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Prognosis

Prognosis refers to the likely outcome or course of a medical condition or disease.
It involves predicting the probable future course and outcome of a patient's disease based on an evaluation of the individual's specific case and the characteristics of the disease.
Prognosis assessment can help guide treatment decisions and provide valuable information about a patient's expected recovery or disease progression.
Accurate prognosis determination is essential for effective disease management and patient care.
Factors such as disease stage, patient age, comorbidities, and response to therapies are commonly considered in prognosis evaluations.
Prognosis information can also be used for epidemiologic purposes, such as estimating the burden of a disease within a population.

Most cited protocols related to «Prognosis»

1
Cutoff Point Analysis for Prognosis
Two different kinds of plots can be generated: overview plots and plots at the cutoff point. Overview plots give a summary of all possible cutoff points with the optimal cutoff marked by a vertical line. The second kinds of plots are Waterfall and Kaplan-Meier plots that are generated for a fixed cutoff point. The overview plots include plots of ORs, HRs and differences in survival. ORs are calculated using the function glm form the R package stats [15] . HRs are calculated using the function coxph from the R package survival [19] . Differences in survival are calculated from the mean survival times in the good prognosis and the poor prognosis group. Mean survival times are estimated from the area under the Kaplan-Meier curve using the maximum time that occurs in the data as uniform time endpoint.
Budczies J., Klauschen F., Sinn B.V., Győrffy B., Schmitt W.D., Darb-Esfahani S, & Denkert C. (2012). Cutoff Finder: A Comprehensive and Straightforward Web Application Enabling Rapid Biomarker Cutoff Optimization. PLoS ONE, 7(12), e51862.
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Publication 2012
Prognosis
2
Multivariate Cox Regression with FDR Correction
During the computation of multiple cut-off values, multiple hypotheses are generated. Therefore, the false discovery rate (FDR) is computed by default in this setting using the Benjamini-Hochberg method [14 (link)] to correct for multiple hypothesis testing. The FDR results are normally shown in the “Results” page.
A requirement for Cox regression is that the hazard is independent of time. To fulfill this requirement, the censoring should be independent of the prognosis, samples entering at different time points in the analysis should have the same prognosis, and the time should be measured as a continuous variable (not in bins). We employed the coxph function of the survival package [10 ] for performing the proportional hazard assumption test.
In some cases, one might want to compare clinical and genomic variables. To enable this, clinical data can be selected not only as filters but also as variables to be included in the multivariate analysis. In these analyses, the “Results” page displays the P values and HR values for each variable included in the multivariate analysis in a table format.
Lánczky A, & Győrffy B. (2021). Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation. Journal of Medical Internet Research, 23(7), e27633.
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Publication 2021
Genome Prognosis
3
Comprehensive Update of miRDB miRNA Target Database
Since the first version of miRDB was established in 2008, thousands of novel miRNAs have been discovered. In addition, annotations of gene targets, especially concerning the identification of 3′-UTR sequences, have been significantly expanded. Thus, we have performed a major update on target prediction data by employing the up-to-date miRNA and target gene annotations. All miRNA sequences and annotations were downloaded from miRBase (version 21) in June 2014 (7 (link)). We have adopted the NCBI RefSeq database for identification of 3′-UTR sequences. In brief, RefSeq sequences were downloaded from NCBI's ftp site (8 (link)) and further parsed with the BioPerl program to obtain the 3′-UTR sequences of the transcripts. Target prediction was then performed with the MirTarget algorithm, which was developed by analyzing high-throughput expression profiling data in a support vector machine framework (9 (link)). Unlike most other prediction algorithms, MirTarget predicts both conserved and nonconserved gene targets by treating target site conservation as an important but non-required sequence feature. The robust performance of MirTarget has been extensively demonstrated. For example, a recent independent analysis shows that MirTarget has superior performance over other public algorithms for identifying miRNA-downregulated gene targets (10 (link)). In this miRDB update, we have also updated the MirTarget algorithm by including additional model training data, which were generated from miRNA-target pairs experimentally identified by RNA-seq (11 (link)). Details of the algorithmic improvement will be described elsewhere.
