The Cancer Genome Atlas Patients Research Articles

Contemporary prognostic signatures and refined risk stratification of gliomas: An analysis of 4400 tumors.

With the significant shift in the classification, risk stratification, and standards of care for gliomas, we sought to understand how the overall survival of patients with these tumors is impacted by molecular features, clinical metrics, and treatment received. We assembled a cohort of patients with histopathologically diagnosed glioma from The Cancer Genome Atlas (TCGA), Project Genomics Evidence Neoplasia Information Exchange, and Dana-Farber Cancer Institute/Brigham and Women's Hospital. This incorporated retrospective clinical, histological, and molecular data alongside a prospective assessment of patient survival. Of 4400 gliomas were identified: 2195 glioblastomas, 1198 IDH1/2-mutant astrocytomas, 531 oligodendrogliomas, 271 other IDH1/2-wild-type gliomas, and 205 pediatric-type glioma. Molecular classification updated 27.2% of gliomas from their original histopathologic diagnosis. Examining the distribution of molecular alterations across glioma subtypes revealed mutually exclusive alterations within tumorigenic pathways. Non-TCGA patients had significantly improved overall survival compared to TCGA patients, with 26.7%, 55.6%, and 127.8% longer survival for glioblastoma, IDH1/2-mutant astrocytoma, and oligodendroglioma, respectively (all P < .01). Several prognostic features were characterized, including NF1 alteration and 21q loss in glioblastoma, and EGFR amplification and 22q loss in IDH1/2-mutant astrocytoma. Leveraging the size of this cohort, nomograms were generated to assess the probability of overall survival based on patient age, the molecular features of a tumor, and the treatment received. By applying modern molecular criteria, we characterize the genomic diversity across glioma subtypes, identify clinically applicable prognostic features, and provide a contemporary update on patient survival to serve as a reference for ongoing investigations.

Dysregulated miRNA Expression and Androgen Receptor Loss in Racially Distinct Triple-Negative Breast Cancer.

Androgen receptor (AR)-negative triple-negative breast cancer (TNBC), often termed quadruple-negative breast cancer (QNBC), disproportionately impacts women of African descent, leading to poorer overall survival (OS). MiRNAs regulate the expression of gene drivers involved in critical signaling pathways in TNBC, such as the AR gene, and their expression varies across races and breast cancer subtypes. This study investigates whether differentially expressed miRNAs influence AR transcription, potentially contributing to the observed disparities between African American (AA) and European American (EA) QNBC patients. Race-annotated TNBC samples (n = 129) were analyzed for AR expression status and revealed the prevalence of QNBC in AA patients compared to EA (76.6% vs. 57.7%) and a significant association of AR loss with poor survival among AAs. The Cancer Genome Atlas (TCGA) RNA-seq data showed that AAs with TNBC (n = 32) had lower AR mRNA levels than EAs (n = 67). Among TCGA patients in the AR-low group, AAs had significantly poorer OS than EAs. In our cohort, 46 miRNAs exhibited differential expression between AAs and EAs with QNBC. Ten of these miRNAs (miR-1185-5p, miR-1305, miR-3161, miR-3690, miR-494-3p, miR-509-3-5p, miR-619-3p, miR-628-3p, miR-873-5p, and miR-877-5p) were predicted to target the AR gene/signaling. The loss of AR expression is linked to poorer prognoses in AA women. The understanding of the specific miRNAs involved and their regulatory mechanisms on AR expression could provide valuable insights into why AA women are more prone to QNBC.

Learning to Train and to Explain a Deep Survival Model with Large-Scale Ovarian Cancer Transcriptomic Data.

Background/Objectives: Ovarian cancer is a complex disease with poor outcomes that affects women worldwide. The lack of successful therapeutic options for this malignancy has led to the need to identify novel biomarkers for patient stratification. Here, we aim to develop the outcome predictors based on the gene expression data as they may serve to identify categories of patients who are more likely to respond to certain therapies. Methods: We used The Cancer Genome Atlas (TCGA) ovarian cancer transcriptomic data from 372 patients and approximately 16,600 genes to train and evaluate the deep learning survival models. In addition, we collected an in-house validation dataset of 12 patients to assess the performance of the trained survival models for their direct use in clinical practice. Despite deceptive generalization capabilities, we demonstrated how our model can be interpreted to uncover biological processes associated with survival. We calculated the contributions of the input genes to the output of the best trained model and derived the corresponding molecular pathways. Results: These pathways allowed us to stratify the TCGA patients into high-risk and low-risk groups (p-value 0.025). We validated the stratification ability of the identified pathways on the in-house dataset consisting of 12 patients (p-value 0.229) and on the external clinical and molecular dataset consisting of 274 patients (p-value 0.006). Conclusions: The deep learning-based models for survival prediction with RNA-seq data could be used to detect and interpret the gene-sets associated with survival in ovarian cancer patients and open a new avenue for future research.

