for 25HC in the brain following peripheral administration of LPS.

Immune-related glycosylation genes based classification predicts prognosis and therapy options of osteosarcoma

Osteosarcoma is the most common primary bone malignancy, with a very poor prognosis. Aberrant glycosylation is close involvement in osteosarcoma. Accordingly, this study aimed at investigating the role of glycosylation genes in the prognosis and therapy options of osteosarcoma. The microenvironment of osteosarcoma was assessed using estimate algorithm. A total of 20 immune-related glycosylation genes (IRGGs) was identified using Pearson correlation analysis. Accordingly, osteosarcoma patients were divided into C1 and C2 type using consensus clustering. Multiple algorithms (Xcell, MCP-counter, ssGSEA, epic, quantiseq), cancer immune cycle analysis, and GSVA were applied to estimate the immune, molecule and metabolism characteristics of osteosarcoma, indicating that C1 type was featured with high immune infiltration, high glycosylation, enriched MEK signaling, and good prognosis, while C2 type was characterized by more metastasis, enriched immunotherapy-positive gene signatures, high tumor mutation burden, and poor prognosis. Results from TIDE algorithm and immunotherapy datasets suggested the C2 type’s preference of immune checkpoint inhibitors (ICIs), while data of GDSC, CMap analysis and cell experiments indicated that C1 type was sensitivity to MEK inhibitor PD0325901. In addition, univariate Cox and Lasso analysis was combined to establish an IRGGs’ risk score containing 6 genes (B3GNT8, FUT7, GAL3ST4, GALNT14, HS3ST2, and MFNG). The data of DCA and ROC indicated its well prediction of prognosis in osteosarcoma. Finally, cellular location analysis showed that the 6 genes not only distributed in tumor cells but also in immune cells. In summary, the classification and risk score based on IRGGs effectively predicted the prognosis and therapy options of osteosarcoma. Further studies on IRGGs may contribute to the understanding of cancer immunity in osteosarcoma.

8743 Regulation Of Tryptophan 2,3-Dioxygenase 2 And Androgen Receptor In Triple-Negative Breast Cancer

Abstract Disclosure: L. Kuo: None. K. Lacey: None. N. Spoelstra: None. J.K. Richer: None. Background: Triple Negative Breast Cancer (TNBC) is an aggressive subtype with a peak recurrence rate within 3-5 years after initial diagnosis. Androgen receptor (AR) and Tryptophan 2,3-dioxygenase 2 (TDO2) are elevated in TNBC cells grown under anchorage-independent conditions and facilitate anoikis resistance. Several studies reported that the luminal AR subset of TNBC showed a positive enrichment of the tryptophan (TRP) catabolism pathway gene signature. Recent studies have demonstrated that aryl hydrocarbon receptor (AhR) inhibition decreases AR promoter transcriptional activity in prostate cancer. We have observed that TNBC with high AR have high levels of genes/proteins in the tryptophan catabolism pathway including the rate limiting enzyme TDO2. Hypothesis: TDO2 activity regulates AR expression through the downstream tryptophan catabolite kynurenine (KYN), which binds and activates AhR and AhR and AR may work together to promote TNBC progression. Methods: Human TNBC cell lines: MDA-MB-453, BT549, SUM159PT ​ were used in this study and the expression of TDO2 and AR were examined by immunohistochemistry (IHC) and western blot. TDO2 was stably knocked down (KD) via shRNA or overexpressed (OE) using lentivirus transduction. Mass spectrometry-based metabolomics were used to measure changes in TRP catabolite levels, and a cytokine array (R&D) was utilized to analyze conditioned media. Results: TDO2 and AR protein are positively correlated in human TNBC cell lines. Although overexpression of TDO2 did not alter AR protein expression in SUM159PT, in the human MDA-MB-453, which have high levels of AR, knockdown of TDO2 (shTDO2) reduced AR protein by 40% and production of kynurenine (KYN) was reduced by 50% (p<0.0001). This suggests that TDO2 activity, specifically KYN binding to AhR, may influence AR protein levels. Conditioned media (CM) from TDO2 OE and KD respectively increased and decreased CXCL5 and IGFBP2. Lastly, TCGA database analysis of TNBC tumors with high levels of both AhR and AR showed significantly shorter patient survival (HR=1.47, log rank p=0.046) compared to those with tumors with lower amounts of both genes. Conclusion: TDO2 and AR are positively correlated in human TNBC cell lines. Tumors with high levels of both AR and AhR result in significantly shorter survival time for TNBC patients. Knockdown of TDO2 significantly reduced KYN production and AR expression in luminal AR TNBC cell lines and future experiments will focus on the underlying mechanism behind this and whether it is dependent on KYN-mediated activation of AhR and if AhR stabilizes AR in TNBC. Presentation: 6/3/2024

