Integrative analysis of the prognostic value and immune microenvironment of mitophagy-related signature for multiple myeloma.

Multiple myeloma (MM) is a fatal malignant tumor in hematology. Mitophagy plays vital roles in the pathogenesis and drug sensitivity of MM. We acquired transcriptomic expression data and clinical index of MM patients from NCI public database, and 36 genes involved in mitophagy from the gene set enrichment analysis (GSEA) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted to construct a risk score prognostic model. Kaplan-Meier survival analysis and receiver operation characteristic curves (ROC) were conducted to identify the efficiency of prognosis and diagnosis. ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) was performed to uncover the level of immune infiltration. QRT-PCR was performed to verify gene expression in clinical samples of MM patients. The sensitivity to chemotherapy drugs was evaluated upon the database of the genomics of drug sensitivity in cancer (GDSC). Fifty mitophagy-related genes were differently expressed in two independent cohorts. Ten out of these genes were identified to be related to MM overall survival (OS) rate. A prognostic risk signature model was built upon on these genes: VDAC1, PINK1, VPS13C, ATG13, and HUWE1, which predicted the survival of MM accurately and stably both in training and validation cohorts. MM patients suffered more adverse prognosis showed more higher risk core. In addition, the risk score was considered as an independent prognostic element for OS of MM patients by multivariate cox regression analysis. Functional pathway enrichment analysis of differentially expressed genes (DEGs) based on risk score showed terms of cell cycle, immune response, mTOR pathway, and MYC targets were obviously enriched. Furthermore, MM patients with higher risk score were observed lower immune scores and lower immune infiltration levels. The results of qRT-PCR verified VDAC1, PINK1, and HUWE1 were dysregulated in new diagnosed MM patients. Finally, further analysis indicated MM patients showed more susceptive to bortezomib, lenalidomide and rapamycin in high-risk group. Our research provided a neoteric prognostic model of MM based on mitophagy genes. The immune infiltration level based on risk score paved a better understanding of the participation of mitophagy in MM.

Jia Y ，Liu R ，Shi L ，Feng Y ，Zhang L ，Guo N ，He A ，Kong G ... -  《BMC CANCER》

A mitophagy-related gene signature associated with prognosis and immune microenvironment in colorectal cancer.

Colorectal cancer (CRC) is a heterogeneous disease and one of the most prevalent malignancies worldwide. Previous research has demonstrated that mitophagy is crucial to developing colorectal cancer. This study aims to examine the association between mitophagy-related genes and the prognosis of CRC patients. Gene expression profiles and clinical information of CRC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) regression analysis were applied to establish a prognostic signature using mitophagy related genes. Kaplan-Meier and receiver operating characteristic (ROC) curves were used to analyze patient survival and predictive accuracy. Meanwhile, we also used the Genomics of Drug Sensitivity in Cancer (GDSC) database and Tumor Immune Dysfunction and Exclusion (TIDE) algorithm to estimate the sensitivity of chemotherapy, targeted therapy and immunotherapy. ATG14 overexpression plasmid was used to regulate the ATG14 expression level in HCT116 and SW480 cell lines, and cell counting kit-8, colony formation and transwell migration assay were performed to validate the function of ATG14 in CRC cells. A total of 22 mitophagy-driven genes connected with CRC survival were identified, and then a novel prognostic signature was established based on 10 of them (AMBRA1, ATG14, MAP1LC3A, MAP1LC3B, OPTN, VDAC1, ATG5, CSNK2A2, MFN1, TOMM22). Patients were divided into high-risk and low-risk groups based on the median risk score, and the survival of patients in the high-risk group was significantly shorter in both the training cohort and two independent cohorts. ROC curve showed that the area under the curves (AUC) of 1-, 3- and 5-year survival were 0.66, 0.66 and 0.64, respectively. Multivariate Cox regression analysis confirmed the independent prognostic value of the signature. Then we constructed a Nomogram combining the risk score, age and M stage, which had a concordance index of survival prediction of 0.77 (95% CI 0.71-0.83) and more robust predictive accuracy. Results showed that CD8+ T cells, regulatory T cells and activated NK cells were significantly more enriched in the high-risk group. Furthermore, patients in the high-risk group are more sensitive to targeted therapy or chemotherapy, including bosutinib, elesclomol, lenalidomide, midostaurin, pazopanib and sunitinib, while the low-risk group is more likely to benefit from immunotherapy. Finally, in vitro study confirmed the oncogenic significance of ATG14 in both HCT116 and SW480 cells, whose overexpression increased CRC cell proliferation, colony formation, and migration. In conclusion, we developed a novel mitophagy-related gene signature that can be utilized not only as an independent predictive biomarker but also as a tool for tailoring personalizing treatment for CRC patients, and we confirmed ATG14 as a novel oncogene in CRC.

Zhang C ，Zeng C ，Xiong S ，Zhao Z ，Wu G ... -  《Scientific Reports》

A novel signature incorporating lipid metabolism- and immune-related genes to predict the prognosis and immune landscape in hepatocellular carcinoma.