The peritumoral edema index and related mechanisms influence the prognosis of GBM patients.

Peritumoral brain edema (PTBE) represents a characteristic phenotype of intracranial gliomas. However, there is a lack of consensus regarding the prognosis and mechanism of PTBE. In this study, clinical imaging data, along with publicly available imaging data, were utilized to assess the prognosis of PTBE in glioblastoma (GBM) patients, and the associated mechanisms were preliminarily analyzed. We investigated relevant imaging features, including edema, in GBM patients using ITK-SNAP imaging segmentation software. Risk factors affecting progression-free survival (PFS) and overall survival (OS) were assessed using a Cox proportional hazard regression model. In addition, the impact of PTBE on PFS and OS was analyzed in clinical GBM patients using the Kaplan-Meier survival analysis method, and the results further validated by combining data from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). Finally, functional enrichment analysis based on TCIA and TCGA datasets identified several pathways potentially involved in the mechanism of edema formation. This study included a total of 32 clinical GBM patients and 132 GBM patients from public databases. Univariate and multivariate analyses indicated that age and edema index (EI) are independent risk factors for PFS, but not for OS. Kaplan-Meier curves revealed consistent survival analysis results between IE groups among both clinical patients and TCIA and TCGA patients, suggesting a significant effect of PTBE on PFS but not on OS. Furthermore, functional enrichment analysis predicted the involvement of several pathways related mainly to cellular bioenergetics and vasculogenic processes in the mechanism of PTBE formation. While these novel results warrant confirmation in a larger patient cohort, they support good prognostic value for PTBE assessment in GBM. Our results indicate that a low EI positively impacts disease control in GBM patients, but this does not entirely translate into an improvement in OS. Multiple genes, signaling pathways, and biological processes may contribute to the formation of peritumoral edema in GBM through cytotoxic and vascular mechanisms.

A Novel Prognostic Model of Hepatocellular Carcinoma per Two NAD+ Metabolic Synthesis-Associated Genes.

Hepatocellular carcinoma (HCC) is a formidable challenge to global human health, while recent years have witnessed the important role of NAD+ in tumorigenesis and progression. However, the expression pattern and prognostic value of NAD+ in HCC still remain elusive. Gene expression files and corresponding clinical pathological files associated with HCC were obtained from the Cancer Genome Atlas (TCGA) database, and genes associated with NAD+ were retrieved from the GSEA and differentially analyzed in tumor and normal tissues. A consensus clustering analysis was conducted by breaking down TCGA patients into four distinct groups, while Kaplan-Meier curves were generated to investigate the disparity in clinical pathology and endurance between clusters. A prognostic model based on NAD+-associated genes was established and assessed by combining LASSO-Cox regression, uni- and multi-variate Cox regression, and ROC curve analyses. Investigations were conducted to determine the expression of distinct mRNAs and proteins in both HCC and non-tumor tissues. A novel two-gene signature including poly (ADP-Ribose) polymerase 2 (PARP2) and sirtuin 6 (SIRT6) was obtained through LASSO-Cox regression and was identified to have favorable prognostic performance in HCC patients from TCGA. Analyses of both single and multiple variables showed that the prognostic model was a distinct prognostic factor in the endurance of liver cancer patients in both the training and trial groups. The nomogram also exhibited clinical significance in the prognosis of HCC patients. Immunohistochemistry, qRT-PCR, and Western blotting revealed that HCC samples exhibited higher PARP2 and SIRT6 expression levels than those of normal controls. This study identified a robust prognostic model comprising two NAD+-associated genes using bioinformatic methods, which is accurate in predicting the survival outcome of HCC patients. This model might benefit the early diagnosis of HCC and further facilitate the management of individualized medical service and clinical decision-making.

