RNA Signature Research Articles

In silico Identification of Hypoxic Signature followed by reverse transcription-quantitative PCR Validation in Cancer Cell Lines

Hypoxic tumor microenvironment is one of the important impediments for conventional cancer therapy. This study aimed to computationally identify hypoxia-related messenger RNA (mRNA) signatures in nine hypoxic-conditioned cancer cell lines and investigate their role during hypoxia. Nine RNA sequencing (RNA-Seq) expression data sets were retrieved from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified in each cancer cell line. Then 23 common DEGs were selected by comparing the gene lists across the nine cancer cell lines. Reverse transcription-quantitative PCR (qRT-PCR) was performed to validate the identified DEGs. By comparing the data sets, GAPDH, LRP1, ALDOA, EFEMP2, PLOD2, CA9, EGLN3, HK, PDK1, KDM3A, UBC, and P4HA1 were identified as hub genes. In addition, miR-335-5p, miR-122-5p, miR-6807-5p, miR-1915-3p, miR-6764-5p, miR-92-3p, miR-23b-3p, miR-615-3p, miR-124-3p, miR-484, and miR-455-3p were determined as common micro RNAs. Four DEGs were selected for mRNA expression validation in cancer cells under normoxic and hypoxic conditions with qRT-PCR. The results also showed that the expression levels determined by qRT-PCR were consistent with RNA-Seq data. The identified protein-protein interaction network of common DEGs could serve as potential hypoxia biomarkers and might be helpful for improving therapeutic strategies.

Detection and Prediction of Neurodegenerative Disorders by Long Noncoding RNA Signature Analysis among Bangladeshi Population

Long noncoding RNAs (lncRNAs) have appeared lately as a new class of genes that may control and mediate cellular processes. It plays a critical role in many diseases, including neurodegenerative disorders. HOTAIR and MKRN2‐42:1 are two lncRNAs that have been seen upregulated in various diseases including in the peripheral blood of the patients suffering from Parkinson’s disease (PD) in different countries. It has also been seen that Meg3v1 and NEAT1 lncRNAs are expressed highly in patients suffering from motor neuron disease (MND) from various countries, though the association between the reason for the onset of this disease and the expression of these specific lncRNAs is not clear. The primary aim of this study was to observe expression variations for specific lncRNAs in the peripheral blood of PD and MND patients compared with healthy individuals and to look for lncRNAs that might be related to the pathogenesis of PD and MND. The aim of this study was also to investigate the association of lncRNA sequence as biomarkers with the risk of having neurodegenerative disorders (PD and MND) in the population of Bangladesh. The purpose of this research also included the possibility of prediction of these neurodegenerative disorders by analyzing and comparing the data which was acquainted from the study where 20 PD patients and 20 healthy subjects and 25 MND patients and 25 healthy subjects of Bangladesh were examined. Their peripheral blood was taken, and plasma was separated. Later, RT‐PCR was performed to observe the changes in the expression of specific lncRNAs. Results showed that expression of HOTAIR and MKRN2‐42:1 was positively correlated (p < 0.05) with the symptoms (5) of Parkinson’s suspects in comparison to the healthy subjects. Similarly, Meg3v1 and NEAT1 lncRNAs were expressed significantly (p < 0.05) in MND patients rather than that in healthy controls. Data were analyzed using GraphPad Prism 7.0, where p < 0.05 was considered statistically significant. Successful prediction of the possibility of neurodegenerative disorder by analyzing data regarding the expression of lncRNAs as biomarkers may help in advance clinicians to stratify high‐risk patients and prepare for appropriate medical intervention.

Development and validation of a novel DNA damage repair-related long non-coding RNA signature in predicting prognosis, immunity, and drug sensitivity in uterine corpus endometrial carcinoma