With updated genomic data and the MirTarget algorithm, we have performed genome-wide miRNA target prediction for all known transcripts (including all isoforms) from five species—human, mouse, rat, dog and chicken. In total, 2.1 million gene targets were predicted to be regulated by 6709 miRNAs in these five species. All the targets have a prediction score in the range of 50–100 as assigned by MirTarget, with a higher score representing more statistical confidence in the prediction result. Detailed statistics of the target prediction are presented in the miRDB website. All the target prediction data as well as the associated genomic annotations were imported into a backend MySQL database for web presentation. The users can search for precompiled results via miRDB web interface, using either miRNA or gene target search terms. Notably, the users have the flexibility of searching a single miRNA/gene target (Figure 1A), or a combination of multiple miRNAs/gene targets (Figure 1B). The users can download target prediction results for individual miRNAs or gene targets to a tab-delimited spreadsheet file via the Target Mining search interface. In addition, the users can download all precompiled target prediction data via the miRDB download page. A representative target prediction result retrieved from miRDB is presented in Figure 1C. There has been a major change in miRNA naming rules recently, resulting in multiple names describing the same miRNA (7 (link)). Thus, historical names for the same miRNAs are also presented in the result page. Data stored in miRDB are interconnected with the miRBase database (7 (link)). In each miRBase miRNA entry, there is a dynamic link directing to specific miRNA target prediction data in miRDB. The miRDB web interface and backend database are hosted by a Linux server at Washington University (http://mirdb.wustl.edu). Besides searching for target sites in 3′-UTR, the users may also locate unconventional target sites in the coding region or 5′-UTR via the Custom Prediction web interface.
Wong N, & Wang X. (2014). miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Research, 43(Database issue), D146-D152.
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Publication 2014
3' Untranslated Regions 5' Untranslated Regions Chickens Gene Annotation Genes Genes, vif Genome Homo sapiens MicroRNAs Mus Protein Isoforms RNA-Seq Term Birth
4
Evaluating PubMed Search Interfaces for Clinical Queries
This randomized trial was designed as a pilot study to measure the relevancy of search results using three different interfaces for the PubMed search system. Each participant was randomly assigned to one of 3 interfaces for searching PubMed (Figure 1). Protocol A, [14 ] used a PubMed/PICO template designed for use on a wireless handheld device. The PubMed/PICO template prompted the searcher for the PICO elements of the question (patient problem, intervention, comparison and outcome), as well as patient age group and gender. There was also an option to select a publication type. The publication types listed were clinical trial, randomized controlled trial, meta-analysis, review or practice guideline. If no publication type was selected, the search default was to include all five study designs. The PubMed/PICO template with these publication options was designed to favor questions of therapy, the most common type of question asked by clinicians [3 (link)]. Protocol B, [15 ] used a PubMed/PICO/Clinical Queries template that prompted for the PICO and allowed the search to be filtered by type of question and scope of strategy (narrow or broad) or by systematic review. Protocol B incorporated the Clinical Queries filters and allowed the searcher to consider a broader range of question types. Because templates for Protocols A and B were designed for handheld devices, participants assigned to these two protocols were given a handheld device of their choice (either a Palm™ Tungsten C or a HP iPAC Pocket PC™) to use during the study. The Web browser home page for the wireless handheld study devices was preconfigured to connect to the appropriate study interface. Protocol C, PubMed, [2 ] used the standard web-based PubMed system on a PC workstation. Protocol C did not include a PICO template for formulating the search strategy, but the participants had access to Clinical Queries if they chose to use them.
Study subjects were recruited from interns and residents on an inpatient general medicine rotation at an academic medical center in the US. Thirty-one subjects were each given three clinical questions and asked to search PubMed for a set of relevant articles that would provide an answer to the questions. The three study questions were taken from a database of actual clinical questions formulated by residents during general medicine rotations between 2001 and 2002 [3 (link)]. Two of the questions (Q2 and Q3) were related to treatment or therapy, the most common type of question asked and one question (Q1) was related to prognosis. (Table 2.) While the PICO framework was developed specifically for therapy questions [16 (link)], the prognosis question was included because PICO is being taught for all types of questions. Protocol assignments and the 3 clinical questions were placed in a concealed envelope, and participants were asked to select one envelope from a group of identical envelopes. Participants were instructed to search each clinical question, as many times as needed, in order to retrieve a final set of articles that would provide the relevant information needed to make a clinical decision in each case.
