Integrated multi-omics analysis and machine learning developed a prognostic model based on mitochondrial function in a large multicenter cohort for Gastric Cancer.

BACKGROUND: Gastric cancer (GC) is a common and aggressive type of cancer worldwide. Despite recent advancements in its treatment, the prognosis for patients with GC remains poor. Understanding the mechanisms of cell death in GC, particularly those related to mitochondrial function, is crucial for its development and progression. However, more research is needed to investigate the significance of the interaction between mitochondrial function and GC cell death. METHODS: We employed a robust computational framework to investigate the role of mitochondria-associated proteins in the progression of GC in a cohort of 1,199 GC patients. Ten machine learning algorithms were utilized and combined into 101 unique combinations. Ultimately, we developed a Mitochondrial-related-Score (MitoScore) using the machine learning model that exhibited the best performance. We observed the upregulation of LEMT2 and further explored its function in tumor progression. Mitochondrial functions were assessed by measuring mitochondrial ATP, mitochondrial membrane potential, and levels of lactate, pyruvate, and glucose. RESULTS: MitoScore showed significant correlations with GC immune and metabolic functions. The higher MitoScore subgroup exhibited enriched metabolic pathways and higher immune activity. Overexpression of LETM2 (leucine zipper and EF-hand containing transmembrane protein 2) significantly enhanced tumor proliferation and metastasis. LETM2 plays a role in promoting GC cell proliferation by activating the mTOR pathway, maintaining mitochondrial homeostasis, and promoting glycolysis. CONCLUSION: The powerful machine learning framework highlights the significant potential of MitoScore in providing valuable insights and accurate assessments for individuals with GC. This study also enhances our understanding of LETM2 as an oncogene signature in GC. LETM2 may promote tumor progression by maintaining mitochondrial health and activating glycolysis, offering potential targets for diagnosis, treatment, and prognosis of GC.

Integration of clinical and transcriptomics reveals programming of the lipid metabolism in gastric cancer.

Lipid metabolism has a profound impact on gastric cancer (GC) progression and is a newly targetable vulnerability for cancer therapy. Given the importance of lipids in cancer cellular processes, in this study we employed lipidomic clinical and transcriptomic data to connect the variations of lipid metabolism changes of GC. We constructed a clinical nomogram based on the lipid factors and other clinical items. Then by using multi-omics techniques, we established a lipid-related gene signature for individualized prognosis prediction in patients with GC. Moreover, a total of 1357 GC cases were then applied to evaluate the robustness of this model. WGCNA was used to identify co-expression modules and enriched genes associated with GC lipid metabolism. The role of key genes ACLY in GC was further investigated. The prognostic value of the lipgenesis signature was analyzed using Cox regression model, and clinical nomogram was established. Among them, we observed overexpression of ACLY significantly increased the levels of intracellular free fatty acid and triglyceride, and activated AKT/mTOR pathway to promote cancer development. In conclusion, our findings revealed that GC exhibited a reprogramming of lipid metabolism in association with an altered expression of associated genes. Among them, ACLY significantly promoted GC lipid metabolism and increased cancer cell proliferation, suggesting that this pathway can be targetable as a metabolic vulnerability in future GC therapy.

Adipose tissue is a predictor of 30-days mortality in patients with bloodstream infection caused by carbapenem-resistant Klebsiella pneumoniae.