BackgroundDNA damage response (DDR) confer resistance to chemoradiotherapy in cancer cells. However, the role of DDR-related lncRNAs (DRLs) in uterine corpus endometrial carcinoma (UCEC) is poorly understood. In this study, we aimed to identify a DRL-related prognostic signature that could guide the clinical treatment of UCEC. MethodsWe extracted transcriptome and clinical data of patients with UCEC from The Cancer Genome Atlas (TCGA) database and identified DRLs using Spearman correlation analysis. Univariate and multivariate Cox analyses were used to determine candidate prognostic DRLs. The samples were randomly divided into training and test cohorts in a 1:1 ratio. A DRL-related risk signature was constructed from the training cohort data using the least absolute shrinkage and selection operator (LASSO) algorithm, and validated using the test and entire cohorts. Subsequently, a prognostic nomogram was developed using a multivariate Cox regression analysis. The functional annotation, immune microenvironment, tumor mutation burden (TMB), immune checkpoint blockade (ICB) efficacy, and drug sensitivity were also comprehensively analyzed between different risk groups. Finally, the function of AC019069.1 was validated in vitro. ResultsA novel risk signature was developed based on nine DRLs. The risk score efficiently predicted the prognosis of patients with UCEC. Based on the median risk score, two subgroups were identified. The DDR-related pathways were upregulated in the high-risk group. Additionally, high-risk patients have low immune activity, poor response to ICB, and weak sensitivity to chemotherapeutic agents, possibly because of the proficient DDR system. Finally, we demonstrated AC019069.1 could promote cell proliferation, decrease apoptosis and maintain genome stability of UCEC cells. ConclusionsThe developed DRL-related signature can predict the prognosis, immune microenvironment, immunotherapy, and chemoradiotherapy responsiveness of UCEC. Our study also revealed the potential value of DDR-targeted therapy in treating high-risk patients with UCEC.

Identification of anoikis-related long non-coding RNA signature as a novel prognostic model in lung adenocarcinoma

Identification of anoikis-related long non-coding RNA signature as a novel prognostic model in lung adenocarcinoma

Basement Membrane-Related Long Non-Coding RNA Signature Predicts the Prognosis of Breast Cancer

In this study, it was reported that basement membrane-associated long non-coding ribonucleic acid predicts prognosis and effectively enhances the individualized treatment of breast cancer by effectively identifying hot and cold tumors. Breast cancer transcriptional sequencing data were downloaded from The Cancer Genome Atlas and searched for prognostic long non-coding ribonucleic acids associated with basement membrane by univariate Cox regression and co-expression analysis. The least absolute shrinkage and selection operator analysis was employed to investigate the long non-coding ribonucleic acid prognostic model that is related to the basement membrane. Then, the following analyses were used to validate and evaluate the model, including univariate Cox regression, Kaplan-Meier analysis, multivariate Cox regression, receiver operating characteristic curve, calibration curves and nomogram. Immunoassay, principal component analysis, immunocytometric analysis and half-maximal inhibitory concentration analysis were conducted on the risk groups. To distinguish cold and hot tumors in terms of drug immunotherapy sensitivity, all inflammation-related long non-coding ribonucleic acids were divided into two groups. A model containing 4 basement membranerelated long non-coding ribonucleic acids was developed in this study. The area under the receiver operating characteristic curve was 0.742, 0.759 and 0.840 for 1, 2 and 3 y, respectively. High-risk patients were associated with tumor invasion and immunity and had a high immune infiltration status. Immune cells and checkpoints were infiltrated and activated in the high-risk group. Hot and cold tumors could be effectively distinguished by tumor clusters. Among the two clusters, cluster 2 was identified as hot tumors, which indicated that they were more sensitive to immunotherapeutic agents. This study provides evidence to support the hypothesis that basement membrane-related long non-coding ribonucleic acids can accurately predict patient prognosis and differentiate between hot and cold tumors. As a result, individualized immunotherapy for breast cancer patients will be improved and patients will have access to new treatment options.

Identification and development of TP53 mutation-associated Long non-coding RNAs signature for optimized prognosis assessment and treatment selection in hepatocellular carcinoma.