The success of the search was measured by comparing the number of relevant citations retrieved to the total number of article retrieved in the final set. The research team identified the relevant articles for each clinical question. The criteria for an article being included as relevant was that it addressed the specific clinical question, including patient, intervention and outcome, and that it was of the best study methodology based on the type of question. For example, a therapy question needed to be answered by a randomized controlled trial, systematic review, or meta-analysis, while a prognosis question required a prospective cohort study. Two researchers conducted PubMed searches for each question. These two and a third researcher selected the relevant articles from the pooled results of the searches. A fourth researcher also reviewed the results and reconciled any disagreement among the other reviewers.
Participants using the handheld devices wrote down their IP address, the time, and the number of citations in the set that best addressed the clinical question. This information was matched with data collected by the project server at NLM and was used to verify the final result set. Participants using the PC workstation were asked to save their final set results to the study account in MyNCBI. Screen captures, a means of saving the image of the search, were made of their complete search history to back up their saved strategies. For all participants, the search terms, date and time of the search, and the Unique Identifiers for the citations retrieved were collected by the system transactions logs and stored on the NLM project server or in MyNCBI. There were no time constraints on any of the participants. The DUMC Institutional Review Board approved the study method and all participants signed an informed consent form.
Schardt C., Adams M.B., Owens T., Keitz S, & Fontelo P. (2007). Utilization of the PICO framework to improve searching PubMed for clinical questions. BMC Medical Informatics and Decision Making, 7, 16.
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Publication 2007
Age Groups Arecaceae Ethics Committees, Research Gender Inpatient Medical Devices Patients Prognosis Teaching Therapeutics Tungsten
5
Polygene Analysis of Obesity Risk
We assessed the cumulative effects of the 97 GWS loci on mean BMI and on their ability to predict obesity (BMI ≥ 30 kg m−2) using the c statistic from logistic regression models in the Health and Retirement Study17 , a longitudinal study of 26,000 European Americans 50 years or older. The variance explained (VarExp) by each SNP was calculated using the effect allele frequency (f) and beta (β) from the meta-analyses using the formula VarExp = β2(1 − f)2f.
For polygene analyses, the approximate conditional analysis from GCTA19 (link),20 (link), was used to select SNPs using a range of P value thresholds (that is, 5 × 10−8, 5 × 10−7, …, 5 × 10−3) based on summary data from the European sex-combined meta-analysis excluding TwinGene and QIMR studies. We performed a within-family prediction analysis using full-sib pairs selected from independent families (1,622 pairs from the QIMR cohort and 2,758 pairs from the TwinGene cohort) and then SNPs at each threshold were used to calculate the percentage of phenotypic variance explained and predict risk (Extended Data Figs 2 and 3). We then confirmed the results from population-based prediction and estimation analyses in an independent sample of unrelated individuals from the TwinGene (n = 5,668) and QIMR (n = 3,953) studies (Extended Data Fig. 3 and Fig. 1c). The SNP-derived predictor was calculated using the profile scoring approach implemented in PLINK and estimation analyses were performed using the all-SNP estimation approach implemented in GCTA.
