Identification of oxidative stress-related hub genes for predicting prognosis in diffuse large B-cell lymphoma.

BACKGROUND: Oxidative stress is a cellular characteristic that might induce the proliferation and differentiation of tumor cells and promote tumor progression in diffuse large B-cell lymphoma (DLBCL). METHODS: The DLBCL gene sequencing dataset, tumor mutation burden data, copy number variation data of Somatic cell mutation data in TCGA were downloaded for data training analysis, along with four DLBCL datasets in GEO for validation analysis. The known oxidative stress related genes (OSRGs) were collected from websites. The weighted gene co-expression network analysis (WGCNA) was conducted on the TCGA DLBCL dataset to obtain gene modules related to oxidative stress and intersected with the known OSRGs to obtain the hub genes, which were used to perform consensus clustering on the samples to obtain new phenotypes. Next, the prognosis related OSRGs were selected through regression analysis algorithms and key genes were identified. These genes were used to establish the prognostic risk model and predictive model, and to compare functional and pathway differences among different risk groups. RESULTS: Through website search, we obtained 297 known OSRGs, and after intersecting with WGCNA results, we obtained 26 OSRGs. The TCGA-DLBC samples were clustered into 2 subtypes with these genes and there were significant differences in immune infiltration between subtypes. After regression analysis, we obtained a total of four key genes, BMI1, CDKN1A, NOX1, and SESN1. The risk prediction model established with these four genes as variables has accurate prognostic prediction ability. The key genes interact with 65 miRNAs, 57 TFs, 47 RBPs, and 62 drugs, respectively, and are closely related to immune infiltration of the disease. Among them, CDKN1A and SESN1 had the highest variability. CONCLUSIONS: The key genes involved in oxidative stress could predict the prognosis of DLBCL and potentially become therapeutic targets.

DrugReSC: targeting disease-critical cell subpopulations with single-cell transcriptomic data for drug repurposing in cancer.

The field of computational drug repurposing aims to uncover novel therapeutic applications for existing drugs through high-throughput data analysis. However, there is a scarcity of drug repurposing methods leveraging the cellular-level information provided by single-cell RNA sequencing data. To address this need, we propose DrugReSC, an innovative approach to drug repurposing utilizing single-cell RNA sequencing data, intending to target specific cell subpopulations critical to disease pathology. DrugReSC constructs a drug-by-cell matrix representing the transcriptional relationships between individual cells and drugs and utilizes permutation-based methods to assess drug contributions to cellular phenotypic changes. We demonstrate DrugReSC's superior performance compared to existing drug repurposing methods based on bulk or single-cell RNA sequencing data across multiple cancer case studies. In summary, DrugReSC offers a novel perspective on the utilization of single-cell sequencing data in drug repurposing methods, contributing to the advancement of precision medicine for cancer.

Development and Validation of a Comprehensive Prognostic and Depression Risk Index for Gastric Adenocarcinoma.

Depressive disorder contributes to the initiation and prognosis of patients with cancer, but the interaction between cancer and depressive disorder remains unclear. We generated a gastric adenocarcinoma patient-derived xenograft mice model, treated with chronic unpredictable mild stimulation. Based on the RNA-sequence from the mouse model, patient data from TCGA, and MDD-related (major depressive disorder) genes from the GEO database, 56 hub genes were identified by the intersection of differential expression genes from the three datasets. Molecular subtypes and a prognostic signature were generated based on the 56 genes. A depressive mouse model was constructed to test the key changes in the signatures. The signature was constructed based on the NDUFA4L2, ANKRD45, and AQP3 genes. Patients with high risk-score had a worse overall survival than the patients with low scores, consistent with the results from the two GEO cohorts. The comprehensive results showed that a higher risk-score was correlated with higher levels of tumor immune exclusion, higher infiltration of M0 macrophages, M2 macrophages, and neutrophils, higher angiogenetic activities, and more enriched epithelial-mesenchymal transition signaling pathways. A higher risk score was correlated to a higher MDD score, elevated MDD-related cytokines, and the dysfunction of neurogenesis-related genes, and parts of these changes showed similar trends in the animal model. With the Genomics of Drug Sensitivity in Cancer database, we found that the gastric adenocarcinoma patients with high risk-score may be sensitive to Pazopanib, XMD8.85, Midostaurin, HG.6.64.1, Elesclomol, Linifanib, AP.24534, Roscovitine, Cytarabine, and Axitinib. The gene signature consisting of the NDUFA4L2, ANKRD45, and AQP3 genes is a promising biomarker to distinguish the prognosis, the molecular and immune characteristics, the depressive risk, and the therapy candidates for gastric adenocarcinoma patients.