The TP53 gene is estimated to be mutated in over 50% of tumors, with the majority of tumors exhibiting abnormal TP53 signaling pathways. However, the exploration of TP53 mutation-related LncRNAs in Hepatocellular carcinoma (HCC) remains incomplete. This study aims to identify such LncRNAs and enhance the prognostic accuracy for Hepatoma patients. Differential gene expression was identified using the "limma" package in R. Prognosis-related LncRNAs were identified via univariate Cox regression analysis, while a prognostic model was crafted using multivariate Cox regression analysis. Survival analysis was conducted using Kaplan-Meier curves. The precision of the prognostic model was assessed through ROC analysis. Subsequently, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were executed on the TCGA dataset via the TIDE database. Fractions of 24 types of immune cell infiltration were obtained from NCI Cancer Research Data Commons using deconvolution techniques. The protein expression levels encoded by specific genes were obtained through the TPCA database. In this research, we have identified 85 LncRNAs associated with TP53 mutations and developed a corresponding signature referred to as TP53MLncSig. Kaplan-Meier analysis revealed a lower 3-year survival rate in high-risk patients (46.9%) compared to low-risk patients (74.2%). The accuracy of the prognostic TP53MLncSig was further evaluated by calculating the area under the ROC curve. The analysis yielded a 5-year ROC score of 0.793, confirming its effectiveness. Furthermore, a higher score for TP53MLncSig was found to be associated with an increased response rate to immune checkpoint blocker (ICB) therapy (p = .005). Patients possessing high-risk classification exhibited lower levels of P53 protein expression and higher levels of genomic instability. The present study aimed to identify and validate LncRNAs associated with TP53 mutations. We constructed a prognostic model that can predict chemosensitivity and response to ICB therapy in HCC patients. This novel approach sheds light on the role of LncRNAs in TP53 mutation and provides valuable resources for analyzing patient prognosis and treatment selection.

Human circulating small non-coding RNA signature as a non-invasive biomarker in clinical diagnosis of acute myeloid leukaemia.

Background: Acute myeloid leukaemia (AML) is the most common acute leukaemia in adults; AML is highly heterogeneous and involves abnormalities at multiple omics levels. Small non-coding RNAs (sncRNAs) present in body fluids are important regulatory molecules and considered promising non-invasive clinical diagnostic biomarkers for disease. However, the signature of sncRNA profile alteration in AML patient serum and bone marrow supernatant is still under exploration. Methods: We examined data for blood and bone marrow samples from 80 consecutive, newly-diagnosed patients with AML and 12 healthy controls for high throughput small RNA-sequencing. Differentially expressed sncRNAs were analysed to reveal distinct patterns between AML patients and controls. Machine learning methods were used to evaluate the efficiency of specific sncRNAs in discriminating individuals with AML from controls. The altered expression level of individual sncRNAs was evaluated by RT-PCR, Q-PCR, and northern blot. Correlation analysis was employed to assess sncRNA patterns between serum and bone marrow supernatant. Results: We identified over 20 types of sncRNA categories beyond miRNAs in both serum and bone marrow supernatant, with highly coordinated expression patterns between them. Non-classical sncRNAs, including rsRNA (62.86%), ysRNA (14.97%), and tsRNA (4.22%), dominated among serum sncRNAs and showed sensitive alteration patterns in AML patients. According to machine learning-based algorithms, the tsRNA-based signature robustly discriminated subjects with AML from controls and was more reliable than that comprising miRNAs. Our data also showed that serum tsRNAs to be closely associated with AML prognosis, suggesting the potential application of serum tsRNAs as biomarkers to assist in AML diagnosis. Conclusions: We comprehensively characterized the expression pattern of circulating sncRNAs in blood and bone marrow and their alteration signature between healthy controls and AML patients. This study enriches research of sncRNAs in the regulation of AML, and provides insights into the role of sncRNAs in AML.

MRNA and long-noncoding RNA signatures for improving the prognosis prediction of cutaneous skin melanoma efficiently.

Non-coding RNAs occupy a significant fraction of the human genome, and their biological significance during the pathological process is proved. More and more lncRNAs are reported in cancer research. To investigate the non-coding RNA's biological relevance with cutaneous skin melanoma, we first compared the survival analysis by combining the most differentially expressed mRNA and non-coding RNA expression values. The result showed that the abundantly expressed mRNAs and lncRNAs have significant effects on the survival of patients. Compared to the mRNAs, these lncRNAs have more impact on the progress of cutaneous skin melanoma. Thus, we combined the two types of RNA factors having significant effects as risk factors to construct the diagnosis model, and the survival analysis confirmed the robustness of the diagnosis model. In summary, a list of eight lncRNA and five mRNA expression signatures can be used to improve the prognosis prediction of cutaneous skin melanoma, as well as help us to understand the pathogenic mechanism and provide a hint for targeting therapy.

Identification of a pyroptosis-related long non-coding RNA Signature for prognosis and its related ceRNA regulatory network of ovarian cancer.