Locke A.E., Kahali B., Berndt S.I., Justice A.E., Pers T.H., Day F.R., Powell C., Vedantam S., Buchkovich M.L., Yang J., Croteau-Chonka D.C., Esko T., Fall T., Ferreira T., Gustafsson S., Kutalik Z., Luan J., Mägi R., Randall J.C., Winkler T.W., Wood A.R., Workalemahu T., Faul J.D., Smith J.A., Zhao J.H., Zhao W., Chen J., Fehrmann R., Hedman Å.K., Karjalainen J., Schmidt E.M., Absher D., Amin N., Anderson D., Beekman M., Bolton J.L., Bragg-Gresham J.L., Buyske S., Demirkan A., Deng G., Ehret G.B., Feenstra B., Feitosa M.F., Fischer K., Goel A., Gong J., Jackson A.U., Kanoni S., Kleber M.E., Kristiansson K., Lim U., Lotay V., Mangino M., Leach I.M., Medina-Gomez C., Medland S.E., Nalls M.A., Palmer C.D., Pasko D., Pechlivanis S., Peters M.J., Prokopenko I., Shungin D., Stančáková A., Strawbridge R.J., Sung Y.J., Tanaka T., Teumer A., Trompet S., van der Laan S.W., van Setten J., Van Vliet-Ostaptchouk J.V., Wang Z., Yengo L., Zhang W., Isaacs A., Albrecht E., Ärnlöv J., Arscott G.M., Attwood A.P., Bandinelli S., Barrett A., Bas I.N., Bellis C., Bennett A.J., Berne C., Blagieva R., Blüher M., Böhringer S., Bonnycastle L.L., Böttcher Y., Boyd H.A., Bruinenberg M., Caspersen I.H., Chen Y.D., Clarke R., Daw E.W., de Craen A.J., Delgado G., Dimitriou M., Doney A.S., Eklund N., Estrada K., Eury E., Folkersen L., Fraser R.M., Garcia M.E., Geller F., Giedraitis V., Gigante B., Go A.S., Golay A., Goodall A.H., Gordon S.D., Gorski M., Grabe H.J., Grallert H., Grammer T.B., Gräßler J., Grönberg H., Groves C.J., Gusto G., Haessler J., Hall P., Haller T., Hallmans G., Hartman C.A., Hassinen M., Hayward C., Heard-Costa N.L., Helmer Q., Hengstenberg C., Holmen O., Hottenga J.J., James A.L., Jeff J.M., Johansson Å., Jolley J., Juliusdottir T., Kinnunen L., Koenig W., Koskenvuo M., Kratzer W., Laitinen J., Lamina C., Leander K., Lee N.R., Lichtner P., Lind L., Lindström J., Lo K.S., Lobbens S., Lorbeer R., Lu Y., Mach F., Magnusson P.K., Mahajan A., McArdle W.L., McLachlan S., Menni C., Merger S., Mihailov E., Milani L., Moayyeri A., Monda K.L., Morken M.A., Mulas A., Müller G., Müller-Nurasyid M., Musk A.W., Nagaraja R., Nöthen M.M., Nolte I.M., Pilz S., Rayner N.W., Renstrom F., Rettig R., Ried J.S., Ripke S., Robertson N.R., Rose L.M., Sanna S., Scharnagl H., Scholtens S., Schumacher F.R., Scott W.R., Seufferlein T., Shi J., Smith A.V., Smolonska J., Stanton A.V., Steinthorsdottir V., Stirrups K., Stringham H.M., Sundström J., Swertz M.A., Swift A.J., Syvänen A.C., Tan S.T., Tayo B.O., Thorand B., Thorleifsson G., Tyrer J.P., Uh H.W., Vandenput L., Verhulst F.C., Vermeulen S.H., Verweij N., Vonk J.M., Waite L.L., Warren H.R., Waterworth D., Weedon M.N., Wilkens L.R., Willenborg C., Wilsgaard T., Wojczynski M.K., Wong A., Wright A.F., Zhang Q., Brennan E.P., Choi M., Dastani Z., Drong A.W., Eriksson P., Franco-Cereceda A., Gådin J.R., Gharavi A.G., Goddard M.E., Handsaker R.E., Huang J., Karpe F., Kathiresan S., Keildson S., Kiryluk K., Kubo M., Lee J.Y., Liang L., Lifton R.P., Ma B., McCarroll S.A., McKnight A.J., Min J.L., Moffatt M.F., Montgomery G.W., Murabito J.M., Nicholson G., Nyholt D.R., Okada Y., Perry J.R., Dorajoo R., Reinmaa E., Salem R.M., Sandholm N., Scott R.A., Stolk