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High mortality of patients with cervical cancer (CC) stresses the imperative of prognostic biomarkers for CC patients. Additionally, the vital status of post‐translational modifications (PTMs) in the progression of cancers has been reported by numerous researches. Therefore, the purpose of this research was to dig a prognostic signature correlated with PTMs for CC. We built a five‐mRNA (GALNTL6, ARSE, DPAGT1, GANAB and FURIN) prognostic signature associated with PTMs to predict both disease‐free survival (DFS) (hazard ratio [HR] = 3.967, 95% CI = 1.985‐7.927; P

Mingyi Ju,1,2,* Longyang Jiang,1,2,* Qian Wei,1,2 Lifeng Yu,1,2 Lianze Chen,1,2 Yan Wang,1,2 Baohui Hu,1,2 Ping Qian,1,2 Ming Zhang,1,2 Chenyi Zhou,1,2 Zinan Li,1,2 Minjie Wei,1– 3 Lin Zhao,1,2 Jiali Han4 1Department of Pharmacology, School of Pharmacy; China Medical University, Shenyang, Liaoning Province, 110122, People’s Republic of China; 2Liaoning Key Laboratory of Molecular Targeted Anti-Tumor Drug Development and Evaluation, Liaoning Cancer Immune Peptide Drug Engineering Technology Research Center, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, China Medical University, Shenyang, Liaoning Province, 110122, People’s Republic of China; 3Liaoning Medical Diagnosis and Treatment Center, Shenyang, Liaoning Province, People’s Republic of China; 4Department of Otolaryngology, The First Hospital of China Medical University, Shenyang, Liaoning, 110001, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jiali HanDepartment of Otolaryngology, The First Hospital of China Medical University, Shenyang, Liaoning, 110001, People’s Republic of ChinaTel +86 18102489275Email jiali_han@163.comLin ZhaoDepartment of Pharmacology, School of Pharmacy, China Medical University, No. 77 Puhe Road, Shenyang North New Area, Shenyang, 110122, Liaoning, People’s Republic of ChinaTel +86 18900911856Email zl_cmu@163.com; lzhao@cmu.edu.cnObjective: Although many curative treatments are being applied in the clinic, a significant number of patients with liver hepatocellular carcinoma (LIHC) suffer from drug resistance. The tumour microenvironment (TME) has been found to be closely associated with resistance, suggesting that identification of predictive biomarkers related to the TME for resistance in LIHC will be very rewarding. However, there has been no study dedicated to identifying a TME-related biomarker that has the potential to predict resistance in LIHC.Methods: An integrated analysis was conducted based on data of patients with LIHC suffering from drug resistance from the TCGA database and four GEO datasets. Subsequently, we also validated the expression levels of the identified genes in paraffin-embedded LIHC samples by immunohistochemistry.Results: In this study, we developed a robust and acute TME-related signature consisted of five immune-related genes (FABP6, CD4, PRF1, EREG and COLEC10) that could independently predict both the RFS and OS of LIHC patients. Moreover, the TME-related signature was significantly associated with the immune score, immune cytolytic activity (CYT), HLA, interferon (IFN) response and tumour-infiltrating lymphocytes (TILs), and it might influence tumour immunity mainly by affecting B cells, CD8+ T cells and dendritic cells. Furthermore, our analysis also indicated that the TME-related signature was correlated with the immunotherapy response and had an enormous potential to predict sorafenib resistance in LIHC.Conclusion: Our findings demonstrated a TME-related signature which can independently predict both the RFS and OS of LIHC patients, highlighting the predictive potential of the signature for immunotherapy response and sorafenib resistance, potentially enabling more precise and personalized sorafenib treatment in LIHC in the future.Keywords: liver cancer, tumour microenvironment, immunotherapy, sorafenib resistance, prognostic signature

Background: Prostate cancer recognized as a “cold” tumor has an immunosuppressive microenvironment in which regulatory T-cells (Tregs) usually represent a major role. Therefore, identifying a prognostic signature of Tregs has promising benefits of improving survival of prostate cancer patients. However, the prognostic signature based on Tregs-specific mRNAs for prostate cancer is lacking. Methods: We systematicly analyzed transcriptional profiles of Tregs and 19 other immune cell types using 42 purified immune cell datasets from GEO to identify Tregs-specific mRNAs, and develop and validate a prognostic signature of Tregs (named “TILTregSig”) for monitoring prognosis of prostate cancer using TCGA and ICGC datasets. We also applied the signature to five immunotherapy response datasets and GSCALite to analyze the potential of the TILTregSig for predicting CIT response and cancer therapeutic resistance.Results: We develop the TILTregSig comprising five mRNAs (SOCS2, EGR1, RRM2, TPP1 and C11orf54) for prostate cancer patients. We find that the TILTregSig is a stronger predictor for tumor immunity compared with tumor mutation burden (TMB) and glycolytic activity which have been reported as immune predictors. Further analyses indicate that the TILTregSig may influence tumor immunity mainly by mediating tumor-infiltrating Tregs. Moreover, the TILTregSig also shows promising potential for predicting cancer immunotherapy (CIT) response and therapeutic resistance in multiple cancers.Conclusions: Our study has highlighted the value of the TILTregSig as a prognostic biomarker of prostate cancer from a tumor-infiltrating Tregs perspective, and strengthened its potential application as predictor of CIT response and cancer therapeutic resistance, which warrants further.