Significance of pyroptosis-related genes in the diagnosis and classification of diabetic kidney disease.

OBJECTIVE: This study aimed to identify the potential biomarkers associated with pyroptosis in diabetic kidney disease (DKD). METHODS: Three datasets from the Gene Expression Omnibus (GEO) were downloaded and merged into an integrated dataset. Differentially expressed genes (DEGs) were filtered and intersected with pyroptosis-related genes (PRGs). Pyroptosis-related DEGs (PRDEGs) were obtained and analyzed using functional enrichment analysis. Random forest, Least Absolute Shrinkage and Selection Operator, and logistic regression analyses were used to select the features of PRDEGs. These feature genes were used to build a diagnostic prediction model, identify the subtypes of the disease, and analyze their interactions with transcription factors (TFs)/miRNAs/drugs and small molecules. We conducted a comparative analysis of immune cell infiltration at different risk levels of pyroptosis. qRT-PCR was used to validate the expression of the feature genes. RESULTS: A total of 25 PRDEGs were obtained. These genes were coenriched in biological processes and pathways, such as the regulation of inflammatory responses. Five key genes (CASP1, CITED2, HTRA1, PTGS2, S100A12) were identified and verified using qRT-PCR. The diagnostic model based on key genes has a good diagnostic prediction ability. Five key genes interacted with TFs and miRNAs in 67 and 80 pairs, respectively, and interacted with 113 types of drugs or molecules. Immune infiltration of samples with different pyroptosis risk levels showed significant differences. Thus, CASP1, CITED2, HTRA1, PTGS2 and S100A12 are potential DKD biomarkers. CONCLUSION: Genes that regulate pyroptosis can be used as predictors of DKD. Early diagnosis of DKD can aid in its effective treatment.

Deciphering the prognostic and therapeutic significance of BAG1 and BAG2 for predicting distinct survival outcome and effects on liposarcoma.

Liposarcoma (LPS) is the second most common kind of soft tissue sarcoma, and a heterogeneous malignant tumor derived from adipose tissue. Up to now, the prognostic value of BAG1 or BAG2 in LPS has not been defined yet. Expression profiling data of LPS patients were collected from TCGA and GEO database. Survival curves were plotted to verify the outcome differences of patients based on BAG1 or BAG2 expression. Univariate and multivariate Cox regression models were used to analyze the prognostic ability of BAG1 or BAG2. Chaperone's regulators BAG1 and BAG2 were identified as prognostic biomarkers for LPS patients, which exhibited distinct expression patterns and survival outcome prediction performances. Patients with high BAG2 expression and/or low BAG1 expression had worse prognosis. Enrichment analysis showed that BAG1 was involved in negative regulation of TGF-ß signaling. Low expression of BAG1 was associated with high abundance of regulatory T cells (Tregs). The 2-gene signature model further confirmed the improved risk assessment performance of BAG1 and BAG2: high risk patients displayed poor prognosis. BAG1 and BAG2 are supposed to be potential prognostic biomarkers for LPS and have impacts on liposarcomagenesis and immune infiltration in distinctive manners, which may function as potential therapy targets (BAG1 agonists/BAG2 inhibitors) for LPS.

Establishment of the Prognostic Signature with Genes Related to G2/M Checkpoint in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer mortality in the world. Prognostic indicators such as clinicopathological characteristics and single molecular signature are far from satisfactory in clinical practice. More and more researches have suggested that polygenic prognostic features could predict the prognosis of cancer more precisely than single genes nowadays. In this study, we performed gene set enrichment analysis (GSEA) to identify the sets of TCGA hallmark genes. Univariate Cox regression analysis was used to select preliminary genes, and then multivariate Cox regression analysis was used to identify genes associated with overall survival (OS). We also used Kaplan-Meier analysis and receiver operating characteristic (ROC) analysis to validate the prognostic gene signature. Lastly, qRT-PCR was used to evaluate the expression of these genes in clinical samples, and immunohistochemical staining was performed to confirm the signature. A 12-gene signature was finally built and the risk score was significantly associated with the survival of the patients. Subsequent validation by qRT-PCR and immunohistochemical staining in clinical specimens confirmed the value of the risk score in predicting the prognosis. We developed a 12-gene signature that could predict the prognosis of HCC patients. This signature is of high precision and would help identifying subgroups of HCC patients with high or low risk of unfavorable survival.