BACKGROUND: Prevalence of carbapenem-resistant Klebsiella pneumoniae (CRKP) bloodstream infection with high mortality has attached physicians' attention. High visceral adipose tissue (VAT) and high subcutaneous adipose tissue (SAT) were confirmed by previous studies that were closely related to increased pneumonia severity, more complications, and higher mortality in COVID-19. Thus, we speculate that CT-quantified body composition may also be connected to all-cause mortality and bacterial clearance in patients with CRKP bloodstream infection (BSI). METHODS: We investigated the associations of CT-quantified body composition with the mortality of CRKP bloodstream infectious patients. All CT images were obtained at the level of the L3/4 spinal level. The prognostic value of the body composition was analyzed using the Cox regression model, and precise clinical nomograms were established. RESULTS: 72 eligible patients both suffered from CRKP bloodstream infection and performed abdominopelvic CT were included. Factors associated with 30-day all-in hospital mortality included total adipose tissue (TAT) [adjusted hazard ratio (HR) = 1.028, 95% confidence interval (CI), 1.003-1.053; P = 0.025], age [HR = 1.030, 95% CI, 1.000-1.061; P = 0.047] and SOFA scores [HR = 1.138, 95% CI 1.049-1.263; P = 0.002]. Compared with low-VAT, patients with high-VAT show a strikingly poor prognosis in both 30-day all-cause mortality (P = 0.0108, Fig. 2A) and 30-day CRKP BSI mortality (P = 0.0049, Fig. 2C). The results of TAT were similar to VAT. CONCLUSIONS: Our study suggested that CT-derived body composition could be a credible and effective alternative to assess the prognosis of patients with BSI owing to CRKP. CT-quantified TAT, age, and SOFA scores were independently associated with 30-day all-cause mortality in these severe infectious patients, while skeletal muscle did not have obvious statistical significance.

Metabolic syndrome related gene signature predicts the prognosis of patients with pancreatic ductal carcinoma. A novel link between metabolic dysregulation and pancreatic ductal carcinoma.

BACKGROUND: Pancreatic cancer is one of the most common malignancies worldwide. In recent years, specific metabolic activities, which involves the development of tumor, caused wide public concern. In this study, we wish to explore the correlation between metabolism and progression of tumor. METHODS: A retrospective analysis including 95 patients with pancreatic ductal adenocarcinoma (PDAC) and PDAC patients from The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), and The Gene Expression Omnibus (GEO) database were involved in our study. Multivariate Cox regression analysis was used to construct the prognosis model. The potential connection between metabolism and immunity of PDAC was investigated through a weighted gene co-expression network analysis (WGCNA). 22 types of Tumor-infiltrating immune cells (TIICs) between high-risk and low-risk groups were estimated through CIBERSORT. Moreover, the potential immune-related signaling pathways between high-risk and low-risk groups were explored through the gene set enrichment analysis (GSEA). The role of key gene GMPS in developing pancreatic tumor was further investigated through CCK-8, colony-information, and Transwell. RESULTS: The prognostic value of the MetS factors was analyzed using the Cox regression model, and a clinical MetS-based nomogram was established. Then, we established a metabolism-related signature to predict the prognosis of PDAC patients based on the TCGA databases and was validated in the ICGC database and the GEO database to find the distinct molecular mechanism of MetS genes in PDAC. The result of WGCNA showed that the blue module was associated with risk score, and genes in the blue module were found to be enriched in the immune-related signaling pathway. Furthermore, the result of CIBERSORT demonstrated that proportions of T cells CD8, T cells Regulatory, Tregs NK cells Activated, Dendritic cells Activated, and Mast cells Resting were different between high-risk and low-risk groups. These differences are potential causes of different prognoses of PDAC patients. GSEA and the protein-protein interaction network (PPI) further revealed that our metabolism-related signature was significantly enriched in immune-related biological processes. Moreover, knockdown of GMPS in PDAC cells suppressed proliferation, migration, and invasion of tumor cells, whereas overexpression of GMPS performed oppositely. CONCLUSION: The results shine light on fundamental mechanisms of metabolic genes on PDAC and establish a reliable and referable signature to evaluate the prognosis of PDAC. GMPS was identified as a potential candidate oncogene with in PDAC, which can be a novel biomarker and therapeutic target for PDAC treatment.

Integrative Radiogenomics Approach for Risk Assessment of Postoperative and Adjuvant Chemotherapy Benefits for Gastric Cancer Patients.