8743 Regulation of Tryptophan 2,3-Dioxygenase 2 and Androgen Receptor in Triple-Negative Breast Cancer

Abstract Disclosure: L. Kuo: None. K. Lacey: None. N. Spoelstra: None. J.K. Richer: None. Background: Triple Negative Breast Cancer (TNBC) is an aggressive subtype with a peak recurrence rate within 3-5 years after initial diagnosis. Androgen receptor (AR) and Tryptophan 2,3-dioxygenase 2 (TDO2) are elevated in TNBC cells grown under anchorage-independent conditions and facilitate anoikis resistance. Several studies reported that the luminal AR subset of TNBC showed a positive enrichment of the tryptophan (TRP) catabolism pathway gene signature. Recent studies have demonstrated that aryl hydrocarbon receptor (AhR) inhibition decreases AR promoter transcriptional activity in prostate cancer. We have observed that TNBC with high AR have high levels of genes/proteins in the tryptophan catabolism pathway including the rate limiting enzyme TDO2. Hypothesis: TDO2 activity regulates AR expression through the downstream tryptophan catabolite kynurenine (KYN), which binds and activates AhR and AhR and AR may work together to promote TNBC progression. Methods: Human TNBC cell lines: MDA-MB-453, BT549, SUM159PT ​ were used in this study and the expression of TDO2 and AR were examined by immunohistochemistry (IHC) and western blot. TDO2 was stably knocked down (KD) via shRNA or overexpressed (OE) using lentivirus transduction. Mass spectrometry-based metabolomics were used to measure changes in TRP catabolite levels, and a cytokine array (R&D) was utilized to analyze conditioned media. Results: TDO2 and AR protein are positively correlated in human TNBC cell lines. Although overexpression of TDO2 did not alter AR protein expression in SUM159PT, in the human MDA-MB-453, which have high levels of AR, knockdown of TDO2 (shTDO2) reduced AR protein by 40% and production of kynurenine (KYN) was reduced by 50% (p<0.0001). This suggests that TDO2 activity, specifically KYN binding to AhR, may influence AR protein levels. Conditioned media (CM) from TDO2 OE and KD respectively increased and decreased CXCL5 and IGFBP2. Lastly, TCGA database analysis of TNBC tumors with high levels of both AhR and AR showed significantly shorter patient survival (HR=1.47, log rank p=0.046) compared to those with tumors with lower amounts of both genes. Conclusion: TDO2 and AR are positively correlated in human TNBC cell lines. Tumors with high levels of both AR and AhR result in significantly shorter survival time for TNBC patients. Knockdown of TDO2 significantly reduced KYN production and AR expression in luminal AR TNBC cell lines and future experiments will focus on the underlying mechanism behind this and whether it is dependent on KYN-mediated activation of AhR and if AhR stabilizes AR in TNBC. Presentation: 6/3/2024