L., Takahashi A., Tanaka T., van ’t Hooft F.M., Vinkhuyzen A.A., Westra H.J., Zheng W., Zondervan K.T., Heath A.C., Arveiler D., Bakker S.J., Beilby J., Bergman R.N., Blangero J., Bovet P., Campbell H., Caulfield M.J., Cesana G., Chakravarti A., Chasman D.I., Chines P.S., Collins F.S., Crawford D.C., Cupples L.A., Cusi D., Danesh J., de Faire U., den Ruijter H.M., Dominiczak A.F., Erbel R., Erdmann J., Eriksson J.G., Farrall M., Felix S.B., Ferrannini E., Ferrières J., Ford I., Forouhi N.G., Forrester T., Franco O.H., Gansevoort R.T., Gejman P.V., Gieger C., Gottesman O., Gudnason V., Gyllensten U., Hall A.S., Harris T.B., Hattersley A.T., Hicks A.A., Hindorff L.A., Hingorani A.D., Hofman A., Homuth G., Hovingh G.K., Humphries S.E., Hunt S.C., Hyppönen E., Illig T., Jacobs K.B., Jarvelin M.R., Jöckel K.H., Johansen B., Jousilahti P., Jukema J.W., Jula A.M., Kaprio J., Kastelein J.J., Keinanen-Kiukaanniemi S.M., Kiemeney L.A., Knekt P., Kooner J.S., Kooperberg C., Kovacs P., Kraja A.T., Kumari M., Kuusisto J., Lakka T.A., Langenberg C., Marchand L.L., Lehtimäki T., Lyssenko V., Männistö S., Marette A., Matise T.C., McKenzie C.A., McKnight B., Moll F.L., Morris A.D., Morris A.P., Murray J.C., Nelis M., Ohlsson C., Oldehinkel A.J., Ong K.K., Madden P.A., Pasterkamp G., Peden J.F., Peters A., Postma D.S., Pramstaller P.P., Price J.F., Qi L., Raitakari O.T., Rankinen T., Rao D.C., Rice T.K., Ridker P.M., Rioux J.D., Ritchie M.D., Rudan I., Salomaa V., Samani N.J., Saramies J., Sarzynski M.A., Schunkert H., Schwarz P.E., Sever P., Shuldiner A.R., Sinisalo J., Stolk R.P., Strauch K., Tönjes A., Trégouët D.A., Tremblay A., Tremoli E., Virtamo J., Vohl M.C., Völker U., Waeber G., Willemsen G., Witteman J.C., Zillikens M.C., Adair L.S., Amouyel P., Asselbergs F.W., Assimes T.L., Bochud M., Boehm B.O., Boerwinkle E., Bornstein S.R., Bottinger E.P., Bouchard C., Cauchi S., Chambers J.C., Chanock S.J., Cooper R.S., de Bakker P.I., Dedoussis G., Ferrucci L., Franks P.W., Froguel P., Groop L.C., Haiman C.A., Hamsten A., Hui J., Hunter D.J., Hveem K., Kaplan R.C., Kivimaki M., Kuh D., Laakso M., Liu Y., Martin N.G., März W., Melbye M., Metspalu A., Moebus S., Munroe P.B., Njølstad I., Oostra B.A., Palmer C.N., Pedersen N.L., Perola M., Pérusse L., Peters U., Power C., Quertermous T., Rauramaa R., Rivadeneira F., Saaristo T.E., Saleheen D., Sattar N., Schadt E.E., Schlessinger D., Slagboom P.E., Snieder H., Spector T.D., Thorsteinsdottir U., Stumvoll M., Tuomilehto J., Uitterlinden A.G., Uusitupa M., van der Harst P., Walker M., Wallaschofski H., Wareham N.J., Watkins H., Weir D.R., Wichmann H.E., Wilson J.F., Zanen P., Borecki I.B., Deloukas P., Fox C.S., Heid I.M., O’Connell J.R., Strachan D.P., Stefansson K., van Duijn C.M., Abecasis G.R., Franke L., Frayling T.M., McCarthy M.I., Visscher P.M., Scherag A., Willer C.J., Boehnke M., Mohlke K.L., Lindgren C.M., Beckmann J.S., Barroso I., North K.E., Ingelsson E., Hirschhorn J.N., Loos R.J, & Speliotes E.K. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature, 518(7538), 197-206.
Publication 2015
Europeans Obesity Phenotype Prognosis

Most recents protocols related to «Prognosis»

1
PVT1 Splice Variants in Medulloblastoma
Not available on PMC !