Gastric cancer (GC) is a typical heterogeneous malignant tumor, whose insensitivity to chemotherapy is a common cause of tumor recurrence and metastasis. There is no doubt regarding the effectiveness of adjuvant chemotherapy (ACT) for GC, but the population for whom it is indicated and the selection of specific options remain the focus of present research. The conventional pathological TNM prediction focuses on cancer cells to predict prognosis, while they do not provide sufficient prediction. Enhanced computed tomography (CT) scanning is a validated tool that assesses the involvement of careful identification of the tumor, lymph node involvement, and metastatic spread. Using the radiomics approach, we selected the least absolute shrinkage and selection operator (LASSO) Cox regression model to build a radiomics signature for predicting the overall survival (OS) and disease-free survival (DFS) of patients with complete postoperative gastric cancer and further identifying candidate benefits from ACT. The radiomics trait-associated genes captured clinically relevant molecular pathways and potential chemotherapeutic drug metabolism mechanisms. Our results of precise surrogates using radiogenomics can lead to additional benefit from adjuvant chemotherapy and then survival prediction in postoperative GC patients.

Rab1A promotes IL-4R/JAK1/STAT6-dependent metastasis and determines JAK1 inhibitor sensitivity in non-small cell lung cancer.

Rab1A overexpression has been observed in several cancer types, however, its significance and the underlying mechanisms in non-small cell lung cancer (NSCLC) remain largely unexplored. This study demonstrated that Rab1A overexpression in NSCLC was significantly correlated to short survival and metastasis. Rab1A overexpression promoted cancer cell migration, invasion, and metastasis both in vitro and in vivo, by activating JAK1/STAT6 signaling through stabilizing IL-4Rα protein. Strikingly, high Rab1A level was associated with sensitivity to JAK1 inhibitor, and Rab1A overexpression rendered cancer cells vulnerable to JAK1-targeted agents. JAK1 inhibitor, Itacitinib adipate, dramatically inhibited high Rab1A NSCLC metastasis, in both cell line and patient derived xenograft models. Collectively, these findings demonstrated that Rab1A plays a critical role in the aggressive properties of NSCLC, revealing a unique mechanism by which it promotes metastasis. In addition, we found that Rab1A is a determinant of JAK1 inhibitor sensitivity, which could be explored for improving JAK1-targeted cancer therapy.

PGE2-JNK signaling axis non-canonically promotes Gli activation by protecting Gli2 from ubiquitin-proteasomal degradation.

Both bench and bedside investigations have challenged the supportive role of Hedgehog (Hh) activity in the progression of colorectal cancers, thus raising a critical need to further deeply determine the contribution of Hh to the growth of colorectal cancer. Combining multiple complementary means, including in vitro and in vivo inflammatory colorectal cancer models, and pathological analysis of clinical colorectal cancer patients samples. We report that colorectal cancer cells hijack prostaglandin E2 (PGE2) to non-canonically promote Hh transcriptional factor Gli activity and Gli-dependent proliferation of colorectal cancer cells in a Smo-independent manner. Mechanistically, PGE2 activates c-Jun N-terminal kinase (JNK), which in turn enables Gli2 to evade ubiquitin-proteasomal degradation by phosphorylating Gli2 at Thr1546. This study not only presents evidence for understanding the contribution of Hh to colorectal cancers, but also provides a novel molecular portrait underlying how PGE2-activated JNK fine-tunes the evasion of Gli2 from ubiquitin-proteasomal degradation. Therefore, it proposes a rationale for the future evaluation of chemopreventive and selective therapeutic strategies for colorectal cancers by targeting PGE2-JNK-Gli signaling route.

Patterns of immune infiltration in gastric cancer and their clinical significance.