7427 Cellular Senescence As A Pro-Tumorigenic Process And Therapeutic Target In Adrenocortical Carcinoma

Abstract Disclosure: L. Smith: None. K. Warde: None. S. Haston: None. J. Martinez-Barbera: None. K. Basham: None. Adrenocortical carcinoma (ACC) is a rare, yet highly aggressive malignancy that arises in the adrenal cortex. Advanced age is a major risk factor for ACC, with the peak age incidence between 50 and 60 years old. Older individuals above age 50 also have significantly worse survival outcomes. A better understanding of how aging contributes to the development and progression of ACC will uncover new treatment strategies. A major hallmark of aging is the systemic accumulation of senescent cells. While senescent cells have been implicated in various age-related diseases, including cancer, their role in ACC remains poorly understood. ACC tumors from older patients have an enrichment of senescence-associated gene signatures. Moreover, advanced ACC patients are treated with etoposide, doxorubicin, and cisplatin (EDP), which are chemotherapies that induce senescence. While EDP offers short-term benefit in some patients, more than half will progress within 6-months. We hypothesize that the accumulation of senescent cells that arise after EDP treatment are ultimately tumor-supportive long-term. As a result, we postulate that the use of senolytic compounds that clear away senescent cells after EDP treatment will lead to improved survival outcomes for ACC patients. We developed a new ACC mouse model that provides a window to study the role of senescence in ACC tumor progression and treatment. Our model is based on adrenal specific loss of ZNRF3, which is a Wnt inhibitor that is frequently inactivated in human ACC tumors. Despite early adrenal hyperplasia, we showed that Znrf3 conditional knockout (cKO) mice do not develop adrenal tumors until 18-months of age, which corresponds to the peak age incidence in humans. Additionally, the adrenals of Znrf3 cKO mice show a robust senescence activation phenotype that precedes adrenal tumorigenesis. We hypothesize that the accumulation of senescent cells is pro-tumorigenic long-term. To test our hypothesis, we combined Znrf3 cKO mice with a newly established mouse model that enables selective ablation of senescent cells (ZKO-FDR mice). Ongoing aging studies will determine whether the removal of early and late senescent cells in ZKO-FDR mice reduces adrenal tumorigenesis. Further, we established new primary tumor cell lines from aged ZKO-FDR mice. These cell lines will facilitate testing of the pro-tumor mechanisms of senescence. Additionally, they will permit the removal of senescent cells, both genetically and pharmacologically with senolytics, as a new treatment strategy in ACC. Overall, this project aims to investigate the pro-tumor biology of age- and therapy-induced senescence in ACC. Results from these studies will advance our understanding of senescence in cancer and aid in the development of new therapeutic strategies for ACC, including senolytics. Presentation: 6/2/2024

The role of 1q abnormalities in multiple myeloma: Genomic insights, clinical implications, and therapeutic challenges

Chromosome 1q copy number variations, collectively termed +1q, are 1 of the most common cytogenetic abnormalities in multiple myeloma. 1q abnormalities are associated with overexpression of a high-risk gene signature promoting cell proliferation, apoptosis resistance, genomic instability, and treatment resistance, and acquisition or expansion of +1q subclones mediate disease development and relapse. While there remains significant controversy as to whether the presence of +1q is itself an independent driver of poor prognosis or is simply a marker of other high-risk features, +1q has recently been incorporated into multiple prognostic scoring models as a new high-risk cytogenetic abnormality. In this review, we present possible underlying genetic mechanisms of high-risk disease in +1q myeloma, implications for subclonal development, its role in modifying the tumor microenvironment, current evidence for clinical significance in newly-diagnosed and relapsed patients, and current controversies in +1q classification and prognostication.

8066 Compensatory Signatures of Islet Cells That Protect Obese Humans From Diabetes

Abstract Disclosure: H. Kim: None. Y. Li: None. H. Zhu: None. K. El Jellas: None. N. Shukla: None. Z. Ren: None. K. Fukuda: None. M. Chen: None. R. Kulkarni: Advisory Board Member; Self; Novo Nordisk, Inversago, Biomea, REDD. The increasing prevalence of obesity and type 2 diabetes (T2D) continues to be a major global socioeconomic burden. Healthy humans adapt to metabolic stress by increasing their functional β-cell mass. Failure of this process, called decompensation, potentially triggers the development of T2D in many but not all obese humans. Furthermore, the molecular mechanism(s) underlying the ability of heterogeneous human β-cells to compensate and protect obese humans from developing T2D remain elusive. Here we report unique gene signatures of β-cell subtypes and their molecular interactions with islet stellate cells during successful compensation in human obesity. Using a single-nucleus multiomics approach, we profiled gene expression and chromatin accessibility of 160,954 islet nuclei from non-diabetic lean, overweight, obese, or T2D human donors. We identified multiple β-cell subtypes and their unique sets of genes, biological pathways, and transcription factors associated with compensation. The genome-wide association studies corroborated the compensatory genes identified in the subsets, showing significant associations with metabolic phenotypes in humans. Unbiased cell-cycle gene and ligand-receptor analyses of islet cell types revealed that activated stellate cells proliferate and interact with the β-cell subtypes that involves the extracellular matrix-integrin protein complex in non-diabetic obesity. These results highlight the physiological roles of islet stellate cells in regulating β-cell compensation and point to therapeutic opportunities in precision medicine to protect obese humans from developing T2D. Presentation: 6/3/2024