A protein expression atlas on tissue samples and cell lines from cancer patients provides insights into tumor heterogeneity and dependencies.

The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) are foundational resources in cancer research, providing extensive molecular and phenotypic data. However, large-scale proteomic data across various cancer types for these cohorts remain limited. Here, we expand upon our previous work to generate high-quality protein expression data for approximately 8,000 TCGA patient samples and around 900 CCLE cell line samples, covering 447 clinically relevant proteins, using reverse-phase protein arrays. These protein expression profiles offer profound insights into intertumor heterogeneity and cancer dependency and serve as sensitive functional readouts for somatic alterations. We develop a systematic protein-centered strategy for identifying synthetic lethality pairs and experimentally validate an interaction between protein kinase A subunit α and epidermal growth factor receptor. We also identify metastasis-related protein markers with clinical relevance. This dataset represents a valuable resource for advancing our understanding of cancer mechanisms, discovering protein biomarkers and developing innovative therapeutic strategies.

Genetic Screen in a Preclinical Model of Sarcoma Development Defines Drivers and Therapeutic Vulnerabilities.

High-grade complex karyotype sarcomas are a heterogeneous group of tumors with a uniformly poor prognosis. Within complex karyotype sarcomas, there are innumerable genetic changes but identifying those that are clinically relevant has been challenging. To address this, we utilized a pooled genetic screening approach, informed by The Cancer Genome Atlas (TCGA) data, to identify key drivers and modifiers of sarcoma development that were validated in vivo. YAP1 and wild-type KRAS were validated as drivers and transformed human mesenchymal stem cells into two distinct sarcoma subtypes, undifferentiated pleomorphic sarcoma and myxofibrosarcoma, respectively. A subset of tumors driven by CDK4 and PIK3CA reflected leiomyosarcoma and osteosarcoma demonstrating the plasticity of this approach and the potential to investigate sarcoma subtype heterogeneity. All generated tumors histologically reflected human sarcomas and had increased aneuploidy as compared to simple karyotype sarcomas. Comparing differential gene expression of TCGA samples to model data identified increased oxidative phosphorylation signaling in YAP1 tumors. Treatment of a panel of soft tissue sarcomas with a combination of YAP1 and oxidative phosphorylation inhibitors led to significantly decreased viability. Transcriptional co-analysis of TCGA patient samples to YAP1 and KRAS model tumors supports that these sarcoma subtypes lie along a spectrum of disease and adds guidance for further transcriptome-based refinement of sarcoma subtyping. This approach can be used to begin to understand pathways and mechanisms driving human sarcoma development, the relationship between sarcoma subtypes, and to identify and validate new therapeutic vulnerabilities for this aggressive and heterogeneous disease.

Abstract LB390: Enhancing prostate cancer diagnosis: A deep learning approach for DNA ploidy prediction from whole slide images

Abstract Prostate cancer is distinguished by unique histological alterations in glandular architecture, observable in Whole Slide Images (WSIs). The disease is further characterized by pronounced genomic instability, exemplified by biomarkers such as DNA ploidy. While the Gleason Score effectively assesses the histological changes, the manual evaluation of DNA ploidy is subjective and time-consuming. Addressing the need for an objective diagnostic tool, our study developed a deep learning model aimed at accurately predicting DNA ploidy using publicly available WSI from the Cancer Genome Atlas (TCGA). Utilizing a ResNet-18 convolutional neural network, we trained the model on WSIs from 200 TCGA patients to extract features and predict total mRNA expression. The model was further refined using data from 19 TCGA patients, focusing on ploidy number prediction. Comparative analysis against a random reference model revealed significant improvements of ploidy prediction in Mean Absolute Error (MAE) by 43.52%, Mean Absolute Percentage Error (MAPE) by 43.59%, and Mean Squared Error (MSE) by 67.86%. These enhancements were statistically validated (p-values: MAE - 7.07E-06, MAPE - 7.69E-06, MSE - 3.14E-05) and substantiated by large Cohen's d values (MAE - 2.88, MAPE - 2.86, MSE - 2.42), confirming the model's advanced predictive accuracy and practical utility. This approach, validated further on a separate TCGA hold-out test set, signifies a major leap in prostate cancer diagnostics. By integrating mRNA prediction checkpoints, our model not only refines ploidy assessment but also sets the stage for correlating these predictions with patient clinical outcomes. These developments promise to enhance existing diagnostic methods, offering a more objective and efficient tool for DNA ploidy assessment in prostate cancer prognosis and treatment planning. Citation Format: Josselyn Sofia Vergara Cobos, Francisco Carrillo-Perez, Marija Pizurica, Noemi Andor, Olivier Gevaert. Enhancing prostate cancer diagnosis: A deep learning approach for DNA ploidy prediction from whole slide images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB390.