OBJECTIVE: The malignant phenotypes of cancer are defined not only by its intrinsic tumor cells but also by the tumor-infiltrating immune cells activated and recruited to the cancer microenvironment. However, a comprehensive introduction of gastric cancer immune cell infiltration has not been identified so far. METHODS: In this study, we comprehensively analyzed the tumor-infiltrating immune cells abundance in gastric cancer for the first time by CIBERSORT. The meta-analysis, single-sample gene set enrichment analysis and hierarchical agglomerative clustering were used to measure and evaluate the respective proportions of 22 cell types of immune infiltration using normalized gene expression data. The fraction of tumor-infiltrating immune cells subpopulations was also evaluated to determine the associations with clinical features and molecular subtypes. RESULTS: Tumor-infiltrating immune cells are extensively involved in the pathogenesis and development of the gastric cancer. We discovered Tfh and activated CD4+ memory T cells were associated with poorer overall survival and Progression-free survival (PFS), but that naïve B cells were opposite for PFS. Unsupervised clustering analysis revealed there existed three tumor-infiltrating immune cells subgroups with distinct survival patterns. Specially, cluster 1 showed significantly better clinical outcome than other two clusters. CONCLUSIONS: Collectively, our data explored the differences of tumor-infiltrating immune cells in gastric cancer, and these variations were likely to be important clues for prognosis and management of its future clinical implementation.

Association between CT-Quantified Body Composition and Recurrence, Survival in Nonmetastasis Colorectal Cancer Patients Underwent Regular Chemotherapy after Surgery.

BACKGROUND: Body mass index, measured at colorectal cancer (CRC) diagnosis has been associated with recurrence and survival outcomes. Computed tomography- (CT-) defined body compositions accurately reflect body mass, but there was no consistent perspective on the influence of visceral adipose tissue (VAT) and skeletal muscle mass (SM) on the prognosis of nonmetastasis CRC, especially in the patients underwent surgery and regularly standard chemotherapy. METHODS: We investigated the associations of CT-quantified body composition (VAT and SM) with CRC patients successively underwent surgery and regular 8-12 of periods standard chemotherapy. All of the CT images were obtained at the level of the L3/4 spinal level. The prognostic value of the body compositions was analyzed using the Cox regression model, and precise clinical nomograms were established. RESULTS: In XELOX-treated patients, progression-free survival (PFS) (P = 0.025) and overall survival (OS) (P = 0.032) were lower in the high-SM than in the low-SM group. The univariate analysis demonstrated that compared with low-SM patients, patients with high-SM showed a strikingly poor prognosis in both OS (P = 0.0512) and PFS in the T4 subgroup (P = 0.0417), while contrary to the T2-3 subgroup. CONCLUSIONS: CT-quantified body compositions have a significant influence on CRC patients successively underwent curative resection and regularly standard chemotherapy with the endpoints of 1-year, 3-year, and 5-year both OS and PFS. Patients with high-SM showed a strikingly poor prognosis in OS and PFS in the T4 subgroup; however, the prognosis role of body composition was opposite in T2-3 patients.

Clinical and genomic characteristics of metabolic syndrome in colorectal cancer.

Metabolic syndrome (MetS) is characterized by a group of metabolic disturbances which leads to the enhanced risk of cancer development. Elucidating the mechanisms between these two pathologies is essential to identify the potential therapeutic molecular targets for colorectal cancer (CRC). 716 colorectal patients from the First and Second Affiliated Hospital of Wenzhou Medical University were involved in our study and metabolic disorders were proven to increase the risk of CRC. The prognostic value of the MetS factors was analyzed using the Cox regression model and a clinical MetS-based nomogram was established. Then by using multi-omics techniques, the distinct molecular mechanism of MetS genes in CRC was firstly systematically characterized. Strikingly, MetS genes were found to be highly correlated with the effectiveness of targeted chemotherapy administration, especially for mTOR and VEGFR pathways. Our results further demonstrated that overexpression of MetS core gene IL6 would promote the malignancy of CRC, which was highly dependent on mTOR-S6K signaling. In conclusion, we comprehensively explored the clinical value and molecular mechanism of MetS in the progression of CRC, which may serve as a candidate option for cancer management and therapy in the future.

TRIP13 promotes lung cancer cell growth and metastasis through AKT/mTORC1/c-Myc signaling.