A novel gene signature based on endoplasmic reticulum stress for predicting prognosis in hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) remains one of the most common human cancers, the death cases induced by HCC are increasing these years. Endoplasmic reticulum stress (ERS) occurs when misfolded proteins cannot be disposed of properly. It is reported that ERS plays a crucial role in the pathogenesis of human malignant tumors. The aim of this study is to construct a novel gene signature based on ERS for predicting prognosis in HCC. Methods: The data of HCC patients were downloaded from public databases. The Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed to construct ERS-related gene signature. The cases were divided into high- and low-risk groups based on the ERS-related gene signature in The Cancer Genome Atlas (TCGA) cohort. Subsequently, the differences in messenger ribonucleic acid (mRNA) expression patterns, immune status, tumor mutation burden (TMB) and copy number variants (CNV) were investigated between high- and low-risk groups. Then, a predictive nomogram according to the ERS-related gene signature and clinicopathological variables was established. At last, we explored the biological functions of TMX1 which had the biggest coefficient and we investigated the effect of BRSK2 on apoptosis in HCC. Results: In our study, a 9-gene ERS-related gene signature was constructed. The results showed that patients in the low-risk group had a better prognosis than the high-risk group patients. The results of receiver operating characteristic (ROC) curves revealed that the area under the curve (AUC) was 0.784 at 1 year, 0.780 at 2 years, 0.793 at 3 years in the training set. While in validation cohort, this index was 0.694 at 1 year, 0.622 at 2 years, 0.613 at 3 years respectively. The analysis of immune status revealed an immunosuppressive microenvironment in the high-risk group. The analysis of TMB and CNV revealed that the high-risk group patients had a higher genomic mutation frequency. In Univariate Cox regression analysis, the hazard ratio of RiskScore was 2.718 [95% confidence interval (CI): 2.173-3.399]. In Multivariate Cox regression analysis, the hazard ratio of RiskScore was 2.422 (95% CI: 1.805-3.25). Then, we established a nomogram according to the RiskScore and Eastern Cooperative Oncology Group performance status. The AUCs of the nomogram were 0.851 at 1 year, 0.860 at 2 years, and 0.866 at 3 years. At last, we found that TMX1 knockdown can inhibit the proliferation and migration of Huh7 and HepG2 cells. In addition, BRSK2 knockdown could promote the apoptosis induced by ERS. Conclusions: In our study, a novel ERS-related gene signature was constructed to predict the prognosis of HCC patients. In addition, TMX1 and BRSK2 could promote the progression of HCC. This study may provide a new understanding for HCC.

Comprehensive analysis of cuproptosis-related genes involved in prognosis and tumor microenvironment infiltration of colorectal cancer.

Background: Colorectal cancer (CRC) is a common malignancy, with high incidence and high mortality rates. Cuproptosis, a novel form of copper-induced programmed cell death, contributes to tumor progression. However, whether cuproptosis-related genes (CRGs) play a role in CRC remains unclear. This study aims to elucidate the role of CRGs in CRC development, patient prognosis, and immune response. Methods: We performed bioinformatics analysis of the differential expression of CRGs between CRC and normal tissues. Least absolute shrinkage and selection operator (LASSO), and univariate and multivariate Cox analyses were employed to identify risk factors, which were used to construct a risk score model. Patients with CRC were categorized into high- and low-risk groups based on their median risk scores. Receiver operating characteristic curve analysis was used to verify the predictive accuracy of the risk model. A nomogram was developed for CRC through univariate and multivariate Cox regression analyses. The chemotherapeutic drug sensitivity was compared between patients with high and low CDKN2A/DLAT expression using the Wilcoxon rank-sum test. Spearman's correlation and TISIDB database analyses were conducted to determine relationships between CDKN2A or DLAT and immune cell infiltration. Results: Eight of ten identified CRGs exhibited significant differential expression between CRC and normal tissues. Among the eight significant differential expression CRGs, CDKN2A and DLAT were identified as independent risk factors for predicting overall survival (OS) in CRC. Patients with CRC in the low-risk group had longer OS than those in the high-risk group. The risk score model had good predictive accuracy for OS. Based on CDKN2A, DLAT and some clinical characteristics, a prognostic nomogram was developed to predict OS for CRC patients and showed good predictive ability. CDKN2A and DLAT expressions were significantly associated with chemotherapeutic drug sensitivity and immune cell infiltration in CRC, and the molecular subtypes and immune subtypes differed between CDKN2A and DLAT. Conclusions: Our research revealed the prognostic value of CRGs, particularly CDKN2A and DLAT, in CRC and demonstrated the relationship between CDKN2A/DLAT and immune infiltration in CRC, thereby contributing to the outcome evaluation of patients with CRC and identifying novel targets for CRC immunotherapy.

Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven 7-Gene Stemness Signature that Predicts Progression.

Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progression into aggressive forms like neuroendocrine prostate cancer (NEPC). This study developed and validated a stemness-associated gene signature using advanced machine learning techniques, including Random Forest and Lasso regression, applied to large-scale transcriptomic datasets. The resulting 7-gene signature (KMT5C, MEN1, TYMS, IRF5, DNMT3B, CDC25B and DPP4) was validated across independent cohorts and patient-derived xenograft (PDX) models. The signature demonstrated strong prognostic value for progression-free, disease-free, relapse-free, metastasis-free, and overall survival. Importantly, the signature not only identified specific NEPC subtypes, such as large-cell neuroendocrine carcinoma, which is associated with very poor outcomes, but also predicted a poor prognosis for PCa cases that exhibit this molecular signature, even when they were not histopathologically classified as NEPC. This dual prognostic and classifier capability makes the 7-gene signature a robust tool for personalized medicine, providing a valuable resource for predicting disease progression and guiding treatment strategies in PCa management.

A Combined Proteomic and Transcriptomic Signature Is Predictive of Response to Anti-PD-1 Treatment: A Retrospective Study in Metastatic Melanoma Patients.

Resistance biomarkers are needed to identify patients with advanced melanoma obtaining a response to ICI treatment and developing resistance later. We searched a combination of molecular signatures of response to ICIs in patients with metastatic melanoma. In a retrospective study on patients with metastatic melanoma treated with an anti-PD-1 agent carried out at Istituto Nazionale Tumori-IRCCS-Fondazione "G. Pascale", Naples, Italy. We integrated a whole proteome profiling of metastatic tissue with targeted transcriptomics. To assess the prognosis of patients according to groups of low and high risk, we used PFS and OS as outcomes. To identify the proteins and mRNAs gene signatures associated with the patient's response groups, the discriminant analysis for sparse data performed via partial least squares procedure was performed. Tissue samples from 22 patients were analyzed. A combined protein and gene signature associated with poorer response to ICI immunotherapy in terms of PFS and OS was identified. The PFS and OS Kaplan-Meier curves were significantly better for patients with high expression of the protein signature compared to patients with low expression of the protein signature and who were high-risk (Protein: HR = 0.023, 95% CI: 0.003-0.213; p < 0.0001. Gene: HR = 0.053, 95% CI: 0.011-0.260; p < 0.0001). The Kaplan-Meier curves showed that patients with low-risk gene signatures had better PFS (HR = 0 0.221, 95% CI: 0.071-0.68; p = 0.007) and OS (HR = 0.186, 95% CI: 0.05-0.695; p = 0.005). The proteomic and transcriptomic combined analysis was significantly associated with the outcomes of the anti-PD-1 treatment with a better predictive value compared to a single signature. All the patients with low expression of protein and gene signatures had progression within 6 months of treatment (median PFS = 3 months, 95% CI: 2-3), with a significant difference vs. the low-risk group (median PFS = not reached; p < 0.0001), and significantly poorer survival (OS = 9 months, 95% CI: 5-9) compared to patients with high expression of protein and gene signatures (median OS = not reached; p < 0.0001). We propose a combined proteomic and transcriptomic signature, including genes involved in pro-tumorigenic pathways, thereby identifying patients with reduced probability of response to immunotherapy with ICIs for metastatic melanoma.