Example 1

The ENSEMBLE database was searched in order to identify the variants that regulate MYC protein in cancer cells. Accordingly, 25 splice variants of PVT1 have been found (FIG. 2). Primers were developed to identify the abundance of each transcript in patient derived medulloblastoma (MB) xenografts (PDX) (FIGS. 3 and 4). The analysis suggested that PVT1_212 is the most abundant PVT1 splice variant in all the 4 subgroups of the MB PDXs, while PVT1_203 being the second most prevalent splice variant. PVT1_212 is most prevalent in the MB Subgroup 3 patients, which has the poorest prognosis among the MB patients (FIG. 5). Three types of PVT1_212 expression pattern were identified in MB PDXs as well in patient samples: Low PVT1_212 expressing group (0-15×): contained mainly Subgroup 4 MBs, Intermediate PVT1_212 expressing group (15-200×): contained Subgroups 3, Shh and Wnt MB, and the high PVT1_212 expressing group (200-1000×): Exclusively Subgroup 3 (FIGS. 6 and 7). This demonstrated that PVT1 expression can be used to stratify MB patients, where the high PVT1_212 expressing group (200-1000×) can designate the 8q24 gain, MYC-driven type of the Group 3 MB patients (generally associated with the poor prognosis).

US11866788B2. Role of PVT1 in the diagnosis and treatment of MYC-driven cancer (2024-01-09). Sanford Burnham Prebys Medical Discovery Institute [US]. Inventors: Anindya Bagchi [US], Ashutosh Tiwari [US].
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Patent 2024
Cells Chryseobacterium taklimakanense Figs Heterografts Homo sapiens Malignant Neoplasms Medulloblastoma Oligonucleotide Primers Patients pralatrexate Prognosis Proteins PVT1 long-non-coding RNA, human
2
Predicting Carbapenem-Resistant Enterobacterales Bloodstream Infections
R (Version 4.0.4) was used for statistical analysis. The Shapiro–Wilk test was used to check the normal distribution of continuous data. Normally distributed continuous data were expressed as means ± standard deviation (SD) and tested using the Student’s t-test. Non-normally distributed continuous data were expressed as medians (first quartile, third quartile) and tested using the Mann–Whitney U-test. Categorical data were expressed as number (n) and percentage (%) and tested using the Chi-square test or Fisher’s exact test.
Candidate variables, selected based on statistical significance in univariate logistic analysis (P<0.05), clinical experience and published data were included in the multivariate logistic analysis using stepwise methods. The final variables in the prediction model were selected by the clinical significance, principle of statistics19 (link) and the results of the multivariate logistic analysis (P<0.05). The final regression model was visualized by a nomogram to predict the CRE BSI. Furthermore, the receiver-operating characteristic (ROC) curve (area under the curve [AUC]) and the C-statistic were used to assess the discrimination ability of this nomogram. The calibration curves and Brier score were used to assess the calibration ability of this nomogram. In addition, enhanced bootstrap internal validation was performed to verify the diagnostic efficiency of the model. Furthermore, decision curve analysis (DCA) was performed to determine the model’s clinical usefulness. P<0.05 was considered statistically significant. An online prediction tool (Shiny App) was prepared using the DynNom package in R. The construction process was conducted according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis guidelines.20 (link)
Liu J., Zhang H., Feng D., Wang J., Wang M., Shen B., Cao Y., Zhang X., Lin Q., Zhang F., Zheng Y., Xiao Z., Zhu X., Zhang L., Wang J., Pang A., Han M., Feng S, & Jiang E. (2023). Development of a Risk Prediction Model of Subsequent Bloodstream Infection After Carbapenem-Resistant Enterobacteriaceae Isolated from Perianal Swabs in Hematological Patients. Infection and Drug Resistance, 16, 1297-1312.
Publication 2023
Diagnosis Discrimination, Psychology Prognosis Student
3
Deriving a Retrospective Kidney Cohort
We used the Alberta Kidney Disease Network (AKDN) database to derive a retrospective, population-based cohort using linked administrative health, laboratory, and kidney failure datasets from Alberta, Canada [13 (link)]. These data were used to derive and internally validate our multivariable risk prediction models. We conducted this study using a prespecified protocol in accordance with the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) Checklist for Prediction model development (Supplementary Table 1). The University of Calgary and the University of Alberta granted ethics approval and waived the need for informed consent.
Harrison T.G., Hemmelgarn B.R., James M.T., Sawhney S., Manns B.J., Tonelli M., Ruzycki S.M., Zarnke K.B., Wilson T.A., McCaughey D, & Ronksley P.E. (2023). Prediction of major postoperative events after non-cardiac surgery for people with kidney failure: derivation and internal validation of risk models. BMC Nephrology, 24, 49.