BACKGROUND: Lung adenocarcinoma (LUAD) is a primary cause of cancer-patient mortality throughout the world. Thyroid hormone receptor interactor 13 (TRIP13) is a gene that expresses a protein involved in cell division, including tumorigenesis. Its expression is high in various human tumors; however, its role in LUAD cells remains undetermined. OBJECTIVE: To investigate the TRIP13's role in the development of LUAD. METHODS: Bioinformation analysis was used to analyze the expression of TRIP13 in LUAD tissues and the impact on the prognosis of LUAD; CRISPR/Cas9 was used to construct the cell lines; CCK-8 was used to explore the cell proliferation; Transwell assays was applied to exam the cell migration and cell invasion abilities; Western blot and immunoprecipitation was used to explore the relation between TRIP13 and AKT/mTORC1/c-Myc signaling pathway. RESULTS: By analyzing LUAD data from The Cancer Genome Atlas and the Gene Expression Omnibus databases, we determined that TRIP13 is highly expressed in LUAD tissues and that this expression level has a negative impact on the patient mortality. TRIP13 has also proved to promote LUAD cell proliferation, migration, and invasion. In this study, we demonstrated that TRIP13 activates AKT/mTORC1/c-Myc signaling in these cells. CONCLUSION: Our results have identified the role and potential mechanism by which TRIP13 affects LUAD cells, which may provide a useful marker for helping to diagnose this disease and create new therapies against it.

The role of FOS-mediated autophagy activation in the indocyanine green-based photodynamic therapy for treating melanoma.

The morbidity and mortality of melanoma which accounts for 90% of cutaneous neoplasm-related deaths is growing over the last few decades. Common treatments for melanoma are limited to poor tissue selectivity, high toxicity and drug resistance. Photodynamic therapy (PDT) is an effective adjuvant therapy and could be a promising therapy for melanoma. Multiple mechanisms are involved in PDT2 and programmed cell death (PCD) which comprises of autophagy and apoptosis is likely to be a critical one. Whereas, the molecular mechanism and subsequent effect of PDT-induced autophagy in melanoma are still unclear. In this study, we first analyzed gene expression data in the TCGA3 and GEO4 databases to clarify that PDT-induced-autophagy improved the prognosis of melanoma. The expression of FOS which generally defined as an immediate-early gene (IEG) and related to cell autophagy was found significantly elevated after PDT. To further investigate whether FOS played a key role in PDT-induced-autophagy of melanoma, we first determined the optimum concentration of ICG solution for autophagy observation. Then, relative FOS expression was detected at mRNA and protein level and cell autophagy was observed by western blot and flow cytometry. We found that ICG-PDT treatment could significantly elevate FOS expression in SKCM5 B16 cells, and FOS promoted ICG-PDT-induced cell autophagy. To sum up, our data indicated that FOS was involved in ICG-PDT-induced-autophagy in melanoma and furthermore improved the prognosis of melanoma.

Immune Infiltration Landscape in Lung Squamous Cell Carcinoma Implications.

Intrinsic cancer cells and the tumor-infiltrating immune cells (TIICs) recruited to the immune microenvironment define the malignant phenotype of lung squamous cell carcinoma (LUSC). Understanding more about the immune microenvironment of LUSC enables the selection of high-risk patients who would derive benefit from immunotherapy. Based on large public LUSC cohorts obtained from TCGA and GEO datasets, 22 types of infiltrating immune cell subgroups were evaluated by CIBERSORT. Meta-analysis, principal component analysis (PCA), single-sample gene set enrichment analysis (ssGSEA), and hierarchical clustering analysis were used to evaluate specific immune responses of LUSC. The distribution of TIICs of LUSC was entirely different from normal. TIIC subpopulations were also found to be closely associated with clinical features and molecular subtypes. Unsupervised clustering analysis revealed that three distinct TIIC subgroups existed with different survival patterns. TIICs are extensively implicated in the pathogenesis and development of LUSC. Characterizing the composition of TIICs influences the metabolism, pathological stage, and survival of tumor patients. It is hoped that this immune landscape could provide a more accurate understanding of the development and immunotherapy of LUSC.