Background Breast cancer (BRCA) is a malignant tumor with high morbidity and mortality, which is a threat to women’s health worldwide. Ferroptosis is closely related to the occurrence and development of breast cancer. Here, we aimed to establish a ferroptosis-related prognostic gene signature for predicting patients’ survival. Methods Gene expression profile and corresponding clinical information of patients from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. The Least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis model was utilized to construct a multigene signature. The Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) pathway and single-sample gene set enrichment analysis (ssGSEA) were performed for patients between the high-risk and low-risk groups divided by the median value of risk score. Results We constructed a prognostic signature consisted of nine ferroptosis-related genes (ALOX15, CISD1, CS, GCLC, GPX4, SLC7A11, EMC2, G6PD and ACSF2). The Kaplan-Meier curves validated the fine predictive accuracy of the prognostic signature (p p p Conclusion Our study indicated that the ferroptosis-related prognostic signature gene could serve as a novel biomarker for predicting breast cancer patients’ prognosis. Furthermore, we found that immunotherapy might play a vital role in therapeutic schedule based on the level and difference of immune-related cells and pathways in different risk groups for breast cancer patients.

Background: Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC.Methods: Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis.Results: A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) < 1), and HLA-F was risky (HR > 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC.Conclusion: The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.

Esophageal cancer (ESCA) is a leading cause of cancer-related mortality, with poor prognosis worldwide. DNA damage repair is one of the hallmarks of cancer. Loss of genomic integrity owing to inactivation of DNA repair genes can increase the risk of cancer progression and lead to poor prognosis. We aimed to identify a novel gene signature related to DNA repair to predict the prognosis of ESCA patients. Based on gene expression profiles of ESCA patients from The Cancer Genome Atlas and gene set enrichment analysis, 102 genes related to DNA repair were identified as candidates. After stepwise Cox regression analysis, we established a five-gene prognostic model comprising DGCR8, POM121, TAF9, UPF3B, and BCAP31. Kaplan-Meier survival analysis confirmed a strong correlation between the prognostic model and survival. Moreover, we verified the clinical value of the prognostic signature under the influence of different clinical parameters. We found that small-molecule drugs (trametinib, selumetinib, and refametinib) could help to improve patient survival. In summary, our study provides a novel and promising prognostic signature based on DNA-repair-related genes to predict survival of patients with ESCA. Systematic data mining provides a theoretical basis for further exploring the molecular pathogenesis of ESCA and identifying therapeutic targets.

NLRP3 inflammasome was introduced as a double-edged sword in tumorigenesis and influenced immunotherapy response by modulating host immunity. However, a systematic assessment of the NLRP3-inflammasome-related genes across human cancers is lacking, and the predictive role of NLRP3 inflammasome in cancer immunotherapy (CIT) response remains unexplored. Thus, in this study, we performed a pan-cancer analysis of NLRP3-inflammasome-related genes across 24 human cancers. Out of these 24 cancers, 15 cancers had significantly different expression of NLRP3-inflammasome-related genes between normal and tumor samples. Meanwhile, Cox regression analysis showed that the NLRP3 inflammasome score could be served as an independent prognostic factor in skin cutaneous melanoma. Further analysis indicated that NLRP3 inflammasome may influence tumor immunity mainly by mediating tumor-infiltrating lymphocytes and macrophages, and the effect of NLRP3 inflammasome on immunity is diverse across tumor types in tumor microenvironment. We also found that the NLRP3 inflammasome score could be a stronger predictor for immune signatures compared with tumor mutation burden (TMB) and glycolytic activity, which have been reported as immune predictors. Furthermore, analysis of the association between NLRP3 inflammasome and CIT response using six CIT response datasets revealed the predictive value of NLRP3 inflammasome for immunotherapy response of patients in diverse cancers. Our study illustrates the characterization of NLRP3 inflammasome in multiple cancer types and highlights its potential value as a predictive biomarker of CIT response, which can pave the way for further investigation of the prognostic and therapeutic potentials of NLRP3 inflammasome.

Skin cutaneous melanoma (SKCM) is the most invasive form of skin cancer with poor prognosis. Growing evidence has demonstrated that tumor immune microenvironment plays a key contributing role in tumorigenesis and predicting clinical outcomes. The aim of this study was to recognize immune classification and a reliable prognostic signature for patients with SKCM. By using single-sample gene set enrichment (ssGSEA) and hierarchical clustering analyses, we evaluated the immune infiltration landscape of SKCM afflicted patients from The Cancer Genome Atlas (TCGA) dataset and named two SKCM subtypes: Immunity-high and Immunity-low. The Immunity-high subgroup was characterized by up-regulation of immune response and favorable survival probability. Seven candidate small molecule drugs which potentially reverse SKCM immune status were identified by using Connectivity map (CMap) database. A prognostic five-immune-associated gene (IAG) signature consisting IFITM1, TNFSF13B, APOBEC3G, CCL8 and KLRK1 with high predictive value was established using the LASSO Cox regression analysis in training set, and validated in testing set as well as Gene Expression Omnibus (GEO) external validation cohort (P < 0.05). Lower tumor purity and active immune-related signaling pathways were obviously presented in low-risk group. Furthermore, a novel composite nomogram based upon the five-IAG signature and other clinical parameters was built with excellent calibration curves. Collectively, comprehensively characterizing the SKCM subtypes based upon immune microenvironment landscape and development of a survival-related IAG signature may provide a promising avenue for improving individualized treatment design and prognosis prediction for patients with SKCM.