To date, there has been great progress in understanding the genetic basis of ischemic stroke (IS); however, several aspects of the condition remain underexplored, including the influence of genetic factors on post-stroke outcomes and the identification of causative loci. We proposed that an analysis of the results obtained from animal models of brain ischemia could be helpful. To this end, we developed a bioinformatic approach for exploring single-nucleotide polymorphisms (SNPs) in human orthologs of rat genes expressed differentially after induced brain ischemia. Using this approach, we identified and analyzed 11 SNPs from 6 genes in 553 Russian individuals (331 patients with IS and 222 controls). We assessed the association of SNPs with the risk of IS and IS outcomes. We found that the SNPs rs858239 (GPNMB), rs907611 (LSP1), and rs494356 (TAGLN) were associated with different parameters of IS functional outcomes. In addition, the SNP rs1261025 (PDPN) was associated significantly with IS itself (p = 0.0188, recessive model). All these associations were demonstrated for the first time. Analysis of the literature suggests that they should be characterized as being inflammation related. This supports the pivotal role of inflammation in both the incidence of stroke and post-stroke outcomes. We believe the findings reported here will help with stroke prognosis in the future.

OBJECTIVE: To study the associations of nine genetic variants with the risk and dynamics of recovery (outcome) of ischemic stroke (IS) using the developed protocol for the search for genomic markers based on a bioinformatic approach to the study of single nucleotide polymorphisms (SNPs) in human orthologues of rat genes differentially expressed under conditions of induced cerebral ischemia.
METHODS: We identified and analyzed nine SNPs in 553 Russians (331 IS patients and 222 controls). The National Institutes of Health Stroke Scale (NIHSS) was used to assess stroke severity. Functional recovery after stroke was assessed using the modified Rankin scale (mRS). The principles of selection of polymorphic markers analyzed in the study were determined according to the protocol developed by us earlier. Selected SNP tags were genotyped using real-time polymerase chain reaction (PCR) TaqMan.
RESULTS: The relationship of SNP with both the risk of IS and the dynamics of its recovery was investigated. SNP rs66782529 (LGALS3) was associated with negative IS outcomes (p=0.048). SNPs rs62278647 and rs2316710 (PTX3) were significantly associated with IS risk (p=0.000029 and p=0.0025, respectively). The associations for rs62278647 and rs2316710 were found only in females, suggesting a gender-related PTX3 polymorphism.
CONCLUSIONS: This study not only reveals some new genetic links to IS and its consequences, but also shows how the study of gene variations in a rat model of cerebral ischemia can be useful in the search for genetic markers of this disease in humans.
UNASSIGNED: Исследовать ассоциации девяти генетических вариантов с риском и динамикой восстановления ишемического инсульта (ИИ), используя разработанный протокол для поиска геномных маркеров, основанный на биоинформатционном подходе к изучению однонуклеотидных полиморфизмов (SNP) в человеческих ортологах крысиных генов, дифференциально экспрессирующихся в условиях индуцированной ишемии головного мозга.
UNASSIGNED: Нами было идентифицировано и проанализировано 9 SNP у 553 россиян (331 пациент с ИИ и 222 лица группы контроля). Для оценки тяжести инсульта использовали шкалу инсульта Национальных институтов здоровья (NIHSS). Функциональное восстановление после инсульта оценивали по модифицированной шкале Рэнкина (mRS). Принципы отбора анализируемых в исследовании полиморфных маркеров определяли согласно разработанному нами ранее протоколу. Выбранные SNP-метки были генотипированы с использованием полимеразной цепной реакции (ПЦР) в реальном времени TaqMan.
UNASSIGNED: Была исследована связь SNP как с риском ИИ, так и с динамикой его восстановления. SNP rs66782529 (LGALS3) ассоциировался с неблагоприятным исходом ИИ (p=0,048). SNP rs62278647 и rs2316710 (PTX3) были значимо связаны с риском ИИ (p=0,000029 и p=0,0025 соответственно). Эти корреляции для rs62278647 и rs2316710 были обнаружены только у женщин, что свидетельствует о связи с полом полиморфизма PTX3.
UNASSIGNED: Данное исследование не только выявляет некоторые новые генетические связи с ИИ и его последствиями, но также показывает, как изучение вариаций генов на крысиной модели ишемии головного мозга может быть полезным для поиска генетических маркеров этого заболевания у человека.

BACKGROUND: Accurate outcome prediction is of great clinical significance in customizing personalized treatment plans, reducing the situation of poor recovery, and objectively and accurately evaluating the treatment effect. This study intended to evaluate the performance of clinical text information (CTI), radiomics features, and survival features (SurvF) for predicting functional outcomes of patients with ischemic stroke.
METHODS: SurvF was constructed based on CTI and mRS radiomics features (mRSRF) to improve the prediction of the functional outcome in 3 months (90-day mRS). Ten machine learning models predicted functional outcomes in three situations (2-category, 4-category, and 7-category) using seven feature groups constructed by CTI, mRSRF, and SurvF.
RESULTS: For 2-category, ALL (CTI + mRSRF+ SurvF) performed best, with an mAUC of 0.884, mAcc of 0.864, mPre of 0.877, mF1 of 0.86, and mRecall of 0.864. For 4-category, ALL also achieved the best mAuc of 0.787, while CTI + SurvF achieved the best score with mAcc = 0.611, mPre = 0.622, mF1 = 0.595, and mRe-call = 0.611. For 7-category, CTI + SurvF performed best, with an mAuc of 0.788, mPre of 0.519, mAcc of 0.529, mF1 of 0.495, and mRecall of 0.47.
CONCLUSIONS: The above results indicate that mRSRF + CTI can accurately predict functional outcomes in ischemic stroke patients with proper machine learning models. Moreover, combining SurvF will improve the prediction effect compared with the original features. However, limited by the small sample size, further validation on larger and more varied datasets is necessary.

Although there has been great progress in understanding the genetic bases of ischemic stroke (IS), many of its aspects remain underexplored. These include the genetics of outcomes, as well as problems with the identification of real causative loci and their functional annotations. Therefore, analysis of the results obtained from animal models of brain ischemia could be helpful. We have developed a bioinformatic approach exploring single nucleotide polymorphisms (SNPs) in human orthologues of rat genes expressed differentially under conditions of induced brain ischemia. Using this approach, we identified and analyzed nine SNPs in 553 Russian individuals (331 patients with IS and 222 controls). We explored the association of SNPs with both IS outcomes and with the risk of IS. SNP rs66782529 (LGALS3) was associated with negative IS outcomes (p = 0.048). SNPs rs62278647 and rs2316710 (PTX3) were associated significantly with IS (p = 0.000029 and p = 0.0025, respectively). These correlations for rs62278647 and rs2316710 were found only in women, which suggests a sex-specific association of the PTX3 polymorphism. Thus, this research not only reveals some new genetic associations with IS and its outcomes but also shows how exploring variations in genes from a rat model of brain ischemia can be of use in searching for human genetic markers of this disorder.