Profiles of Immune Infiltration and Prognostic Immunoscore in Lung Adenocarcinoma.

Lung tissue is abundant with immune cells that form a powerful first defense against exotic particles and microbes. The malignant phenotype of lung adenocarcinoma (LUAD) is defined not only by intrinsic tumor cells but also by tumor-infiltrating immune cells (TIICs) recruited to the immune microenvironment. Understanding more about the immune microenvironment of LUAD could function in sorting out patients more likely with high risk and benefit from immunotherapy. Twenty-two types of TIICs were estimated based on large public LUAD cohorts from the TCGA and GEO datasets using the CIBERSORT algorithm. Then principal component analysis (PCA), meta-analysis, and single-sample gene set enrichment analysis (ssGSEA) were used to measure and evaluate the specific immune responses and relative mechanisms. Moreover, an immunoscore model based on the percent of immune cells was constructed via the univariate and multivariate Cox regression models, which provided an in-depth overview of the LUAD immune microenvironment and shed light on the immune regulatory mechanism. The differential expression genes (DEGs) were acquired based on the immunoscore model, and prognostic immune-related genes were further identified. GSEA and the protein-protein interaction network (PPI) further revealed that these genes were mostly enriched in many immune-related biological processes. It is hoped that this immune landscape could provide a more accurate understanding for LUAD development and tumor immune therapy.

Immune Infiltration Landscape in Clear Cell Renal Cell Carcinoma Implications.

The malignant phenotypes of cancer are defined not only by its intrinsic tumor cells but also by the tumor infiltrating immune cells (TIICs) recruited to the cancer microenvironment. Clear cell renal cell carcinoma (ccRCC) immune microenvironment plays an important role in the tumorigenesis. This research investigated the characteristics of immune cell invasion of renal cell carcinoma and provided clues for future clinical implementation. Retrospectively, ccRCC gene expression was analyzed with appropriate clinicopathological data from the Cancer Genome Atlas (TCGA) and GEO database up to December 2019. The CIBERSORT algorithm, meta-analysis, principal component analysis (PCA), Single-Sample Gene Set Enrichment Analysis (ssGSEA) and hierarchical agglomerative clustering were used to measure and evaluate the respective proportions of 22 cell types of immune infiltration using normalized gene expression data. We also focused on evaluating the association with TIICs subpopulations and clinical features and molecular subtypes. TIICs subpopulation, especially Macrophages subgroup, T follicular helper (Tfh) cells and CD8 T cells, all contribute to tumorigenesis. Unsupervised clustering analysis revealed that there existed two distinct TIICs subgroups with different survival patterns. TIICs are extensively involved in the pathogenesis and development of the ccRCC. Characterizing the composition of TIICs influences the metabolism of tumors, activity, level, stage, and survival of patients. Collectively, the TIIC analysis has the potential to assist in the assessment and selection of ccRCC prognosis and treatment.

Methylation analysis highlights novel prognostic criteria in human-metastasized melanoma.

Melanoma accounts for 90% of the deaths associated with cutaneous neoplasms, and the 5-year survival rate of patients with the advanced stage is about 20%. Many mechanisms are involved in melanoma progression, but dynamic epigenetic changes are likely to be critical contributors, especially for DNA methylation. However, we know little about the methylation events involved in melanoma lymph node metastasis (LNM), a deficit that is of particular concern because it has a growing incidence and mortality. To identify DNA methylated-associated changes involved in the formation of metastatic melanoma, we explored the different methylated genes (DMGs) between primary and LNM melanoma by Illumina Human Methylation 450 K BeadArray GSE44661. By integrating DNA methylation and messenger RNA expression data from The Cancer Genome Atlas database, we identified these DNA methylation biomarkers. Pathway analysis highlighted these DMGs, which were closely related to the carcinogenesis of melanoma, such as cell cycle regulation and RNA transcription process. Furthermore, according to the univariate and multivariate Cox regression analysis, we constructed a four-DMG prognostic signature model, which could precisely predict the outcome of melanoma in a more exact way. In summary, this four-DMG based risk score model successfully predicts the survival of melanoma. It is independent of other clinical characteristics and is good for prognosis prediction.