Identification of two heterogeneous subtypes of hepatocellular carcinoma with distinct pathway activities and clinical outcomes based on gene set variation analysis.

Background: High heterogeneity is an essential feature of malignant tumors. This study aims to reveal the drivers of hepatocellular carcinoma heterogeneity for prognostic stratification and to guide individualized treatment. Methods: Omics data and clinical data for two HCC cohorts were derived from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Atlas (ICGC), respectively. CNV data and methylation data were downloaded from the GSCA database. GSVA was used to estimate the transcriptional activity of KEGG pathways, and consensus clustering was used to categorize the HCC samples. The pRRophetic package was used to predict the sensitivity of samples to anticancer drugs. TIMER, MCPcounter, quanTIseq, and TIDE algorithms were used to assess the components of TME. LASSO and COX analyses were used to establish a prognostic gene signature. The biological role played by genes in HCC cells was confirmed by in vitro experiments. Results: We classified HCC tissues into two categories based on the activity of prognostic pathways. Among them, the transcriptional profile of cluster A HCC is similar to that of normal tissue, dominated by cancer-suppressive metabolic pathways, and has a better prognosis. In contrast, cluster B HCC is dominated by high proliferative activity and has significant genetic heterogeneity. Meanwhile, cluster B HCC is often poorly differentiated, has a high rate of serum AFP positivity, is prone to microvascular invasion, and has shorter overall survival. In addition, we found that mutations, copy number variations, and aberrant methylation were also crucial drivers of the differences in heterogeneity between the two HCC subtypes. Meanwhile, the TME of the two HCC subtypes is also significantly different, which offers the possibility of precision immunotherapy for HCC patients. Finally, based on the prognostic value of molecular subtypes, we developed a gene signature that could accurately predict patients' OS. The riskscore quantified by the signature could evaluate the heterogeneity of HCC and guide clinical treatment. Finally, we confirmed through in vitro experiments that RFPL4B could promote the progression of Huh7 cells. Conclusion: The molecular subtypes we identified effectively exposed the heterogeneity of HCC, which is important for discovering new effective therapeutic targets.

Comprehensive analysis and experimental verification of the mechanism of anoikis related genes in pancreatic cancer.

Background: Pancreatic cancer (PC), characterized by its aggressive nature and low patient survival rate, remains a challenging malignancy. Anoikis, a process inhibiting the spread of metastatic cancer cells, is closely linked to cancer progression and metastasis through anoikis-related genes. Nonetheless, the precise mechanism of action of these genes in PC remains unclear. Methods: Study data were acquired from the Cancer Genome Atlas (TCGA) database, with validation data accessed at the Gene Expression Omnibus (GEO) database. Differential expression analysis and univariate Cox analysis were performed to determine prognostically relevant differentially expressed genes (DEGs) associated with anoikis. Unsupervised cluster analysis was then employed to categorize cancer samples. Subsequently, a least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted on the identified DEGs to establish a clinical prognostic gene signature. Using risk scores derived from this signature, patients with cancer were stratified into high-risk and low-risk groups, with further assessment conducted via survival analysis, immune infiltration analysis, and mutation analysis. External validation data were employed to confirm the findings, and Western blot and immunohistochemistry were utilized to validate risk genes for the clinical prognostic gene signature. Results: A total of 20 prognostic-related DEGs associated with anoikis were obtained. The TCGA dataset revealed two distinct subgroups: cluster 1 and cluster 2. Utilizing the 20 DEGs, a clinical prognostic gene signature comprising two risk genes (CDKN3 and LAMA3) was constructed. Patients with pancreatic adenocarcinoma (PAAD) were classified into high-risk and low-risk groups per their risk scores, with the latter exhibiting a superior survival rate. Statistically significant variation was noted across immune infiltration and mutation levels between the two groups. Validation cohort results were consistent with the initial findings. Additionally, experimental verification confirmed the high expression of CDKN3 and LAMA3 in tumor samples. Conclusion: Our study addresses the gap in understanding the involvement of genes linked to anoikis in PAAD. The clinical prognostic gene signature developed herein accurately stratifies patients with PAAD, contributing to the advancement of precision medicine for these patients.