Aim: To identify the pyroptosis-related long non-coding RNAs (lncRNAs) in ovarian cancer and construct a prognostic signature based on them. Methods: Expression data from TCGA was used to explore differentially expressed pyroptosis-related lncRNAs in ovarian cancer. A risk signature was established by LASSO and cox regression analysis and then validated. Databases such as ESTIMATE, CIBERSORT, TIMER, XCELL were used to identify the relation between this signature and the immune microenvironment of ovarian cancer. Gene Set Enrichment Analysis was introduced to identify the pathways and functions that the signature may participate in. Based on miRcode and starBase databases, microRNAs related to the lncRNAs in our signature and the positively co-expressed pyroptosis- related genes were screened and a competing endogenous RNA (ceRNA) network was then constructed. Quantitative reverse transcription PCR was conducted to validate the expression levels of two lncRNAs in this ceRNA network. Results: A 13 pyroptosis-related lncRNA prognostic signature (MYCNOS, AL161772.1, USP30-AS1, ZNF32-AS2, AC068733.3, AC012236.1, AC015802.5, KIAA1671-AS1, AC013403.2, MIR223HG, KRT7-AS, PTPRD-AS1 and LINC01094) was constructed. Patients in high-risk group had a significantly worse prognosis than that of low-risk (P<0.0001). Immune infiltration analysis found that patients identified as high-risk had a higher infiltration of macrophages and tumor-associated fibroblasts. Further pathway analysis revealed that the signature may be involved in epithelial mesenchymal transition, extracellular matrix receptor interaction, and focal adhesion. Finally, a competitive endogenous inhibition relationship was discovered between LINC01094, KRT7-AS, MYCNOS, ZNF32-AS2, AC012236.1 and pyroptosis- related genes such as IRF1, NOD1, GSDMC, NLRP1, PLCG1, GSDME and GZMB, in which LINC01094 and KRT7-AS were found to be overexpressed in three ovarian cancer cell lines. Conclusion: We constructed a pyroptosis-related lncRNA signature and correlate it to the immune microenvironment. A ceRNA regulatory network related to pyroptosis was also constructed, which provides novel insights useful for the study of pyroptosis in ovarian cancer.

An angiogenesis‑related lncRNA signature for theprognostic prediction of patients with bladdercancer and LINC02321 promotes bladder cancerprogression via the VEGFA signaling pathway.

The mechanism underlying bladder cancer metastasis is associated with tumor angiogenesis. The present study aimed to evaluate the predictive role and value of an angiogenesis‑associated long non‑coding (lnc)RNA signature in patients with bladder cancer and the role of long intergenic non‑coding RNA (LINC)02321 in the progression of this malignancy. Angiogenesis‑related lncRNAs were screened using Pearson correlation analysis and the signaturewas constructed using Cox regression analysis and evaluated using the receiver operating characteristic curve. LINC02321, which expressed the largest difference in bladder cancer, was screened using reverse transcription‑quantitative PCR. The role of LINC02321 in the malignant progression of bladder cancer was evaluated using Transwell, wound healing and Cell Counting Kit 8 assays. A total of six angiogenesis‑associated lncRNAs (USP30‑AS1, LINC02321, PSMB8‑AS1, KRT7‑AS, LINC01767 and OCIAD1‑AS1) were identified as candidates for the prognostic signature using Cox regression analysis. The overall survival of patients in the low‑risk group was significantly longer compared with that in the high‑risk group, with the highest area under the curve value being 0.807. A nomogram was constructed based on the traditional clinical indicators (age, sex, grade, American Joint Committee on Cancer stage) and risk score of patients. Compared with the traditional clinical indicators, the risk score demonstrated better clinical prediction capacity for predicting the prognosis of patients with bladder cancer. The Cancer Genome Atlas prediction and RT‑qPCR experimental results demonstrated that only LINC02321 was highly expressed in bladder cancer tissue and promoted the proliferation, invasion, migration and cisplatin resistance of the malignancy. Gene set enrichment, Pearson's correlation analysis and experimental results demonstrated that the VEGFA signalling pathway may be involved in the LINC02321‑regulated progression of bladder cancer. In conclusion, the six angiogenesis‑associated lncRNA signatures reported in the present study may be used to predict the prognosis of patients with bladder cancer, and LINC02321 promoted malignant progression of bladder cancer via the VEGFA signalling pathway.