Multiregional transcriptomic profiling provides improved prognostic insight in localized non-small cell lung cancer

Lung Cancer remains the leading cause of cancer deaths in the USA and worldwide. Non-small cell lung cancer (NSCLC) harbors high transcriptomic intratumor heterogeneity (RNA-ITH) that limits the reproducibility of expression-based prognostic models. In this study, we used multiregional RNA-seq data (880 tumor samples from 350 individuals) from both public (TRACERx) and internal (MDAMPLC) cohorts to investigate the effect of RNA-ITH on prognosis in localized NSCLC at the gene, signature, and tumor microenvironment levels. At the gene level, the maximal expression of hazardous genes (expression negatively associated with survival) but the minimal expression of protective genes (expression positively associated with survival) across different regions within a tumor were more prognostic than the average expression. Following that, we examined whether multiregional expression profiling can improve the performance of prognostic signatures. We investigated 11 gene signatures collected from previous publications and one signature developed in this study. For all of them, the prognostic prediction accuracy can be significantly improved by converting the regional expression of signature genes into sample-specific expression with a simple function—taking the maximal expression of hazardous genes and the minimal expression of protective genes. In the tumor microenvironment, we found a similar rule also seems applicable to immune ITH. We calculated the infiltration levels of major immune cell types in each region of a sample based on expression deconvolution. Prognostic analysis indicated that the region with the lowest infiltration level of protective or highest infiltration level of hazardous immune cells determined the prognosis of NSCLC patients. Our study highlighted the impact of RNA-ITH on the prognostication of NSCLC, which should be taken into consideration to optimize the design and application of expression-based prognostic biomarkers and models. Multiregional assays have the great potential to significantly improve their applications to prognostic stratification.

Identification of a seven-gene prognostic model for renal cell carcinoma associated with CD8+T lymphocyte cell.

CD8+ T lymphocytes are important elements of the tumor microenvironment, hence their involvement in the development and progression of tumors is complex. Data on the precise tumor-infiltrating lymphocytes gene signature in renal cell carcinoma (RCC) remain limited. Therefore, this study created a tumor-infiltrating lymphocytes-related predictive model for patients with RCC using data from The Cancer Genome Atlas. The most important genes associated with CD8 + T lymphocytes were identified using weighted gene co-expression network analysis. Functional categories of important genes were revealed using gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes signaling pathway analyses. A CD8 + T lymphocyte-related prognostic model with 7 important genes was simultaneously created using the least absolute shrinkage and selection operator, univariate and multivariate Cox regressions, and the 7 genes were expressed particularly in CD8 + T lymphocytes according to single-cell sequencing data obtained from the Gene Expression Omnibus. This study identified a seven-gene prognostic model associated with CD8 + T lymphocytes that may significantly influence risk stratification in patients with RCC. The genes included in the model are apolipoprotein B mRNA editing catalytic polypeptide 3G, CD3 gamma, eomesodermin, protein tyrosine phosphatase, non-receptor type 7, signal regulatory protein gamma, Fas ligand, and T-cell immunoreceptor with Ig and ITIM domains.

Construction and validation of an immune-related gene signature predictive of survival and response to immunotherapy for colorectal cancer.