Identification of a prognostic disulfidptosis-related gene signature in hepatocellular cancer.

Background: Disulfidptosis regulate various biological processes in cancer. However, there is limited research on the genes related to disulfidptosis in predicting the prognosis of hepatocellular carcinoma (HCC). We aimed to develop a reliable disulfidptosis-related gene signature, which will characterize different HCC subtypes and predict their prognosis. Methods: The Cancer Genome Atlas (TCGA)-HCC dataset, comprising RNA sequencing data and clinical information, was obtained from the TCGA database. The crucial disulfidptosis-related genes were selected for bioinformatic analysis in HCC. HCC tumor classification was established through a consistent cluster analysis. The prognosis and immune-cell infiltration were investigated in association with a disulfidptosis-related HCC model. Results: In TCGA-HCC patients, a total of 3,621 prognostic genes and 30 key prognostic disulfidptosis-related genes were identified. Using key prognostic disulfidptosis-related genes, TCGA-HCC patients were categorized into low- and high-risk clusters. The upregulated differentially expressed genes (DEGs) in high-risk cluster 1 (C1) could significantly impact cell cycle, DNA replication, and the p53 signaling pathway, whereas the pathways associated with the downregulated DEGs in high-risk C1 could significantly impact metabolism of xenobiotics by cytochrome P450, the PPAR signaling pathway, and tyrosine metabolism. Furthermore, the immune activity of the high-risk C1 group was different to that of the low-risk cluster 2 (C2) group. The 13 disulfidptosis-related genes were finally screened using least absolute shrinkage and selection operator (LASSO) regression analysis, including ANP32E, BOP1, RPN1, SLC7A11, PPIH, PCBP2, ME1, PRDX1, FLNC, INF2, MYH11, LRPPRC, and HNRNPM. Conclusions: The genes related to disulfidptosis are closely associated with tumor classification and immunity in patients with HCC. This is the first gene signature related to disulfidptosis demonstrated a strong predictive performance for the prognosis of HCC, which provide new perspectives for the diagnosis and treatment of HCC.

Unlocking the potential of signature-based drug repurposing for anticancer drug discovery.

Cancer is the leading cause of death worldwide and is often associated with tumor relapse even after chemotherapeutics. This reveals malignancy is a complex process, and high-throughput omics strategies in recent years have contributed significantly in decoding the molecular mechanisms of these complex events in cancer. Further, the omics studies yield a large volume of cancer-specific molecular signatures that promote the discovery of cancer therapy drugs by a method termed signature-based drug repurposing. The drug repurposing method identifies new uses for approved drugs beyond their intended initial therapeutic use, and there are several approaches to it. In this review, we discuss signature-based drug repurposing in cancer, how cancer omics have revolutionized this method of drug discovery, and how one can use the cancer signature data for repurposed drug identification by providing a step-by-step procedural handout. This modern approach maximizes the use of existing therapeutic agents for cancer therapy or combination therapy to overcome chemotherapeutics resistance, making it a pragmatic and efficient alternative to traditional resource-intensive and time-consuming methods.

Targeting ferroptosis for improved radiotherapy outcomes in HPV-negative head and neck squamous cell carcinoma.

To enhance the efficacy of radiotherapy (RT) in human papillomavirus (HPV)-negative head and neck squamous cell carcinoma (HNSCC), we explored targeting ferroptosis, a regulated cell death process. We developed a gene signature associated with ferroptosis using Cox proportional hazard modeling in HPV-negative HNSCC patients who underwent RT. This ferroptosis-related gene signature (FRGS) was a significant predictor of overall survival and recurrence-free survival in HPV-negative HNSCC patients who received RT. Subtype B of the FRGS, characterized by decreased expression of ferroptosis inducers [nuclear receptor coactivator 4 (NCOA4) and natural resistance-associated macrophage protein 2 homolog/divalent metal transporter 1 (NRAMP2/DMT1)] and increased expression of suppressors [phospholipid hydroperoxide glutathione peroxidase (GPX4) and ferritin heavy chain (FTH1)], was associated with poorer prognosis, potentially indicating the inhibition of ferroptosis. Furthermore, our in vitro and in vivo studies demonstrated that treatment with statins, such as atorvastatin and simvastatin, induced ferroptosis and sensitized radioresistant HNSCC cells to irradiation, improving radiosensitivity and potentially enhancing the response to RT. Additionally, in xenograft models, the combination of statins and RT led to a significant reduction in tumor initiation. These findings provide valuable insights for enhancing treatment and improving prognosis in HPV-negative HNSCC by targeting ferroptosis and utilizing statins to sensitize tumors to RT-induced cell death.

