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Record W4409893390 · doi:10.1186/s12885-025-14195-9

The role of MLH1, MSH2 and MSH6 in the development of colorectal cancer in Uganda

2025· article· en· W4409893390 on OpenAlexfundno aff
Richard Wismayer, Rosie Matthews, Celina Whalley, Julius Kiwanuka, Fredrick Elishama Kakembo, S. Thorn, Henry Wabinga, Michael Odida, Ian Tomlinson

Bibliographic record

VenueBMC Cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsnot available
FundersInstitute of Genetics
KeywordsMSH6Surgical oncologyMLH1Colorectal cancerMedicineMSH2OncologyCancerInternal medicineGeneral surgeryDNA mismatch repair

Abstract

fetched live from OpenAlex

INTRODUCTION: In Uganda, colorectal cancer (CRC) is steadily increasing according to the Kampala Cancer Registry. In the West, microsatellite instability is detected in 90% of hereditary nonpolyposis colon cancers (HNPCC) which account for 1-2% of all CRC, and 15% of sporadic CRC. Germline mutations in MLH1 and MSH2 account for 90% of HNPCC in the West, whilst the remainder of cases are due to mutations in MSH6 and PMS2. The aim of this study was to determine the microsatellite instability (MSI) status and determine the proportions of MLH1, MSH2, and MSH6 pathological mutations in Ugandan CRC patients. METHODOLOGY: This was a cross-sectional study carried out between 1st January 2008 to 15th September 2021. Patients were recruited prospectively from 16th September 2019 to 16th September 2021, from Masaka Regional Referral Hospital, Mulago National Referral Hospital, Uganda Martyrs' Hospital Lubaga and Mengo Hospital. From 1st January 2008 to 15th September 2019, CRC FFPE tissue blocks were obtained from the archives of the Department of Pathology, Makerere University. Data was abstracted from the medical case files for demographics, topography and stage. The histopathological subtype and grade of CRC were obtained by two consultant pathologists from the H&E slides. DNA was extracted from CRC formalin-fixed paraffin-embedded (FFPE) tissue blocks. Library preparation was completed using the Qiagen custom design panel. The custom panel represented 56 genes. The MLH-1, MSH2, MSH6, BRAF and KRAS genes were sequenced using the above library preparation and NGS sequencing. The MSI status was obtained if one of the MSI genes, MLH1, MSH2 or MSH6 was pathologically mutated. If none of the genes was pathologically mutated it was considered MSI negative, microsatellite stable (MSS). Immunohistochemistry was carried out to determine whether MLH1 and PMS2 was MMR proficient or deficient. Categorical data was summarized using frequencies and proportions corresponding to each of the three histopathological subtypes and MSI status subtypes. Continuous and categorical variables were analyzed using the chi-square and Fischer's exact tests. A p -value ≤ 0.05 was considered statistically significant for all the analyses. RESULTS: Out of 127 CRC patients, the mean(SD) age of MSI cases was 55.6(16.9) years and of MSS cases was 55.4(15.5) years. The majority were MSS, 75(59.06%) followed by MSI, 52(40.9%). There were 14(11.02%) MLH-1 mutations, 30(23.62%) MSH2 mutations, and 26(20.47%) MSH6 mutations. BRAF mutational analysis showed only 5(3.9%) having pathologic missense BRAF V600 mutations. KRAS mutations consisted of only 8(6.3%) having pathologic missense KRAS mutations. CONCLUSIONS: The high rate of MSI in Ugandan colorectal tumours was mainly associated with a lack of BRAF mutations and a high frequency of MSH2 and MSH6 MMR gene mutations. In CRC patients, identification of the causative mutation is recommended, however in a resource-limited setting, MSI testing and immunohistochemistry is more cost effective. In Ugandan CRC patients who meet at least one of the Bethesda criteria, MSI testing and immunohistochemistry may therefore be offered to obtain the MSI status of the tumour.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.304
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations5
Published2025
Admission routes1
Has abstractyes

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