Comprehensive analysis of cuproptosis-related long non-coding RNA signature and personalized therapeutic strategy of breast cancer patients.

Breast cancer (BC) is considered to be one of the primary causes of cancer deaths in women. Cuproptosis was suggested to play an important role in tumor proliferation and tumor immune microenvironment. Therefore, an investigation was conducted to identify the relationship between cuproptosis-related long non-coding RNAs (lncRNAs) and BC prognosis. Based on The Cancer Genome Atlas (TCGA), nine cuproptosis-related lncRNAs were identified by Pearson's analysis and Cox regression analysis to create a cuproptosis-related lncRNA signature. Subsequently, patients with BC were divided into high-risk and low-risk groups. The Kaplan-Meier curves and a time-dependent receiver operating characteristic (ROC) analysis were employed to elucidate the predictive capability of the signature. After that, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted by Gene Set Enrichment Analysis (GSEA), and the lncRNA-mRNA co-expression network was established by Cytoscape software. Furthermore, the ESTIMATE score was calculated, and the immune cell type component analysis was conducted. Eventually, immunotherapy response analysis was applied to identify the predictive power of cuproptosis-related lncRNAs to tumor immunotherapy response, including immune checkpoint gene expression levels, tumor mutational burden (TMB), and microsatellite instability (MSI). Patients with BC in the low-risk groups showed better clinical outcomes. The KEGG pathways in the high-risk groups were mainly enriched in immune response and immune cell activation. Furthermore, the ESTIMATE scores were higher in the low-risk groups, and their immune cell infiltrations were dramatically different from those of the high-risk groups. The low-risk groups were shown to have higher infiltration levels of CD8+ T cells and TMB-high status, resulting in better response to immunotherapies. The findings of this study revealed that the nine-cuproptosis-related lncRNA risk score was an independent prognostic factor for BC. This signature was a potential predictor for BC immunotherapy response. What we found will provide novel insight into immunotherapeutic treatment strategies in BC.

Evaluation of Exosomal Coding and Non-Coding RNA Signature in Obese Adolescents.

Exosomes may contribute to the pathogenesis of obesity through their action as communication mediators. As we have previously demonstrated, in obese adolescents, some circulating miRNAs modified the C-type natriuretic peptide (CNP) expression and were associated with changes in metabolic functions. At present no data are available on miRNA transport by exosomes in this condition. To verify and compare the presence and the expression of CNP/NPR-B/NPR-C, and some miRNAs (miR-33a-3p/miR-223-5p/miR-142-5p/miRNA-4454/miRNA-181a-5p/miRNA-199-5p), in circulating exosomes obtained from the same cohort of obese (O, n = 22) and normal-weight adolescents (N, n = 22). For the first time, we observed that exosomes carried CNP and its specific receptors only randomly both in O and N, suggesting that exosomes are not important carriers for the CNP system. On the contrary, exosomal miRNAs resulted ubiquitously and differentially expressed in O and N. O showed a significant decrease (p &lt; 0.01) in the expression of all miRNAs except for miR-4454 and miR-142-5p. We have found significant correlations among miRNAs themselves and with some inflammatory/metabolic factors of obesity. These relationships may help in finding new biomarkers, allowing us to recognize, at an early stage, obese children and adolescents at high risk to develop the disease complications in adult life.

Construction of a m5C-related long non-coding RNA signature for the prognosis of hepatocellular carcinoma.

RNA modification serves as a kind of posttranscriptional modification. Besides N6-methyladenosine (m6A), 5-methylcytosine(m5C) is also an important RNA modification. Long non-coding RNAs (lncRNAs) play an important role in tumor progression. Thus, we performed bioinformatic analysis to establish a m5C-related lncRNA signature(m5ClncSig) for hepatocellular carcinoma (HCC). The RNA sequencing data and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Pearson correlation coefficient analysis was applied to conduct m5C-related genes and m5C-related lncRNAs co-expressing network. Univariate Cox regression was used to screen the m5C-related lncRNAs with prognosis value. LASSO regression was applied to establish m5ClncSig. Functional analysis including KEGG and GO were performed. The relation between m5ClncSig and immunity was assessed by CIBERSORT and ESTIMATE. RP11-498C9.15 was selected for in vitro validation. A m5ClncSig was established containing 8 lncRNAs with significantly prognosis value. According to risk score calculated by m5ClncSig, high-risk group had worse clinical outcomes than low-risk group. The risk score was validated as an independent prognosis factor. Moreover, the abundances of 11 types of immune cells were significantly different between high-risk group and low-risk group while 8 immune-related genes expressed differently between these two groups. RP11-498C9.15 was validated as a risk factor in HCC progression.