Colorectal cancer is a common malignant tumor with the second incidence rate and the third mortality rate worldwide. In this study, we identified and validated an immune-related gene signature, explored the clinical and molecular characteristics of the signature-defined risk groups, and assessed its ability in predicting prognosis, immune cell infiltration and immunotherapy responses. The Cancer Genome Atlas database was used as the training set while GSE39582 database as the validation set. Immune-related hub genes were selected by the Least Absolute Shrinkage and Selection Operator-penalized Cox regression model, and the signature was then constructed by the selected genes and their relevant coefficients. Prognostic performance of the signature and the signature-base nomogram models were assessed by time-dependent receiver operating characteristic curves and calibration plots in both training and validation cohorts. Clinical and mutation-related data were downloaded and analyzed to explore their associations with signature-defined risk groups. Proportions of infiltrated immune cells was estimated via CIBERSORT algorithm and immunotherapy response was evaluated by immunophenoscore and tumor immune dysfunction and exclusion scores. Seven among 790 immune-related differentially-expressed genes were selected and use to construct the signature. The signature and signature-base nomograms showed promising prognostic performance in both training and validation cohorts. Signature-defined high-risk group was associated with advanced disease, poor pathological prognostic factors and less active immune infiltration microenvironment. Besides, the response to immunotherapy of high-risk group was predicted to be poorer by immunophenoscore and tumor immune dysfunction and exclusion scores. Our signature proved its efficacy in predicting prognosis, tumor immune microenvironment and responses to immunotherapy in colorectal cancer.

Prognostic value of a lactate metabolism gene signature in lung adenocarcinoma and its associations with immune checkpoint blockade therapy response.

Lung adenocarcinoma (LUAD) is a study that examines the prognostic value of lactate metabolism genes in tumor cells, which are associated with clinical prognosis. We analyzed the expression and clinical data for LUAD from The Cancer Genome Atlas database, using the GSE68465 dataset from the Gene Expression Omnibus and the MSigDB database. LASSO Cox regression and stepwise Cox regression were used to identify the optimal lactate metabolism gene signature. Differences in immune infiltration, tumor mutation burden (TMB), and response to immune checkpoint blockade (ICB) therapy were evaluated between groups. LASSO and Cox regression analyses showed an eight-lactate metabolism gene signature for model construction in both TCGA cohort and GSE68465 data, with higher survival outcomes in high-risk groups. The lactate metabolism risk score had an independent prognostic value (hazard ratio: 2.279 [1.652-3.146], P < .001). Immune cell infiltration differed between the risk groups, such as CD8+ T cells, macrophages, dendritic cells, mast cells, and neutrophils. The high-risk group had higher tumor purity, lower immune and stromal scores, and higher TMB. High-risk samples had high tumor immune dysfunction and exclusion (TIDE) scores and low cytolytic activity (CYT) scores, indicating a poor response to ICB therapy. Similarly, most immune checkpoint molecules, immune inhibitors/stimulators, and major histocompatibility complex (MHC) molecules were highly expressed in the high-risk group. The 8-lactate metabolism gene-based prognostic model predicts patient survival, immune infiltration, and ICB response in patients with LUAD, driving the development of therapeutic strategies to target lactate metabolism.

Therapeutic JAK1 Inhibition Reverses Lupus Nephritis in a Mouse Model and Demonstrates Transcriptional Changes Consistent With Human Disease.

Janus kinase family members are essential for signaling by multiple cytokines, including many implicated in systemic lupus erythematosus (SLE) pathogenesis. To test whether inhibition of JAK1 signaling can be efficacious in SLE, we used a JAK1-selective inhibitor (ABT-317) and evaluated its ability to ameliorate disease in murine SLE. Efficacy of ABT-317 was evaluated using NZB/W-F1 mice treated prophylactically and therapeutically. Primary endpoints were proteinuria, survival, and saliva production. Other endpoints included histological analysis of kidneys and salivary glands, flow cytometric analysis of splenic cell populations, and gene expression analysis by RNA sequencing in the kidneys, salivary glands, and blood. Publicly available human kidney gene transcription data were used to assess the translatability of the mouse findings. ABT-317 was efficacious when dosed prophylactically and prevented disease for up to two months after treatment cessation. When dosed therapeutically, ABT-317 quickly reversed severe proteinuria and restored saliva production, as well as diminished kidney and salivary gland inflammation. ABT-317-induced changes in glomerular morphology coincided with normalization of a human nephrotic gene signature, suggesting translatability to human lupus nephritis (LN). JAK1 inhibition prevented and reversed kidney and salivary gland manifestations of murine lupus with long-lasting effects after treatment cessation. These data, along with the presence of JAK1 and nephrotic gene signatures in human LN glomeruli, suggest that a JAK1-selective inhibitor may be an effective therapeutic in the treatment of human SLE and LN.