Dysregulation of LINC00324 promotes poor prognosis in patients with glioma.

LINC00324 is a long-stranded non-coding RNA, which is aberrantly expressed in various cancers and is associated with poor prognosis and clinical features. It involves multiple oncogenic molecular pathways affecting cell proliferation, migration, invasion, and apoptosis. However, the expression, function, and mechanism of LINC00324 in glioma have not been reported. We assessed the expression of LINC00324 of LINC00324 in glioma patients based on data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) to identify pathways involved in LINC00324-related glioma pathogenesis. Based on our findings, we observed differential expression of LINC00324 between tumor and normal tissues in glioma patients. Our analysis of overall survival (OS) and disease-specific survival (DSS) indicated that glioma patients with high LINC00324 expression had a poorer prognosis compared to those with low LINC00324 expression. By integrating clinical data and genetic signatures from TCGA patients, we developed a nomogram to predict OS and DSS in glioma patients. Gene set enrichment analysis (GSEA) revealed that several pathways, including JAK/STAT3 signaling, epithelial-mesenchymal transition, STAT5 signaling, NF-κB activation, and apoptosis, were differentially enriched in glioma samples with high LINC00324 expression. Furthermore, we observed significant correlations between LINC00324 expression, immune infiltration levels, and expression of immune checkpoint-related genes (HAVCR2: r = 0.627, P = 1.54e-77; CD40: r = 0.604, P = 1.36e-70; ITGB2: r = 0.612, P = 6.33e-7; CX3CL1: r = -0.307, P = 9.24e-17). These findings highlight the potential significance of LINC00324 in glioma progression and suggest avenues for further research and potential therapeutic targets. Indeed, our results confirm that the LINC00324 signature holds promise as a prognostic predictor in glioma patients. This finding opens up new possibilities for understanding the disease and may offer valuable insights for the development of targeted therapies.

Abstract 4931: The Cancer Proteome Atlas: Expanded datasets and empowered with an AI-driven chatbot

Abstract Reverse-phase protein arrays (RPPAs) represent a powerful functional proteomic approach to elucidate cancer-related molecular mechanisms and develop novel cancer therapies. To facilitate community-based investigation of the large-scale protein expression data generated by this platform, we have developed a user-friendly, open-access bioinformatic resource, The Cancer Proteome Atlas (TCPA, http://tcpaportal.org), which contains several applications. The first one focuses on RPPA data of patient tumors, which contains &amp;gt;8,000 samples of 32 cancer types from The Cancer Genome Atlas (TCGA) and other independent patient cohorts. The second application focuses on the RPPA data of &amp;gt;1,500 cancer cell lines with publicly available, high-quality DNA, RNA and drug screening data. The third one focuses on perturbed RPPA profiles of &amp;gt;14,000 samples given drug treatments. We recently expanded our RPPA protein set from 200 to 500 proteins Importantly, this highly selected set is enriched in therapeutic targets and biomarkers and covers key oncogenic pathways such as PI3K/AKT, RAS/MAPK, Src/FAK, TGFβ/SMAD, JAK/STAT, epithelial-mesenchymal transition, DNA damage repair, Hippo, Notch, cell cycle, apoptosis, and immune-oncology. Using this upgraded RPPA platform, we generated an harmonized, expanded RPPA dataset of 8000 TCGA patient samples and 1000 CCLE cell line samples. To further address the informatic challenges of analyzing such large, diverse datasets, in addition to the GUI interfaces, we recently developed a chatbot through which users can analyze the RPPA data through human nature languages and obtain the results and related analytic reports without a learning curve. Such a chatbot empowers a broad research community to explore high-quality RPPA datasets and generate testable hypotheses in an effective and intuitive manner, representing the direction of next-generation data analytics. Citation Format: Han Liang, Wei Liu, Jun Li, Yitao Tang. The Cancer Proteome Atlas: Expanded datasets and empowered with an AI-driven chatbot [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4931.