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Publication 2023
Diagnosis Kidney Diseases Kidney Failure Prognosis
4
Comprehensive Enrichment Analyses for RBPs
In this study, comprehensive enrichment analyses covering 4 aspects were conducted. First, the “clusterProfiler” R package was utilised to perform KEGG along with the GO enrichment analyses targeting the RBPs containing different RBDs (canonical RBDs or non-canonical RBDs). Next, KEGG and GO analyses were also performed regarding distinct modules which were significantly correlated with prognosis identified by the WGCNA. Thirdly, to elucidate the mechanism underlying our prognostic model, GSEA (V.4.1.0, http://software.broadinstitute.org/gsea/) was employed to assess BP, CC, MF and KEGG enrichment based on differently expressed genes between different risk groups predicted by our novel prognostic models (FDR < 0.001, |NES| > 2). Finally, emerging literature have demonstrated the relationship between RBPs and immune status. Therefore, we further used ssGSEA to quantify the enrichment scores of diverse immune cell subpopulations and related functions or pathways. The infiltrating score of 16 immune cells and the activity of 13 immune-related functions or pathways were calculated with ssGSEA in the “gsva” R package. And the NES scores of different risk groups were compared using Wilcoxon method.
Cao L., Duan L., Zhang R., Yang W., Yang N., Huang W., Chen X., Wang N., Niu L., Zhou W., Chen J., Li Y., Zhang Y., Liu J., Fan D, & Liu H. (2023). Development and validation of an RBP gene signature for prognosis prediction in colorectal cancer based on WGCNA. Hereditas, 160, 10.
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Publication 2023
Genes Immune System Processes Population at Risk Population Group Prognosis RNA Recognition Motif
5
Prognostic Co-Expression Network of RBPs
WGCNA is a systematic biology method for determining the association patterns among genes across different samples. It can be used to identify highly covarying gene sets (modules) and to identify candidate biomarkers or therapeutic targets based on the association between modules and sample phenotypes [25 (link)]. This approach focuses on exploring associations between external traits and co-expression gene sets instead of individual genes, which is more comply with biological laws. It has been widely used in various cancer researches. In this study, the expression pattern of the 1734 differently expressed RBPs and their matching clinical features (age, gender, overall survival time, survival status, and stage) in the TCGA cohort were employed to create a co-expression network using the R “WGCNA” package (V.4.0.2). The WGCNA approach was performed as documented previously [25 (link)]. First, to remove outlier samples, a hierarchical clustering analysis of CRC tumour samples on the basis of the expression of RBPs was performed. After that, we screened the estimated soft threshold power (β) to ensure the construction of scale-free networks, which is more in line with the law of biology. Herein, β = 5 (Figure S1 scale free R2 = 0.885) was employed. Considering the TOM-based dissimilarity measure, average linkage hierarchical clustering with a min-Module size (gene group) of 20 was carried out. Moreover, RBPs with similar expression modes were categorized into the same modules and similar modules were merged. Next, we calculated the module eigengenes (MEs) and gene significance (GS). MEs exhibit the first principal component-linked module, whose value representing all genes in the module. GS was defined as the association of genes with traits and was employed to quantify the relationship of individual genes with the clinical traits of interest. Based on these two parameters, modules that are remarkably related with the OS time or tumour stage were uncovered as prognosis-related modules. The PPI network of the genes from these prognosis-related modules were constructed using the STRING website and the cut-off confidence was set as 0.9 (https://string-db.org/cgi/input.pl, version 11.0) and Cytoscape software (Version 3.8.2).
Cao L., Duan L., Zhang R., Yang W., Yang N., Huang W., Chen X., Wang N., Niu L., Zhou W., Chen J., Li Y., Zhang Y., Liu J., Fan D, & Liu H. (2023). Development and validation of an RBP gene signature for prognosis prediction in colorectal cancer based on WGCNA. Hereditas, 160, 10.
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Publication 2023
Biological Markers Biopharmaceuticals Gender Gene Expression Gene Modules Gene Regulatory Networks Genes Malignant Neoplasms Neoplasms Phenotype Prognosis Therapeutics

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Prism 7 by GraphPad
Sourced in United States, Japan, United Kingdom, Austria, Canada, Germany, Poland, Belgium, Lao People's Democratic Republic, China, Switzerland, Sweden, Finland, Spain, France
GraphPad Prism 7 is a data analysis and graphing software. It provides tools for data organization, curve fitting, statistical analysis, and visualization. Prism 7 supports a variety of data types and file formats, enabling users to create high-quality scientific graphs and publications.