CIEC: Cross-tissue Immune Cell Type Enrichment and Expression Map Visualization for Cancer.

Single-cell transcriptome sequencing technology has been applied to decode the cell types and functional states of immune cells, revealing their tissue-specific gene expression patterns and functions in cancer immunity. Comprehensive assessments of immune cells within and across tissues will provide us with a deeper understanding of the tumor immune system in general. Here, we present Cross-tissue Immune cell type or state Enrichment analysis of gene lists for Cancer (CIEC), the first web-based application that integrates database and enrichment analysis to estimate the cross-tissue immune cell type or state. CIEC version 1.0 consists of 480 samples covering primary tumor, adjacent normal tissue, lymph node, metastasis tissue, and peripheral blood from 323 cancer patients. By applying integrative analysis, we constructed an immune cell-type/state map for each context and adopted our previously developed Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology Based Annotation System (KOBAS) algorithm to estimate the enrichment for context-specific immune cell type/state. In addition, CIEC also provides an easy-to-use online interface for users to comprehensively analyze the immune cell characteristics mapped across multiple tissues, including expression map, correlation, similar genes detection, signature score, and expression comparison. We believe that CIEC will be a valuable resource for exploring the intrinsic characteristics of immune cells in cancer patients and for potentially guiding novel cancer-immune biomarker development and immunotherapy strategies. CIEC is freely accessible at http://ciec.gene.ac/.

Leveraging programmed cell death signature to predict clinical outcome and immunotherapy benefits in postoperative bladder cancer

Bladder cancer is the fourth most common malignancy in men with poor prognosis. Programmed cell death (PCD) exerts crucial functions in many biological processes and immunotherapy responses of cancers. Cell death signature (CDS) is novel gene signature comprehensively considering the characteristics of 15 patterns of programmed cell death, which could affect the prognosis and immunotherapy benefits of cancer patients. Integrative machine learning procedure including 10 algorithms was conducted to construct a prognostic CDS using TCGA, GSE13507, GSE31684, GSE32984 and GSE48276 datasets. Immunophenoscore, intratumor heterogeneity (ITH), tumor immune dysfunction and exclusion (TIDE) score and five immunotherapy cohorts were used to evaluate the predictive value of CDS in immunotherapy response. The prognostic CDS constructed by StepCox[backward] + Ridge algorithms was regarded as the optimal prognostic model. The CDS had a stable and powerful performance in predicting overall survival of bladder cancer patients with the AUCs at 3-year, 5-year, and 7-year ROC of 0.740, 0.763 and 0.820 in TCGA cohort. Moreover, CDS score acted as an independent risk factor for overall survival rate of bladder cancer patients. Low CDS score had a higher abundance of immuno-activated cells, higher PD1&CTLA4 immunophenoscore, higher TMB score, lower TIDE score, lower immune escape score, lower ITH score, lower cancer-related hallmarks score in bladder cancer. The CDS score was higher in non-responders in pan-cancer patients receiving immunotherapy. Our study constructed a novel prognostic CDS, which could serve as an indicator for predicting the prognosis in postoperative bladder cancer cases and immunotherapy benefits in pan-cancer. Low CDS score indicated a better prognosis and immunotherapy benefits.

Identification of Cuproptosis-Associated Prognostic Gene Expression Signatures from 20 Tumor Types.

We investigated the mRNA expression of 124 cuproptosis-associated genes in 7489 biopsies from 20 different tumor types of The Cancer Genome Atlas (TCGA). The KM plotter algorithm has been used to calculate Kaplan-Meier statistics and false discovery rate (FDR) corrections. Interaction networks have been generated using Ingenuity Pathway Analysis (IPA). High mRNA expression of 63 out of 124 genes significantly correlated with shorter survival times of cancer patients across all 20 tumor types. IPA analyses revealed that their gene products were interconnected in canonical pathways (e.g., cancer, cell death, cell cycle, cell signaling). Four tumor entities showed a higher accumulation of genes than the other cancer types, i.e., renal clear cell carcinoma (n = 21), renal papillary carcinoma (n = 13), kidney hepatocellular carcinoma (n = 13), and lung adenocarcinoma (n = 9). These gene clusters may serve as prognostic signatures for patient survival. These signatures were also of prognostic value for tumors with high mutational rates and neoantigen loads. Cuproptosis is of prognostic significance for the survival of cancer patients. The identification of specific gene signatures deserves further exploration for their clinical utility in routine diagnostics.