Ferroptosis-Related Genes Are Associated with Radioresistance and Immune Suppression in Head and Neck Cancer.

Background: Ferroptosis is associated with tumor development; however, its contribution to radioresistant head and neck cancer (HNC) remains unclear. In this study, we used bioinformatics analysis and in vitro testing to explore ferroptosis-related genes associated with HNCs radiosensitivity. Materials and Methods: GSE9714, GSE90761, and The Cancer Genome Atlas (TCGA) datasets were searched to identify ferroptosis-related differentially expressed genes between radioresistant and radiosensitive HNCs or radiation-treated and nonradiation-treated HNCs. A protein-protein interaction analysis on identified hub genes was then performed. Receiver operating characteristic curves and Kaplan-Meier survival analysis were used to assess the diagnostic and prognostic potential of the hub genes. Cell counting kit-8, transwell assay, and flow cytometry were applied to examine the role of hub gene collagen type IV, alpha1 chain (COL4A1) on the proliferation, migration, invasion, and apoptosis of TU686 cells. Results: Hub genes MMP10, MMP1, COL4A1, IFI27, and INHBA showed diagnostic potential for HNC and were negatively correlated with overall survival and disease-free survival in the TCGA dataset. Also, IL-1B, IFI27, INHBA, and COL4A1 mRNA levels were significantly increased in TCGA patients with advanced clinical stages or receiving radiotherapy, whereas COL4A1, MMP10, and INHBA expressions were negatively correlated with immune infiltration. Furthermore, the knockdown of COL4A1 inhibited cell proliferation, migration, and invasion while promoting apoptosis in TU686 cells. Conclusion: Ferroptosis-related hub genes, such as COL4A1, are potential diagnostic and prognostic indicators as well as therapeutic targets for HNC.

Image-Based Subtype Classification for Glioblastoma Using Deep Learning: Prognostic Significance and Biologic Relevance.

To apply deep learning algorithms to histopathology images, construct image-based subtypes independent of known clinical and molecular classifications for glioblastoma, and produce novel insights into molecular and immune characteristics of the glioblastoma tumor microenvironment. Using whole-slide hematoxylin and eosin images from 214 patients with glioblastoma in The Cancer Genome Atlas (TCGA), a fine-tuned convolutional neural network model extracted deep learning features. Biclustering was used to identify subtypes and image feature modules. Prognostic value of image subtypes was assessed via Cox regression on survival outcomes and validated with 189 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set. Morphological, molecular, and immune characteristics of glioblastoma image subtypes were analyzed. Four distinct subtypes and modules (imClust1-4) were identified for the TCGA patients with glioblastoma on the basis of the image feature data. The glioblastoma image subtypes were significantly associated with overall survival (OS; P = .028) and progression-free survival (P = .003). Apparent association was also observed for disease-specific survival (P = .096). imClust2 had the best prognosis for all three survival end points (eg, after 25 months, imClust2 had >7% surviving patients than the other subtypes). Examination of OS in the external validation using the unseen CPTAC data set showed consistent patterns. Multivariable Cox analyses confirmed that the image subtypes carry unique prognostic information independent of known clinical and molecular predictors. Molecular and immune profiling revealed distinct immune compositions of the tumor microenvironment in different image subtypes and may provide biologic explanations for the patterns in patients' outcomes. Our image-based subtype classification on the basis of deep learning models is a novel tool to refine risk stratification in cancers. The image subtypes detected for glioblastoma represent a promising prognostic biomarker with distinct molecular and immune characteristics and may facilitate developing novel, individualized immunotherapies for glioblastoma.

DNA damage response alterations in clear cell renal cell carcinoma: clinical, molecular, and prognostic implications