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Stata 12 by StataCorp
Sourced in United States, United Kingdom, Denmark, Belgium, Spain, Canada, Austria
Stata 12.0 is a comprehensive statistical software package designed for data analysis, management, and visualization. It provides a wide range of statistical tools and techniques to assist researchers, analysts, and professionals in various fields. Stata 12.0 offers capabilities for tasks such as data manipulation, regression analysis, time-series analysis, and more. The software is available for multiple operating systems.
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Prism 5 by GraphPad
Sourced in United States, Germany, United Kingdom, Israel, Canada, Austria, Belgium, Poland, Lao People's Democratic Republic, Japan, China, France, Brazil, New Zealand, Switzerland, Sweden, Australia
GraphPad Prism 5 is a data analysis and graphing software. It provides tools for data organization, statistical analysis, and visual representation of results.
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Prism 6 by GraphPad
Sourced in United States, United Kingdom, Canada, China, Germany, Japan, Belgium, Israel, Lao People's Democratic Republic, Italy, France, Austria, Sweden, Switzerland, Ireland, Finland
Prism 6 is a data analysis and graphing software developed by GraphPad. It provides tools for curve fitting, statistical analysis, and data visualization.
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Spss 22 by IBM
Sourced in United States, United Kingdom, Germany, Japan, Belgium, Austria, China, Denmark, Spain
SPSS 22.0 is a statistical software package developed by IBM. It is designed to analyze and manipulate data, providing users with a range of statistical tools and techniques for various applications, including market research, survey data analysis, and social science research.
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R software by R Project for Statistical Computing
Sourced in United States, Austria, Japan, Belgium, New Zealand, United Kingdom, France
R is a free, open-source software environment for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others.
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Sas 9 by SAS Institute
Sourced in United States, Austria, Japan, Belgium, United Kingdom, Cameroon, China, Denmark, Canada, Israel, New Caledonia, Germany, Poland, India, France, Ireland, Australia
SAS 9.4 is an integrated software suite for advanced analytics, data management, and business intelligence. It provides a comprehensive platform for data analysis, modeling, and reporting. SAS 9.4 offers a wide range of capabilities, including data manipulation, statistical analysis, predictive modeling, and visual data exploration.
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Spss version 22 by IBM
Sourced in United States, Japan, United Kingdom, Austria, Germany, Czechia, Belgium, Denmark, Canada
SPSS version 22.0 is a statistical software package developed by IBM. It is designed to analyze and manipulate data for research and business purposes. The software provides a range of statistical analysis tools and techniques, including regression analysis, hypothesis testing, and data visualization.

More about "Prognosis"

Prognosis, the forecast of a patient's likely outcome or course of a medical condition, is a crucial aspect of effective disease management and patient care.
This predictive assessment involves evaluating the individual's specific case, disease characteristics, and various factors such as stage, age, comorbidities, and response to therapies.
Accurate prognosis determination can guide treatment decisions and provide valuable insights into a patient's expected recovery or disease progression.
Prognosis optimization is a key focus in the field of medical research, with various statistical software tools playing a vital role.
Programs like Prism 8, GraphPad Prism 7, Stata 12.0, GraphPad Prism 5, Prism 6, SPSS 22.0, R software, and SAS 9.4 offer advanced analysis capabilities to support prognosis assessment.
These tools can help researchers and clinicians identify the most effective protocols, enhance reproducibility, and improve research accuracy through seamless protocol comparison and identification of the optimal approaches.
The importance of prognosis extends beyond individual patient care, as it also has epidemiologic applications, such as estimating the burden of a disease within a population.
By understanding the likely course and outcome of medical conditions, healthcare providers and policymakers can make informed decisions to allocate resources and develop targeted interventions.
Staying up-to-date with the latest advancements in prognosis optimization is crucial for delivering high-quality patient care and advancing medical research.
AI-driven platforms like PubCompare.ai offer a cutting-edge solution, empowering researchers to unlock the power of prognosis optimization through seamless protocol comparison and identification of the most effective approaches.
With these insights, clinicians can enhance their decision-making, improve patient outcomes, and contribute to the overall progress of the healthcare system.