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

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

Screening and validating genes associated with cuproptosis in systemic lupus erythematosus by expression profiling combined with machine learning.

Cell death has long been a focal point in life sciences research, and recently, scientists have discovered a novel form of cell death induced by copper, termed cuproptosis. This paper aimed to identify genes associated with cuproptosis in Systemic Lupus Erythematosus (SLE) through machine learning, combined with single-cell sequencing (scRNA-seq), to screen and validate related genes. The analytical results were then experimentally verified. Two published microarray gene expression datasets (GSE65391 and GSE61635) from SLE and control peripheral blood samples were downloaded from the GEO database. The GSE65391 dataset was used as the training group, while the GSE61635 dataset served as the validation group. Differentially expressed genes (DEGs) from GSE65391 identified 12 differential genes. Nine diagnostic genes, considered potential biomarkers, were selected using the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) analysis. The Receiver Operating Characteristic (ROC) curves for both the training and validation groups were used to calculate the Area Under the Curve (AUC) to assess discriminatory properties. CIBERSORT was used to assess the relationship between these diagnostic genes and a reference set of infiltrating immune cells. Single-cell RNA sequencing data (GSE162577) from SLE patients were also obtained from the GEO database and analyzed. Experimental validation of the most important SLE biomarkers was performed. Twelve significantly different cuproptosis-related genes were identified in the GSE65391 training set. Immune cell analysis revealed 12 immune cell types and identified nine signature genes, including PDHB, GLS, DLAT, LIAS, MTF1, DLST, DLD, LIPT1, and FDX1. In the GSE61635 validation set, seven genes were weakly expressed, and two genes were strongly expressed in the treatment group. According to the ROC curves, PDHB and GLS demonstrated significant diagnostic value. Additionally, correlation analysis was conducted on the nine characteristic genes in relation to immune infiltration. The distribution of key genes in immune cells was determined using single-cell RNA sequencing data. Finally, the mRNA expression of the nine diagnostic genes was validated using qPCR.

Application of Gene Expression Microarray for the Classification of Ph-Like B-Cell Acute Lymphoblastic Leukemia.

Ph-like ALL has gene expression profile similar to Ph-positive ALL but without the BCR::ABL1 fusion. The disease presents higher rates of severe clinical features and is associated with unfavorable outcomes. There is still no standard pipeline for molecular characterization of the disease, and no valid predictor gene panel is available worldwide. We performed expression microarray on 25 B-cell ALL and 6 Ph-positive B-cell ALL to cluster and identify the transcriptional signature of Ph-like ALL. qRT-PCR was used to confirm the expression of candidate genes. Four out of 25 samples (16%) shared gene expression signatures related to and clustered with control Ph-positive samples. Analysis of genes differentially expressed in Ph-like B-cell ALL and evidentially functional in normal blood cell development and leukemogenesis, we selected genes as potential biomarkers for Ph-like B-cell ALL in our dataset: ADGRE2, CD9, EPHA7, FAM129C, TCL1A, and VPREB1. Those genes were filtered by Ph-like gene signatures obtained from distinct reliable data, resulting in five genes, CA6, CHN2, JAK1, JCHAIN, and PON2, selected for validation by qRT-PCR. The Ct values of genes, including CA6 (p = 0.0017), PON2 (p = 0.0210), TCL1A (p = 0.0064), and VPREB1 (p = 0.0338), were significant in Ph-like ALL. GSEA analysis identified VPREB1 as enrichment in the KRAS signaling pathway, and several genes that interact with VPREB1 were reported as critical molecules involved in the leukemogenesis of B-cell ALL. In summary, we demonstrate using a gene expression microarray for classifying Ph-like B-cell ALL and highlight VPREB1 as a potential biomarker for this disease.