BackgroundDNA damage repair (DDR) pathways modulate cancer risk, progression, and therapeutic responses. Nonetheless, the characteristics and significance of DDR alterations in clear cell renal cell carcinoma (ccRCC) remain undefined. This study aimed to explore the predictive role, molecular mechanism, and tumor immune profile of DDR genes in ccRCC.MethodsWe prospectively sequenced 757 tumors and matched blood DNA samples from Chinese patients with ccRCC using next-generation sequencing (NGS) and analyzed data from 537 patients from The Cancer Genome Atlas (TCGA). A comprehensive analysis was performed.ResultsFifty-two percent of Chinese patients with ccRCC harbored DDR gene mutations and 57% of TCGA patients. The immunotherapy treatment prognosis of patients with DDR gene mutations was superior to that of patients without DDR gene mutations (p = 0.047). DDR gene mutations were associated with more gene mutations and a higher tumor mutation load (TMB, p < 0.001). Moreover, patients with DDR gene mutations have a distinct mutational signature compared with those with wild-type DDR. Furthermore, the DDR-mut group had elevated neoantigen load (including single-nucleotide variants (SNV) and indel neoantigen load, p = 0.037 and p = 0.002, respectively), TCR Shannon (p = 0.025), and neutrophils (p = 0.010). DDR gene mutations exhibited a distinct immune profile with significantly higher expression levels of TNFSF9, CD70, ICAM1, and indoleamine-2,3-dioxygenase (IDO) and lower expression levels of VTCN1 and IL12A.ConclusionsOur data suggest that the detection of somatic mutations in DDR genes can predict the efficacy of immunotherapy in patients with ccRCC. Furthermore, we revealed the unique molecular and immune mechanisms underlying ccRCC with DDR gene mutations.

TCGAnalyzeR: An Online Pan-Cancer Tool for Integrative Visualization of Molecular and Clinical Data of Cancer Patients for Cohort and Associated Gene Discovery.

For humans, the parallel processing capability of visual recognition allows for faster comprehension of complex scenes and patterns. This is essential, especially for clinicians interpreting big data for whom the visualization tools play an even more vital role in transforming raw big data into clinical decision making by managing the inherent complexity and monitoring patterns interactively in real time. The Cancer Genome Atlas (TCGA) database's size and data variety challenge the effective utilization of this valuable resource by clinicians and biologists. We re-analyzed the five molecular data types, i.e., mutation, transcriptome profile, copy number variation, miRNA, and methylation data, of ~11,000 cancer patients with all 33 cancer types and integrated the existing TCGA patient cohorts from the literature into a free and efficient web application: TCGAnalyzeR. TCGAnalyzeR provides an integrative visualization of pre-analyzed TCGA data with several novel modules: (i) simple nucleotide variations with driver prediction; (ii) recurrent copy number alterations; (iii) differential expression in tumor versus normal, with pathway and the survival analysis; (iv) TCGA clinical data including metastasis and survival analysis; (v) external subcohorts from the literature, curatedTCGAData, and BiocOncoTK R packages; (vi) internal patient clusters determined using an iClusterPlus R package or signature-based expression analysis of five molecular data types. TCGAnalyzeR integrated the multi-omics, pan-cancer TCGA with ~120 subcohorts from the literature along with clipboard panels, thus allowing users to create their own subcohorts, compare against existing external subcohorts (MSI, Immune, PAM50, Triple Negative, IDH1, miRNA, metastasis, etc.) along with our internal patient clusters, and visualize cohort-centric or gene-centric results interactively using TCGAnalyzeR.

Potential biomarkers as a predictive factor of response to primary chemotherapy in breast cancer patients.

In this study, we identified miRNAs and their potential mRNA targets that are intricately linked to primary chemotherapy response in patients with invasive ductal carcinomas. A cohort of individuals diagnosed with advanced invasive breast ductal carcinoma who underwent primary chemotherapy served as the cornerstone of our study. We conducted a comparative analysis of microRNA expression among patients who either responded or did not respond to primary systemic therapy. To analyze the correlation between the expression of the whole transcriptome and the 24 differentially expressed (DE) miRNAs, we harnessed the extensive repository of The Cancer Genome Atlas (TCGA) database. We mapped molecular mechanisms associated with these miRNAs and their targets from TCGA breast carcinomas. The resultant expression profile of the 24 DE miRNAs emerged as a potent and promising predictive model, offering insights into the intricate dynamics of chemotherapy responsiveness of advanced breast tumors. The discriminative analysis based on the principal component analysis identified the most representative miRNAs across breast cancer samples (miR-210, miR-197, miR-328, miR-519a, and miR-628). Moreover, the consensus clustering generated four possible clusters of TCGA patients. Further studies should be conducted to advance these findings.

Expression of Serpin Family E Member 1 (SERPINE1) Is Associated with Poor Prognosis of Gastric Adenocarcinoma.