507. Peripheral blood RNA signatures associated with human babesiosis, quality of life and neurological symptoms

Abstract Background Human babesiosis is an emerging tick-borne disease in the US that is primarily caused by the protozoan Babesia microti (Bm). Although Babesiosis can cause life-threatening infections, the pathophysiology including host and parasite determinants associated with disease severity are largely unknown. The aim of this study is to identify parasite and host signatures of human babesiosis and correlate these with disease severity and long-term symptoms. Methods We enrolled prospectively patients with acute babesiosis (microcopy positive blood smear for Bm, confirmed by PCR) at Stony Brook University Hospital during 2020-2021. Blood samples and surveys were completed at initial diagnosis (Visit=1;V1) and at months 1, 6 and 12 (V2,V3,V4). Total RNA from whole peripheral blood was isolated using the PAXgene Blood RNA Kit from individuals infected with Bm and healthy controls. Quality of life (QOL) surveys were administered to patients. Results Peripheral blood transcriptome analysis of 17 patients (median age: 63 years, range 42-78; 24% female) who presented with babesiosis were distinctly different from uninfected individuals (n=9). Babesiosis was associated with a peripheral blood Type I and Type II Interferon transcriptional signature. Other dominant transcriptional signatures included those involved in activation of mononuclear cells, oxidative phosphorylation, inducible nitric oxide synthase and regulation of homeostasis as well as inducible nitric oxide synthase. Ingenuity Pathway Analysis for clinical chemistry and hematology also highlight changes associated with cardiotoxicity, hepatotoxicity, and nephrotoxicity as well as increased levels of red blood cells. Patients with babesiosis showed a persistent deterioration of QOL in cognition [mean V1=33.1; V3=36.3] and satisfaction [mean V1=28.4; V3=34]. Also a decrease in QOL over time despite effective treatment [mean at V1 82 (SD: 5.9), V2 80 (8.1), V3 76 (4.7) (p &amp;gt;0.05). Heat Map Summary. Volcano Plot Gene Pathways Analysis Conclusion Babesiosis is associated with a marked alteration in the peripheral blood transcriptome of patients that may also provide insight into both the pathophysiology of the disease including severity and complications. Furthermore, even after successful treatment, babesiosis may result in long-term impacts on quality of life indicators. Disclosures All Authors: No reported disclosures.

Biomarker Development Trial of Satraplatin in Patients with Metastatic Castration-Resistant Prostate Cancer.

In the phase III SPARC trial, satraplatin, an oral platinum analogue, demonstrated anticancer activity in men with metastatic castration-resistant prostate cancer (mCRPC). Repeat biopsies are uncommon in mCRPC, limiting the feasibility of tissue-based biomarkers. This phase II study sought to evaluate the feasibility and utility of blood-based biomarkers to identify platinum-sensitive mCRPC. Patients with mCRPC who had progressed on docetaxel were enrolled at a single center from 2011 to 2013. Subjects received satraplatin 80 mg/m2 by mouth daily on days 1-5 and prednisone 5 mg PO twice daily, on a 35-day cycle. Serial peripheral blood samples were collected for biomarker assessment. Thirteen docetaxel-refractory mCRPC patients were enrolled, with a median age of 69 years (range 54-77 years) and median PSA of 71.7 ng/mL (range 0.04-3057). Four of 13 patients (31%) responded to satraplatin (defined as a PSA decline of ≥30%). Responders demonstrated improved time to disease progression (206 vs. 35 days, HR 0.26, 95% CI, 0.02-0.24, P = .003). A 6-gene peripheral blood RNA signature and serum tissue inhibitor of metalloproteinase-1 (TIMP-1) levels were assessed as biomarkers, but neither was significantly associated with response to satraplatin. In this small series, one-third of mCRPC patients responded to platinum-based chemotherapy. Peripheral blood biomarker measurement is feasible in mCRPC, though the biomarkers we investigated were not associated with platinum response. Other biomarkers, such as DNA damage repair mutations, should be evaluated.