Esophageal cancer lymph node metastasis-associated gene signature optimizes overall survival prediction of esophageal cancer.

Esophageal cancer (EC) is characteristic of early regional lymph node metastasis (LNM) and most patients with metastasis have a poor prognosis. However, the current diagnostic techniques do not enable precise differentiation of EC LNM, prognostic stratification, and individual survival estimation. To identify potential molecular biomarkers for EC patients with LNM, we explored differently expressed genes in The Cancer Genome Atlas database between 77 non-LNM cases and 88 LNM cases by limma package R. Then, according to univariate and multivariate Cox regression analyses, we constructed an 8-messenger RNA (mRNA) prognostic signature model, which could predict the outcome in a more exact way. The area under the curve of the risk score is significantly higher than other clinical information, indicating that the 8-mRNA-based risk score is a good indicator for prognosis. Then, combined with other individual risk factors, such as age, sex, T stage, M stage, etc, we could precisely calculate the individual 1-, 3-, and 5-year survival rates. The Gene Set Enrichment Analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analysis indicate that the risk model is mainly associated with cancer-related pathways, such as cell division, cellular meiosis, and cell cycle regulation. In summary, the 8-mRNA-based risk score model that we developed successfully predicts the survival of EC. It is independent of clinical information and performing better than other clinical information for prognosis.

Gankyrin Drives Malignant Transformation of Gastric Cancer and Alleviates Oxidative Stress via mTORC1 Activation.

Gastric cancer, as a malignant epithelial tumor, is a major health threat leading to poor overall survival and death. It is usually diagnosed at an advanced stage due to asymptomatic or only nonspecific early symptoms. The present study demonstrated that gankyrin contributes to the early malignant transformation of gastric cancer and can be selected to predict the risk of gastric cancer in those patients harboring the precancerous lesions (dysplasia and intestinal metaplasia). In addition, a new insight into gastric cancer was provided, which stated that gankyrin alleviates oxidative stress via mTORC1 pathway activation. It can potentiate the mTORC1 by PGK1-AKT signaling that promotes the tumor process, and this phenomenon is not completely consistent with the previous report describing colorectal cancer.

Role of differentially expressed genes and long non-coding RNAs in papillary thyroid carcinoma diagnosis, progression, and prognosis.

Currently, the combination of ultrasonography and fine-needle aspiration biopsy (FNAB) can not discriminate between benign and malignant tumor of thyroid in some cases. The main issue in assessing the patients with thyroid nodules is to distinguish thyroid cancer from benign nodules, and reduce diagnostic surgery. To identify potential molecular biomarkers for patients with indeterminate FNAB, we explored the differentially expressed genes (DEGs) and differentially expressed long non-coding RNAs (DElncRNAs) in TCGA database between 318 papillary thyroid carcinoma (PTC) tissues and 35 normal thyroid gland tissues by DESeq R. Furthermore, DEGs were verified by gene expression profile GSE33630. Ten top DEGs and DElncRNAs were identified as candidate biomarkers for diagnosis and Lasso (Least Absolute Shrinkage and Selection Operator) logistic regression analysis were performed to improve the diagnostic accuracy of them. Besides, partial molecular biomarkers of top DEGs and DElncRNAs were closely related to the tumor stage (T), lymph node metastasis (N), metastasis (M) and pathological stage of PTC, which could reflect behavior of tumor progression. According to multivariate Cox analysis, the combination of two DEGs (METTL7B and KCTD16) and two DElncRNAs (LINC02454 and LINC02471) could predict the outcome in a more exact way. In conclusion, top DEGs and DElncRNAs could raise diagnosis of PTC in indeterminate FNAB specimens, and some could function as molecule biomarkers for tumor progression and prognosis.