No abstract

Colorectal cancer (CRC) is one of the most common and deadly malignancies. Novel biomarkers for the diagnosis and prognosis of this disease must be identified. Besides, metabolism plays an essential role in the occurrence and development of CRC. This article aims to identify some critical prognosis-related metabolic genes (PRMGs) and construct a prognosis model of CRC patients for clinical use. We obtained the expression profiles of CRC from The Cancer Genome Atlas database (TCGA), then identified differentially expressed PRMGs by R and Perl software. Hub genes were filtered out by univariate Cox analysis and least absolute shrinkage and selection operator Cox analysis. We used functional enrichment analysis methods, such as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, to identify involved signaling pathways of PRMGs. The nomogram predicted overall survival (OS). Calibration traces were used to evaluate the consistency between the actual and the predicted survival rate. Finally, a prognostic model was constructed based on six metabolic genes (NAT2, XDH, GPX3, AKR1C4, SPHK1, and ADCY5), and the risk score was an independent prognostic prognosticator. Genetic expression and risk score were significantly correlated with clinicopathologic characteristics of CRC. A nomogram based on the clinicopathological feature of CRC and risk score accurately predicted the OS of individual CRC cancer patients. We also validated the results in the independent colorectal cancer cohorts GSE39582 and GSE87211. Our study demonstrates that the risk score is an independent prognostic biomarker and is closely correlated with the malignant clinicopathological characteristics of CRC patients. We also determined some metabolic genes associated with the survival and clinical stage of CRC as potential biomarkers for CRC diagnosis and treatment.

Colorectal cancer (CRC) is one of the most prevalent and fatal malignancies, and novel biomarkers for the diagnosis and prognosis of CRC must be identified. RNA-binding proteins (RBPs) are essential modulators of transcription and translation. They are frequently dysregulated in various cancers and are related to tumorigenesis and development. The mechanisms by which RBPs regulate CRC progression are poorly understood and no clinical prognostic model using RBPs has been reported in CRC. We sought to identify the hub prognosis-related RBPs and to construct a prognostic model for clinical use. mRNA sequencing and clinical data for CRC were obtained from The Cancer Genome Atlas database (TCGA). Gene expression profiles were analyzed to identify differentially expressed RBPs using R and Perl software. Hub RBPs were filtered out using univariate Cox and multivariate Cox regression analysis. We used functional enrichment analysis, including Gene Ontology and Gene Set Enrichment Analysis, to perform the function and mechanisms of the identified RBPs. The nomogram predicted overall survival (OS). Calibration curves were used to evaluate the consistency between the predicted and actual survival rate, the consistency index (c-index) was calculated, and the prognostic effect of the model was evaluated. Finally, we identified 178 differently expressed RBPs, including 121 up-regulated and 57 down-regulated proteins. Our prognostic model was based on nine RBPs (PNLDC1, RRS1, HEXIM1, PPARGC1A, PPARGC1B, BRCA1, CELF4, AEN and NOVA1). Survival analysis showed that patients in the high-risk subgroup had a worse OS than those in the low-risk subgroup. The area under the curve value of the receiver operating characteristic curve of the prognostic model is 0.712 in the TCGA cohort and 0.638 in the GEO cohort. These results show that the model has a moderate diagnostic ability. The c-index of the nomogram is 0.77 in the TCGA cohort and 0.73 in the GEO cohort. We showed that the risk score is an independent prognostic biomarker and that some RBPs may be potential biomarkers for the diagnosis and prognosis of CRC.

This study aims to visualize research hotspots and trends of “ferroptosis in cancer”, “necroptosis in cancer”, “pyroptosis in cancer”, and “cuproptosis in cancer” through a bibliometric analysis to facilitate understanding of future developments in basic and clinical research and to provide a new perspective on cancer treatment. From January 1, 2012 to October 31, 2022, in the field of “ferroptosis in cancer”, a total of 2467 organizations from 79 different countries published 3302 articles. 2274 organizations from 72 different countries published 2233 articles in the field of “ necroptosis in cancer”. 1366 institutions from 58 different countries contributed 1445 publications in the field of “pyroptosis in cancer”. In the field of “ cuproptosis in cancer”, the number of articles published in the last 10 years is relatively low, with a total of 109 articles published by 116 institutions from four different countries. In the field of “ferroptosis in cancer”, Tang Daolin had published 66 documents, ranked the first, while Dixon SJ is the most cited author, cited 3148 times; In the fields of “necroptosis in cancer”, Vandenabeele peter had published 35 papers and Degterev had been cited 995 times, ranked the first, respectively; Kanneganti thirumala-devi had published 24 papers, is the highest number of publications in the fields of “pyroptosis in cancer”, while Shi JJ was the most cited author with being cited 508 times. Both Huang Yan and Wang Tao published three papers and tied for first place and Tsvetkov p ranks first with being cited 107 times in “cuproptosis in cancer”. “Cell”, “Cell”, “Nature”, and “Science” was the most frequently co-cited journal on “ferroptosis in cancer”, “necroptosis in cancer”, “pyroptosis in cancer”, and “cuproptosis in cancer”, respectively. Further exploration of inhibitors of different Programmed cell death (PCD) and their targeted therapies are potential treatment options for cancer, but more direct clinical evidence as well as higher level clinical trials remain to be explored. Further clarification of the mechanisms of crosstalk between these PCDs may provide effective cancer treatments. And the role of different types of PCDs, especially the novel ones discovered, in cancer can be expected to remain a hot topic of research in the cancer field for quite some time to come.