Liver hepatocellular carcinoma (LIHC) is a highly malignant tumor with high metastasis and recurrence rates. Due to the relation between lipid metabolism and the tumor immune microenvironment is constantly being elucidated, this work is carried out to produce a new prognostic gene signature that incorporates immune profiles and lipid metabolism of LIHC patients. We used the "DEseq2" R package and the "Venn" R package to identify differentially expressed genes related to lipid metabolism (LRDGs) in LIHC. Additionally, we performed unsupervised clustering of LIHC patients based on LRDGs to identify their subgroups and immuno-infiltration and Gene Ontology (GO) enrichment analysis on the subgroups. Next, we employed multivariate, LASSO and univariate Cox regression analyses to determine variables and to create a prognostic profile on the basis of immune- and lipid metabolism-related differential genes (IRDGs and LRDGs). We separated patients into low- and high-risk groups in accordance with the best cut-off value of risk score. We conducted Decision Curve Analysis (DCA), Receiver Operating Characteristic curve analysis as a function of time as well as Survival Analysis to evaluate this signature's prognostic value. We incorporated the clinical characteristics of patients into the risk model to obtain a nomogram prognostic model. GEO14520 and ICGC-LIRI JP datasets were employed to externally confirm the accuracy and robustness of signature. The gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were applied for investigating the underlying mechanisms. Immune infiltration analysis was implemented to examine the differences in immune between both risk groups. Single-cell RNA sequencing (scRNA-SEQ) was utilized to characterize the genes that were involved in the distribution of signature and expression characteristics of different LIHC cell types. The patients' sensitivity in both risk groups to commonly used chemotherapeutic agents and semi-inhibitory concentrations (IC50) of the drugs was assessed using the GDSC database. On the basis of the differentially expressed genes (DEGs) in the two groups, the CMAP database was adopted for the prediction of potential small-molecule compounds. Small-molecule compounds were molecularly docked with prognostic markers. Lastly, we investigated the prognostic gene expression levels in normal and LIHC tissues with immunohistochemistry (IHC) and quantitative reverse transcription polymerase chain reaction(qRT-PCR). We built and verified a prognostic signature with seven genes that incorporated immune profiles and lipid metabolism. Patients were classified as low- and high-risk groups depending on their prognostic profiles. The overall survival (OS) was markedly lower in the high-risk group as compared to low-risk group. Time-dependent ROC curves more precisely predicted patients' survival at 1, 3 and 5 years; the area under the ROC curve was 0.81 (1 year), 0.75 (3 years) and 0.77 (5 years). The DCA curves showed the value of the prognostic genes in this signature for clinical applications. We included the patients' clinical characteristics in the risk model for both multivariate and univariate Cox regression analyses, and the findings revealed that the risk model represents an independent factor that influences OS in LIHC patients. With immune analysis, GSVA and GSEA, we identified that there are remarkable differences between the two risk groups in immune pathways, lipid metabolism, tumor development, immune cell infiltration and immune microenvironment, response to immunotherapy, and sensitivity to chemotherapy. Moreover, those with higher risk scores presented greater sensitivity to the chemotherapeutic agents. Experiments in vitro further elucidated the roles of SPP1 and FLT3 in the LIHC immune microenvironment. Furthermore, four small-molecule drugs that could target LIHC were screened. In vitro qRT-PCR , IHC revealed that the SPP1,KIF18A expressions were raised in LIHC in tumor samples, whereas FLT3,SOCS2 showed the opposite trend. We developed and verified a new signature comprising immune- and lipid metabolism-associated markers and to assess the prognosis and the immune status of LIHC patients. This signature can be applied to survival prediction, individualized chemotherapy, and immunotherapeutic guidance for patients with liver cancer. This study also provides potential targeted therapeutics and novel ideas for the immune evasion and progression of LIHC.

Yang T ，Luo Y ，Liu J ，Liu F ，Ma Z ，Liu G ，Li H ，Wen J ，Chen C ，Zeng X ... -  《Frontiers in Oncology》

Risk score constructed with neutrophil extracellular traps-related genes predicts prognosis and immune microenvironment in multiple myeloma.

Multiple myeloma (MM) exhibits considerable heterogeneity in treatment responses and survival rates, even when standardized care is administered. Ongoing efforts are focused on developing prognostic models to predict these outcomes more accurately. Recently, neutrophil extracellular traps (NETs) have emerged as a potential factor in MM progression, sparking investigation into their role in prognostication. In this study, a multi-gene risk scoring model was constructed using the intersection of NTEs and differentially expressed genes (DEGs), applying the least absolute shrinkage and selection operator (LASSO) Cox regression model. A nomogram was established, and the prognostic model's effectiveness was determined via Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). The ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) were employed to evaluate the level of immune infiltration. The sensitivity of chemotherapy drugs was assessed using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Ultimately, the presence of the detected genes was confirmed through quantitative real-time polymerase chain reaction (qRT-PCR) analysis in MM cell specimens. 64 NETs-DEGs were yielded, and through univariate Cox regression and LASSO regression analysis, we constructed a risk score composed of six genes: CTSG, HSPE1, LDHA, MPO, PINK1, and VCAM1. MM patients in three independent datasets were classified into high- and low-risk groups according to the risk score. The overall survival (OS) of patients in the high-risk group was significantly reduced compared to the low-risk group. Furthermore, the risk score was an independent predictive factor for OS. In addition, interactions between the risk score, immune score, and immune cell infiltration were investigated. Further analysis indicated that patients in the high-risk group were more sensitive to a variety of chemotherapy and targeted drugs, including bortezomib. Moreover, the six genes provided insights into the progression of plasma cell disorders. This study offers novel insights into the roles of NETs in prognostic prediction, immune status, and drug sensitivity in MM, serving as a valuable supplement and enhancement to existing grading systems.

Gao G ，Liu R ，Wu D ，Gao D ，Lv Y ，Xu X ，Fu B ，Lin Z ，Wang T ，He A ，Bai J ... -  《-》

Identification of a glycolysis- and lactate-related gene signature for predicting prognosis, immune microenvironment, and drug candidates in colon adenocarcinoma.

Liu C ，Liu D ，Wang F ，Xie J ，Liu Y ，Wang H ，Rong J ，Xie J ，Wang J ，Zeng R ，Zhou F ，Peng J ，Xie Y ... -  《Frontiers in Cell and Developmental Biology》