Functional gene signature offers a powerful tool for characterizing clinicopathological features and depicting tumor immune microenvironment of colorectal cancer.

BACKGROUND: Colorectal cancer, a prevalent malignancy worldwide, poses a significant challenge due to the lack of effective prognostic tools. In this study, we aimed to develop a functional gene signature to stratify colorectal cancer patients into different groups with distinct characteristics, which will greatly facilitate disease prediction. RESULTS: Patients were stratified into high- and low-risk groups using a prediction model built based on the functional gene signature. This innovative approach not only predicts clinicopathological features but also reveals tumor immune microenvironment types and responses to immunotherapy. The study reveals that patients in the high-risk group exhibit poorer pathological features, including invasion depth, lymph node metastasis, and distant metastasis, as well as unfavorable survival outcomes in terms of overall survival and disease-free survival. The underlying mechanisms for these observations are attributed to upregulated tumor-related signaling pathways, increased infiltration of pro-tumor immune cells, decreased infiltration of anti-tumor immune cells, and a lower tumor mutation burden. Consequently, patients in the high-risk group exhibit a diminished response to immunotherapy. Furthermore, the high-risk group demonstrates enrichment in extracellular matrix-related functions and significant infiltration of cancer-associated fibroblasts (CAFs). Single-cell transcriptional data analysis identifies CAFs as the primary cellular type expressing hub genes, namely ACTA2, TPM2, MYL9, and TAGLN. This finding is further validated through multiple approaches, including multiplex immunohistochemistry (mIHC), polymerase chain reaction (PCR), and western blot analysis. Notably, TPM2 emerges as a potential biomarker for identifying CAFs in colorectal cancer, distinguishing them from both colorectal cancer cell lines and normal colon epithelial cell lines. Co-culture of CAFs and colorectal cancer cells revealed that CAFs could enhance the tumorigenic biofunctions of cancer cells indirectly, which could be partially inhibited by knocking down CAF original TPM2 expression. CONCLUSIONS: This study introduces a functional gene signature that effectively and reliably predicts clinicopathological features and the tumor immune microenvironment in colorectal cancer. Moreover, the identification of TPM2 as a potential biomarker for CAFs holds promising implications for future research and clinical applications in the field of colorectal cancer.

70-Gene signature-guided adjuvant systemic treatment adjustments in early-stage ER+ breast cancer patients: 7-year follow-up of a prospective multicenter cohort study.

BACKGROUND: A previous prospective multicenter study revealed the change of the oncologists' chemotherapy advice due to the 70-Gene signature (GS) test result in half of the estrogen receptor-positive (ER+) invasive early-stage breast cancer patients with disputable chemotherapy indication. This resulted in less patients receiving chemotherapy. This study aims to complement these results by the 7-year oncological outcomes according to the 70-GS test result and the oncologists' pre-test advice. METHODS: Patients operated for early-stage ER+ breast cancer with disputable chemotherapy indication, had been prospectively included between 2013 and 2015. Oncologists were asked whether they intended to administer adjuvant chemotherapy before deployment of the 70-GS test. Information on adjuvant systemic treatment and oncological outcome was obtained through active follow-up by data managers of the Netherlands Cancer Registry. The primary endpoint of this study was distant metastasis-free survival (DMFS) according to the genomic risk. Exploratory analyses were done to evaluate DMFS in relation to the oncologists' pre-test advice. RESULTS: After a median follow-up of 7 years, distant metastases were diagnosed in 23 of the 606 patients (3.8%) and 36 (5.9%) patients had died. The DMFS rate for the 357 70-GS genomic low-risk patients was 94.2% (95% CI 91.2-96.2) and 89.1% for the 249 genomic high-risk patients (95% CI 84.3-92.4). Of the low-risk patients 3% had received chemotherapy compared to 80% of the high-risk patients. For the subgroups based on the pre-test oncologists' advice (no chemotherapy/chemotherapy/unsure) there were no clinically relevant differences in DMFS (89.8, 93.2 and 92.0%, respectively), while comparable proportions of patients had received chemotherapy. CONCLUSIONS: In patients with early-stage ER+ breast cancer with a disputable chemotherapy indication it is sensible to deploy the 70-GS to better select patients for adjuvant chemotherapy.