The aberrant expression of serpin family E member 1 (SERPINE1) is associated with carcinogenesis. This study assessed the alteration of SERPINE1 expression for an association with gastric adenocarcinoma prognosis. The Cancer Genome Atlas (TCGA) dataset was applied to investigate the impact of SERPINE1 expression on the survival of patients afflicted with gastric cancer. Subsequently, 136 samples from the Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University were subjected to qRT-PCR and Western blot to validate the expression level of SERPINE1 between tumor and adjacent normal tissues. The correlation between the expression of SERPINE1 with the clinicopathological features in TCGA patients was analyzed using Wilcoxon signed-rank and logistic regression tests. The potential molecular mechanism associated with SERPINE1 expression in gastric cancer were confirmed using gene set enrichment analysis (GSEA). The TCGA data showed that SERPINE1 was overexpressed in tumor tissues compared to normal mucosae and associated with the tumor T stage and pathological grade. SERPINE1 overexpression was associated with the poor overall survival (OS) of patients. The findings were confirmed with 136 patients, that is, SERPINE1 expression was associated with poor OS (hazard ratio (HR): 1.82; 95% confidence interval (CI): 0.84-1.83; p = 0.012)) as an independent predictor (HR: 2.11, 95% CI: 0.81-2.34; p = 0.009). The resulting data were further processed by GSEA showed that SERPINE1 overexpression was associated with the activation of EPITHELIAL_MESENCHYMAL_TRANSITION, TNFA_SIGNALING_VIA_NFKB, INFLAMMATORY_RESPONSE, ANGIOGENESIS, and HYPOXIA. SERPINE1 overexpression is associated with a poor gastric cancer prognosis.

Establishment of a prognosis prediction model for lung squamous cell carcinoma related to PET/CT: basing on immunogenic cell death-related lncRNA

BackgroundImmunogenic cell death (ICD) stimulates adaptive immunity and holds significant promise in cancer therapy. Nevertheless, the influence of ICD-associated long non-coding RNAs (lncRNAs) on the prognosis of patients with lung squamous cell carcinoma (LUSC) remains unexplored.MethodsWe employed data from the The Cancer Genome Atlas (TCGA)database to identify ICD-related lncRNAs associated with the prognosis of LUSC using univariate Cox regression analysis. Subsequently, we utilized the LOSS regression model to construct a predictive risk model for assessing the prognosis of LUSC patients based on ICD-related lncRNAs. Our study randomly allocated187 TCGA patients into a training group and 184 patients for testing the predictive model. Furthermore, we conducted quantitative polymerase chain reaction (qPCR) analysis on 43 tumor tissues from LUSC patients to evaluate lncRNA expression levelsPearson correlation analysis was utilized to analyze the correlation of risk scores with positron emission tomography/computed tomography (PET/CT) parameters among LUSC patients.ResultsThe findings from the univariate Cox regression revealed 16 ICD-associated lncRNAs linked to LUSC prognosis, with 12 of these lncRNAs integrated into our risk model utilizing the LOSS regression. Survival analysis indicated a markedly higher overall survival time among patients in the low-risk group compared to those in the high-risk group. The area under the Receiver operating characteristic (ROC) curve to differentiate high-risk and low-risk patients was 0.688. Additionally, the overall survival rate was superior in the low-risk group compared to the high-risk group. Correlation analysis demonstrated a positive association between the risk score calculated based on the ICD-lncRNA risk model and the maximum standard uptake value (SUVmax) (r = 0.427, P = 0.0043) as well as metabolic volume (MTV)of PET-CT (r = 0.360, P = 0.0177) in 43 LUSC patients.ConclusionWe have successfully developed a risk model founded on ICD-related lncRNAs that proves effective in predicting the overall survival of LUSC patients.

Establishment and validation of a prognostic immune-related lncRNA risk model for acute myeloid leukemia.