Identification of cuproptosis-related long noncoding RNA signature for predicting prognosis and immunotherapy response in bladder cancer

Bladder cancer (BC) is the most common malignant tumour of the urinary system and one of the leading causes of cancer-related death. Cuproptosis is a novel form of programmed cell death, and its mechanism in tumours remains unclear. This study aimed to establish the prognostic signatures of cuproptosis-related lncRNAs and determine their clinical prognostic value. RNA sequencing data from The Cancer Genome Atlas were used to detect the expression levels of cuproptosis-related genes in BC. Cuproptosis-related lncRNAs linked to survival were identified using co-expression and univariate Cox regression. Furthermore, consensus cluster analysis divided the lncRNAs into two subtypes. Subsequently, we established a signature model consisting of seven cuproptosis-related lncRNAs (AC073534.2, AC021321.1, HYI-AS1, PPP1R26-AS1, AC010328.1, AC012568.1 and MIR4435-2Hg) using least absolute shrinkage and selection operator regression. Survival analysis based on risk score showed that the overall survival and progression-free survival of patients in the high-risk group were worse than those in the low-risk group. Multivariate Cox analysis demonstrated the independent prognostic potential of this signature model for patients with BC. Moreover, age and clinical stage were also significantly correlated with prognosis. The constructed nomogram plots revealed good predictive power for the prognosis of patients with BC and were validated using calibration plots. Additionally, enrichment analysis, Single sample gene set enrichment analysis and immune infiltration abundance analysis revealed significant differences in immune infiltration between the two risk groups, with high levels of immune cell subset infiltrations observed in the high-risk group accompanied by various immune pathway activation. Moreover, almost all the immune checkpoint genes showed high expression levels in the high-risk group. Moreover, TIDE analysis suggested that the high-risk group was more responsive to immunotherapy. Finally, eight drugs with low IC50 values were screened, which may prove to be beneficial for patients in the high-risk group.

An immune infiltration-related long non-coding RNAs signature predicts prognosis for hepatocellular carcinoma.

Background: With a high incidence and dismal survival rate, hepatocellular carcinoma (HCC) tops the list of the world's most frequent malignant tumors. Immunotherapy is a new approach to cancer treatment, and its effect on prolonging overall survival (OS) varies from patient to patient. For a more effective prognosis and treatment of HCC, we are committed to identifying immune infiltration-related long non-coding RNAs (IIRLs) with prognostic value in hepatocellular carcinoma. Methods: In our study, we calculated immune scores of 369 hepatocellular carcinoma samples from the Cancer Genome Atlas (TCGA) database by using an estimation algorithm, and obtained long non-coding RNAs (lncRNAs) associated with immune infiltration by using Weighted Gene Co-expression Network analysis (WGCNA). For training cohort, univariate Cox, least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analysis were used to determine prognostic IIRLs, we established a prognostic IIRLs signature. By testing cohort and entire cohort, we confirmed that the signature is practical. The prognosis of people with different clinicopathological stages and risk scores were predicted by the nomogram we constructed. In addition, Immune cell infiltration analysis and prediction of therapeutic drugs were performed. Results: 93 IIRLs were obtained by WGCNA. Furthermore, the prognostic value of these IIRLs were evaluated by using univariate Cox, Lasso and multivariate Cox analysis. Four IIRLs were used to create a signature with a prognosis. Time-related receiver operating characteristic (ROC) curve revealed that this model had an acceptable prognostic value for HCC patients. By using univariate and multivariate Cox regression analysis, this risk score has been shown to be an independent prognostic factor for HCC. The nomogram we made showed good predictions. Except for that, the treatment with immune checkpoint inhibitors (ICI) was likely to be more effective for low-risk patients. Conclusion: Based on four IIRLs, a prognostic signature was created in this research showed good accuracy in predicting OS. This study also provided valuable references for Immunotherapy of hepatocellular carcinoma.