Construction of M2 macrophage-related gene signature for predicting prognosis and revealing different immunotherapy response in bladder cancer patients.

BACKGROUND: Bladder cancer development is closely associated with the dynamic interaction and communication between M2 macrophages and tumor cells. However, specific biomarkers for targeting M2 macrophages in immunotherapy remain limited and require further investigation. METHODS: In this study, we identified key co-expressed genes in M2 macrophages and developed gene signatures to predict prognosis and immunotherapy response in patients. Public database provided the bioinformatics data used in the analysis. We created and verified an M2 macrophage-related gene signature in these datasets using Lasso-Cox analysis. RESULTS: The predictive value and immunological functions of our risk model were examined in bladder cancer patients, and 158 genes were found to be significantly positively correlated with M2 macrophages. Moreover, we identified two molecular subgroups of bladder cancer with markedly different immunological profiles and clinical prognoses. The five key risk genes identified in this model were validated, including CALU, ECM1, LRP1, CYTL1, and CCDC102B, demonstrating the model can accurately predict prognosis and identify unique responses to immunotherapy in patients with bladder cancer. CONCLUSIONS: In summary, we constructed and validated a five-gene signature related to M2 macrophages, which shows strong potential for forecasting bladder cancer prognosis and immunotherapy response.

Evaluating the Radiation Sensitivity Index and 12-chemokine gene expression signature for clinical use in a CLIA laboratory.

Background: The radiation sensitivity index (RSI) and 12-chemokine gene expression signature (12CK GES) are two gene expression signatures (GES) that were previously developed to predict tumor radiation sensitivity or identify the presence of tertiary lymphoid structures in tumors, respectively. To advance the use of these GES into clinical trial evaluation, their assays must be assessed within the context of the Clinical Laboratory Improvement Amendments (CLIA) process. Methods: Using HG-U133Plus 2.0 arrays, we first established CLIA laboratory proficiency. Then the accuracy (limit of detection and macrodissection impact), precision (variability by time and operator), sample type (surgery vs. biopsy), and concordance with reference laboratory were evaluated. Results: RSI and 12CK GES were reproducible (RSI: 0.01 mean difference, 12CK GES 0.17 mean difference) and precise with respect to time and operator. Taken together, the reproducibility analysis of the scores indicated a median RSI difference of 0.06 (6.47% of range) across samples and a median 12CK GES difference of 0.92 (12.29% of range). Experiments indicated that the lower limit of input RNA is 5 ng. Reproducibility with a second CLIA laboratory demonstrated reliability with the median RSI score difference of 0.065 (6% of full range) and 12CK GES difference of 0.93 (12 % of observed range). Conclusions: Overall, under CLIA, RSI and 12CK GES were demonstrated by the Moffitt Cancer Center Advanced Diagnostic Laboratory to be reproducible GES for clinical usage.

Characterization of a ferroptosis-related gene signature predicting survival and immunotherapeutic response in lung adenocarcinoma.

Lung cancer remains the leading cause of cancer-related death worldwide, and drug resistance represents the main obstacle responsible for the poor mortality and prognosis. Here, to identify a novel gene signature for predicting survival and drug response, we jointly investigated RNA sequencing data of lung adenocarcinoma patients from TCGA and GEO databases, and identified a ferroptosis-related gene signature. The signature was validated in the validation set and two external cohorts. The high-risk group had a reduced survival than the low-risk group (P < 0.05). Moreover, the established gene signature was associated with tumor mutation burden, microsatellite instability, and response to immune checkpoint blockade. In addition, four candidate oncogenes (RRM2, SLC2A1, DDIT4, and VDAC2) were identified to be candidate oncogenes using in silico and wet experiments, which could serve as potential therapeutic targets. Collectively, this study developed a novel ferroptosis-related gene signature for predicting prognosis and drug response, and identified four candidate oncogenes for lung adenocarcinoma.