Acute myeloid leukemia (AML) is a cancer arising in the bone marrow and is the most common type of adult leukemia. AML has a poor prognosis, and currently, its prognosis evaluation does not include immune status assessment. This study established an immune-related long non-coding RNA (lncRNA) prognostic risk model for AML based on immune lncRNAs screening. To construct training and validation cohorts, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public databases were accessed to obtain gene expression profiles and clinical data. The correlation between lncRNAs and immunity genes was analyzed using the "limma" package, and the immune-related lncRNAs were obtained. Through least absolute shrinkage and selection operator regression, a prognostic model was established with immune-related lncRNAs. Using the median risk score, patients were divided into high- and low-risk groups. The Kaplan-Meier method was used for survival analysis, whereas the accuracy of the risk model was evaluated using time-dependent receiver operating characteristic curves, risk score distribution, survival status, and risk heat maps. We utilized univariate and multivariate Cox regression to examine the association between risk score and clinical variables and AML survival and prognosis. In the immune-related lncRNA prognostic risk model, the prognosis was better for low-risk than for high-risk patients, indicating risk score of this model as an independent indicator of prognosis. The area under the curve value for 1-, 3-, and 5-year survival of TCGA patients was 0.817, 0.859, and 0.909, respectively, whereas that of GEO patients (of dataset GPL96-GSE37642) was 0.603, 0.652, and 0.624, respectively. Gene set enrichment analysis revealed the enrichment of multiple pathways, such as antigen processing, B-cell receptor signaling pathway, natural killer cell-mediated cytotoxicity, and chemokines, in high-risk patients. In this study, immune-related lncRNA prognostic risk models effectively predicted AML survival and provided potential treatment targets.

Comprehensive Analysis of KREMEN2 as an Immunotherapeutic and Prognostic Biomarker in Pan-Cancer.

Kremen2 has been shown to play an important role in multiple cancers formation as a negative regulatory factor in the Wnt signaling pathway. Our study aimed to explore the potential value of KREMEN2 in pan-cancer and investigate the molecular mechanisms associated with tumor development, providing a basis for prognostic factors and new therapeutic targets for cancer. Raw RNA-seq data for 32 types of cancers were obtained from The Cancer Genome Atlas (TCGA), while Xena database provided overall survival (OS) and progression-free survival (PFI) data for TCGA patients. R language was used to identify the association between KREMEN2 and immune response, tumor mutational burden (TMB), and microsatellite instability (MSI). Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA) were conducted in pan-cancer. A Nomogram prediction model and weighted gene co-expression network analysis (WGCNA) were constructed in colorectal cancer (CRC). KREMEN2 was found highly expressed in 17 types of tumor tissues compared to normal tissues. KREMEN2 was only correlated with some tumor pathological stages. KREMEN2 with high expression had poor prognosis in pan-cancer. KREMEN2 expression was significantly associated with immune infiltration, immune checkpoints, immune-related genes, commonly regulated tumor-related genes, TMB, and MSI. Moreover, GSVA and GSEA analyses suggested that KREMEN2 played a role in cell cycle in pan-cancer. KREMEN2 expression had a significant impact on the performance of Nomogram prediction model in CRC, and WGCNA analysis indicated that KREMEN2 performed special functions in CRC. The comprehensive pan-cancer analysis revealed that KREMEN2 is a promising tumor prognostic biomarker and a potential anti-tumor immunotherapeutic target in human tumors.

Pharmacological NF-κB inhibition decreases cisplatin chemoresistance in muscle-invasive bladder cancer and reduces cisplatin-induced toxicities.

Most patients with muscle-invasive bladder cancer (MIBC) are not cured with platinum chemotherapy. Up-regulation of nuclear factor kappa light-chain enhancer of activated B cells (NF-κB) is a major mechanism underlying chemoresistance, suggesting that its pharmacological inhibition may increase platinum efficacy. NF-κB signaling was investigated in two patient cohorts. The Cancer Genome Atlas (TCGA) was used to correlate NF-κB signaling and patient survival. The efficacy of cisplatin plus the NF-κB inhibitor dimethylaminoparthenolide (DMAPT) versus cisplatin or DMAPT alone was tested in vitro. Xenografted and immunocompetent MIBC mouse models were studied in vivo. Platinum-naive claudin-low MIBC showed constitutive NF-κB signaling and this was associated with reduced disease-specific survival in TCGA patients. Chemotherapy up-regulated NF-κB signaling and chemoresistance-associated genes, including SPHK1, PLAUR, and SERPINE1. In mice, DMAPT significantly improved the efficacy of cisplatin in both models. The combination preserved body weight, renal function, and morphology, reduced musclefatigue and IL-6 serum levels, and did not aggravate immuno-hematological toxicity compared with cisplatin alone. These data provide a rationale for combining NF-κB inhibition with platinum-based chemotherapy and conducting a clinical trial in MIBC patients.