A Transcriptomic-Based Tool to Predict Gemcitabine Sensitivity in Advanced Pancreatic Adenocarcinoma

A Transcriptomic-Based Tool to Predict Gemcitabine Sensitivity in Advanced Pancreatic Adenocarcinoma

Construction and validation of a novel cuproptosis-related long noncoding RNA signature for predicting the outcome of prostate cancer.

Background: Prostate cancer (PCa) is one of the most common tumors of the urinary system. Cuproptosis is a novel mode of controlled cell death that is related to the development of various tumor types. However, the functions of cuproptosis-related long noncoding RNAs (CRLs) in PCa have not yet been well studied. Methods: In this study, data of PCa patients were obtained from The Cancer Genome Atlas (TCGA) and from the Changhai Hospital. Univariate and multivariate Cox regression analyses and LASSO regression analysis were conducted to screen CRLs linked to the prognosis of PCa patients. A risk score model was constructed on the basis of CRLs to predict prognosis. PCa patients were categorized into high- and low-risk cohorts. The predictive value of the risk score was evaluated by Kaplan-Meier survival analysis, receiver operating characteristic curves, and nomograms. In addition, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore possible pathways involving CRLs in PCa. Immune function analysis confirmed the correlation between CRLs and immunity in PCa. Finally, we explored the tumor mutational burden and drug response in the high- and low-risk cohorts. Results: First, we identified seven CRLs (C1orf229, C9orf139, LIPE-AS1, MCPH1-AS1, PRR26, SGMS1-AS1, and SNHG1) that were closely related to prognosis in PCa. The risk score model based on the selected CRLs could accurately predict the prognosis of PCa patients. The high-risk cohort was associated with worse disease-free survival (DFS) time in PCa patients (p < 0.001). ROC curve analysis was performed to confirm the validity of the signature (area under the curve (AUC) at 1year: 0.703). Nomograms were constructed based on the risk score and clinicopathological features and also exhibited great predictive efficiency for PCa. GO and KEGG analyses showed that the CRLs were mainly enriched in metabolism-related biological pathways in PCa. In addition, immune function analysis showed that patients in the high-risk cohort had higher TMB and were more sensitive to conventional chemotherapy and targeted drugs including doxorubicin, epothilone B, etoposide, and mitomycin C. Conclusion: We constructed a novel CRL-related risk score model to accurately predict the prognosis of PCa patients. Our results indicate that CRLs are potential targets for drug therapy in PCa and provide a possible new direction for personalized treatment of PCa patients.

Prognostic implications of necroptosis-related long noncoding RNA signatures in muscle-invasive bladder cancer.

Background: Bladder cancer (BLCA) is the sixth most common cancer in men, with an increasing incidence of morbidity and mortality. Necroptosis is a type of programmed cell death and plays a critical role in the biological processes of bladder cancer (BLCA). However, current studies focusing on long noncoding RNA (lncRNA) and necroptosis in cancer are limited, and there is no research about necroptosis-related lncRNAs (NRLs) in BLCA. Methods: We obtained the RNA-seq data and corresponding clinical information of BLCA from The Cancer Genome Atlas (TCGA) database. The seven determined prognostic NLRs were analyzed by several methods and verified by RT-qPCR. Then, a risk signature was established based on the aforementioned prognostic NLRs. To identify it, we evaluated its prognostic value by Kaplan-Meier (K-M) survival curve and receiver operating characteristics (ROC) curve analysis. Moreover, the relationships between risk signature and clinical features, functional enrichment, immune landscape, and drug resistance were explored as well. Results: We constructed a signature based on seven defined NLRs (HMGA2-AS1, LINC02489, ETV7-AS1, EMSLR, AC005954.1, STAG3L5P-PVRIG2P-PILRB, and LINC02178). Patients in the low-risk cohort had longer survival times than those in the high-risk cohort, and the area under the ROC curve (AUC) value of risk signature was higher than other clinical variables. Functional analyses, the infiltrating level of immune cells and functions, ESTIMATE score, and immune checkpoint analysis all indicated that the high-risk group was in a relatively immune-activated state. In terms of treatments, patients in the high-risk group were more sensitive to immunotherapy, especially anti-PD1/PD-L1 immunotherapy and conventional chemotherapy. Conclusion: The novel NLR signature acts as an invaluable tool for predicting prognosis, immune microenvironment, and drug resistance in muscle-invasive bladder cancer (MIBC) patients.